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title: 'Implementation of evidence-based multiple focus integrated intensified TB
screening to end TB (EXIT-TB) package in East Africa: a qualitative study'
authors:
- Kahabi Isangula
- Doreen Philbert
- Florence Ngari
- Tigest Ajeme
- Godfather Kimaro
- Getnet Yimer
- Nicholaus P. Mnyambwa
- Winters Muttamba
- Irene Najjingo
- Aman Wilfred
- Johnson Mshiu
- Bruce Kirenga
- Steve Wandiga
- Blandina Theophil Mmbaga
- Francis Donard
- Douglas Okelloh
- Benson Mtesha
- Hussen Mohammed
- Hadija Semvua
- James Ngocho
- Sayoki Mfinanga
- Esther Ngadaya
journal: BMC Infectious Diseases
year: 2023
pmcid: PMC10013287
doi: 10.1186/s12879-023-08069-3
license: CC BY 4.0
---
# Implementation of evidence-based multiple focus integrated intensified TB screening to end TB (EXIT-TB) package in East Africa: a qualitative study
## Abstract
### Introduction
Tuberculosis (TB) remains a major cause of morbidity and mortality, especially in sub-Saharan Africa. We qualitatively evaluated the implementation of an Evidence-Based Multiple Focus Integrated Intensified TB Screening package (EXIT-TB) in the East African region, aimed at increasing TB case detection and number of patients receiving care.
### Objective
We present the accounts of participants from Tanzania, Kenya, Uganda, and Ethiopia regarding the implementation of EXIT-TB, and suggestions for scaling up.
### Methods
A qualitative descriptive design was used to gather insights from purposefully selected healthcare workers, community health workers, and other stakeholders. A total of 27, 13, 14, and 19 in-depth interviews were conducted in Tanzania, Kenya, Uganda, and Ethiopia respectively. Data were transcribed and translated simultaneously and then thematically analysed.
### Results
The EXIT-TB project was described to contribute to increased TB case detection, improved detection of Multidrug-resistant TB patients, reduced delays and waiting time for diagnosis, raised the index of TB suspicion, and improved decision-making among HCWs. The attributes of TB case detection were: (i) free X-ray screening services; (ii) integrating TB case-finding activities in other clinics such as Reproductive and Child Health clinics (RCH), and diabetic clinics; (iii), engagement of CHWs, policymakers, and ministry level program managers; (iv) enhanced community awareness and linkage of clients; (v) cooperation between HCWs and CHWs, (vi) improved screening infrastructure, (vii) the adoption of the new simplified screening criteria and (viii) training of implementers. The supply-side challenges encountered ranged from disorganized care, limited space, the COVID-19 pandemic, inadequate human resources, inadequate knowledge and expertise, stock out of supplies, delayed maintenance of equipment, to absence of X-ray and GeneXpert machines in some facilities. The demand side challenges ranged from delayed care seeking, inadequate awareness, negative beliefs, fears towards screening, to financial challenges. Suggestions for scaling up ranged from improving service delivery, access to diagnostic equipment and supplies, and infrastructure, to addressing client fears and stigma.
### Conclusion
The EXIT-TB package appears to have contributed towards increasing TB case detection and reducing delays in TB treatment in the study settings. Addressing the challenges identified is needed to maximize the impact of the EXIT-TB intervention.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-023-08069-3.
## Introduction
The persistent burden of Tuberculosis (TB) in sub-Saharan Africa (SSA) is linked to missed diagnoses, delayed diagnoses, and challenges with access to high-quality care, which all continue to contribute to a higher risk of death, suffering, and catastrophic financial consequences [1]. While it is well recognized that early TB diagnosis and prompt treatment are cornerstones to TB control, early case detection, and timely treatment also reduce morbidity and mortality associated with TB [2]. TB is a health priority in SSA where lack of modern facilities for proper diagnosis and management has left majority of patients undiagnosed, and these continue to spread the disease. In most SSA countries, TB case finding is through passive case finding and where possible, provider-initiated active case finding of symptomatic people in a predetermined target group such as HIV-infected individuals [1–3]. Under passive case finding, an individual is required to report to a health facility for care. In Tanzania, Kenya, Uganda, and Ethiopia, for an individual to be recognized as a presumptive TB patient, they need to report to a health facility with a cough of two or more weeks with or without accompanying symptoms [4–6], limiting the opportunity to capture those who report less duration cough, women and children attending Reproductive and Child Health (RCH) clinics and diabetic patients. To a large extent, passive case finding depends on individuals’ self-initiative to visit a healthcare facility and report a cough with proper duration, socio-economic status, and knowledge, and on the degree of alertness of health workers to suspect a patient [7].
In the East African (EA) region, TB case detection is below what is required to achieve the World Health Organization (WHO) END TB strategic target of reducing TB incidence by $50\%$ by 2025 [4–6] and is even lower among women and children. The 2021 National TB control (NTP) reports show that TB case notifications per 100,000 population were 134 in Tanzania, 135 in Kenya, 155 in Uganda, and 84 in Ethiopia, and in most countries only $40\%$ were females [2–6]. These values appear to be less than the estimated incidences for 2021 of 208 in Tanzania, 252 in Kenya, 199 in Uganda and 119 in Ethiopia per 100,000 population [2–6]. Moreover, TB case detection at Care and Treatment Centres (CTC) is based on the screening of symptomatic patients and none is done at diabetic clinics. Furthermore, although TB contact tracing is one of the core NTP activities in all four countries [2–6], it is not sufficiently done even among contact children due to financial constraints. It is known that women are good attendants of RCH clinics either for their reproductive health services or the health of their children, therefore, the current practice limits the opportunity of capturing TB cases among women and their children, and hence limits the efforts of NTP’s of putting more infectious TB cases into care. In addition, although chest X-ray (CXR) has been recently promoted and recommended by WHO as a useful tool for TB screening and triaging algorithms [8], none of the NTPs in the region has adopted it into policy. CXR has been reported to be the most sensitive TB screening tool (with very low specificity though) since a significant proportion of TB patients are asymptomatic [9]. CXR when used to triage who should be tested with GeneXpert has been reported to reduce the number of individuals to be tested with the assay, and thus, reduces high costs associated with GeneXpert and thus improve the efficiency of GeneXpert [10].
Given inadequate TB case detection and high mortality especially among HIV patients, RCH clients and diabetic patients, interventions to increase detection, and reduce diagnostic and treatment delays and mortality are needed to contribute towards improving the situation in SSA. Therefore, an Evidence-Based *Multiple focus* Integrated Intensified TB Screening package (EXIT-TB) was implemented in EA. The EXIT-TB package consisted of integrating TB case detection activities in RCH and diabetic clinics, systematic TB symptoms screening among health facilities attendees using healthcare workers or community health workers (CHW’s), followed by CXR wherever available, screening for TB irrespective of symptoms among HIV infected individuals with advanced disease attending CTCs, targeted contact tracing for all TB patients with a child household member, and use of stool GeneXpert among children who could not expectorate, and a TB diagnostic test (either smear microscope or GeneXpert). This paper explores the accounts of participants from Tanzania, Kenya, Uganda, and Ethiopia regarding the contribution and implementation of the EXIT-TB intervention and suggestions for scaling up.
## Design
The parent study was a multi-country cluster randomized controlled trial where the unit of randomization was the facility [11]. The EXIT-TB package was implemented in a total of 4 urban and 3 rural facilities in each participating country. Twenty-eight facilities (7 in each country) were randomized to either early intervention or deferred arm. The intervention was done in the first 12 months in 16 facilities and deferred in 12 facilities (control sites). Implementing the intervention in all 28 clinics simultaneously was a major challenge—hence we opted for this approach to scale up sequentially. While the EXIT-TB package was implemented, a qualitative descriptive approach was envisaged in gathering insights from the participants on EXIT-TB package implementation. This approach was deemed appropriate to answer three key questions to this inquiry: (i) *What is* the contribution of the EXIT-TB package on TB case detection in the study settings? ( ii) what are the challenges encountered and possible solutions that were taken during the implementation of the EXIT-TB package? and, (iii) What are the key considerations for scaling up the EXIT-TB package? A qualitative descriptive approach is appropriate for this inquiry as it aims to develop an understanding and describe the contribution of the EXIT-TB package on TB case detection without testing an existing theory [12]. This approach offered an effective way of gaining a deep and rich understanding of HCWs, CHWs, and stakeholders’ perceptions and experiences in the chosen context, as this may differ from other contexts in terms of culture, expectations, and resources within health care settings.
## Settings
In each country, regions/districts/counties and facilities were selected for the EXIT-TB implementation. Dar es Salaam in Tanzania, Nakaseke and Kiboga in Uganda, Siaya in Kanya Dire Dawa, and Addis Ababa in Ethiopia were selected because they have the highest TB rates and human immunodeficiency virus (HIV) co-infection in the region [3, 13–15]. Furthermore, the EXIT-TB package was implemented in seven healthcare facilities in each country with four facilities commencing implementation early (early implementation sites) as compared to the rest. Therefore, qualitative data were collected in these 4 early implementation facilities in each country because participants had much longer exposure to the intervention.
## EXIT-TB intervention package
The EXIT-TB package involving integrated TB case-finding activities was implemented from April 2019 to January 2022. The package was implemented at the reproductive and child health clinics (RCH), diabetics and HIV clinics in addition to the outpatients’ departments (OPD) using systematic TB symptom screening at these service delivery points [11]. We introduced a stamp of TB symptoms in all patient forms to aid in TB symptom screening. This was followed by further clinical evaluation by the clinicians at the OPD, RCH, and diabetic and HIV clinics. Healthcare workers and/or CHWs at the entry point of the OPD, RCH, diabetic and HIV clinics were trained on how to screen for TB symptoms. Following clinician evaluation, all symptomatic patients defined as patients who either reported a cough and/or haemoptysis of any duration or excessing weight loss, or excessive night sweats or fever were further screened using CXR (with exception of pregnant women, diabetic patients, and HIV infected individuals who were directly subjected to TB diagnostic test). Contact tracing among children with a household member with TB and CXR of all symptomatic children were also performed. Following CXR findings, patients were triaged accordingly, and presumptive TB patients were either tested using smear microscopy or GeneXpert (depending on availability). Presumptive TB patients were grouped as follows (i) Those with CXR suggestive of TB regardless of the presence of other TB cardinal symptoms, (ii) Patients with a short duration cough with CXR suggestive of TB regardless of other TB cardinal symptoms, (iii) Patients with a long duration cough defined as cough of two or more weeks regardless of the CXR findings, (iv) Diabetic patients with cough and/or haemoptysis of any duration, (v) HIV infected individuals (stage 1 and 2) with any of the TB symptoms regardless of the duration of the TB symptoms, (vi) HIV infected individuals with advanced diseases (stage 3 and 4) regardless of the presence of cough, and (vii) Pregnant women with a cough with or without any other TB cardinal symptoms or haemoptysis regardless of duration. To minimize the cost of CXR, the project facilitated the procurement of an X-ray machine in one of the facilities in Kenya and met the cost for presumptive patients in other facilities (in all countries) for patients from low-income families. Only two facilities (one in Kenya and one in Tanzania) had no CXR services. In Kenya, the project paid for CXR services for presumptive patients from low-income families in a facility located about 2 kms from the project site however they were required to meet the transport costs. In Tanzania, the project facilitated the payment of the cost of CXR for presumptive patients from low-income families in a nearby private facility that was within walking distance of the project site. All diagnosed TB patients were treated according to the National TB treatment guidelines.
## Study population, sample size, and sampling method
A total of 73 in-depth interviews (IDI) were conducted with purposefully selected service providers, patients, policymakers, and stakeholders as we wanted to gather perspectives on EXIT-TB from different healthcare facilities and countries. While equal representation is not a primary focus in qualitative studies [11], the level and ownership of facility (public, private & FBO and dispensary, health centre, and hospital) and country were considered during participants’ enrolment. No age preference was made for this qualitative inquiry other than being a service provider who participated in EXIT-TB implementation, a policy marker who is aware of the EXIT-TB package; a patient who received EXIT-TB intervention; OR a stakeholder working on TB control issues in respective countries.
It is important to note that the National TB Programs are the arms of the Ministry of Health charged with the responsibility of the prevention of TB to a point where it is no longer a major public health concern. In Tanzania, the NTLP (https://ntlp.go.tz/) coordinates all TB, TB-HIV and Leprosy control activities TB activities involve prevention, screening, and treatment. In Uganda, NTLP (https://www.health.go.ug/programs/tb-leprosy-control-program/) is responsible for setting policies, planning, training, procurement of supplies and drugs and setting diagnostic and quality standards for central and peripheral laboratories. Similarly in Kenya, NTLP (https://www.nltp.co.ke/) coordinates all services pertaining to TB, Leprosy and Lung diseases from prevention and health promotion, monitoring and evaluation, community engagement, communication, laboratory, care and treatment, policy and planning, monitoring, evaluation and research, supply chain and pharmacovigilance, and administration and finance. Finally, in Ethiopia, NTP (https://www.moh.gov.et/site/initiatives-4-col/Tuberculosis_and_Leprosy) coordinates TB services and finances.
## Data collection tools
Semi-structured IDI guides were developed and translated through a consultative process involving experts from study countries. First, the English versions of the interview guides were translated into Swahili (Tanzania), Luganda (Uganda) and Amharic (Ethiopia), then back-translated to English, and checked for conceptual equivalence. Questions within the interview guide ranged from those examining experiences of participation in the implementation of the EXIT-TB package, the contribution of the package to the facility, challenges encountered during implementation to suggestions for scaling up. Four [4] research assistants with degrees in Social Sciences and Medicine were recruited in each country and trained on the use of interview guides and techniques about this study. The interview guides were pre-tested in purposefully selected settings which were later excluded from the study. After pre-testing, the guides were refined to ensure readiness for use in the actual data collection process. Support supervision of research assistants was conducted throughout the data collection and analysis stages to ensure data quality.
## Recruitment of participants
Participant recruitment was guided by the desire to maximise data source triangulation (a key aspect of qualitative research rigor). We, therefore, sought to include participants who performed various duties during the implementation of the EXIT-TB package. Clinicians provided clinical consultation services for TB presumptive patients and made the diagnosis. Nurses were involved in TB screenings, patient education and treatment supervision. Community health workers and volunteers were involved in initial TB screening and patient enrollment. Together with community health workers, link assistants provided linkage of resumptive patients from the community to health facilities and tracking of lost to follow-ups. Heads of facilities and departments were involved in the coordination of EXIT-TB activities. Finally, TB coordinators and stakeholders at the local and national levels provided technical support and coordination of EXIT-TB activities at their respective levels. Therefore, the recruitment for service providers commenced with a purposeful selection of healthcare facilities for implementation of the EXIT-TB package in respective countries. Before IDIs, a courtesy visit was made to appropriate local authorities for approval to visit the facility. This was followed by a physical introductory visit to the facility where the study information was provided to the incharge, and subsequently, project implementation personnel were identified. This was followed by subsequent visits by research assistants to schedule and conduct IDIs. Recruitment for EXIT-TB patients were conducted through CHWs and service providers. Recruitment for policymakers and stakeholders was done by initial phone call after obtaining phone numbers from the Regional/county and district/sub-county authorities. During phone calls with policymakers and stakeholders, interviews were scheduled considering participant preferences of place, date and time.
## Conducting interviews
Qualitative interviews were conducted between May and July 2022, three months after the completion of the EXIT-TB package implementation. Interviews were conducted in a place and date preferred by the participants. Before the commencement of IDI, participants were given information about the study, risks, and benefits of participation (an information sheet was part of the interview). Verbal consent for the interview and voice recording was sought in advance and recorded as part of the interview. Then, interview sessions lasted for approximately 30–60 min in a safe environment. The data collection stopped when data saturation was achieved. Because of the COVID-19 pandemic, all participants and research assistants were provided with face masks and hand sanitizers. Social distance was maintained throughout the interviews.
## Data management and analysis
IDI data transcription and translation was done simultaneously by research assistants, and transcripts were verified by the research team in respective countries. Interview transcripts were deidentified, pseudonyms were generated for each participant, and the data was uploaded into the NVivo 12 software (QSR International) for management and deductive thematic coding. A stepwise approach was used for the thematic analysis of the interview transcripts [16]. First, the research team examined the research questions and generated several themes based on consensus. This resulted in an analytical matrix of the main themes and subthemes. Individual transcripts and phrases (codes) representing participants’ responses to investigators’ questions were exported to relevant themes and related sub-themes within NVivo. A consensus-based approach was then used by the research team to decide on including codes that do not fit within the pre-developed sub-themes and themes; the codes were excluded when they did not provide critical value to the study, as confirmed by subjective and objective evaluations. The coded data within NVivo were then exported to Microsoft Word (Microsoft Corporation) for interpretative analysis and report generation.
## Participants demographic characteristics
The demographic characteristics of participants are represented in and Table 1. In total, 73 participants aged 22–70 years were included in this qualitative audit, with most participants aged between 41 and 50 years. Most participants 43 ($58.9\%$) were male. Participants included clinicians 24 ($32.9\%$), TB coordinators and stakeholders 17 ($23.3\%$), community health workers 14 ($19.1\%$), medical In-charges 10 ($13.7\%$) and nurses 8 ($11.0\%$).Table 1Participants’ demographicsTanzania n (%)Kenya n (%)Uganda n (%)Ethiopia n (%)TotalAge 21–301 (3.7)4 (30.8)1 (7.1)3 (15.8)9 (12.3) 31–405 (18.5)7 (53.8)7 [50]6 (31.6)25 (34.2) 41–5014 (51.9)1 (7.7)5 (35.7)7 (36.8)27 (37.0) 50 + 7 (25.9)1 (7.7)1 (7.1)3 (15.8)12 (16.4)Sex Male12 (44.4)8 (61.5)12 (85.7)11 (57.9)43 (58.9) Female15 (55.6)5 (38.5)2 (14.3)8 (42.1)30 (41.1)Cadres Clinician5 (18.5)6 (46.2)5 (35.7)8 (42.1)24 (32.9) CHWs/CHVs7 (25.9)3 (23.1)4 (28.6)0 [0]14 (19.1) Medical Officer in charge7 (25.9)1 (7.7)1 (7.1)1 (5.3)10 (13.7) TB coordinators/Stakeholders7 (25.9)3 (23.7)4 (28.6)3 (15.8)17 (23.3) Nurse1 (3.7)0 [0]0 [0]7 (36.8)8 (11.0)Total2713141973
## Increased TB case detection
There was a consensus among most participants that the implementation of the EXIT-TB project has increased TB case detection. The descriptions during qualitative interviews strongly reflected project monitoring and evaluation data in the 16 selected facilities that indicated an increase in TB case detection from 320 in Tanzania, 253 in Kenya, 169 in Uganda and 39 in Ethiopia per 100,000 population during the 2017 baseline survey to 505 in Tanzania, 334 in Kenya, 208 in Uganda and 78 in Ethiopia per 100,000 population at the end of the EXIT-TB project in 2022.The contributors of the EXIT-TB package to increased TB case detection described by participants were six-fold (Table 2). The first contributor was the use of CXR services to screen for TB after symptom screening. In some countries, CXR services were made free, especially for those who couldn’t afford them. Free CXR especially among those who couldn’t afford the cost recurrently emerged as a key driver of increased uptake of TB screening. One participant in Uganda mentioned free CXR services worked better because most people could not afford the cost of X-ray services before the project. Free CXR were described as facilitating access of poor people to this essential service in the TB diagnosis process. Relatedly, there were affirmations in Tanzania and Ethiopia that the project improved access to TB services among the poor and low-income population. In Tanzania, some participants described a tendency of peer referral for free CXR services, particularly in areas with low-income populations. In Ethiopia, the project was cited to have facilitated the treatment of poor communities who could otherwise fail to meet the cost of diagnosis and medical care. Table 2The contribution of the EXIT-TB PackageContribution of EXIT-TB packageSpecific contributorsIllustrative quotesIncreased TB case detectionUse of X-Ray servicesThe main contribution of EXIT-TB was improved case identification. Before the project, the facility was identifying less than 50 cases in a year. But when EXIT- TB came, for the first time we were able to find more than 100 cases in one year. So active systematic TB symptoms screening improved case identification, especially within the EXIT-TB project. We were able to diagnose more patients through the use of X-rays as both a screening and diagnostic tool. This is because EXIT-TB was supporting free X-Ray services, especially among those who couldn’t afford them. [ The project] was paying for the X-rays. So, you're able to diagnose more TB cases, even using X-rays, something that we were not able to do before EXIT-TB (Clinician, Uganda)Engagement of CHWsWorking with [CHWs] facilitated the smooth implementation of the EXIT- TB programme by helping patients who were in the programme to get the right services at the right time and reduce costs. It was cost-effective for the patients. They received the right services from the time they entered the facility beginning with health education provided to them to increase their confidence in the TB screening process…those who were found to have TB were immediately put on medications and followed up (Clinical Officer, Tanzania)Increased community awareness and linkageOne of our responsibilities was to make the community members know and understand the existence of TB in the community. Awareness raising was effective in the catchment area because if someone goes to the community today and asks questions about TB people will be able to answer. We did not only teach them about the existence of the disease but also how the disease is transmitted, how to prevent it and where to go for treatment. We conducted symptoms screening and referred symptomatic patients for further investigation. In the course of the investigation, we were able to find patients who are TB positive and directed them to treatment (CHW, Uganda)HCWs support to CHWsHealthcare workers provided support and guidance to us. We were free to move to different departments. Providers prioritised us and gave us an opportunity to talk to patients and we were given a priority to see clinicians (CHWs, Kenya)Screening infrastructureWe used to do TB Screening and triage at OPD, but it was just an open space. Then when EXIT-TB came in, we realised that we need more space with partitions for specific tasks. We, therefore, created three partitions, one for screening, one for coughing patients and another one for registering new patients, and then we have the other one for HIV screening (Nursing Officer, Uganda)New screening criteria and trainingEXIT-TB came up with a different approach from the existing national guideline. For example, the national guideline considered coughs of fourteen days or more as a proxy for TB screening. However, with the exit TB project implementation, this practice was changed and anyone with a cough of any duration was screened. This new practice facilitated early detection. The screening was also mandatory for newly diagnosed HIV patients, those in stage 3 and 4 patients. Patients with cough of any duration were screened and sent to clinicians for further evaluation and diagnosis. Whenever a patient presented with any TB symptom it was a must to be screened and this was very decisive and facilitated early detection and reduced patient suffering. Previously, patients were referred to the laboratory for diagnosis at a very late stage…after developing the disease and after they became very ill. However, this project accepted patients even those without a single symptom especially those with HIV stage 3 or stage 4… they were all screened and diagnosed (TB Focal Person, Ethiopia)Reduced delays in TB CareSpeedful screening, sample collection and processing, and results provisionOne of the good things we started was giving priority to patients with coughs. Previously they were treated with other patients. The other thing is that there was no cough clinic at the beginning, but it was established during the time of the EXIT TB project and enabled us to isolate coughing patients from other clients. Isolated clients got early treatment (ART Head, Ethiopia)Whenever we went to the hospital, we were screened quickly for TB infection, sent to the doctor and samples taken quickly. If the result is negative, we were told about the precautions that we must take and in case someone tests positive, they put to start the medications right away (EXIT-TB patient, Uganda)Improved capacity and decision-making among HCWsTraining, co-learning and peer mentorshipThrough this study, we got more knowledge, and we met our TB targets because during that time TB was a problem. People did not have adequate knowledge about TB and especially in the entire area and even within the hospital. some did not have enough knowledge… even our staff within the hospital didn’t have enough knowledge about TB. Most of these staff got experience as they were implementing Exit-TB, and we used to have CMEs which further strengthened their knowledge of TB, how it is transmitted and how to screen. We were able to sensitise them, give them knowledge in their different departments, and mentored and supervised them. Before the project, we were getting a small number of TB clients but when EXIT- TB came, we were able to increase the number of TB clients… (TB Clinic Focal Person, Uganda)Reduced Lost to follow-up clientsStrong contact management systems, referral system, engagement of TB focal persons and link assistantsWe were able to increase our TB case identification by around $30\%$ or more because of the contact management that we put in place. We were able to identify other cases from the community, especially children and refer them to the facility. That is another one. Also, we were able to minimise the issues of lost clients because once the client comes into the system, we were able to closely follow up through the engagement of the TB focal person. If the client has not reported to the facility, then we will report to TB focal person and link assistants who conducted defaulter management activities. I think we only lost one case throughout the project compared to previous years where we would lose up to four cases. The lost client was an extreme case because a client committed a legal offence and ran away. We exhausted all efforts to contact him unsuccessfully. Reduced lost clients increased our treatment success rates (Clinical Officer, Kenya) The second contributor was the engagement of community health workers (CHWs) to offer health education and perform screening at different service delivery points within the facility. CHWs emerged as the dominant group of providers who facilitated a range of TB-related services. Most CHWs affirmed conducting ‘health talks’ to prepare patients to undertake to screen. They also conducted screening at different service points, assisted presumptive patients to be reviewed by doctors, and assisted with access to laboratory and other needed services within facilities. One CHW in Uganda described health talk topics as part of the EXIT-TB package and these topics included: what TB is, what the symptoms are, how TB can be prevented, what to do if they have the symptoms and how they get infected. CHWs in Uganda described receiving patients at the emergency department and facilitating screening after admission into wards. CHWs in Kenya described being involved in contact tracing and client follow-up. The engagement of CHWs in screening was further considered to facilitate patients to receive the right services on time and reduced cost on the part of patients through timely diagnosis and treatment initiation and follow-up.
The third contributor was the increased community awareness about TB as well as the increased linkage of clients, especially children from the community to the facilities. CHWs were described to have been engaged in raising community awareness of TB. Awareness rising included teaching community members about TB transmission, prevention, and treatment. Some CHWs indicated conducting both screening and testing as well as linkage of symptomatic clients to facilities for further management. The linkage of clients from communities to healthcare facilities especially children with a positive TB contact emerged as an important role of CHWs. A CHW in Kenya mentioned having a ‘referral booklet’ that documented presumptive TB patients in the community and referred them to nearby health facilities.
The fourth contributor was the tendency of HCWs to provide support and guidance to CHWs, for instance delegating them to talk to patients, allowing them to move around the facility and consult clinicians at any time, and giving them priority at different departments including laboratory, and offering them a good working environment. The fifth contributor to increased screening and TB case detection was improved screening infrastructure. When asked about the changes EXIT-TB brought to the facility, some participants cited improvements in infrastructure for TB screening. The creation of dedicated space for screening and triage of patients, sample collection and registration were mentioned to have occurred within some facilities as compared to before when all these activities were conducted in one area.
The sixth contributor to increased screening and diagnosis of new TB cases was the adoption of the ‘new screening criteria’ and training of implementors. Issues related to training as part of the EXIT-TB package are detailed below. Participants indicated that the EXIT-TB project embraced new screening criteria that were different from what is described in the existing guidelines. In all countries, for instance, TB suspects as per existing guidelines, are people with a cough for more than two weeks. However, with the EXIT-TB package, anyone with a cough was screened regardless of the duration. Further, EXIT-TB made it mandatory for diabetic patients and HIV-positive patients in stages 3 and 4 regardless of symptoms. Integrating TB case-finding activities in all RCH clinics also contributed to better EXIT-TB performance. The application of these new screening criteria was linked to increased early detection of TB patients and facilitated timely treatment as compared to a previous practice where patients were delayed being presumed due to the long-duration cough criteria. Some participants went further to link the EXIT-TB project with increased detection of Multidrug-Resistant (MDR) TB because of increased use of GeneXpert. For example, an official of at the Kenya NTLP affirmed thatWe were able to diagnose three MDR patients during Exit-TB implementation by using GeneXpert. You know, diagnosing one MDR case is considered the big thing because that is multidrug resistant TB. The patients came through the outpatient department and if we hadn’t placed screeners there, we could have missed them. We were able to diagnose two MDR cases who are HIV negative. This means if we only relied on HIV department, we could have missed them. This was a big deal for us (National level TB personnel, Kenya)
## Reduced delays in TB care
Although concerns about, machinery problems, expertise deficits and workload dominated some of the discussion (see below), a few participants affirmed reduced delays and waiting time in TB diagnosis. Speedful screening, speedful sample collection and processing and speedful results provision emerged as drivers of reduced delays. However, this was described as possible in areas where there were no machinery, expertise, or workload challenges. Reduced delays were also cited to result from prioritisation of TB clients and the establishment of special clinics or isolation units for clients with cough who then received much faster care as compared to previous practices where they were treated together with other clients.
## Improved capacity and decision-making among HCWs
Although few participants in Tanzania affirmed noticing no difference in terms of capacity to implement TB activities between EXIT-TB and routine TB Care, there was a broad consensus among the majority that the project has increased the capacity of HCWs to conduct TB screening and diagnosis, improved decision making among providers and reduced nosocomial infections transmission between providers and clients. For example, a Nursing Officer in Ethiopia mentioned that the project gave HCWs confidence and courage to conduct a TB risk assessment of clients and make decisions on isolation and treatment and, reduced transmission of respiratory infections from patients to HCPs and vice versa. Increased capacity among HCWs was linked to increased knowledge and skills acquired through training, co-learning and peer mentorship provided during the implementation of TB activities as part of the EXIT-TB project. For example, a TB Focal person in Ethiopia affirmed working together with other EXIT TB professionals in different roles which facilitated co-learning and increased work effectiveness. There were also affirmations that the project increased knowledge among community members on TB issues that contributed to increased TB diagnoses.
## Reduced number of lost to follow up clients
Amidst concerns of some clients not returning for sputum testing or treatment (see below), some participants mentioned that EXIT-TB reduced loss to follow-up rates because of strong contact management systems, strong community-facility referral system, engagement of TB focal persons and link assistants who implemented defaulter management activities. The reduced number of clients lost to follow-up was linked to increased treatment success rates.
## Theme 2: Challenges faced in delivering EXIT-TB package
Although few participants affirmed encountering no challenges, most participants mentioned several challenges and barriers. The challenges and barriers encountered during the implementation of EXIT-TB can be heuristically categorised into two groups: supply-side challenges and demand-side challenges (Table 3). These barriers appeared common in all implementation countries. However, the supply-side challenges dominated compared to the demand-side challenges. Table 3Challenges faced during the implementation of EXIT-TB PackageChallengesSpecific issuesIllustrative QuotesSupply side challengesService delivery barriers• Disorganized services• Delayed consultations• Short consultation time• Missed screening• Missed investigations• Poor screening integration in routine care• Limited space for TB services• COVID-19 pandemicThe barriers were so many, but the main barrier was limited contact tracing after identifying patients and starting them on treatment. Since we had small human resources, very few contacts tracing was done, and some clients were not followed up very well (Clinician, Kenya)The challenge emerged when COVID-19 started because we were told to close our facility for three months. After all, this was a dedicated COVID-19 care centre. Patients were shifted to another hospital (name) therefore we did not have time to serve them well (TB Focal Person, Tanzania)The challenge was COVID-19. You see signs suggesting TB but when you look at the patient and ask some questions about TB there are no symptoms of TB but when you do the COVID-19 test it comes out positive. So, you need to go through both COVID-19 and TB registers (Clinical Officer, Uganda)Human resource barriers• Negativity towards the project due to unmet financial expectations• Fear of TB infection among HCWs• Inadequate staffing and workload• Inadequate knowledge• Fear among providers during COVID-19• Inadequate financial motivation/incentives to HCWs• Technical barriersInadequate expertise among some screenersInadequate expertise in operating some TB diagnostic equipment (e.g., GeneXpert machines)Inadequate expertise in collecting sputum samples from childrenInadequate expertise for interpretation of CXR and absence of radiologists in some facilitiesThe health workers in (name) Hospital especially the clinicians at the OPD did not welcome the (EXIT-TB) package at the beginning. They were arguing that young children of 7 years and below are not allowed to undergo X-rays as they may face negative health consequences and they were telling us to get sputum samples instead. Nevertheless, those who were unable to provide samples were required to undergo chest X-Ray, but clinicians insisted that it will hurt them if they are aged between 5 and 9 years. This created a disagreement for some time but later the EXIT-TB focal person and District TB focal person enlightened them on the purpose of the study, and that chest is also used routinely to diagnose diseases even among young children, and later started showing cooperation by accepting children for X-Ray services (CHW, Uganda)The challenge was a negative attitude among providers towards TB disease; I don't know why. But the attitude of healthcare workers towards TB disease is so negative. Not many HCWs are interested in offering TB care.… healthcare workers sometimes neglect TB cases (because of fear of infection) and this also makes it difficult to treat them (Stakeholder, Uganda)What I can say is the people who were helping us in the identification of clients lacked the technical skills. Therefore, we needed frequent mentorship to improve their knowledge and smoothen the implementation of the program (OPD Incharge, Uganda)The unavailability of radiologists for urgent cases was a challenge. As a physician, I could go to the X-Ray room and read the results correlating with patients’ symptoms. But other health professionals-health officers and Nurses, might not be able to read the result. Thus, the availability of full-time radiologists or training other cadres was very important (Physician, Ethiopia)Equipment and supply barriers• Inadequate supply of stool containers particularly in Tanzania;• Absence of X-ray machines or dysfunctional X-ray machines• Stock-out of X-ray films and cartilages for GeneXpert machinesEquipment and supplies were troubling sometimes…., Sputum containers for children were inadequate. X-ray films were another barrier because we did not have enough expertise to read X-rays and we had to go where there is a screen (TB Focal person, Tanzania)For children under five years of age, we were taking stool samples for testing in the laboratory and if the results come out positive, we initiated treatment but if negative, s/he was counselled by the doctors. We screened young children by asking questions to their parents. We hand them with stool containers … but sometimes we run out of containers (CHW, Tanzania)There were times when many patients were screened and sent for X-rays and GeneXpert, however, the radiologist couldn’t interpret the results from X-rays and therefore could not give the results on time…. There was also a shortage of supplies, shortage of x-ray films, malfunctioning of GeneXpert, cartilage shortage, X-ray machine failure and service interruption. Most of these challenges were because of machine failures (TB Focal Person, Ethiopia)Demand side challengesNegative clients’ behaviours• Delayed care seeking• Late clinic attendance• Coming on non-clinic days• Defaulting careThe challenge was that patients had less understanding of the disease which necessitated health education. Once you give education and direct them to screening points, some of them refuse because their understanding is poor. They thought that going for screening is an indication that they have TB therefore they started self-stigmatisation but after intensive education, this improved a little bit (TB Focal Person, Tanzania)A major challenge within the community was a lack of awareness about the disease. They correlated any weight loss with HIV infection. Also, some of us believe that TB might expose us to stigma and discrimination (EXIT-TB patient, Ethiopia)[Patients] fear (to screen) because of fear of stigmatization because they have TB symptoms. You find people fear saying that maybe they have a cough. But we used health education sessions to minimise the fears. People with TB symptoms were separated and seen quicker to reduce the rate of maybe transmission if they have TB” (Clinical Officer, Kenya)[Patients] fear (to screen) because of fear of stigmatisation because they have TB symptoms. You find people afraid to say that maybe they have a cough. But we used health education sessions to minimise the fears. People with TB symptoms were separated and seen quicker to reduce the rate of maybe transmission if they have TB” (Clinical Officer, Kenya)Another challenge is when you are told to go do an X-ray in a different facility because there are no such services in the screening facility. Some of us could not go to the referral facility because of high transport costs (EXIT-TB patient, Uganda)Limited community awareness and negative beliefs and fears• Poor knowledge of TB diagnosis, treatment, and prevention• Equating screening to having a TB infection• Fear of coming to the facility• Fear of stigmaFinancial barriers• Failure to meet the cost of care• Failure to meet the cost of transport
## Supply-side challenges
Participants’ description of supply-side challenges can be grouped into (i) those related to service delivery; (ii) those related to human resource issues and (iii) those related to diagnostic equipment, and supplies. Each of these challenges are examined next.
## Service delivery barriers
Service delivery barriers included disorganised patient flow across points of care which resulted in unnecessary delays in screening; delays in medical consultation and short consultation time for TB presumptive patients because of high clinicians’ workload; clinicians not performing screening when not done at the entry points, clinicians not performing thorough investigations for TB presumptive patients, poor integration of TB screening in routine clinical investigations and limited space for offering EXIT-TB services within HIV clinics. A key service delivery concern was the COVID-19 pandemic. The challenges introduced by the COVID-19 pandemic were the closure of some facilities particularly those identified as COVID-19 centres, increased demand for personal protective equipment (PPEs) including facemasks which created deficits, fear of some clients attending facilities because of fear of COVID-19 infection (see below) and fear among providers to offer care to patients presenting with cough as they may be dealing with COVID-19 which increased chances of infection. Another challenge was the need to perform TB screening alongside COVID-19 screening which added more documentation responsibilities on the part of the provider. Some of these issues are highlighted in Table 3 below.
## Human resource barriers
Most participants cited a range of human resource challenges. Human resource barriers included negativity among some clinicians towards the EXIT-TB package at the beginning particularly in Ethiopia and Uganda, although participants in Tanzania and Kenya indicated good reception of the project among HCWs. Negativity towards the project, for instance in Uganda was linked to unmet expectations of financial gain from ‘the research project’, fear of being infected with TB even if they were taught how to prevent themselves from TB infection by the project and fear of negative consequences of CXR among young children. A TB focal person in Uganda attributed the negative attitudes towards the project among providers to the expectation of making money from the project on the part of the staff, which made it difficult to work with them when such expectations were unmet. Likewise, a clinician in Uganda indicated that some HCWs were fearful of working in TB clinics because of fear of infection and preferred to remain in the wards. Relatedly, a volunteer in one hospital in Uganda mentioned that clinicians were hesitant to request for CXR for children under the age of 7 years because of fear that it may contribute to negative health consequences in future. On the contrary, the negativity of HCWs towards the project in Ethiopia was linked to a poor understanding of the project. For example, a TB focal person in Ethiopia indicated that some providers had negative attitudes towards the project at the beginning because they did not have a good understanding of it, and they thought that it only sought to benefit specific people. However, sensitization by the EXIT-TB team was used to address these negativities. Other challenges included inadequate staffing for TB screening; inadequate knowledge among some staff about TB screening, and fear among some CHWs to perform screening and other TB-related activities during the COVID-19 pandemic. Inadequate staffing was cited to contribute to some patients not being screened particularly during weekends when only one clinician is available to deal with patients with all kinds of medical conditions. Understaffing and workload also contributed to poor documentation even among volunteers because of multiple documentation demands and being overwhelmed by clients.
When asked about the level of motivation and the support obtained from leadership, some participants described challenges of reduced motivation and poor coordination at the beginning of the project. Lack of motivation was linked to a lack of financial incentives and high workload. Gaps in coordination at the commencement of the project were linked to a feeling that the project is an added responsibility among providers than routine work partly because of being overburdened by the increased number of clients with no accompanying salary top-ups. This explains why there were concerns about non-payment of financial incentives among some participants in Ethiopia and Tanzania despite working tirelessly on the project. Affirmations of considering the project as additional responsibility and workload were common among participants in Ethiopia as compared to other countries. One participant commented:One of the causes for the lack of motivation among the staff was patient load and imbalance between the number of patients attending and the number of staff giving the service. The other thing is that this project work was considered an additional responsibility, so some groups of staff needed additional financial incentives on top of their salary to do this job (OPD Head, Ethiopia) There were disagreements among participants on the technical expertise barriers. One group of participants from all countries affirmed facing no technical barriers because of the training provided as part of EXIT-TB implementation. Training on screening among CHWs and training among clinicians on the interpretation of CXR was mentioned regularly by many participants across all countries. Phrases such as ‘there was no problem’, ‘we did not face technical troubles’, ‘we were trained very well’ and ‘we did not encounter challenges’ dominated among many participants. A District TB and Leprosy Coordinator (DTLC) in Tanzania linked the absence of technical challenges to the training of providers on ‘TB and GeneXpert, and that there was a tendency to refer clients for CXR to the nearest facility if the focal facility had no such service and expertise. On the contrary, one group of participants specifically cited technical challenges such as inadequate expertise among some screeners, inadequate expertise in operating some TB diagnostic equipment (e.g., GeneXpert machines); inadequate expertise in collecting sputum samples from children; inadequate expertise for interpretation of CXR and absence of radiologists in some facilities. A clinical officer in Kenya affirmed that “some clinicians did not have expertise in diagnosing TB through CXR”. Similar affirmations of difficulty in CXR interpretation were noted among a few clinicians in Uganda. One participant in Uganda indicated that people who were trained in CXR interpretation were not directly working on CXR-related activities. One participant in Ethiopia indicated that there was an insufficient number of people who could interpret CXR which contributed to delayed results to patients with some waiting for days. This explains why some participants felt that additional training or training of other cadres of providers on CXR interpretation should have been offered during project implementation. It is important to note that concerns about the absence of radiologists were more common in Ethiopia which necessitated using Compact Disks (CDs) to send results to other facilities. This practice was cited to delay results and treatment initiation and increased the chances of disease transmission if the results are suggestive of TB.
## Equipment and medical supply barriers
There were some disagreements on barriers related to diagnostic equipment and supplies. While most participants in all countries mentioned encountering many equipment and supply challenges, some participants affirmed encountering no challenges during EXIT-TB implementation. On the one hand, those who affirmed encountering no challenge were mainly those who performed coordination roles, and they linked this to the efficient delivery of materials and supplies from implementing partners. For example, a district TB and Leprosy coordinator in Tanzania affirmed that in case of a lack of X-ray films, NIMR was supplying them timely. On the other hand, the many who affirmed encountering equipment and supply challenges reported that most of them were eventually solved, or alternatives implemented at some point. These challenges included: inadequate supply of stool containers particularly in Tanzania; absence of X-ray machines in some facilities or dysfunctional X-ray machines in some facilities which necessitated referring clients to other facilities and stock-out of cartilages for GeneXpert machines. Problems with X-rays and GeneXpert functionality and inadequate materials related to these machines, particularly films and Cartilages respectively emerged as the dominant barriers across countries. A participant in Tanzania mentioned receiving expired X-ray films which could not generate X-ray images although these were then replaced. A participant in Kenya indicated not using the GeneXpert machine for the last two months because of the absence of cartridges necessitating the use of smear microscopy, which was perceived as less sensitive, less likely to detect drug resistance, requires two samples- spot and early morning samples and has long waiting time for the results (24–48 h). GeneXpert was perceived as highly sensitive, more likely to detect drug resistance detection, only requiring spot samples which reduces the burden of returning for early morning samples among clients and had short result processing time. There were also concerns about the absence of PPEs and delays in X-ray results. Delays in X-ray results were partly linked to limited expertise in the interpretation of the results, particularly in Ethiopia.
## Demand side challenges
Participants’ description of demand side challenges can be grouped into (i) negative clients’ behaviours; (ii) limited community awareness about TB and negative beliefs and fears; (iii) inability to meet financial demands of further care when needed. Each of these challenges are examined in detail next.
## Negative clients’ behaviours’
Some participants mentioned negative client behaviours including delayed healthcare seeking among those with TB symptoms, coming late to the facilities, attending the facilities on the days in which TB screening is not performed (e.g., weekends) and/or not returning to the facility when they fail to produce sputum on the day they were screened or after initiation of treatment.
## Limited community awareness about TB, negative beliefs and fears
Lack of awareness among community members of what TB is, its transmission and prevention, as well as treatment, emerged as common in the accounts of participants in all countries. Relatedly, some participants mentioned negative beliefs and fears among clients. There were beliefs that going for TB screening means having the disease and hesitancy among some people because of beliefs that people who are conducting TB screening are generating income from identifying TB clients. Furthermore, there were fears of the stigma associated with the isolation of those with TB symptoms (to minimize transmission) and fears that testing TB positive may expose them to stigma and discrimination from community members. Likewise, there were fears of screening for TB among some clients because some relate TB screening to HIV screening which contributed to the denial of having TB symptoms because of fear that they will also be screened for HIV. Some described fears of coming to the hospitals because of equating TB symptoms such as cough to COVID-19 during the pandemic, and refusal of putting on a face mask because of fear of stigma. Some however indicated that intensive health education and prioritisation of TB patients to receive faster care were used to offset these fears and beliefs although they persisted among some clients.
## Financial challenges
Financial challenges among clients dominated participants’ descriptions. Common financial challenges included clients’ non-attendance to X-ray services because of fear of being charged money and failure to meet financial demand for conducting Chest X-rays in a referral facility from where initial screening was performed. There were also challenges of failure to meet the cost of X-ray films were required to pay (e.g., in Uganda) and long distances to facilities in the absence of adequate funds to meet the cost of travel. It is important to note that one participant in Ethiopia mentioned the provision of transport of patients to the nearest facility for X-ray services as a way of addressing financial challenges; however, such support was cited to end when the EXIT-TB project ended.
## Theme 3: Suggestions for scaling up the EXIT-TB package
The suggestions for scaling up the EXIT-TB package were fourfold. First, increasing human resources for instance TB screeners and providers. One clinical officer in Kenya suggested that ‘more screeners and staff are needed in facilities to facilitate scaling up. Second, ensuring effective communication between leaders and ground-level implementers. Effective communication of challenges between implementers who encounter these challenges on the ground and leaders who are responsible for addressing some of these challenges was emphasised in Ethiopia. Third, few participants recommended financial incentives to healthcare providers. EXIT-TB focal persons in Tanzania regarded financial incentives to providers as a motivation for them to ‘identify more TB cases’ because they are few compared to the workload. Fourth and final, training and capacity building of implementers. Some recommended training before implementation, refresher training and additional rounds of training particularly on X-ray implementation as this emerged problematic among some clinicians.
The dominant recommendations for improving access to equipment and supplies were related to X-ray services. First, recommendations to ensure physical access to X-ray services. Many participants in all countries recommended the Government ensures the availability of X-ray machines in all facilities to reduce the time and financial resources needed to go for X-ray services in facilities other than where initial screening was conducted. Second, recommendations were to ensure financial access to X-ray services. Many participants recommended the continuation of subsidised or free X-ray services. Note that free X-ray services emerged as one of the drivers of project success. This explains why most participants recommended subsiding or waiving the cost of X-ray services as part of scaling up of EXIT-TB package. One EXIT-TB focal person in Tanzania went ahead to recommend the expansion of basic insurance coverage to meet the cost of X-rays. This recommendation was made given the minimum benefits offered by the Community Health Fund (CHF)- an affordable insurance scheme in Tanzania that does not cover the cost of X-ray services. Third, ensuring the availability of human resources required for X-ray services i.e., radiologists. Fourth and final, there was a recommendation to ensure a sustainable supply of X-ray films. Related to this, was a recurrent recommendation of ensuring the availability of GeneXpert cartridges as well as other materials such as registers.
Some participants emphasised the need to improve infrastructure for TB services. Such recommendations were common in facilities where space for offering TB services was challenging. For instance, a participant in Kenya recommended redesigning rooms used for TB services to increase space. Relatedly, few participants suggested improved project financing through Government takeover particularly the Ministry of Health and implementing partners to help a wider scale-up. This suggestion was made in view that it is the Government’s responsibility to control TB transmission. The strategies for Government takeover suggested in Ethiopia included integration of this program into the existing health system, assigning personnel to this program without a need for payment apart from their salaries and use of existing resources. Related to this, some participants recommended an extension of implementation time for the project based on its notable success. Some participants commented:For the government to implement this at a large scale, that needs to ensure the availability of X-rays in all facilities as part of the package (CHW, Tanzania)I will advocate for improving the supply of TB testing materials to ensure that the machines run with no interruptions. Emphasising continuous screening with no laboratory materials is a waste of effort. So, at least they need to ensure a regular supply of cartridges for GeneXpert for sustainability (Clinical Officer, Uganda)It would be very good if this program was not interrupted… We should not wait to be funded externally again, it would be better if the government can assign personnel in this area by paying their salary and we health professionals continue implementing this program without inquiring about an extra payment. Also, we can use the government resources we already have and let the program continue. For example, there are two Ambulances here in our centre so we can use one in the countryside and one here in the city. The Government needs to implement this package. If the government incorporates this EXIT-TB program into its health system and implements it throughout the country, I believe TB can be eradicated like malaria from our country (Health Extension Nurse, Ethiopia) Fears and negative beliefs and behaviours emerged as challenges of the EXIT-TB project, necessitating the need to address stigma and fears associated with TB. One clinician in Kenya suggested that ‘there is a stigma about TB and then there is that fear factor” without offering any details. To address these fears and stigma, some participants suggested improving community awareness about TB diseases through community education and health talks. Few participants expressed concerns about loss to follow-up clients e.g., those who are not returning after failing to produce sputum on the day they were screened or those initiated on treatment (see above). There were also concerns about inadequate contact tracing that emerged among some participants (see above). Consequently, some participants recommended more use of link assistants for tracking lost clients within the communities and linking them to the facilities. There were also recommendations to increase CHW allowance and/or facilitate transport support for CHWs to conduct contact tracing. Some participants commented:The things that would contribute to success. Scaling up includes educating and giving health talks to communities. Training the community by emphasizing why they should get screened is important to reduce fears and stigma towards the disease (Community Volunteer, Uganda)*There is* a need to maximise the engagement of link assistants because they assist in identifying people with coughs and link them to clinicians. Once the clinicians investigate and find positive patients, some of them get lost along the way. Therefore, link assistants can be useful in tracking and linking them to TB treatment services (Clinical Officer, Kenya)
## Discussion
This paper examined the contribution of EXIT-TB, challenges encountered during implementation and suggestions for scaling up the EXIT-TB package in Tanzania, Kenya, Uganda, and Ethiopia. The implementation of the EXIT-TB package was done in recognition of low TB case detection in EA despite a decline in prevalence, incidence, and death rates globally [1, 17–22]. IDIs were conducted with service providers, policymakers, and other implementing partners with a focus on their experiences with EXIT-TB implementation, changes noticed and their insights on important considerations for scaling up the package in a similar or another setting.
The findings indicate a broad consensus that EXIT-TB has contributed to an increase in TB screening and diagnosis in the study settings. The findings further indicated an improvement in TB service delivery by reducing waiting time for screening and diagnosis. Healthcare providers appear to have benefited from EXIT-TB implementation by gaining more competence in conducting TB screening and diagnosis and improving their decision-making capacity. The provision of chest X-rays as screening and diagnostic tools and free X-ray services for those who could not afford them facilitated more TB case finding. Likewise, TB case finding integration to other clinics, community sensitization and cooperation between providers at the facility and community as well as better linkage of clients from communities to facilities emerged as major contributors to the success noted. One of the important practices emerging in our study was the engagement of CHWs in community sensitization and screening at the facility as a driver of increased screening. Another important finding was the use of new simple screening criteria as the key drivers of TB screening uptake. During EXIT-TB implementation, anyone presenting with a cough of any duration was screened contrary to the guideline in implementation countries where a cough of more than two weeks is often considered. Our findings strongly mirror the findings of most of the previous studies. Evidence continues to indicate that integrated TB screening packages have significantly increased TB diagnosis in many countries. For instance, a recent study in Ghana suggests that an integrated TB screening package with a simplified process for TB screening, linkage, integration of screening services across service delivery points and referral and engagement of community health care providers are the major drivers of increased TB screening [11, 23]. Another study in Eswatini indicated an increase in TB screening among pregnant women because of the integration of TB/HIV services in Reproductive Maternal Neonatal and Child Health settings despite some concerns with symptom screening [24]. Furthermore, it is important to note that the contribution of CHWs in driving the success of TB screening interventions has been widely documented [25-27]. In India for instance, CHW has been documented to be critical in the success of active TB case findings [25]. In Mozambique, CHWs have been the driving force behind the success of facility-based TB screening [26]. This indicates that the value of employing CHWs in integrated TB screening activities is indispensable. This explains why Sinha, Shenoi & Friedland [27], have illustrated the effectiveness of CHWs across the entire cascade of TB care and outlined additional opportunities for CHWs to address challenges particular to the TB pandemic. Taken together, these findings indicate that integration of TB screening into routine care, affordability of screening tests, use of CHWs for community sensitization and screening and use of simplified screening criteria are critical in increasing TB screening and diagnosis.
The study unmasked a range of supply and demand-side challenges related to the implementation of the EXIT-TB package and the entire TB program in the countries. On the one hand, key supply-side challenges included practice-and resource challenges. Important practice challenges included the concerns of disorganised care in some facilities and negative attitudes towards the project and TB-Diagnostic procedure among some HCWs. Relatedly, important resource challenges included infrastructure barriers and human resources for health issues in terms of expertise and quantity as well as an inadequate stock of essential materials. Dysfunctional or absence of X-Ray and GeneXpert services in some healthcare facilities emerged as a recurrent challenge. Looking across the literature, similar practice and resource challenges have been widely documented on the supply side as facing not only TB screening but also healthcare service provision as a whole. Specifically, to the implementation of integrated TB packages, practice challenges such as the negativity of HCWs and resource challenges such as infrastructure, equipment, essential materials, and human resource gaps have been documented to Impact TB screening in some African countries [2, 4, 5, 11, 14, 23, 28, 29]. For instance, a recent qualitative study examining factors that influence the implementation of TB screening among PLHIV in selected HIV clinics in Ghana [23] reported negative attitudes and low commitment of HCWs to TB screening and limited facility infrastructure as the main barriers. Consequently, the need to increase HCWs’ commitment towards TB screening interventions was recommended by the authors. Within East Africa, concerns of understaffing, inadequate diagnostic materials, service disorganization and malpractice have been recently identified by our team as impacting TB Diagnosis in Kenya, Uganda, and Tanzania [11]. This indicates that the success of TB Screening interventions requires addressing both practice and resource challenges in healthcare facilities.
On the other hand, the key demand side challenges emerging from our study included delayed care seeking, immature discontinuation of the screening process because of failure to return to the facility, negative beliefs, fears of stigma towards screening and financial challenges. Similar to supply-side challenges, demand-side challenges have been widely discussed in the literature. A common approach in most literature is to document both supply and demand sides concurrently. For example, studies in Uganda have documented concerns of infrastructure, understaffing and expertise concerns on the supply side and stigma and financial challenges on the demand side as the barriers to TB screening [28, 29]. However, a few works of literature have specifically highlighted the demand side barriers to TB screening. For example, a qualitative study of TB patients in Mozambique identified concerns of stigma related to diagnosis and treatment, inadequate knowledge, and negative beliefs as among the barriers to TB diagnosis and treatment [30]. The existence of these challenges may explain why participants suggested improvement of service delivery, access to diagnostic equipment and supplies, physical infrastructure, and financing, addressing client fears and stigma, and improving the linkage of clients from communities to facilities for scaling up of EXIT-TB packages. This indicates that the success of the integrated TB screening package largely depends on the efforts to address both the supply and demand side challenges more broadly.
## Limitations
While the implementation of the EXIT-TB package in multiple countries is a major strength, this may also be a limitation. The implementation of the EXIT-TB package across many countries with many healthcare facilities covering rural and urban settings. While a focus on fewer countries could have resulted in richer data, we believe that multi-country studies are important in generating evidence that can be easily adopted globally. Some researchers have provided evidence to support this notion [31].
## Conclusion
The findings of this study indicated that the EXIT-TB intervention was described to have facilitated an increase in TB case detection and reduced delay in Tanzania, Kenya, Uganda, and Ethiopia. However, similar to other public health programs, the implementation of the EXIT-TB package is not without challenges. Both supply and demand side challenges were mentioned indicating that addressing these challenges is necessary to maximise the success of the EXIT-TB package. More specifically, addressing the challenges of service delivery, infrastructure, equipment and supplies as well as demand side challenges of TB knowledge, screening fears and linkage is needed to maximise the impact of the EXIT-TB package.
## Supplementary Information
Additional file 1. National institute for medical research - Muhimbili EXIT-TB Project.
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27. Sinha P, Shenoi SV, Friedland GH. **Opportunities for community health workers to contribute to global efforts to end tuberculosis**. *Glob Public Health* (2020.0) **15** 474-484. DOI: 10.1080/17441692.2019.1663361
28. Nansera D, Bajunirwe F, Kabakyenga J, Asiimwe PKJ, Mayanja-Kizza H. **Opportunities and barriers for implementation of integrated TB and HIV care in lower level health units: experiences from a rural western Ugandan district**. *Afr Health Sci* (2010.0) **10** 312-319. PMID: 21416031
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30. De Schacht C, Mutaquiha C, Faria F. **Barriers to access and adherence to tuberculosis services, as perceived by patients: a qualitative study in Mozambique**. *PLoS ONE* (2019.0) **14** e0219470. DOI: 10.1371/journal.pone.0219470
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|
---
title: 'Bioelectronic microfluidic wound healing: a platform for investigating direct
current stimulation of injured cell collectives†'
authors:
- Sebastian Shaner
- Anna Savelyeva
- Anja Kvartuh
- Nicole Jedrusik
- Lukas Matter
- José Leal
- Maria Asplund
journal: Lab on a Chip
year: 2023
pmcid: PMC10013350
doi: 10.1039/d2lc01045c
license: CC BY 3.0
---
# Bioelectronic microfluidic wound healing: a platform for investigating direct current stimulation of injured cell collectives†
## Abstract
Upon cutaneous injury, the human body naturally forms an electric field (EF) that acts as a guidance cue for relevant cellular and tissue repair and reorganization. However, the direct current (DC) flow imparted by this EF can be impacted by a variety of diseases. This work delves into the impact of DC stimulation on both healthy and diabetic in vitro wound healing models of human keratinocytes, the most prevalent cell type of the skin. The culmination of non-metal electrode materials and prudent microfluidic design allowed us to create a compact bioelectronic platform to study the effects of different sustained (12 hours galvanostatic DC) EF configurations on wound closure dynamics. Specifically, we compared if electrotactically closing a wound's gap from one wound edge (i.e., uni-directional EF) is as effective as compared to alternatingly polarizing both the wound's edges (i.e., pseudo-converging EF) as both of these spatial stimulation strategies are fundamental to the eventual translational electrode design and strategy. We found that uni-directional electric guidance cues were superior in group keratinocyte healing dynamics by enhancing the wound closure rate nearly three-fold for both healthy and diabetic-like keratinocyte collectives, compared to their non-stimulated respective controls. The motility-inhibited and diabetic-like keratinocytes regained wound closure rates with uni-directional electrical stimulation (increase from 1.0 to $2.8\%$ h−1) comparable to their healthy non-stimulated keratinocyte counterparts ($3.5\%$ h−1). Our results bring hope that electrical stimulation delivered in a controlled manner can be a viable pathway to accelerate wound repair, and also by providing a baseline for other researchers trying to find an optimal electrode blueprint for in vivo DC stimulation.
An on-chip bioelectronic platform for exploring precise direct current electric field stimulation of injured keratinocytes with salt-bridgeless electrodes.
## Introduction
For most of us, a wound is a minor nuisance, which heals itself without much conscious effort. However, for people with certain chronic diseases (e.g., diabetes mellitus, peripheral vascular disease), compromised immune systems (e.g., systemic lupus erythematosus), or even with common systemic factors such as poor nutrition and aging, acute wounds are more prone to become chronic. In fact, the high prevalence of chronic wounds constitutes an enormous socioeconomic burden (≈1 to $3\%$ of the total healthcare spending in developed countries and growing as the median age of populations grows older),1 as well as suffering for the actual patients.2,3 Strategies to promote faster healing for these patient groups are therefore urgently needed. The healing process is often categorized into four sequential, yet overlapping phases: hemostasis, inflammation, growth, and maturation. There are many cell types involved in these concurrent phases (in general order of appearance): activated platelets, neutrophils, monocytes, macrophages, mast cells, dendritic cells, T cells, endothelial cells, pericytes, hematopoietic stem cells, fibroblasts, myofibroblasts, melanocytes, and keratinocytes.4 In the wound healing process these cell types are recruited, whether they are cells in close vicinity to the wound site or cells that have to traverse long distances via the circulatory system.
Chemical, mechanical, and electrical gradients all contribute to recruiting or guiding the aforementioned cells to the wound: processes called chemotaxis,5 haptotaxis/durotaxis,6,7 and electrotaxis/galvanotaxis,8 respectively. Notably, electrotaxis refers to the ability of cells to align their migration with electric fields (EFs). Neutrophils,9 monocytes,10 lymphocytes,10 macrophages,11 endothelial cells,12 fibroblasts,13 and keratinocytes14 have all been revealed to be electrotactic. Interestingly, wounds naturally form small EFs when the skin's epithelial layer is broken. This transepithelial potential (∼10 mV to 60 mV), which actively pumps sodium ions (Na+) basally inwards and chlorine ions (Cl−) apically outwards, is short-circuited after injury where positive current flows radially towards the wound center.15 *There is* a large inter-individual variability in the strength of this naturally-occurring (i.e., endogenous) EF, which depends on the systemic nature of the patient (e.g., age, disease). For example, it was shown that the lateral EF of 18–25 year-olds (107 mV mm−1 to 148 mV mm−1) is nearly $48\%$ larger compared to that of 65–80 year-olds (56 mV mm−1 to 76 mV mm−1).16 Taken together with the fact that most skin cells exhibit electrotactic ability, the discovery of these endogenous EFs within wounds has led to the hypothesis that electrical cues are essential for migratory processes in wound healing.17,18 When it comes to exogenously supplying direct current EFs to wounds, there is evidence that undermines the significance of induced EF direction in in vivo wound healing. It has been indicated that applied ionic flow, regardless of EF direction, is a key driver in accelerating in vivo wound closure through up-regulation of mitogen-activated protein kinase (MAPK) signaling pathways and rapid reorganization of cellular components.19–21 Nevertheless, the hypothesis that the electrotactic-induced polarization of skin cells is paramount to properly distributed wound healing still remains an ever-evolving topic of interest, especially since earlier studies have not fully unraveled the merits of both EF magnitude and distribution on electrically-guided closure of physiological wounds.
Keratinocytes, the most prevalent cell type in the skin, are densely packed within any given lateral layer, and are also tightly arranged in vertical tiers (i.e., stratified) where they become more differentiated the closer they get to the outermost, apical layer. In the skin, as well as in confluent cultured cell layers, keratinocytes migrate as a collective.22 Prior in vitro studies on electrotaxis of skin cells have typically focused on single cells, thus neglecting how the complex organization of cells in actual skin impacts the migratory behavior. However, collective cell migration is more indicative of in vivo cell dynamics for cell types like keratinocytes. On a group level, coordination within a cell collective starts with the protrusion of cells at the group's edge (i.e., leader cells) and is propagated through cell-substrate forces (i.e., traction of cell membrane-bound focal adhesions with the substrate)23,24 and cell–cell forces (i.e., normal and shear forces via adherens junctions).25,26 The first demonstration of collective keratinocyte electrotaxis was shown by Zhao et al., but they only explored EF directionality on single wound edges as compared to how endogenous converging EFs merge a perimeter of wound edges.27 Recently, there has been a resurgence in studying electrotaxis-mediated group migration with uni-directional28–30 and converging EFs.31 For instance, Zajdel et al. showed that two patterned monolayers of keratinocytes, with a 1.5 mm gap, can be influenced by an EF stimulus (i.e., 200 mV mm−1) and close the gap between two epithelial sheets twice as fast as compared to non-stimulated controls.31 This work set an important precedent, and calls for further translational efforts to make EF-accelerated repair accessible to patients who are at risk of chronic wounds. A crucial step in this process would be to more closely replicate the process of wounding, where mechanical stress (e.g., physical removal of cells) induces ATP and gap junction-mediated calcium waves across a monolayer of keratinocytes.32 Furthermore, as healthy skin typically heals well, it is paramount to analyse this effect in cultured disease models, which are associated with impaired wound healing and keratinocyte motility.
In this work, the aim was three-fold: (a) explore the influence of electrical guidance cues (EF distribution) on the rate of wound closure, (b) demonstrate how a non-metal DC stimulation electrode material can be stable and safe for cells even without typically needed salt bridges, (c) and establish a diabetic wound model to examine if electrical stimulation improves otherwise poor wound closure dynamics. In order to facilitate this, we developed a microfluidic version of the “classical” scratch wound assay, which allows us to explore the parameter space in which EF stimulation accelerates wound repair. Multiple fluidic concepts are analyzed to identify the layout that best mimics the standard scratch assay method, but with the superior experimental control provided by the microfluidic platform. Direct current (DC) compatible electrode materials are a key ingredient for EF stimulation in vitro, and will furthermore be essential for clinical translation.33,34 Here, we show that electrodes based on a combination of laser-induced graphene (LIG) and a PEDOT:PSS hydrogel, integrated within the platform, were capable of sustaining DC stimulation over hours. This is not only important for in vitro application, but likewise a prerequisite for subsequent clinical translation of the concept. Leveraging this wound-on-a-chip environment, we were able to explore the electrical wound healing concept, first for healthy cells and then using a culture-based model mimicking diabetic keratinocytes. Not only were we able to demonstrate that EF stimulation was effective for accelerating the wound healing for the healthy cells but also, importantly, we could restore the impaired mobility of diabetic-like cells as well.
## Microfluidic design for tailoring electric field distributions around wounds
An in vivo wound naturally generates an EF, which points radially towards the wound center.35 Electrically speaking, the wound center acts like a current sink (i.e., cathode) surrounded by an ionic current source (i.e., anode).15 When designing the microfluidic device, it is this principle that we mimic. An ideal setup would involve having an infinitesimally-small cathode at the wound center, such that all EF vectors guide electrotactic cells to the center. However, the limited ionic charge storage capacity of the electrode materials prevents such miniaturization. An alternative to miniaturizing the cathode, which can visually occlude the closing wound, is to explore how the layout of the microfluidic system can be tailored to allow EF stimulation to converge towards the wound center. Instead, our microfluidic design included a merging microfluidic network where the wounded cells will be centered and different combinations of electrode configurations around the wound can be explored.
The core platform design question is how to design the microchannel network that houses the cells and wound (Fig. 1). While a standard straight channel design would lend itself nicely to applying an EF from one side of the wound (i.e., uni-directional EF), one cannot provide a converging field. A “t-junction” design is another composition that could tackle both a uni-directional EF and a converging EF, depending on how the electrodes are placed and connected to the constant current source. However, in the converging case, the current will take the path of least resistance and turn the bend quickly into the cathode-containing channel, thus leaving a current and EF “dead zone” where the centrally-located wound resides (Fig. S1†). Upon computational investigation of different designs, we came up with a “peace sign” architecture, which mitigates the current “dead zone” by angling the anode-containing channels to reduce the current bending and by adding a fourth channel that enables a more practical physical removal of cells, which we call a “scratch runway” (Fig. S1†). The width of this fourth channel (900 μm) was chosen so that a p10 pipette tip (≃700 μm) could be used as the wounding tool, which is a typical way36 to create in vitro wounds. Then, this lower branch yields the option to have perfusive hydrostatic flow in order to ensure culture health throughout days-long experiments. In order to demonstrate this additional functionality, we used different colored dyes to validate that an effective converging flow can be accomplished without the use of any active component (i.e., no perfusion pump or similar equipment needed) (Fig. S3†). However, for all functional tests, the perfusive flow option was not utilized as to decouple cell migration due to replenishing nutrients versus just the applied EF (12 hours long).
**Fig. 1:** *Microfluidic design to allow different electric field (EF) distributions around the wound. (a) Microchannel design options that were investigated. Red (+) signifies the anode(s) and black (−) denotes the cathode. Yellow dashed lines show the current and EF direction. Light purple shows the microchannel filled with a cell monolayer. White boxes show where the wound would be created. (b) Process flow of how the bioelectronic microfluidic platform is realized. For grouping of important steps, three panels are shown in the following sequential left-to-right order: yellow, blue, and red. In preparation (yellow), multiple microfluidic double-sided adhesive pieces are laser-structured, cleaned, and applied to standard Petri dishes. In parallel, two laser cut pieces of acrylic are solvent bonded to create microfluidic lids to be placed later. In cell seeding/wounding (blue), a high density suspension of keratinocytes is spotted over the open microchannels and allowed to seed before adding more medium and incubating overnight. Afterwards, a pipette tip is fitted to a vacuum aspirator to perform the wounding process. After wounding, the bulk medium is aspirated and the microfluidic adhesive's top protective liner is removed. In lidding/stimulation (red), the microfluidic lid is added to the newly-exposed adhesive to complete the device and medium is added to the microchannels and connecting reservoirs. Electrodes are assembled based on the desired EF strategy, and submerged into the reservoirs. The dish's lid is added and the complete package is placed into an incubated microscope. Note that each device has one channel for stimulation and three non-stimulated controls. (c) Finite element analysis of EF distribution within the microchannel for both electrode configurations where black arrows show the current direction and magnitude. The top illustration shows the dimensions of the microchannel and the typical profile of the wound. The orange and green lines show exemplary profiles to note the EF strength at the center of the wound in x and y. The bottom graph shows the aforementioned orange and green profiles for both electrode configurations. Note that an electrode input current of 25 and 20 μA is chosen so that center of the channel is about 200 mV mm−1 for the uni-directional and converging cases, respectively. The images on the right show the final realized devices, where the inset graphics show the current flow. The yellow X shows the current going from the open well into the closed microchannel and the yellow dot shows it coming out of the microchannel and into the well.*
The next challenge is how to develop a scratch assay protocol inside a microfluidic channel where one can seed, grow, and then scratch a monolayer of cells all within an enclosed domain. Non-contact techniques of creating a scratch in a closed channel, such as UV exposure through a shadow mask or using a laser to ablate cells, fail to result in reproducible conditions as unsuccessful removal of debris has propagating negative effects on the leading edges of the wound. For this reason, we developed an approach that allows for cells to be seeded onto an adhesive-masked Petri dish where the cells adhere to the exposed area of the dish (Fig. 1). The protocol for making the electrotaxis wound healing device leverages common prototyping equipment (i.e., CO2 laser) for both microfluidic adhesive structuring37 and electrode fabrication38 (see Methods section for more details). Using a double-sided adhesive with protective liners on both sides allows for removal of the bottom-side liner to adhere the exposed adhesive to the dish, all the while keeping on the top liner to protect the top adhesive from getting wet, thus acting as a sacrificial layer. Also adjacent to this step of device preparation, thick (8 mm) and thin (0.5 mm) acrylic sheets are laser cut to yield reservoirs/wells and fluidic vias, respectively. These two layers are solvent (i.e., dichloromethane) bonded to act as the microfluidic lid and are set aside until the final assembly steps. Next comes cell seeding, overnight incubation, and subsequent injury of the cell monolayers in the traditional way (i.e., mechanical removal via a pipette tip36) (Fig. S2†). We found that wedging a vacuum aspirator metal tip into a sterile plastic pipette tip allowed for more reproducible removal of cells with less cellular debris left in the wake of the scratch path. After wounding and aspirating of the bulk medium, the adhesive's top liner is removed so that the acrylic lid could be manually aligned and pressed onto the exposed adhesive to complete the final enclosure of the microchannels, which was then immediately filled with medium (Video S1† to see how the liner is peeled and the lid is attached). To be clear, the individual reservoirs are only fludically connected via the microchannels. A set of non-metal conducting hydrogel electrodes (i.e., PEDOT:PSS hydrogel-coated laser-induced graphene) are assembled with custom 3D-printed pogo pin electrical connection adapters and holders. Note that the circles at the end of each branching microchannel act as the beginning of the open reservoirs, such that only the four branches are confined to a micron-size geometry in all dimensions. For further illustration of the fluidic architecture and reservoir/microchannel interface, see Fig. S1 and S3.† Two configurations were analyzed in this study (Fig. 1c). The first will be referred to as “uni-directional EF” and corresponds to an EF across the wound (i.e. not a converging field). This strategy is the most straightforward to implement, as it requires only two electrodes, which are placed at either side of the wound (Fig. 1a). The electrotactic effect in this case will mainly apply to one side of the wound. If only electrotaxis-related forces would dominate the migratory behavior as seen in single cell keratinocyte cultures, then one would expect both wound edges to go with the EF direction and essentially move the same wound laterally.14 Nevertheless, the majority of studies that explore electrical wound healing in vivo exploit this type of setup, despite it not fully accounting for electrotaxis to act on both edges of the wound.39,40 The second configuration is here referred to as “pseudo-converging EF” since there is an anode on each side of the wound where a timed relay can switch between the two anodes to push the wound close from both sides in an alternating fashion. Consequently, in this scheme, the electrotactic effect will apply symmetrically to both sides of the wound. Additionally, the disconnected and polarized anode can passively recharge with ions from solution while the other anode is delivering charge.34 Using finite element analysis (FEA) of the three-dimensional microfluidic device, we identified the input current needed to achieve ≃200 mV mm−1 at the center of the wound, which has been shown to be an optimal EF strength for in vitro keratinocyte electrotaxis.41–43 *There is* a precedent that demonstrates how FEA simulation is accurate in predicting measured experimental EFs in microchannels.44 As validated by the FEA model, the geometric confinement around the monolayer of wounded cells provided by the microchannel allows for precise control of the field distribution (Fig. 1c). The current and normalized EF direction is indicated via the black arrows and the EF magnitude is depicted via the scaled color gradient and size of the black arrows. In order to visualize the EF magnitude along and across the wound, two profile lines are drawn (blue and red, respectively, in Fig. 1c). The plot shows the EF magnitude and corresponding microchannel current density along these lines for a given electrode input current (25 μA and 20 μA for the uni-directional and pseudo-converging cases, respectively). The hollow circles show that the uni-directional case yields a more uniform EF distribution across (i.e., x-direction, red) and along (i.e. y-direction, blue) the wound compared to the pseudo-converging case. The center of the wound is defined to be where both lines intersect (for a comparison to non-angled branched designs, see Fig. S1†). The input currents were selected to match a value of 200 mV mm−1 at this intersection point.
## Minimal pH shifts and joule heating during direct current stimulation
For reproducibly generating electrotactic behavior, it is fundamental that electrotactic effects are decoupled from other possible interferences. For instance, faradaic reactions at the electrode–electrolyte interface can induce redox reactions, which lead to a lowering of pH at the anode (i.e., more H+) and a rise of pH at the cathode (i.e., less H+). Since previous studies have explicitly pointed to pH as a determinant factor of electrotaxis45 and since the confined volume inside the microfluidic compartment causes the system to be more sensitive to such variances, we explicitly validated the pH stability within the microfluidic system under DC stimulation. In order to monitor the pH, the microchannel floor is coated with the colorimetric pH-sensitive polymer polyaniline (i.e., PANI) (Fig. 2, Video S1†). The color changes with the oxidation state of the conjugated polymer. At low pH (≃3 to 5), PANI is in its emeraldine salt oxidation state and gives a gradient color from yellow-green to dark green as the pH increases. At a more neutral pH (≃6 to 8), PANI gradually changes to its emeraldine base oxidation state and begins to transition from dark green to green-blue to blue. Finally, at higher pH (≃9 to 12), PANI progresses to a fully oxidized state called pernigraniline, where the color goes from blue to dark blue to dark purple.46
**Fig. 2:** *Measuring pH shifts due to direct current (DC) stimulation. (a) Polyaniline (PANI) coated dishes subjected to solutions already measured using a benchtop pH meter. (b) Calibration curve relating the hue of the PANI-coated dishes to the pH. The curve fitting was done with a “bi-dose response” sigmoidal fit. (c) Demonstration of how the PANI-coated substrate can sense local electrochemically-induced changes in the pH. Cathode is on the left and anode on the right. Electrodes used are the same materials, but smaller (3 mm diameter), as the electrodes used throughout the paper. Solution is unbuffered 0.9% NaCl and current is constant at 0.40 mA. White arrows point to local pH changes. (d) Example of the final assembly of PANI-coated scratch assay devices when filled with hydrochloric acid (HCl) spiked saline (pH = 5.4) and phosphate-buffered saline (pH = 7.4). (e) Time-lapse example images of 1× phosphate-buffered saline (PBS) filled device with PANI-coated microchannels. The stimulation protocol is a uni-directional EF configuration and stimulated for 22 h at 25 μA (see Fig. 1). Yellow arrows show the current path from the reservoir down into and across the microchannel. Note that the images on the bottom two rows were taken with a wide-field camera, whereas the those on the top row were taken with a 5× objective on an incubated (37 °C & 5% CO2) microscope. All quantitative data were plotted using the 5× images in order to minimize any impact from ambient light fluctuations. (f) Quantitative output of pH changes as a function of DC stimulation time. Colors correspond to the imaging locations shown in (e) of the center image. The black trace represents the non-stimulated control microchannel. The blue trace represents the average of 3 locations (before, center, and after wound zone) of the stimulated microchannel. The red and gray traces are taken at the interface between the reservoir and microchannel entry/exit, which show the anode and cathode reservoirs, respectively.*
The purpose of the PANI coating is to identify at which cutoff time the DC stimulation will induce notable pH shifts in the microchannel for the given input currents. First, PANI's pH-sensitivity was tested by coating a thin layer onto small Petri dishes and filling them with 15 known pH buffers to create a calibration curve relationship between the pH and the consequent PANI color (i.e., hue) (Fig. 2a and b). The sensitivity to indicate pH change during 20 s of DC stimulation was first verified using a non-buffered saline solution ($0.9\%$) with relatively high current density (5.7 mA cm−2), which is about 250× greater than what was planned for the microchannels, in order to rapidly see pH changes below the electrode pair. As soon as 10 s into stimulation was reached, the area under the cathode and anode began to turn more basic (purple) and acidic (green), respectively (Fig. 2c). This demonstrated PANI's ability to display rapid visual pH dynamics as a function of electrochemical faradaic by-products before moving into the device architecture.
A phosphate-buffered saline (PBS) solution of pH 7.4 is used as the testing electrolyte, which also has a similar osmolarity and conductivity to the keratinocyte medium. PANI is coated in the microchannel analogous to the way cells are seeded in the device (Fig. 1b, step 3 and 2d). We here focused on the uni-directional EF case, which represents the highest current injected into the system, and therefore can be expected to correlate with stronger potential pH shifts. Also, the pseudo-converging EF does not induce an acidic pH swing of the same degree due to minimizing the injected faradaic current by way of using two anodes that rely on relay-switching and passive ion recharging (Fig. S4†). Furthermore, the spatiotemporal color/pH stability is verified using the non-stimulated control channel (Fig. 2f – black). Imaging is performed in two ways. The first experiment was carried out with a transmission light microscope fitted with a 5× objective to control light intensity and minimize ambient fluctuations (Fig. 2e, top row). The second was performed with a wide angle camera lens to concurrently capture a global view of all control and stimulation channels (Fig. 2e, middle and bottom rows). As expected, PANI at the transition between the anode reservoir and microchannel (i.e., left circle and channel) turns more acidic and vice versa for the cathode well (Video S2†). From this, we conclude that at least 12 h of DC stimulation is possible without inducing significant pH shifts in the microchannel's wound zone for this combination of input current, electrode size, stimulation configuration, pH-buffering capacity, and microfluidic design. We would like here to emphasize that no cross-flow was used to dilute potential electrochemical by-products, and also no salt-bridges nor any other supporting systems are utilized, which otherwise are essential components when using other metal-based electrode systems.
Another possible interference of applying DC across a fluidic resistor (i.e., microchannel) is that joule heating effects could increase the metabolism of the cells and allow them to migrate faster. In order to account for this, it is important to have an idea of the amount of joule heating as a function of DC stimulation time. Thermocouples are susceptible to the effects of electromagnetic fields, particularly induced voltages from the EF, and are typically physically much larger than the microchannels employed in this work. Therefore, we opted to focus on a FEA simulation-based approach. Specifically, joule heating effects within the electrolyte were computed using the multiphysics coupling of electromagnetic time-independent equations (current conservation based on Ohm's law and scalar electric potential) and heat transfer time-dependent equations (energy conversation using Fourier's law). Both uni-directional and converging cases are explored within a 12 h stimulation cutoff. The model involved simplifying the geometry to just the stimulation microchannel network, connected reservoirs, and surrounding plastic substrate and lid, as well as the electrodes sitting on top of the reservoirs (Fig. S5†). Even after an energy transfer of 2.74 J (25 μA, 12 h, 101.6 kΩ) and 1.87 J (20 μA, 12 h, 108.3 kΩ) for the uni-directional and converging EF cases, respectively, the temperature in the wound zone only rises by less than 0.1 °C. Since more current is forced through the smallest width microchannel branch in the pseudo-converging EF case, the electrical resistance was higher leading to higher joule heating (the maximum temperature increase was 0.03 °C and 0.11 °C after 12 hours of DC stimulation for the uni-directional and pseudo-converging cases, respectively). Using the combined experimental and computational approach to account for pH shifts and joule heating due to DC stimulation, we could conclude that the cells are safe from electrochemically-induced faradaic reactions and heat-induced apoptosis during the 12 h stimulation of cells in our platform at these currents.
## DC stimulation expedites wound closure of keratinocytes
Preliminary electrotaxis studies of single cell keratinocytes are in consensus that there is cathodic directionality of migration, but there are mixed reports on small or significant increases of migration speed compared to non-stimulated controls.42,43,47 In the body, however, keratinocytes are organized as packed layers, and only a few studies have attempted to study the more skin-relevant situation of electrotaxis in confluent cell layers. For in vitro electrotaxis of keratinocyte monolayers, evidence has consistently shown an increase in motility compared to non-stimulated controls.27,41 Recently, it was shown that monolayers of keratinocytes have both cathodic migration directionality and increased migratory speed (∼3×) when subjected to an external direct current EF (200 mV mm−1).41 Globally, collective cells move like an elastic material with a constant tug-of-war between the cells at the advancing edge (i.e., leader cells) and the conglomerate of cells behind them (i.e., follower cells), where there is an interplay of forces that act on the individual and collective group levels.26 *This is* where collective cell migration and individual cell migration differ as the latter is not directly influenced by their neighbors, thus highlighting the importance of studying the more wound-realistic crowd migration of damaged epithelial sheets whose leader cells are steering the way.
Healthy keratinocytes are seeded, grown to full confluency, wounded, and DC stimulated according to Fig. 1. The stimulation protocol was either 12 h of a uni-directional or pseudo-converging EF, and each stimulation replicate had multiple internal non-stimulation controls. Remarkably, DC stimulation resulted in faster wound closure in all cases (Fig. 3a and b). At the end of stimulation, the wounds subjected to a uni-directional EF ($$n = 3$$, in orange) were ≈$100\%$ closed, those with a converging EF ($$n = 3$$, in green) were ≈$72\%$ closed, and the controls ($$n = 9$$, in black) ≈$42\%$ closed. For the uni-directional EF case, the effect was even stronger and the full wound closure was seen even at 10 h when the controls were only ≈$36\%$ closed, which is nearly a 3× increase in closure rate. Kymographs are provided for each case in order to show how the wound closes over time for all 72 frames (Fig. 3b, bottom row). Each row of pixels in the kymograph corresponds to a specific time point where seven lines within the wound region of interest are averaged, plotted, and color-mapped in correlation to the phase-contrast image's intensity. Cell tracking also confirms that a uni-directional EF promotes more directedness across the wound and a larger displacement compared to the non-stimulated control (Fig. 3c). If the wound closure was purely following the logic of electrotactic behaviour in single cells, one would expect that the closure speed would be faster for the converging EFs, where both edges of the wound experience a field that should drive them towards the wound center. The faster closure speeds were instead seen for the uni-directional EF, which is rationalized in the discussion. Cell tracking showed that the anode switching in the pseudo-converging EF case did not provide a boost in lateral directed migration, but rather more of a vertical directed displacement as compared to the non-stimulated control (Fig. S6†). In order to confirm the direct influence of the current and EF direction on group migration, a supplementary pseudo-converging EF stimulation experiment where the current polarity was toggled between ±20 μA was performed to show migration direction reversal (Video S5†). Note that the pseudo-converging EF stimulation scheme in Fig. 3 involved a relay-based switching of the left and right anodes every 30 min to allow for cyclic passive recharging of the non-connected and depleted anode with cations from solution, which can be seen in the rapid rising potential at the beginning of each discharge that is predominately due to capacitive current (Fig. 3d). This scheme allowed the electrodes to operate at a lower potential, which would be beneficial to reduce electrochemical side-effects. There was a subtle increase of the potential over time, which is likely due to the sudden switching of anodes adding stress on the cathode. Owing to the superior wound closure performance of the uni-directional EF scheme, it was opted to be the focal stimulation case for the diabetic model of keratinocytes.
**Fig. 3:** *Bioelectronic wound healing assay of healthy keratinocytes demonstrates faster wound closure with stimulation. (a) Time-lapse images during 12 h DC stimulation for non-stimulated control (black, left panel), uni-directional EF (orange, middle panel), and pseudo-converging EF (green, right panel). (b) Plots of the wound closure over time, where the wound area is normalized to the first image (n = 3 for all conditions). Below the plots are the corresponding kymographs of the wound region of interest (ROI). Each ROI has seven line slices in the x-direction across the wound and these seven lines are averaged and stacked for each time point in the kymograph (image taken every 10 min for 12 h of stimulation yields 72 rows of averaged pixels). The kymograph color scale corresponds to the phase-contrast image intensity. (c) Cell tracking of a sub-population of cells from both wound edges. The directedness of the cell's path is determined by noting the xy-location at each frame and calculating the cosine of the displacement angle. A value of −1 shows directed migration to the left, +1 shows migration to the right, and 0 would show non-directed migration. (d) Example profiles of potential versus time for both electrode configurations. Note that for the converging case, an extra anode is connected (compared to the uni-directional case) and a relay switches the anode every 30 min to push cells from both sides of the wound, as well as passively recharging the unconnected anode with ions from the medium.*
## DC stimulation promotes recovery of inhibited keratinocyte wound closure motility
In order to explore the hypothesis that EF stimulation not only accelerates wound closure for healthy cells, but also is of relevance for patients with impaired wound healing, it is compelling to establish a protocol to mimic the less motile wound closure phenotype of diabetic wounds. Once established, this could be translated to testing inhibited cells under a direct current EF to see if it helps recover the lost motility. Two approaches to model diabetes are tackled in this paper. The first is to subject the seeded keratinocytes to a hyperglycemic environment (i.e., high glucose concentration), which restrains migration speed via sequential suppression of the p-Stat-1 pathway and the α2β1-integrin-mediated MMP-1 pathway.48 The other approach is to inhibit the p38 mitogen-activated protein kinase (MAPK) pathway. This pathway is directly involved in the transition of keratinocytes from cells which are destined to terminally differentiate into the outermost skin layer (i.e., stratum corneum), and helps transform them into highly migratory cells upon wounding, which is part of the regeneratory process.49,50 The p38/MAPK pathway has been shown to be activated during the epithelial regeneration process.51,52 This same pathway has been shown to be down-regulated in high glucose environments, which is a common phenotype of diabetic wounds.53,54 Thus, if this pathway can be restored (up-regulated), then it might be possible to restore the cell migration via an autophagy-dependent manner.54 Jiang et al. showed that down-regulation of CD9, a gene encoding protein involved in cell motility, promotes keratinocyte migration. Also, p38/MAPK inhibition increases CD9 expression, thus suppressing migration.52 Building off that knowledge, it was also shown that keratinocytes subjected to direct current EFs (200 mV mm−1) will have their CD9 expression down-regulated via the 5′ adenosine monophosphate-activated protein kinase (AMPK) pathway.55 *The sum* of these factors led us to believe that if keratinocytes are slowed via an inhibited p38/MAPK pathway (increased CD9 expression), then direct current EFs could down-regulate CD9 and override the migration inhibition via the alternative AMPK pathway.
After seeding keratinocytes so that they were fully confluent the next day, they were subjected to either keratinocyte growth medium spiked with different concentrations of d(+)-glucose (6 mM to 100 mM), medium spiked with p38/MAPK inhibitor (0.5 μM to 50 μM), or a combination of both (Fig. 4). The cells were kept in the glucose environment overnight before wounding and imaging, while the p38/MAPK inhibited cells were subjected for 3 h. Importantly, all inhibitor treatments tested did not affect cell viability even after 24 h of treatment (Fig. S7†). Both treatments on their own were successful at slowing down wound closure, starting at 100 mM for glucose and 25 μM for p38/MAPK inhibitor. The wound closure rate was more than halved compared to the untreated controls (Fig. 4b, black trace, Video S3†). The combinations of both treatments (the amalgam of 50 or 100 mM glucose and 25 or 50 μM inhibitor) were also successful at reducing migration speed, but were much less consistent amongst replicates compared to the single treatments (Video S4†). All things considered, it was decided that the effectiveness and reproducibility of p38/MAPK inhibitor (25 μM) were the best conditions moving forward since it decouples possible compounding issues of dual treatments, in addition to targeting a specific pathway that is potentially more directly linked to electrotaxis.
**Fig. 4:** *Inhibitory treatments of keratinocytes slow down migration and DC stimulation helps recover this lost motility. (a) Keratinocytes seeded on a 12-well plate are subjected to normal growth medium (control), medium with d(+)-glucose, medium with p38/MAPK inhibitor, or a combination of both. After treatment, a scratch assay is performed and time-lapse images are taken. (b) Plots of the wound closure over time, where the wound area is normalized to the first image (n = 3 for all conditions). Note that the p38/MAPK inhibitor stock solution is diluted with dimethyl sulfoxide (DMSO). Therefore, a control group was added with the same final concentration of DMSO that was used in all inhibitor cases in order to account for DMSO's effect on wound closure. (c) Time-lapse images during 12 hour DC stimulation for non-stimulated control (in grey) and uni-directional EF (in gold). (d) Plots of the wound closure over time, where the wound area is normalized to the first image (n = 3 for all conditions). The orange and black traces are a carry-over from Fig. 3 in order to facilitate comparison with healthy keratinocytes.*
Importantly, the positive effect of EF stimulation on wound closure was demonstrated here to hold true also for p38/MAPK-inhibited keratinocytes. It was clear that direct current EF guidance cues help close the wound faster than non-stimulated controls (Fig. 4c). After 12 h of uni-directional EF stimulation, inhibited cells ($$n = 3$$, in gold) were ≈$34\%$ closed compared to only ≈$12\%$ closed for non-stimulated controls ($$n = 3$$, in grey). To put this ∼3× increase in closure speed into perspective, DC stimulation aided the inhibited cells to nearly recover to the wound closure speed as the once healthy keratinocytes (Fig. 4d, gold vs. black traces, Video S6†). Our findings support the idea that electrical field guidance can act to support faster wound closure and, in particular, the potential relevance for addressing impaired wound healing associated with diabetes.
## Discussion
The harmonization of microfluidic design, electrode material choice, and assembly protocol that were shown in this work provides a new platform for exploring the effects of electric fields on wounded cells of choice. The platform is based on readily-available materials and commonly-used prototyping equipment, and could easily be manufactured and customized by others according to our protocols. Below, we discuss three major topics that were leveraged in this work, their key parameters, and the future outlook: DC electrode materials, guidance cues, and disease models.
A major hurdled obstacle that we report here is the application of electrodes that do not require salt bridges, which allows for easier translation to 3D models.33 The use of recently developed non-metal, supercapacitive hydrogel electrodes facilitates not only a compact design in this 2D assay work, but also ease of translation to more 3D architectures, and importantly, it is an enabling technology for translation into a clinically useful device.38 However, it is of critical importance to focus on the electrode's charge storage limitations and the stimulation protocol's total charge to be delivered. The pseudocapacitor hydrogel electrode used here has a relatively high charge storage capacity (CSC ≃ 40 mC cm−2 to 50 mC cm−2).38 This CSC is crucial for determining over how long the current can be delivered capacitively (t = q/i, where q is the charge stored in the electrode and i is the input current) before it shifts to a predominately faradaic current, which is reliant on pH-shifting electrochemical reactions. This is why it is imperative to also account for the amount of pH buffering agent (molarity). On the one hand, the availability of nearby buffering agents in ex vivo or in vivo constructs might not be as abundant as in carefully engineered in vitro systems; however on the other hand, perfusion in functional tissue can help supply reinforcing buffering agents. Additionally, we demonstrate that over-polarized electrodes after stimulation are able to recharge with ions from the surrounding biological electrolyte and that switching between two anodes mitigated faradaic-induced pH shifts at the anodes (Fig. S4e†). This opens up possibilities to employ multi-electrode arrayed anodes and cathodes that have sub-groups that actively discharge (i.e., unipolar, constant current), while other sub-groups passively recharge (i.e., disconnected), and then cyclically interchange between these two sub-groups. It should be noted here that metal electrodes typically do not possess this ability to recharge, as their DC charge injection involves corrosion, a reaction that cannot easily be reversed. In addition, corrosion typically elutes toxic metal ions into tissues. Meanwhile, the metal-free electrodes used here work with ions available in abundance in the biological electrolyte.
Right now, the fields of bioelectronics and wound healing are just ‘scratching’ the surface of how best to use guidance cues to control cell collectives. This work unveiled the surprising result that constantly pushing a wound to close from one side via EF guidance cues was more effective than alternatingly directing the wound to close from both sides. At its face value, this shows electrotaxis, particularly the EF direction, is not the only main driver in accelerated in vitro wound closure via electrical stimulation. Since the wound edge that needs to travel against the EF direction is still able to close the gap, albeit slower, this suggests either kenotaxis,24 free edge motility due to activation of epidermal growth factor receptor (EGFR),56,57 or up-regulation of MAPK pathways due to exogenous ionic flow19 are supportingly important in collective cell wound healing in the presence of electrical stimulation. As for why a pseudo-converging EF provided slower wound closure compared to a uni-directional EF, we hypothesize that this is due to time-varying shifts in the polarization direction of the leader cells, which could have compounding negative traction effects each time the anode is switched. This compounding effect could also explain why the initial slopes of closure rates were similar for both stimulation schemes, but ultimately slows down for the pseudo-converging EF case. These uni-directional EF results can be leveraged when designing future electrical wound dressings since a truly converging EF design would require the problematic task of placing a cathode within the wound's exudate. Furthermore, with an extensive variety of wound morphologies, the centrally-placed cathode becomes more difficult to standardize, whereas this burden would be less so with the simplicity of fixing an electrode on opposite sides of the wound. Here, additional electrode montages could be employed to focus on wound closure from different sides or edges. With that being said, there is still a need to dive further into how guidance cues impact group migration. Recently, Shim et al. demonstrated that increasing levels of calcium proportionally increases cadherin-mediated cell–cell adhesion strength in between adjacent keratinocytes, reduces the average cell migration speed, and dampens the effect external direct current EFs has on directionality, which goes to show there is an interplay of cell–cell forces, cell–substrate forces, and external guidance cues in effective collective migration control.58 In conjunction with bioelectric cues, mixtures of mechanical cues (e.g., extracellular matrix coatings) and chemical cues (e.g., passive or active flowing of pro- or anti-migratory soluble factors) are compelling to investigate and straightforward to implement in a platform such as the one presented here. Also, it is feasible to leverage microfluidic laminar flow to cleverly focus the flow59 of compounds of interest in a spatial manner (also possible here in Fig. S3†). These external cue-mediated 2D sheet migration insights may unveil new mechanisms, and are of importance for translating findings from single cells to more complex and tissue-relevant architectures.
Modeling a disease in vitro has its limitations, but offers opportunities to study individualistic cause and effects. Diabetes in vitro models have been thoroughly explored in the aspects of neuropathy,60 pregnancy,61 and wound healing.54 Hyperglycemic in vitro studies of wound healing rates seem to depend on the epidermal growth factor (EGF) concentration in glucose-spiked media, which suggests that solely using glucose concentration as the independent variable is not targeted enough.62 However, targeting downstream effected pathways of hyperglycemia, like the p38/MAPK pathway, offers a more directed approach to induce a diabetic-like state in the most wound-prevalent cells, keratinocytes.53,54 Prior studies have generated evidence that other pathways also play an influential role in diabetic wounds, including the diacylglycerol pathway, hexosamine pathway, protein kinase C pathway, and polyol pathway.63 This illustrates that there is more room to explore the impact of multiple guidance cues on a variety of relevant diabetic wound pathways at the foundational level. The treatment of healthy keratinocytes was used in this study in order to facilitate direct comparison using the same cell line, culture medium, and seeding methodology. However, it has its limitations in fully encompassing diabetic keratinocyte behavior. Using the presented bioelectronic platform, investigating keratinocytes from diabetic patients and even inducing type 2 diabetes hallmarks in healthy keratinocytes via exposure to diabetic fibroblasts are both fruitful alternatives worthy of future investigation.64 In addition to healthy and diabetic keratinocyte collectives, co-culture models are of interest as they introduce more intercellular crosstalk and potential collision dynamics that were not explored in this work, but nonetheless, could be investigated using this platform. The next logical step of 3D human diabetic skin equivalents64,65 could also be potentially explored by substituting the cell seeding step for inserting pre-formed skin constructs into the open microchannel before lidding.
Aside from diabetes, more ambiguous diseases like systemic lupus erythematosus (SLE) also have affected wound healing. Diseases like SLE stand to benefit from more fundamental research on responsible pathways that could potentially be overridden by electrical stimulation in order to improve their typically poor healing of wounds. There is also evidence that transcutaneous electrical stimulation promotes healing of intact skin for such ailments like pressure ulcers of paraplegic individuals.66 However, for all these aforementioned diseases, there needs to be more exploration into the mechanistic effects of electrical stimulation and more encompassing dose–response investigations from the collective cell group scale all the way up to the organ level. Electrical stimulation on the single-cell level to multicellular 3D constructs all require careful consideration of how and where the current and EF pass through target regions of interest. The microfluidic regime helps channel and direct EFs in a predeterministic fashion during the design phase, which unlocks robust stimulation platforms to study phenomena beyond just wound healing.
## Conclusion
Compared to the canonical wound healing model, this bioelectronic microfluidic platform enables new grounds to directly investigate the role of precise delivery of EF magnitudes with different options for EF directionality in wound closure. This level of EF controllability could not be achieved by simply submerging electrodes in the conventional wound healing platform of seeded well-plates. While this demonstration used seeded keratinocyte monolayers, it could also be utilized with tissue models by replacing the seeding, growth, and wounding steps with the placement of tissue in the microchannel before lidding. Furthermore, this platform allowed us to lay the foundation for a future wound healing concept based on electrical stimulation from supercapacitive non-metal electrodes. We demonstrated the working principle of this concept using culture models of skin wounds, and showed that EF guidance cues can increase the wound closure speed up to 3×, in comparison to non-stimulated controls. We furthermore showed that effective wound closing stimulation relies on a carefully controlled environment, dosage, and directionality of the electric field. Under the conditions that these factors can also be accounted for in real skin wounds, we are convinced that electrical stimulation could contribute as an additional guidance cue in regenerating tissue and thereby promote faster re-epithelialization.
## Methods
All chemicals were purchased from Sigma Aldrich, unless otherwise noted.
## Finite element analysis of electric field distribution and joule heating
COMSOL Multiphysics® software (version 5.3) was used to simulate both EF distribution and joule heating, using the Electric Currents and Heat Transfer modules, respectively. SolidWorks (version 2021) was used to design the three-dimensional models used for COMSOL. For the EF distribution, electrodes sat on top of the reservoirs and were modeled to have the electrical conductivity of PEDOT:PSS hydrogels (σ = 2000 S m−1).67 *The medium* was modeled after 1× (i.e., 10 mM) phosphate-buffered saline (PBS, σ = 1.54 S m−1). Only the form factor of the medium and the electrodes were modeled. The input current density (placed at the face of the anode(s)) was sweeped in order to identify at which current the desired EF strength would be reached at the center of the channel. The cathode was set to a potential of 0 V. For joule heating, the same electrical conditions were implemented, but changed to a time-dependent solver. This model also included modeling fully medium-filled reservoirs and the plastic substrate and lid. The following values were used for heat transfer-related material properties (values are from COMSOL's material database unless otherwise cited): PBS ($k = 2$ W m−1 K−1, ρ = 1000 kg m−3, Cp = 4 J kg−1 K−1);68 acrylic ($k = 0.19$ W m−1 K−1, ρ = 1190 kg m−3, Cp = 1420 J kg−1 K−1, σ = 1 × 10−14 S m−1); PEDOT:PSS ($k = 0.348$ W m−1 K−1,69ρ = 1060 kg m−3,70Cp = 1415 J kg−1 K−1).71 The initial conditions include a convective heat flux of external temperature of 310.15 K with a heat transfer coefficient of 5 W m−2 K−1, a diffusive surface of all plastic components with a surface emissivity of 0.95 and an ambient temperature of 310.15 K. The fluid was modeled to have zero velocity and a pressure of 1 atm.
## Microfluidic fabrication
All components of the microfluidic device, besides the sterile polystyrene dish, were fabricated with a 30 W carbon dioxide (CO2) laser (Universal Laser Systems, VLS 2.30). Specifically, 7.5 W at 70 mm s−1 was used for a kiss-cut and 24 W at 70 mm s−1 was used for a through all-cut for the acrylic-based double-sided pressure-sensitive adhesive (Adhesives Research, 90445Q). The bottom-side liner (i.e., without the kiss-cut) was peeled off to expose the bottom-side adhesive, and then it was pressure bonded by hand to a new Petri dish. Batches of dish/adhesive were placed in a vacuum desiccator overnight to remove any air bubbles that occurred during bonding. These were stored on a shelf until further use. The acrylic (Modulor, Germany) two-part lid included a thin 0.5 mm base that only has fluidic vias and a thicker 8.0 mm reservoir-defining layer. These two parts were solvent bonded together using dichloromethane (Modulor, Germany).
## Electrode fabrication
The fabrication of laser-induced graphene (LIG) electrodes coated with pure PEDOT:PSS hydrogels was recently established by our group.38 In short, a CO2 laser (same as above) was used to carbonize a polyimide sheet (Kapton HN, 75 μm thick) with a rasterization protocol at 4.8 W and 15.2 mm s−1. The freshly carbonized LIG was air plasma-treated (Femto, Diener Electronics) for 5 min at 100 W and 10 sccm to make it more hydrophilic and functionalizable. It was then soaked in $10\%$ w/v hexamethylenediamine (HMDA) for 4 h at room temperature. After washing with DI water and drying with nitrogen, it was dip-coated (Nadetech ND-DC Dip Coater) in a $1\%$ w/v hydrophilic polyurethane solution in $90\%$ ethanol (AdvanSource, HydroMed D3) with a retraction speed of 100 mm min−1, and then subsequently placed on a hot plate for 1 h. Electrode connection lines (i.e., between the electrical bump pad and electroactive area) were coated with an acrylate-based varnish (Essence 2 in 1, Cosnova). A PEDOT:PSS dispersion ($1.3\%$) with $15\%$ dimethyl sulfoxide was drop-cast (200 μl onto the 12 mm diameter LIG electrodes after being placed onto a hot plate (60 °C overnight, and then 130 °C for 90 min). The electrodes were stored in 1× PBS (Sigma Aldrich, P3813). Electrical connections were done with a pogo-pin assembly (Mill-Max, 858-22-002-30-001101) fitted with M3 threaded inserts. Custom polyethylene terephthalate glycol (PET-G) 3D-printed (Prusa Research, i3 MK3S) parts were made for compression clamping of the pogo pin assembly (seen in green in Fig. 1) and for consistent angled alignment of electrodes into reservoirs (seen in orange in Fig. 1).
## Coating of pH-sensitive polymer
Polyaniline (PANI) is polymerized via chemical oxidation72 by mixing an equal volume and molarity of pre-chilled aniline monomer (1.42 M) in hydrochloric acid (HCl, (1.42 M)) with pre-chilled ammonium persulfate (APS, (1.42 M)) in deionized water and depositing this polymerizing mixture onto the open microchannels (Video S1†). Note that the aniline solution and the APS solution were prepared fresh and were stored at −20 °C for 30 min before mixing to slow down the polymerization in order to allow sufficient time for deposition. The mixture turned from a colorless solution into a deep black hydrogel over the next 30 min (Video S1†). The in situ polymerization embeds itself into the Petri dish polystyrene, such that the unbound PANI hydrogel can be washed away leaving behind a thin layer of green PANI (i.e., emeraldine salt).
For calibration, PANI was coated onto small polystyrene Petri dishes (35 mm diameter, Falcon TC-treated). Stock pH buffers ranging from pH 2.69 to 11.25 were made using different combinations of spiking hydrochloric acid (HCl) or sodium hydroxide (NaOH) into primary salt solutions made of either potassium hydrogen phthalate (KHP), potassium dihydrogen phosphate (KH2PO4), sodium tetraborate (Na2B4O7), or sodium bicarbonate (NaHCO3). A benchtop pH meter was used to verify all pH values (VWR pHenomenal pH 1100 L). After depositing 1 mL of the pH buffer into separate PANI-coated dishes, they were imaged on an incubated inverted microscope (Zeiss Axio Observer) with a 20× objective at 37 °C. Images were processed through a Python script to report a hue value. Specifically, the script averages RGB values across the image and reports a corresponding hue. Based on the maximum and minimum values of RGB, the hue is then calculated by using the Python module “colorsys” to change from RGB to HSV coordinates. These hue values were correlated to the known pH values to generate a calibration curve. The curve was fitted with a bi-dose response sigmoidal curve (Origin 2021).
For coating PANI in the DC stimulation device, the polymerization and casting processes are the same, but now performed on the polystyrene-exposed parts of the microfluidic-defining adhesive (Video S1†). After washing away unbound PANI, the liner was then removed and a lid was subsequently added, just like in Fig. 1b. PBS (1×) was degassed in a vacuum desiccator for 30 min to minimize bubble formation in the microchannel. Then, degassed PBS and electrodes were added to the device. Imaging was performed on an incubated microscope over the course of 20-plus straight hours of monophasic DC stimulation (25 μA using a potentiostat/galvanostat (Metrohm, Autolab PGSTAT204)). For the pseudo-converging EF case, a custom-built relay was used between the current source's working electrode lead and the two anodes.
## Culturing keratinocytes
Human epidermal keratinocytes immortalized with HPV-16 E6/E7 were acquired courtesy of Prof. Dr. rer. nat. Thorsten Steinberg (Department of Dental, Oral and Jaw Medicine; University Clinics of Freiburg). Keratinocytes were cultured throughout experiments in serum-free keratinocyte growth medium (KGM2, PromoCell, #C-39016) supplemented with a cocktail of factors and CaCl2 provided by the same manufacturer (SupplementMix, PromoCell, #C-20011), as well as neomycin (Sigma-Aldrich, #N1142) at a final concentration of 20 μg mL−1 and kanamycin (Sigma-Aldrich, #K0254) at a final concentration of 100 μg mL−1. The cell culture was incubated at 37 °C and $5\%$ CO2 at $95\%$ humidity and routinely passaged when $80\%$ to $90\%$ confluency was reached. The growth medium was exchanged three times per week. For the experiments, keratinocytes were used from passages 34 to 49.
## Treatments of keratinocytes to mimic the diabetic phenotype
For experiments with elevated glucose concentrations, a 1 M aqueous stock solution of d (+)-glucose (Sigma Aldrich, #G7021) was added to the culture medium to achieve a desired concentration (6, 12, 25, 50, or 100 mM). The glucose-rich medium was prepared fresh each time before treatment. Confluent cell layers were treated for 24 h before proceeding to the wounding.48 For experiments mimicking a diabetic wound environment, a 25 mM stock solution of p38-MAPK inhibitor (Cell *Signalling via* Selleck Chem, Adezmapimod – SB203580, #S1076) in DMSO was added to the culture medium to achieve a desired concentration (0.5, 5, 25, or 50 μM). The inhibitor-containing medium was prepared fresh the same day as treatment. A control condition to test the effect of DMSO on cell viability and migration was established by using the same final DMSO concentration ($0.1\%$ v/v) as above, but without an inhibitor. Confluent cell layers were treated for 3 h before proceeding to the wounding.54 In order to assess the viability of cells after treatment, live/dead cell double staining was performed with SYTO 16 (Invitrogen, #S7578) and propidium iodide (Invitrogen, #P1304MP). For staining, culture medium containing both dyes with a final concentration of 500 nM each was prepared. Cells were protected from light and incubated at 37 °C for 30 min, then washed with 37 °C PBS and imaged with an incubated inverted microscope (Zeiss Axio Observer).
## Seeding cells onto devices, wounding monolayers, and device assembly
Before seeding on the bioelectronic wound healing assay devices, the devices were air plasma-treated (30 W, 3 min, 10sccm) to improve cell adhesion to the substrate. In preparation, keratinocytes were detached from culture flasks by incubation with $0.05\%$ trypsin and $0.02\%$ EDTA solution (Sigma Alrich, #T3924) at 37 °C for 5 min. To neutralize trypsin, medium containing $10\%$ fetal bovine serum (Sigma Aldrich, #F0804) was used. Harvested cells were centrifuged (1200RPM for 10 min) and resuspended at 4.5 × 106 cells per mL−1. Cell suspensions (100 μl) were spotted directly over open microchannels (see Fig. 1b, step 3) and incubated for 3 h to allow for cell attachment. Afterwards, the excess of cells was washed away with 37 °C PBS solution and aspirated before adding 10 mL of fresh growth medium. The cell seeding concentration was titrated in order to find the optimal amount so that the devices were fully confluent the next day (Fig. S1†).
Monolayers were scratched using a sterile p10 pipette tip (≃700 μm) that was connected to a vacuum aspirator (Vacusip, Integra Biosciences). The motion of the scratch was always done starting at the base of the microchannel scratch alley and finishing where the four channels converge. After the wound formation, the medium was aspirated, and then the devices were washed with sterile PBS (1×), fresh growth medium was added, and they were finally placed back in the incubator for 4 h. In the meantime, the acrylic lid, electrodes, 3D printed adapters and wires were washed with $70\%$ ethanol and then further sterilized in an S1 cell culture hood (Safe 2020, Thermo Scientific) via UV-treatment for 1 h. After incubation, the medium was aspirated until only a small amount of medium resided in the microchannels, leaving the liner as dry as possible in order to minimize the probability of the fluid transferring onto the dry adhesive. The liner was then peeled, the two-part acrylic lid was aligned and fixed using alignment marks, and the medium immediately replenished by initially flowing 100 μl directly into the microchannels to displace trapped air. The rest of the reservoirs were filled using a standard serological pipette. The electrodes were assembled and placed into the reservoir, and the corresponding wires were routed through the lid, which was applied to prevent evaporation.
## Imaging and direct current stimulation
Seeded and assembled devices were placed on an incubated inverted microscope (Zeiss Axio Observer with a Definite Focus 2) and maintained at 37 °C and $5\%$ CO2. Phase-contrast images were acquired every 10 min using a 5× objective in order to capture the entire microfluidic network. The DC stimulation was carried out in the exact same way as described in the preliminary pH-monitoring experiments (e.g., constant current using a potentiostat/galvanostat – Autolab PGSTAT204).
## Statistical analysis
All cell-based experiments were completed in triplicate. All data plots were assembled using the data analysis software Origin 2021, where the shaded regions of the line plots represent the standard deviation. Output images were put through an ImageJ plugin in order to quantify the wound area closure over time.73 Kymographs were collected using the ImageJ plug-in KymographBuilder. Cell tracking was done using CellTracker.74
## Author contributions
S. S. and M. A. conceived the project. S. S. designed and fabricated all platforms (fluidic and electrochemical). S. S. performed all FEA simulations/analyses and electrode characterization. L. M. and J. L. performed image analysis of PANI-based experiments. A. S., A. K., and N. J. performed all cell culturing, cell seeding, and cell motility inhibition assays. S. S. performed all stimulation-based experiments. S. S. analyzed, compiled, and plotted the data. S. S. made the figures. S. S. wrote the manuscript. All authors reviewed the manuscript.
## Conflicts of interest
There are no conflicts to declare.
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|
---
title: Tripterygium glycosides improve abnormal lipid deposition in nephrotic syndrome
rat models
authors:
- Bidan Zheng
- Dongfang Lu
- Xiuping Chen
- Yinghua Yin
- Weiying Chen
- Xiaowan Wang
- Huanmei Lin
- Peng Xu
- Aihua Wu
- Bo Liu
journal: Renal Failure
year: 2023
pmcid: PMC10013393
doi: 10.1080/0886022X.2023.2182617
license: CC BY 4.0
---
# Tripterygium glycosides improve abnormal lipid deposition in nephrotic syndrome rat models
## Abstract
### Objective
The purpose of this study was to determine the effect of tripterygium glycosides (TGs) on regulating abnormal lipid deposition in nephrotic syndrome (NS) rats.
### Methods
Sprague-Dawley (SD) rats were injected with 6 mg/kg doxorubicin to construct nephrotic syndrome models ($$n = 6$$ per group), and then administered with TGs (10 mg/kg·d−1), prednisone (6.3 mg/kg·d−1), or pure water for 5 weeks. Biomedical indexes, such as urine protein/creatinine ratio (PCR), blood urea nitrogen (BUN), serum creatinine (Scr), serum albumin (SA), triglycerides (TG), total cholesterol (TC)were investigated to evaluate the renal injury of rats. H&E staining experiment was used to assess the pathological alterations. Oil Red O staining was used to assess the level of renal lipid deposition. Malondialdehyde (MDA) and glutathione (GSH) were measured to assess the extent of oxidative damage to the kidney. TUNEL staining was used to assess the status of apoptosis in the kidney. Western blot analysis was performed to examine the levels of relevant intracellular signaling molecules.
### Results
After treatment with TGs, those tested biomedical indexes were significantly improved, and the extent of kidney tissue pathological changes and lipid deposition in the kidney was diminished. Treatment with TGs decreased renal oxidative damage and apoptosis. Regarding the molecular mechanism, TGs significantly increased the protein expression levels of Bcl-2 but decreased the levels of CD36, ADFP, Bax, and Cleaved caspase-3.
### Conclusion
TGs alleviates renal injury and lipid deposition induced by doxorubicin, suggesting that it may be a new strategy for reducing renal lipotoxicity in NS.
## Graphical Abstract
## Introduction
Lipotoxicity is marked by an unusual excess accumulation of lipids in non-adipose tissues, which causes deleterious effects [1]. The kidney is one of the main target organs for lipotoxicity damage [2]. Renal parenchymal cell lipid deposition causes cellular dysfunction and apoptosis [3]. Doxorubicin-induced nephrosis in rats is a representative animal model of nephrotic syndrome [4]. Doxorubicin induces nephrotoxicity via renal oxidative stress, inflammation, and apoptosis [5]. In addition, doxorubicin disrupts lipid metabolism, causing the development of lipotoxicity [6], which aggravates kidney damage.
TGs is multicomponent extracts from the Chinese herb, *Tripterygium wilfordii* (thunder god vine), which exhibit excellent immunosuppressive and anti-inflammatory activities, and it shows satisfactory efficacy in nephrotic syndrome [7]. Studies have suggested that TGs plays an important role in regulating lipid metabolism. TGs effectively improves the function of impaired kidneys by promoting triglycerides (TG) catabolism via modulation of adipose triglyceride lipase [8]. The two main active components of TGs, triptolide, and celastrol, could reduce excessive lipid accumulation [9,10]. However, it remains unknown whether TGs have a favorable therapeutic effect on lipotoxicity caused by NS. Thus, the present study performed a series of experiments to determine the effects of TGs on NS-induced lipotoxicity.
## Animals
Eight-week-old SPF adult male Sprague–Dawley rats (200 ± 20 g) were purchased from Guangdong Medical Laboratory Animal Center (Guangzhou, China) and maintained under specific pathogen-free conditions, 20 ± 2 °C temperature, 50 ± $10\%$ humidity, regular 12-h dark/light cycles, and the rats were allowed free access to food and water. After a week of acclimatization, the rats were randomly divided into the following four groups ($$n = 5$$–6 per group): control group, model group, TGs group, and prednisone group. All in vivo experiments were performed according to protocols approved by the Animal Care and Use Committee of Guangdong Provincial Hospital of Traditional Chinese Medicine.
## Experimental design
All rats (except the control group) were given 6 mg/kg DOX via a single tail vein injection to construct a nephrotic syndrome model. After 3 weeks, the rats were regrouped according to the PCR and treated as follows: the TGs group was treated with 10 mg/kg·d−1 TGs (i.g.); the prednisone group was treated with 6.3 mg/kg·d−1 prednisone (i.g.); and the model and control groups were given equal amounts of distilled water. The body weight was recorded once a week. Rat urine was collected to detect urinary protein and creatinine on the 5th week of dosing. After 5 weeks of continuous dosing, all rats were euthanized with sodium pentobarbital. Blood was collected, and serum was separated by centrifugation at 3500 × g for 15 min. Kidneys were removed and stored according to the experimental needs.
## Kidney index
All animals were weighed before being euthanized. Kidney tissues were collected and weighed immediately after the rats were sacrificed, and the kidney index was calculated using the following formula: kidney index = kidney weight (g)/body weight (g).
## Serological parameters
Assay kits were used to measure the contents of TG (Cat No. A110-1-1), TC (Cat No. A111-1-1), BUN (Cat No. C013-2-1), SA (Cat No. A028-2-1), and Scr (Cat No. C011-2-1), which were all purchased from Nanjing Jincheng Institute of Biotechnology (Jiangsu, China).
## Histopathological examination
Kidney tissues were immersed in $4\%$ paraformaldehyde for 24 h, embedded in paraffin, and cut into 5-μm thick sections. Hematoxylin and eosin (H&E) staining was performed following the manufacturer’s instructions (Beijing Leagene Biotech Co., Ltd., DH0006). Three different rats’ sections from each group.
## Oil Red O staining
Oil Red O solution was prepared by dissolving 0.5 g of Oil Red O powder (Sigma, O0625) in 100 mL of isopropanol, and three parts of dissolved Oil Red O were mixed with two parts of water and then filtered. Renal tissues were frozen in OTC compound, cut into 7-μm thick sections, and fixed with $4\%$ paraformaldehyde. The sections were rinsed with ddH2O for 5 min, rinsed with $60\%$ isopropanol for 1 min, stained with Oil Red O solution for 30 min, and finally rinsed with $60\%$ isopropanol. Nucleus were counterstained with hematoxylin. The stained sections were observed using an Olympus microscope. Three rats in each group were randomly selected for examination.
## Western blot analysis
The appropriate amount of renal cortex was placed into homogenate tubes with an equal proportion of RIPA lysis buffer (Thermo Fisher) mixed with a protease inhibitor cocktail (Roche) and homogenized using a tissue grinder. Protein lysates were then prepared for western blot analysis. Proteins were isolated by electrophoresis on $12.5\%$ SDS–PAGE gels, transferred onto PVDF membranes, and blocked with $5\%$ non-fat milk for 2 h. The membranes were incubated overnight at 4 °C with the following primary antibodies: Cleaved caspase-3 (CST#9661, Cell Signaling Technologies), Bcl-2 (ab196495, Abcam), Bax (CST#2772, Cell Signaling Technologies), ADFP (ab108323, Abcam), CD36 (ab133625, Abcam), and GAPDH (CST#5174, Cell Signaling Technologies). The membranes were subsequently washed and then incubated for 60 min at room temperature with HRP-conjugated anti-mouse or anti-rabbit secondary antibodies. An ECL reagent kit (Millipore, USA) and gel imaging equipment (Bio-Rad, ChemiDocTM Touch, USA) were used to detect the presence of protein bands on the membrane. The quantification of the band intensities was performed using Image Lab 5.2.1 software (BIO-RAD), and the band intensities were normalized to GAPDH.
## Quantification of GSH and MDA
The homogenates were centrifuged at 4500 rpm for 15 min at 4 °C, and the supernatants were taken to determine the levels of oxidative stress biomarkers, such as GSH (Cat No. A006-2-1) and MDA (Cat No. A003-1-2), using corresponding kits according to the manufacturer’s protocols (Nanjing Jiancheng Bioengineering Institute, Nanjing, China).
## TUNEL and DAPI staining
Frozen kidney tissues were cut into 7-μm thick sections and fixed in $4\%$ paraformaldehyde for 30 min. The sections were then incubated in PBS containing $0.4\%$ Triton X-100 for 5 min followed by incubation in TUNEL solution (Beyotime Biotechnology, Shanghai, China, Cat No. C1090) for 1 h in a humidified chamber, and the sections were counterstained with DAPI (Solarbio, Cat No. C0065). The slides were sealed with an anti-fluorescence quencher and observed under a fluorescence microscope (Olympus, Japan), and images were acquired. Three experimental animals in each group were randomly selected for this experiment.
## Statistical analysis
Graphing was performed with GraphPad Prism 9 and statistical analyses were performed using IBM SPSS 25 for Windows. Multiple group comparisons were performed using one-way ANOVA, and Tukey or Dunnett T3 methods were used for post-hoc analysis. Data are expressed as the mean ± SD, and p-values ≤ 0.05 indicated statistically significant differences.
## TGs protect kidneys against doxorubicin-mediated injury
Rats were injected with DOX after 7–8 weeks, and PCR, BUN, Scr, kidney index, TG, and TC were significantly increased (Figures 1(A–F)), but significantly decreased body weight and SA (Figures 1(G,H)). These results demonstrated that the models were successful. In addition, TGs and prednisone ameliorated Dox-mediated renal injury, and the treatment effect of TGs was comparable to that of prednisone. However, TGs and prednisone did not reverse the body weight loss in rats. Compared to TGs, prednisone showed better effectiveness in reducing PCR and BUN.
**Figure 1.:** *TGs protect kidneys against doxorubicin-mediated injury. (A) 24 h Urine protein-to-creatinine ratio (PCR). (B) Blood urea nitrogen. (C) Serum creatinine concentration. (D) Kidney index. (E) Serum TG levels in rats (n = 5–6). (F) Serum TC levels in rats (n = 5–6). (G) Body weight (n= 5–6 per group). (H) Serum albumin (n= 5–6 per group). Significance between groups was determined by ANOVA followed by Dunnett’s T3. Model group vs. Control group (*p ≤ 0.05 and **p ≤ 0.01); TGs group and Prednisone group vs. Model group (#p ≤ 0.05, ##p ≤ 0.01, and NS p > 0.05); TGs group vs. Prednisone group ($$p ≤ 0.01 and NS p > 0.05).*
## TGs protect against alterations in the kidney architecture of NS
The histopathological changes in the kidney tissues of rats in each group were observed by HE staining. The results showed that severe renal tubular damage occurred after DOX injection, including renal tubular dilation with granular degeneration and tubule brush border shedding as well as vacuolar degeneration of renal tubular epithelial cells, protein casts, and inflammatory cell infiltration (Figure 2). The therapeutic outcome in the TG group was equivalent to that in the prednisone group.
**Figure 2.:** *TGs protect against alterations in the kidney architecture of NS. HE staining (n = 3; scale bar: 50 µm). Yellow arrows indicate brush border detachment and absence. Blue arrows indicate protein casts. Yellow triangles indicate focal inflammatory cell infiltration.*
## TGs alleviate abnormal lipids in NS
To analyze the effect of TGs on lipid deposition in kidney tissue, Oil Red O staining was performed. Lipid droplets were not observed in normal rats. However, large orange-red lipid droplets were observed in the model group, and the orange-red droplets were mainly concentrated in renal tubules (Figure 3(A)). In addition, TGs reduced lipid deposition in the kidney tissue doxorubicin-induced NS rats. WB analysis also confirmed that TGs significantly decreased the expression of the CD36 and ADFP (two lipid-related proteins) (Figures 3(B–D)). Thus, these findings indicated that the therapeutic outcome of TG was better than that of prednisone.
**Figure 3.:** *TGs alleviate abnormal lipids in NS. (A) Oil Red O and hematoxylin staining of kidney sections (n = 3; scale bar: 50 µm). (B–D) Expression of CD36 and ADFP in renal tissues (n = 3). Significance between groups was determined by ANOVA followed by Tukey. Model group vs. Control group (**p ≤ 0.01); TGs group or Prednisone group vs. Model group (#p ≤ 0.05 and ##p ≤ 0.01); TGs group vs. Prednisone group (NS p > 0.05).*
## TGs inhibit renal oxidative stress in NS
Abnormal lipid metabolism induces lipid peroxidation, leading to oxidative stress. To evaluate whether TGs improve lipid-related oxidative stress, we quantified the amounts of GSH and MDA in rat kidney tissues. The results showed that DOX reduced GSH content (but not significantly) and increased MDA content in the kidneys. Treatment with TGs or prednisone restored the content of GSH and reduced the DOX-mediated increase in MDA (Figure 4). These findings indicated that TGs or prednisone significantly inhibit lipid peroxidation and restore antioxidant capacity to a certain extent in the kidneys of NS model rats. The TG therapeutic outcome was equivalent to that in the prednisone group.
**Figure 4.:** *TGs inhibit renal oxidative stress in NS. (A) Levels of GSH in renal tissue homogenate. (B) Levels of MDA in renal tissue homogenate. (n = 5–6). Significance between groups was determined by ANOVA followed by Dunnett’s T3. Model group vs. Control group (**p ≤ 0.01, and NS p > 0.05); TGs group or Prednisone group vs. Model group (#p ≤ 0.05 and ##p ≤ 0.01); TGs group vs. prednisone group (NS p > 0.05).*
## TGs inhibit renal apoptosis of NS
To determine the apoptosis levels of the kidneys, TUNEL assays and WB analyses were performed. TUNEL staining showed a significantly higher level of cell death in the model group than in the control group, TGs group and prednisone group significantly decreased the number of TUNEL-positive cells (Figure 5(A)). WB analysis indicated that the TGs and prednisone groups exhibited reduced levels of Bax and active Caspase-3 but increased levels of the Bcl-2 antiapoptotic protein compared to the model group (Figures 5(B–E)). The TGs therapeutic outcome was equivalent to that in the prednisone group.
**Figure 5.:** *TGs inhibit renal apoptosis of NS. (A) TUNEL and DAPI staining of kidney sections (n = 3; scale bar: 100 µm). (B–E) Bax, Bcl-2, and Cleaved caspase-3 expression in the kidney (n = 3). Significance between groups was determined by ANOVA followed by Tukey. Model group vs. Control group (**p ≤ 0.01); TGs group or Prednisone group vs. Model group (#p ≤ 0.05 and ##p ≤ 0.01); TGs group vs. prednisone group ($p ≤ 0.05, and NS p > 0.05).*
## Discussion
As early as 1982, the concept of lipid nephrotoxicity attracted much attention [11]. Hyperlipidemia is prevalent in NS and is considered to be a feature of severe NS [12]. Severe impairment of lipid clearance is a leading cause of abnormal lipid metabolism in NS, resulting in a surplus of fatty acids and glyceride converted to TG, which accumulate in the form of intracellular lipid droplets [13]. Non-adipose tissue has a limited capacity to store TGs [14], and excessive lipid deposition causes nephrotoxicity due to cellular dysfunction as well as accelerates energy metabolism, induces oxidative damage, and leads to cell death [15,16]. Therefore, reducing abnormal lipid deposition in the kidney mitigates kidney damage and slows the progression of NS. In the present study, DOX injection significantly increased the levels of PCR, BUN, Scr, TG, and TC but significantly decreased the levels of SA in rats compared to the model group, which indicated successful model construction. Treatment with TGs significantly reduced kidney damage in the NS model rats.
Oil Red O is a fat-soluble dye with can specifically bind to TG in tissues or cells to dye fat cells red [17]. As a scavenger receptor, CD36 is a key element in fatty acid uptake [18]. Overexpression of CD36 increases fatty acid uptake and directs abnormal lipid deposition and excessive oxidative stress [19,20]. CD36 is significantly upregulated in kidney disease and can reflect the severity of tissue injury in kidney disease to some extent [21]. Adipose differentiation-related protein (ADFP) is an important lipid droplet surface protein that is mainly involved in cellular fatty acid intake, lipid droplet formation, and lipid stores [22]. ADFP plays a role in preventing lipase entry into lipid droplets and slowing lipid digestion, allowing lipids to accumulate in lipid droplets [23]. Without ADFP, lipid droplets are degraded by the proteasome; thus, ADFP is an indicator of lipid accumulation [24,25].
In the present study, Oil Red O staining and western blot analysis indicated an accumulation of lipid droplets in the kidneys of model rats, but the amount of lipid droplets was significantly reduced after treatment with TGs. Moreover, the expression of CD36 and ADFP was also downregulated in the TGs group. These results demonstrated that TGs reduce abnormal lipid deposition in the kidneys of NS rats, thus improving renal injury.
Aberrant accumulation of lipids results in induced cellular oxidative stress [26]. As a representative product of lipid peroxidation, MDA directly reflects the extent of lipid peroxidation damage [27]. Because GSH is an important endogenous antioxidant in the body that scavenges oxidative free radicals and prevents a variety of diseases, it reflects the ability of the tissue to resist oxidative damage [28]. In the present study, the MDA content was significantly higher and the GSH level was significantly lower in the model group compared to the control group, which indicated that the kidneys of the model rats were in a state of lipid peroxidation. Moreover, treatment with TGs reduced the MDA content and increased the GSH level, thereby recovering the oxidative-antioxidative balance and reducing the cellular damage caused by lipid peroxidation.
Excessive lipid deposition may exceed the repair capacity of cells and lead to apoptosis [29]. Doxorubicin has a strong cytotoxic effect and induces cell apoptosis. Combining lipid deposition and doxorubicin may exacerbate injury at the same time. Bax is an important proapoptotic protein in the Bcl-2 family [30], and it is predominantly present in an inactive conformation, maintaining organismal stability in part through interaction with antiapoptotic Bcl-2 proteins; thus, Bax and Bcl-2 play a key role in cell death and survival [31]. Caspase 3 is a key member of the caspase family, and activation of Caspase 3 is an indispensable step in mitochondria-dependent apoptosis [32]. Its spliceosome, Cleaved caspase-3 is a key downstream factor in the apoptotic cascade and functions as an important executor of apoptosis [33]. In the present study, TGs prevented apoptosis in NS rats by decreasing the expression of Bax and Cleaved caspase-3 as well as restoring the expression of Bcl-2.
In summary, the present findings suggested that TGs treatment improves renal injury induced by doxorubicin by reducing renal lipid deposition, inhibiting renal lipid peroxidation, and mitigating cell apoptosis. However, the precise mechanism and signaling pathway through which TGs improve renal lipid deposition require additional detailed studies.
## Ethical approval
The animal protocol was reviewed and approved by the Experimental Animal Ethics Committee of Guangdong Provincial Hospital of Traditional Chinese Medicine, approval number [2020004].
## Author contributions
Bo Liu, Aihua Wu, and Peng Xu conceived and designed the experiments. Bidan Zheng, Dongfang Lu, Xiuping Chen, Yinghua Yin, and Weiying Chen performed the experiments. Bidan Zheng, Huanmei Lin, and Dongfang Lu analyzed and interpreted the data. Bidan Zheng and Dongfang Lu wrote the manuscript. Bo Liu and Aihua Wu critically revised the manuscript. Bo Liu, Aihua Wu, Peng Xu, and Xiaowan Wang supervised the findings of the work and approved the manuscript for submission. All authors agreed with the final version of this manuscript.
## Disclosure statement
The graphical abstract was created by Figdraw. The authors declare no conflicts of interest.
## Data availability statement
All data generated during the study can be obtained upon reasonable request from the corresponding author.
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|
---
title: COL28 promotes proliferation, migration, and EMT of renal tubular epithelial
cells
authors:
- Linlin Li
- Hong Ye
- Qiaoling Chen
- Lixin Wei
journal: Renal Failure
year: 2023
pmcid: PMC10013395
doi: 10.1080/0886022X.2023.2187236
license: CC BY 4.0
---
# COL28 promotes proliferation, migration, and EMT of renal tubular epithelial cells
## Abstract
Type XXVIII collagen (COL28) is involved in cancer and lung fibrosis. COL28 polymorphisms and mutations might be involved in kidney fibrosis, but the exact role of COL28 in renal fibrosis is unknown. This study explored the function of COL28 in renal tubular cells by examining the expression of COL28 mRNA and the effects of COL28 overexpression in human tubular cells. COL28 mRNA expression and localization were observed in normal and fibrotic kidney tissues from humans and mice using real-time PCR, western blot, immunofluorescence, and immunohistochemistry. The consequences of COL28 overexpression on cell proliferation, migration, cell polarity, and epithelial-to-mesenchymal transition (EMT) induced by TGF-β1 were examined in human tubular HK-2 cells. COL28 expression was low in human normal renal tissues, mainly observed in the renal tubular epithelial cells and especially in proximal renal tubules. COL28 protein expression in human and mouse obstructive kidney disease was higher than in normal tissues ($p \leq 0.05$) and more significant in the UUO2-Week than the UUO1-Week group. The overexpression of COL28 promoted HK-2 cell proliferation and enhanced their migration ability (all $p \leq 0.05$). TGF-β1 (10 ng/ml) induced COL28 mRNA expression in HK-2 cells, decreased E-cadherin and increased α-SMA in the COL28-overexpression group compared with controls ($p \leq 0.05$). ZO-1 expression decreased while COL6 increased in the COL28-overexpression group compared with controls ($p \leq 0.05$). In conclusion, COL28 overexpression promotes the migration and proliferation of renal tubular epithelial cells. The EMT could also be involved. COL28 could be a therapeutic target against renal- fibrotic diseases.
## Introduction
The last 25 years saw the growth and aging of the world’s population, leading to major changes in epidemiologic trends, including an increase in the incidence and prevalence of chronic kidney disease (CKD). Globally, from 1990 to 2016, CKD incidence increased by $89\%$ (to reach 21,328,972), CKD prevalence increased by $87\%$ (to reach 275,929,799), and CKD-related mortality increased by $98\%$ (to 1,186,561) [1].
Glomerulosclerosis and interstitial fibrosis inevitably occur during the development of chronic kidney diseases, eventually leading to renal failure. The characteristics of renal fibrosis include the destruction of renal tissue structure, excessive accumulation of extracellular matrix in the renal interstitium, and the loss of renal function [2]. The mechanism of renal fibrosis involves enhanced oxidative stress response, apoptosis of renal intrinsic cells and immune cells, inflammatory response, proliferation, activation of fibroblasts, and transformation of epithelial cells into fibroblasts [3]. A key feature of renal interstitial fibrosis is the epithelial-mesenchymal transition (EMT) and the secretion of extracellular matrix (ECM) components [4].
The collagen family includes several proteins widespread throughout the body and are important for tissue and organ scaffolding, cell adhesion and migration, cancer, angiogenesis, tissue morphogenesis, and repair [5]. Different types of collagens are found in the ECM and play vital roles in the structural characteristics of different tissues, constitute scaffolds for cell adhesion and migration, and play important roles in various diseases [6,7]. Many collagen-related diseases are caused by gene mutations encoding a collagen polypeptide chain [8–12]. These mutations can lead to a spectrum of conditions since different collagen proteins also directly regulate the phenotypic state of cells by transmitting signals to mesenchymal cells, epithelial cells, and endothelial cells that affect their proliferation, differentiation, polarization, and survival [13–15].
Type XXVIII collagen (COL28) is a recently described (in 2006) homotrimeric molecule. Its ɑ-chain contains a 528-amino acid collagenous domain and two von Willebrand factor A (VWA) modules involved in protein-protein interactions [16]. The expression of COL28 displays a heterogeneous localization around Schwann cells and nerve bundles, and COL28 is a marker of non-myelinated zones of the peripheral somatosensory system [17]. Zhao et al. [ 18] reported that mutations in the COL28A1 gene were associated with spontaneous preterm birth. Chen et al. [ 19] showed that COL28A1, as a biomarker, is associated with benefits from immune checkpoint inhibitor treatment in patients with melanoma. Yang et al. [ 20] screened 13 key genes related to the prognosis of glioblastoma multiforme, among which COL28A1 was the most important. COL28 is involved in lung fibrosis and might be a therapeutic target [21]. Besides its role in various diseases, COL28 is expressed in the kidney [16], and COL28A1 polymorphisms appear to modulate the development of diabetic nephropathy [22], a disease involving renal fibrosis.
The authors’ team has found a missense mutation in COL28 that might cause tubulointerstitial fibrosis and uremia. A proband (33-years old) of a uremic family underwent sequencing, revealing a missense mutation in COL28 that resulted in a change in amino acid 722. Bioinformatics predicted that this change would directly affect the function of COL28. A renal biopsy revealed chronic tubulointerstitial disease (data not shown). Therefore, it was hypothesized that COL28 could play an important role in maintaining the normal function of renal tubules. Therefore, this study explored the functions of COL28 in tubular cells by examining the expression of COL28 mRNA and localization of its protein in kidney tissue in humans and mice with normal kidneys and fibrotic renal diseases. The COL28 gene was overexpressed in human tubular HK-2 cells to determine the consequences of COL28 overexpression on cell proliferation, migration, cell polarity, and EMT induced by TGF-β1. The results could provide a better understanding of fibrotic kidney disease and provide novel potential therapeutic targets.
## Animals
C57BL/6J SPF male mice aged 8 weeks and weighing 25–30 g were obtained from the Shanghai SLAC Laboratory Animal Co. (Shanghai, China). The animals were housed in three mice per cage and with free access to chow and water. All animals were kept inadequate sanitary conditions. The animals were divided into three experimental groups (six mice/group): the control group, which was submitted only to anesthesia (isoflurane $2\%$ inhalation) and laparotomy; the unilateral ureteral occlusion (UUO) 1-week group, which was submitted to anesthesia (isoflurane $2\%$ inhalation) and left UUO [23], then sacrificed (by cervical dislocation after inhalation anesthesia with isoflurane $2\%$) on the 7th day after surgery; the UUO 2-week group, which was submitted to anesthesia (inhalation anesthesia with isoflurane $2\%$) and left UUO [23], then sacrificed (by cervical dislocation after inhalation anesthesia with isoflurane $2\%$) on the 14th day after surgery. All animal experiments were approved by the Animal Ethics Committee of Fu Jian Medical University (approval #2020070101) and performed in compliance with the Guidelines for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH Publication, 8th Edition, 2011).
## Tissue collection
Eight patients who underwent nephrectomy due to renal cancer in the urology department of Fujian Medical University Union Hospital were enrolled from January 2018 to December 2019. Normal non-cancerous tissues were taken 3 cm away from cancer. Renal tissues were stained by hematoxylin and eosin (H&E) to exclude structural abnormalities and cancer. All patients were diagnosed with renal tumors. All underwent total nephrectomy.
Eight patients who underwent surgical treatment for obstructive nephropathy were also enrolled during the same period. Paraffin-embedded tissue sections from these patients were used for Mason staining and immunohistochemistry. Their diagnosis was nephrolithiasis and severe hydronephrosis.
The hospital’s ethics committee approved the study (2020ky070). All methods were carried out in accordance with relevant regulations and guidelines. All participants signed the informed consent form.
## Histopathological examination
Harvested tissues were fixed in $10\%$ neutral buffered formaldehyde solution for 24 h and then paraffin-embedded before sectioning at 3 µm. After deparaffinization using xylene, the sections were stained with H&E and Masson trichrome. Five non-overlapping fields were selected at 200× magnification (E600; Nikon, Tokyo, Japan) and examined by two pathologists separately. The total renal histopathological score (0, normal; 1, mild impairment; 2, moderate impairment; 3, severe damage) was calculated based on eight items: interstitial fibrosis, interstitial edema, interstitial infiltration, tubular atrophy, red tube, protein casts, tubule vacuolar degeneration, and tubular dilatation [24]. The blue-stained (i.e., fibrotic) areas were quantified by morphometric analysis using Image-Pro Plus 6.0 software (Media Cybernetics, Silver Spring, MD, USA).
## Immunohistochemistry
Immunohistochemistry was performed on 3-μm formalin-fixed paraffin-embedded (FFPE) sections to detect the protein expression and localization of COL28 in human kidneys. The primary antibody used in this study was rabbit anti-collagen XXVIII (1:1000, ab188533, Abcam, Cambridge, The United Kingdom). The sections were incubated overnight in primary antibody buffer at 4 °C. After washing with PBS, they were stained with ElivisionTM super HRP (Mouse/Rabbit) IHC Kit (Maixin Biotech, Ltd., Fuzhou, China). After immunostaining, the sections were counterstained with hematoxylin. Representative pictures were captured using microscopy (Leica Microsystems, Wetzlar, Germany).
## Immunofluorescence
Immunofluorescence was performed on 4-μm FFPE sections to detect the co-localization of COL28 with AQP1, AQP3, and THP1 in normal human kidneys. The sections were treated in a repair box with EDTA antigen repair buffer (pH 8.0), and antigen repair was carried out using a microwave-based antigen retrieval technique. Sections were observed under a fluorescence microscope, and images were collected.
## Cell culture
HK-2 cells (human kidney proximal tubular cells line) were from the American Type Culture Collection (Manassas, VA, USA) and cultured in Dulbecco’s modified Eagle’s medium/F12 medium (Hyclone, Logan, UT, USA) containing $10\%$ FBS (Hyclone) and $1\%$ antibiotics (100 U/ml penicillin and 100 μg/ml streptomycin) (Life Technologies Co., Grand Island, NY, USA). The cells were incubated at 37 °C in a humidified incubator with $5\%$ CO2. For TGF-β1 treatment, the cells were cultured in a serum-free medium with or without recombinant human TGF-β1 (CA#10021, PeproTech, Rocky Hill, NJ, USA) with different concentrations.
## COL28 overexpression
The COL28A1-expressing lentivirus vector (Lv-COL28A1) and the negative universal control (Lv-NC) containing the green fluorescent protein EGFP and a puromycin resistance gene were constructed by Shanghai Genechem Co., Ltd. (Shanghai, China). Before infection with the virus, the HK-2 cells were seeded onto a 6-well plate (1.0 × 105 cells/well) and allowed to grow to $30\%$ confluency. The Lv-COL28A1 and Lv-NC were transfected into the cells with an enhanced infection solution (Shanghai Genechem Co., Ltd.) and polybrene (Sigma, St. Louis, MO, USA). The transfected HK-2 cells were selected with puromycin (3 μg/ml), and the expression of the fluorescence protein EGFP was used to monitor the infection efficiency by fluorescence microscopy after 72 h. The Lv-COL28A1-positive cells were designated as COL28-OE, and the Lv-NC cells were designated as COL28-NC.
## Cell viability assay
HK-2 cells were seeded into 96-well culture plates (1 × 104 cells/well) and treated with various concentrations of TGF-β1 (0,1, 2, 5, 10, and 15 ng/ml) for 24 h. Cell viability was evaluated using a Cell Counting Kit-8 (CCK-8; Genview, Australian). Absorbance was measured at 450 nm (Spectra Max i3X, Molecular Devices, LLC, Sunnyvale, CA, USA).
## Colony formation assay
The cells were divided into three groups: the control group (CON; HK-2 cells), COL28-NC, and COL28-OE. First, the cells in the three groups were made into single-cell suspensions using $0.25\%$ trypsin. Afterward, the cells were coated onto plates (1.5 × 103 cells per well) and incubated at 37 °C and $5\%$ CO2 for 2 weeks. After 2 weeks, the cells were washed two times with PBS, fixed with 5 mL of methanol (Sigma, St. Louis, MO, USA), and stained with $0.1\%$ Giemsa solution (Sigma) within 10 min.
## Wound healing assay
The mobility of the cells was evaluated by a wound-healing assay. The cells in the three groups (CON, COL28-NC, and COL28-OE) were incubated in 6-well tissue culture plates for 24 h to form a monolayer. A 20-µL pipette tip was used to scratch a line and remove the cells. Each well was washed twice with 1 mL of PBS to remove the detached cells. Then, 2 mL of DMEM/F12 was added to each well, and the plate was incubated for 24, 48, and 72 h. Cells were washed twice with PBS. The width of the cell-free space was measured at 0, 24, 48, and 72 h using a microscope (Carl Zeiss GmbH, Oberkochen, Germany).
## Transwell assays
Migration assays were carried out using Transwell chambers (8 µm pore size, Corning Inc., Corning, NY, USA). The cells in the three groups (CON, COL28-NC, and COL28-OE) suspended in a serum-free medium were loaded onto the upper chamber, while a medium containing $10\%$ FBS was added to the lower chamber. After 48 h of incubation at 37 °C, the Transwell chambers were removed from the incubator. The cells in the upper chamber were removed, and the migrated cells in the lower chamber were stained with crystal violet for 20 min. Finally, the number of migrated cells was counted under a microscope.
## Quantitative real-time PCR
Total RNA was isolated using Trizol (Invitrogen Inc., Carlsbad, CA, USA). RNA (2 µg) was reverse-transcribed using the high-capacity cDNA Reverse Transcriptase kit (Roche Applied Science, Penzberg, Germany). The primers are listed in Table 1. Real-time PCR amplification was performed using the SYBR Green PCR Master Mix Kit (Invitrogen). The relative quantity of mRNA was normalized to β-actin and calculated using the 2-ΔΔCt method.
**Table 1.**
| Genes | Sequences, 5’->3’ | Length of the product (bp) |
| --- | --- | --- |
| COL28A1 | F: CAGCCCTTCAGTTTAGCAG | 173 |
| COL28A1 | R: ATCCTTACGCCCTTCTCTC | 173 |
| CDH1 | F: AGTCACTGACACCAACGATAAT | 205 |
| CDH1 | R: ATCGTTCACTGGATTTGTG | 205 |
| ACTA2 | F: CCTGAAGTACCCGATAGAACATG | 273 |
| ACTA2 | R: TCTCCAGAGTCCAGCACGAT | 273 |
| TJP1 | F: AAAGAGAAAGGTGAAACACTGC | 135 |
| TJP1 | R: TTTTAGAGCAAAAGACCAACCG | 135 |
| ACTB | F: GGGCCGGACTCGTCATAC | 144 |
| ACTB | R: CCTGGCACCCAGCACAAT | 144 |
## Western blot
Western blot was used to detect COL28 protein expression and localization in the normal renal cortex and medulla according to the manufacturer’s protocol. The densitometric analysis of the images was performed using Image J software.
## Statistical analysis
Data were shown as means ± standard deviations and analyzed using GraphPad Prism 6.0 (GraphPad Software Inc., San Diego, CA, USA). The unpaired two-tailed t-test was used for comparison between the two groups. One-way analysis of variance (ANOVA) and Tukey’s posthoc test was used to compare more than two groups. Two-sided p-values <0.05 were considered statistically significant.
## COL28 is mainly expressed and located in proximal renal tubules of normal kidney tissue
Western blot and qPCR were used to detect the COL28 mRNA and protein expression and localization in the normal renal cortex and medulla. The results showed that COL28 mRNA and protein expression in the renal cortex was higher than in the renal medulla (Figure 1(A–C)), suggesting that COL28 was mainly expressed in the renal cortex. Immunohistochemistry showed that the COL28 protein was mainly expressed in renal tubules, especially in the cortex, and there was little staining in medullary tubules (Figure 1(D)). Glomeruli and renal interstitium were rarely stained. Immunofluorescence showed that COL28 co-located with AQP1, the marker protein of the proximal tubule, but not with THP, the marker protein of the Loop of Henle, and AQP3 of the collector, suggesting that COL28 was mainly expressed in the proximal tubule of kidney in normal human tissues (Figure 1(E)).
**Figure 1.:** *Expression and localization of COL28 in human normal kidney tissues. COL28 expression in the cortex and medulla of normal kidney tissue was analyzed by western blot (A) and immunohistochemistry (D). COL28 mRNA (B) and protein (C) expression were quantified and calibrated with the expression of β-actin. *p < 0.05, **p < 0.01. The localization of COL28 in normal kidney tissues (E). The experiments were repeated three times using eight samples each time. Data are presented as mean ± SD. Bar = 100 µm. Red represents COL28, green represents AQP1, AQP3, and THP, and blue represents the cell nuclei.*
## COL28 expression is increased in human and mouse renal tissue with obstructive kidney disease
COL28 was evaluated in obstructive nephropathy. COL28 staining in renal tubules increased with the aggravation of obstructive lesions (Figure 2(A–B)). In the UUO mouse models, H&E and Masson’s staining showed that renal tubules were dilated, flattened, and detached in the UUO1W group. A few inflammatory cells infiltrated the stroma. The expression of COL28 was significantly increased in the UUO1W group, and all tubules were stained, especially the dilated tubules (Figure 2(C–F)). In the UUO2W group, renal tubule atrophy and necrosis were obvious. Renal interstitial fibrosis was obvious. The staining of COL28 was most obvious in the renal tissues of the UUO2W group, especially in the atrophic tubules, showing block-like staining (Figure 2(C–F)). Compared with the CON and UUO1W groups, the total renal histopathological score and fibrotic area percentage of the UUO2W group were higher (both $p \leq 0.05$), while the expression of COL28 in the UUO2W group was increased compared with the UUO1W group (Figure 2(G–H)). We collected the pathological sections of eight mice in the control, UUO1W, and UUO2W groups, carried out COL28 immunohistochemistry and Masson staining, and analyzed the correlation between the optical density of COL28 staining and the collagen volume fraction of Masson staining. The results showed that the optical density of COL28 staining in mice was positively correlated with the volume fraction of collagen. It is suggested that with the aggravation of renal fibrosis in mice, COL28 staining is also gradually increased (Figure 2(I)).
**Figure 2.:** *COL28 expression in human obstructive kidney tissue and mouse obstructive kidney tissue. Image of normal and obstructive renal tissue stained by COL28 immunohistochemistry (A). Bar = 100 µm. Quantification of the expression levels of COL28 proteins in normal and obstructive kidney tissues (B). Graphical representation of the protein (C) and mRNA (D) expression levels of COL28 in mouse control, UUO 1-week, and UUO 2-week groups. *p < 0.05, **p < 0.01. Image of mason (E) and COL28 immunohistochemistry staining (F) in the mouse control, UUO 1-week, and UUO 2-week groups. Bar = 200 µm. Total renal histopathological score (G) and Masson straining surface area (H) in the mouse control, UUO 1-week, and UUO 2-week groups. (I) Correlation between the total renal histopathological score and Masson straining surface area. *p < 0.05.*
## Overexpression of COL28 promotes HK-2 cell proliferation
To evaluate the effect of COL28 overexpression on HK-2 proliferation, COL28 was overexpressed in HK-2 cells (Figure 3(A–C)). The effect of COL28 overexpression on the proliferation of HK-2 cells was detected by the plate cloning method. There were no differences in the numbers of colonies among the COL28-OE, CON, and COL28-NC groups, but the diameter of each colony was larger in the COL28-OE group compared with the two other groups ($p \leq 0.05$) (Figure 3(D–E)). The cell viability of the COL28-OE group was higher than in the CON and COL28-NC groups ($p \leq 0.05$) (Figure 3(G)). Therefore, COL28 overexpression can promote the proliferation of HK-2 cells.
**Figure 3.:** *Overexpression of COL28 promotes HK-2 cell proliferation and migration. The mRNA (A) and protein (B-C) expression of COL28 in HK-2 cells. Effect of COL28 overexpression on the proliferation of HK-2 cells detected by the plate cloning method (D-E). *p < 0.05. Comparison of the cell viability in the CON, COL28-NC, and COL28-OE groups by CCK-8 (G). *p < 0.05. Cell migration ability in the three groups by the wound healing test (F&H) **p < 0.01. Cell migration ability in the three groups by the Transwell assay (I-J). **p < 0.01. All experiments were performed three times.*
## Overexpression of COL28 promotes HK-2 cell migration
The cell wounding assay was used to detect the effect of overexpression of COL28 on the migration ability of HK-2 cells. Compared with the CON and COL28-NC groups, the scratch areas of the cells in the COL28-OE group were reduced, and the cell migration ability was the strongest (Figure 3(F,H)). The Transwell assay showed that the number of cells passing through the membrane in the COL28-OE group was higher than in the CON and COL28-NC groups ($p \leq 0.01$) (Figure 3(I–J)). These results suggest that COL28 overexpression can promote the migration ability of HK-2 cells.
## Overexpression of COL28 promotes HK-2 cell EMT induced by TGF-β1
The preliminary experiments showed that the cell viability was decreased with TGF-β1 15 ng/ml compared with the controls ($p \leq 0.05$) (Figure 5(A)). The cells gradually changed from the original oval and paving-stone shape to a fibroblast-like appearance with the increase in TGF-β1 concentration (Figure 5(B)). E-cadherin mRNA expression decreased with the increasing TGF-β1 concentration, while the mRNA expression of α-SMA and COL28 increased (Figure 5(C)).
After 10 ng/ml TGF-β1 induction, α-SMA expression in the COL28-OE group was higher than in the CON and COL28-NC groups ($p \leq 0.01$). The expression of E-cadherin in the COL28-OE group was lower than in the CON and COL28-NC groups ($p \leq 0.01$) (Figure 4(A–B)). These results suggest that the expression of COL28 aggravates the EMT induced by TGF-β1. The relative expression levels of ZO-1 mRNA and protein in the COL28-OE group were lower than in the COL28-NC group ($p \leq 0.01$) (Figure 4(C–E)). The overexpression of COL28 could significantly inhibit the expression of ZO-1 in HK-2 cells.
**Figure 4.:** *Effect of overexpression of COL28 on the expression of E-cadherin and α-SMA protein in HK-2 cells after induction with 10 ng/ml TGF-β1 (A-B). *p < 0.05 **p < 0.01. Overexpression of COL28 in HK-2 cells inhibited ZO-1 protein (C-D) and mRNA (E) expression. **p < 0.01. COL6 protein expression levels in the CON, COL28-NC, and COL28-OE groups (F-G). *p < 0.05 **p < 0.01. COL6 protein expression in human fibrotic renal tissue was higher than in human normal renal tissue (H, I). **p < 0.01. All experiments were performed three times.* **Figure 5.:** *COL28 Overexpression promotes HK-2 cell EMT induced by TGF-β1. HK-2 cell viability was detected by CCK-8 under stimulation of TGF-β1 at different concentrations (A). Morphological changes in HK-2 cells are induced by different concentrations of TGF-β1 (B). Expression of E-cadherin, α-SMA, and endogenous COL28 mRNA in HK-2 cells induced by TGF-β1 at different concentrations (C). $p < 0.01 vs. TGF-β1 0 ng/ml, *p < 0.01 vs. TGF-β1 2 ng/ml, #p < 0.01 vs. TGF-β1 5 ng/ml. All experiments were performed three times.*
## COL28 overexpression increases COL6 protein expression in HK-2 cells
After the stimulation of HK-2 cells with TGF-β1 10 ng/ml, the expression levels of the COL6 protein in the COL28-OE group were higher than in the CON and COL28-NC groups (Figure 4 (F–G)). Only a small amount of COL6 was expressed in the normal human renal interstitium, while in the human fibrotic kidney, COL6 staining in the stroma was significantly enhanced ($p \leq 0.01$) (Figure 4(H–I)). The results suggest that the overexpression of COL28 could promote the expression of COL6 in HK-2 cells.
## Discussion
COL28 is involved in cancer and lung fibrosis [19–21]. Polymorphisms and mutations in COL28 might be involved in kidney fibrosis [22], but the role of COL28 in renal fibrosis is unknown. Therefore, this study aimed to explore the function of COL28 in tubular cells by examining the expression of COL28 mRNA and localization of its protein in kidney tissue and the effects of COL28 overexpression in human tubular cells. The results strongly suggest that COL28 expression is high in renal fibrosis, both in human and mouse models. COL28 overexpression promotes the proliferation, migration, and EMT of renal tubular epithelial cells and increases COL6 expression. Therefore, COL28 might be a therapeutic target against renal fibrotic diseases.
COL28 is the latest discovered collagen and thus has been only sparsely investigated. In the collagen superfamily, COL28 is similar to COL6 in the structure of its α-chain. They belong to the class of beaded filament-forming collagen. COL6 plays several key functions in various tissues, including promoting tumor growth and progression and regulating autophagy and cell differentiation [25–27], but whether COL28, which has a structure similar to COL6, also has similar functions is unknown. Reese-Petersen et al. [ 28] found that pro-COL28 levels in peripheral blood were high in patients with lung cancer and patients with heart failure with preserved ejection fraction. In addition, Pro-COL28 levels are high in diseases featuring a high ECM turnover [28]. COL28 could be involved in fibroproliferative conditions of the heart and lungs [28]. In the present study, immunohistochemistry showed that COL28 was mainly located in the proximal tubules of the renal cortex. Immunofluorescence showed that COL28 co-located with AQP1 (expressed on proximal tubules) but not with THP and AQP3 (expressed in the Loops of Henle and collecting ducts, respectively). These results confirmed that COL28 is expressed in the kidney, especially in the proximal tubules of the kidney in normal kidney tissues. The mechanism of renal fibrosis involves enhanced oxidative stress response, apoptosis of renal intrinsic cells and immune cells, inflammatory response, the proliferation of renal cells, activation of fibroblasts, and transformation of epithelial cells into fibroblasts [3]. A key feature of renal interstitial fibrosis is the epithelial-mesenchymal transition (EMT) and the secretion of extracellular matrix (ECM) components [4]. Cell cycle dysregulation leading to excessive renal cell proliferation is a feature of fibrotic kidney diseases and is a potential treatment target against renal fibrosis [29]. The present study suggests that COL28 can activate cell proliferation in kidney diseases. Although it is not the only factor affecting cell proliferation, COL28 is involved in the process. Future studies should investigate the mechanisms leading to increased COL28 expression and its involvement in the proliferation of renal tubular epithelial cells.
The induction of UUO in mice is a standard model of nonimmunological tubulointerstitial fibrosis [30]. The main renal damage caused by UUO is tubular damage and interstitial fibrosis. In the pathological tissues of human obstructive kidney disease collected in the present study, the COL28 staining on the proximal renal tubules was significantly enhanced compared with controls. The mouse UUO model confirmed that with the aggravation of interstitial fibrosis, the COL28 mRNA and protein expression levels in renal tissue were increased. These results are consistent. Therefore, the expression of COL28 is high in renal interstitial fibrosis, and its expression is correlated, to some extent, with the severity of the obstruction. Nevertheless, COL28 expression was reduced in renal tissues with severe fibrosis, i.e., in which the renal proximal tubules are completely detached, necrotic, lost, infiltrated by inflammatory cells, or completely replaced by fibrotic tissue. Therefore, it can be speculated that COL28 shows a process of first increased expression and finally decreased expression during renal tubular injury and interstitial fibrosis. Future studies will have to examine that hypothesis.
During urinary tract obstruction, resident fibroblasts become activated, and myofibroblasts are produced from several sources, including epithelial cells (via the epithelial-mesenchymal transition [EMT]), pericytes, endothelial cells, and bone-marrow-derived cells [31–34]. Myofibroblasts observed in the kidney after UUO can originate from tubular cells after EMT, but also pericytes and resident fibroblasts. Activated fibroblasts and myofibroblasts cells play important roles in interstitial fibrosis [31]. In the present study, COL28 overexpression in HK-2 cells promoted the proliferation of HK-2 cells and increased the migration ability of renal tubular epithelial cells. These results indicated that overexpressing COL28 could activate the renal tubular epithelial cells and promote the generation of myofibroblasts from renal tubular epithelial cells.
EMT is an important pathologic pathway involved in renal interstitial fibrosis [35]. The first and most critical step in EMT is reducing E-cadherin expression. Once E-cadherin is reduced, cell-to-cell adhesion decreases, the cells lose their tight intercellular junctions, and finally lose epithelial cell polarity; after EMT, the cells also show increased α-SMA expression, suggesting that the cells acquired a myofibroblast-like phenotype, enhancing their proliferation and invasive potentials [35,36]. At last, basement membrane degradation is also a feature of kidney EMT [37]. ZO-1 is one of the important proteins belonging to tight junctions (TJs) [38,39]. ZO-1 is important to maintain the polarity of epithelial cells and is also involved in forming the cytoskeleton, cell proliferation, and differentiation [40,41]. During EMT, the decrease of ZO-1 is accompanied by a decrease of Claudin, occludin expression, and the cleavage of E-cadherin on the plasma membrane [42–44]. COL28 overexpression could inhibit the expression of ZO-1 and E-cadherin in HK-2 cells, and promote the expression of α-SMA, suggesting that COL28 can affect the integrity of tight junctions of epithelial cells, destroy the adhesion between cells, inhibit the formation of renal tubular epithelial cells polarity, and promote EMT [43], thus aggravating kidney injury.
Accumulation of the extracellular matrix (ECM) protein significantly contributes to glomerulosclerosis and TIF. ECM is mainly synthesized and secreted by fibroblasts/myofibroblasts in the glomerulus and tubulointerstitium. Growing evidence suggests that EMT of renal tubular epithelial cells is one source of matrix-producing fibroblasts and myofibroblasts, which is one of the potential mechanisms involved in kidney fibrosis [45]. Clinical and in vitro studies suggest that COL6 might stimulate cell proliferation, leading to tissue fibrosis [46]. Deposition of COL6 was high in patients with kidney diseases compared with control patients, and COL6 was higher in diabetic glomeruli, associated with α-actin-positive myofibroblasts [47–49]. In fibrotic renal tissue, the expression of COL6 was significantly upregulated. Under the induction of TGF-β1, COL28 overexpression could further stimulate the production of the COL6 protein in HK-2 cells, suggesting that COL28 can aggravate renal interstitial fibrosis by inducing the expression of COL6.
This study has some shortcomings. First, only the functional changes of HK-2 cells after overexpression of COL28 were studied, but knockdown was not studied. The expression of COL28 in HK-2 cells is already very low under physiological conditions. In the qRT-PCR experiment, the average CT value was greater than 32. If COL28 is further knocked down at such a low expression level, it would be difficult to evaluate the effect after knockdown, and it would be difficult to carry out subsequent cell function experiments. Secondly, COL28 could aggravate renal interstitial fibrosis by promoting cell proliferation, migration, and EMT, but the exact mechanisms were not explored. A recent transcriptomics study by the authors suggests that COL28 overexpression aggravates renal interstitial fibrosis by increasing the EMT of renal tubular epithelial cells; interfering with HKDC1 expression appears to reverse the COL28-induced EMT and alleviate fibrosis [50]. Transcriptomic analyses of the changes of HK-2 cells overexpressing COL28 are still necessary under different pathological conditions, and overexpression/silencing of different genes to explore the meaningful differentially expressed genes and cell pathways related to proliferation, migration, and EMT.
In conclusion, COL28 expression is high in renal fibrosis, both in human and mouse models. COL28 overexpression promotes the proliferation and migration of renal tubular epithelial cells and increases COL6 expression. The EMT could also be involved in the process. Therefore, COL28 might be a therapeutic target against renal fibrotic diseases.
## Author contributions
Linlin Li performed the whole experiments related to this study, drafted the manuscript, and did the revision process. Hong Ye and Qiaoling Chen participated in the experiments. Lixin Wei acted as the corresponding author, provided expertise, and did the manuscript revision. All authors have read and agreed to the published version of the manuscript.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
## Data availability statement
All data generated or analyzed during this study are included in this published article.
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|
---
title: Prevalence, evolution, replication and transmission of H3N8 avian influenza
viruses isolated from migratory birds in eastern China from 2017 to 2021
authors:
- Yanwen Wang
- Mengjing Wang
- Hong Zhang
- Conghui Zhao
- Yaping Zhang
- Jinyan Shen
- Xiaohong Sun
- Hongke Xu
- Yujiao Xie
- Xinxin Gao
- Pengfei Cui
- Dong Chu
- Yubao Li
- Wenqiang Liu
- Peng Peng
- Guohua Deng
- Jing Guo
- Xuyong Li
journal: Emerging Microbes & Infections
year: 2023
pmcid: PMC10013397
doi: 10.1080/22221751.2023.2184178
license: CC BY 4.0
---
# Prevalence, evolution, replication and transmission of H3N8 avian influenza viruses isolated from migratory birds in eastern China from 2017 to 2021
## ABSTRACT
The continued evolution and emergence of novel influenza viruses in wild and domestic animals poses an increasing public health risk. Two human cases of H3N8 avian influenza virus infection in China in 2022 have caused public concern regarding the risk of transmission between birds and humans. However, the prevalence of H3N8 avian influenza viruses in their natural reservoirs and their biological characteristics are largely unknown. To elucidate the potential threat of H3N8 viruses, we analyzed five years of surveillance data obtained from an important wetland region in eastern China and evaluated the evolutionary and biological characteristics of 21 H3N8 viruses isolated from 15,899 migratory bird samples between 2017 and 2021. Genetic and phylogenetic analyses showed that the H3N8 viruses circulating in migratory birds and ducks have evolved into different branches and have undergone complicated reassortment with viruses in waterfowl. The 21 viruses belonged to 12 genotypes, and some strains induced body weight loss and pneumonia in mice. All the tested H3N8 viruses preferentially bind to avian-type receptors, although they have acquired the ability to bind human-type receptors. Infection studies in ducks, chickens and pigeons demonstrated that the currently circulating H3N8 viruses in migratory birds have a high possibility of infecting domestic waterfowl and a low possibility of infecting chickens and pigeons. Our findings imply that circulating H3N8 viruses in migratory birds continue to evolve and pose a high infection risk in domestic ducks. These results further emphasize the importance of avian influenza surveillance at the wild bird and poultry interface.
## Introduction
Avian influenza viruses (AIVs) pose persistent threats to birds, mammals and humans due to their rapid mutation, complicated reassortment, and cross-species transmission from birds to mammals. In the past two decades, highly pathogenic H5N1, H5N6 and H5N8 viruses have continued to evolve in wild and domestic birds and caused more than eight hundred human infections [1–4]. The H7N9 virus that emerged in chickens in 2013 led to five waves of human infections between 2013 and 2017 and underwent rapid mutation [5–7]. H9N2, the predominant subtype among the low-pathogenic viruses, circulates widely in poultry and wild birds and has adapted to replicate in mammals, resulting in more than 90 human infection cases since 1996 [3,8,9]. In recent years, avian-origin H3N8, H7N4, H10N3, and H10N8 viruses have also been reported to infect humans who have close contact with domestic birds [10–13]. The ongoing threat from different subtypes of avian influenza viruses emphasizes the importance of surveillance in their natural reservoirs.
Wild birds, especially migratory waterfowl, such as wild ducks, gulls and shorebirds, are known to be natural hosts of avian influenza viruses. Wild bird migration between countries and breeding play key roles in the maintenance and dissemination of viruses [14,15]. The highly pathogenic H5N8 viruses that caused recent outbreaks in poultry in Europe and Asia were confirmed to be closely related to those in migratory birds, and strains in wild birds have been found to be closely related to novel H5N6 and H5N1 reassortants that have evolved and been causing outbreaks in America, Europe, Africa and Asia since 2021 [2,16–19]. Previous studies found that the H7 low-pathogenicity virus from wild birds contributed to the emergence of highly pathogenic viruses [20]. Our recent study found that H10 viruses of the North American lineage have been introduced into Asia by migratory birds [21]. Surveillance of dominant and emerging viruses in migratory birds and monitoring cross-transmission to commercial poultry and mammals will contribute to early detection of a pandemic threat posed by novel avian influenza viruses.
H3N8 influenza viruses have been detected in a wide range of mammalian hosts, including dogs [22], horses [23], pigs [24], donkeys [25], harbour seals [26], and recently humans [10,27], and have been associated with ongoing outbreaks in dogs and horses. H3N8 avian influenza viruses primarily circulate in wild birds and domestic ducks, and their genetic and biological characteristics are still relatively poorly understood. Previously, studies found that an H3N8 avian influenza virus isolated from harbour seals preferentially bound to human-type receptors, which could be transmitted among ferrets via respiratory droplets and replicated efficiently in human lung cells [26]. A recent study demonstrated that wild bird-origin H3N8 viruses acquired the ability to bind to human-type receptors, and some viruses were transmitted efficiently via contact among guinea pigs. The PB1 S524G mutation conferred avian H3N8 virus airborne transmissibility in ferrets [28]. Li et al. found that a wild bird H3N8 virus developed enhanced human-type receptor binding ability and exhibited good adaptation in mice [48]. Human infection with H3N8 AIV was first reported in Henan Province, China, on April 10, 2022. A 4-year-old boy developed fever and lethargy followed by severe acute respiratory distress syndrome, including dyspnea, hypoxemia and pneumonia, in a short period of time. Avian H3N8 virus was detected in alveolar lavage fluid and peripheral blood samples [10]. In May 2022, a second H3N8 infection case was reported in Hunan Province, China. A 5-year-old boy developed fever and chills on May 9, 2022, and recovered after symptomatic treatment [27]. Subsequent surveillance and phylogenetic studies indicated that the two human H3N8 isolates were genetically close to the emerged chicken H3N8 reassortants, which may have originated from duck H3Nx viruses (HA donor), wild bird HxN8 viruses (NA donor) and chicken H9N2 viruses (internal gene constellation donor) [29–31]. These findings, together with human infection reports, suggest that H3N8 AIV poses an increasing threat to public health.
Since 2017, we have conducted annual avian influenza surveillance in migratory birds in the Yellow River Delta wetland in eastern China to monitor the infection risk of wild bird-origin AIVs to domestic birds and mammals [1,21,32–34]. In this study, we analyzed five years of avian influenza virus surveillance data from the Yellow River Delta wetland and fully uncovered the ecological, genetic and biological characteristics of representative avian H3N8 viruses in migratory birds. These findings provide important information about the evolution and dissemination of wild bird-origin H3N8 viruses and provide insights for the surveillance of these viruses at the wild bird and commercial poultry interface.
## H3N8 viruses in migratory birds in eastern China
From the autumn migratory season of 2017 to 2021, we conducted continuous active avian influenza surveillance in migratory birds in the Yellow River Delta wetland located in eastern China, which overlaps with the EAA migratory flyway (Figure 1A, Figure S1). A total of 162 viruses that belonged to 28 subtypes were identified and isolated from the 15,899 samples (Figure 1B–D). The AIV isolation rates in each migration season varied from $0.1\%$ to $8.77\%$, while 2225 samples collected at seven time points between 2017 and 2021 were negative for AIVs (Figure 1C). Twenty-one of the 162 viruses were isolated from 3032 wild duck and gull samples from four independent sampling dates and identified as the H3N8 subtype (Figure 1D, Table S1) (the sequence data have been deposited in the GISAID EpiFlu Database under accession numbers EPI2245719-EPI2245772, EPI2245774, EPI2245776-EPI2245888). Six and eleven H3N8 viruses were isolated from wild ducks in 2017 and 2019, respectively, while four viruses were isolated from 806 migratory gull samples in 2021 (Table S1). No H3N8 virus was detected in 2018 or 2020. The annual sampling and virus information indicated that migratory birds provide a large gene pool, driving the prevalence and evolution of different influenza viruses. These identified subtypes further revealed that H3N8, H4N6, H6N1 and H9N2 viruses were the dominant viruses in migratory birds in eastern China. Figure 1.Prevalence of different subtypes of avian influenza viruses detected in wild birds in the Yellow River Delta wetland in eastern China, 2017–2021. ( A) Sampling sites; (B) sampling size and number of isolates for each year; (C) sampling frequency, sampling numbers, AIV isolation numbers, and AIV isolation rates at each collection date; (D) subtypes identified.
## Prevalence of H3N8 viruses in birds
To better understand the ecology and epidemiology of H3N8 viruses, we mapped the globally circulated H3N8 viruses in different hosts. To date, nine H3 and NA surface combinations, including H3N1, H3N2, H3N3, H3N4, H3N5, H3N6, H3N7, H3N8 and H3N9, have been identified in animals, and a total of 11,534 HA sequences of H3Nx (N1–N9) viruses obtained from birds and mammals are available in databases (GenBank and GISAID, updated to November 25, 2022) (Figure 2A). Among these nine identified subtypes, H3N2 (7194 HA sequences) and H3N8 (3568 HA sequences) viruses are the predominant subtypes, and they have been detected in multiple animals. A total of 3568 H3N8 viral HA sequences were available in the databases, and these viruses were mainly detected in wild bird (1815 strains), equine (1088 strains), duck (289 strains), and canine (222 strains) viruses. Only 30 H3N8 strains were detected in chickens. Some H3N8 viruses were occasionally found in harbour seals, swine and camels (Figure 2B). The H3N8 virus was first detected in 1963 and has circulated in animals for more than half a century (Figure 2C). Because this study focused on H3N8 viruses circulating in wild birds, we summarized the identified specific hosts of global H3N8 viruses in wild birds. To date, H3N8 viruses have been detected in at least 64 different species of wild birds. Significantly, migratory wild ducks, including mallards (Anas platyrhynchos) (753 strains), pintails (Anas acuta) (206 strains), Anas discors (179 strains), *Anas crecca* (158 strains), *Anas clypeata* (68 strains) and Anas rubripes (54 strains), are the primary natural hosts of avian H3N8 viruses (Figure 2D). A total of 91 avian H3N8 viral HA sequences are available in the databases (including the sequences detected in this study), and these viruses were detected in wild and domestic birds in 17 provinces in China, including 55 strains obtained from wild ducks and domestic ducks and 21 viruses detected in chickens. Of note, most of the chicken viruses (16 of 21) were detected in 2022. Two human H3N8 strains were obtained in Henan and Hunan provinces in 2022. Interestingly, nearly all the H3N8 viruses were detected in the provinces of China located along the East Asia-Australasia (EAA) migratory flyway (Figure 2E). These categorized results suggest that H3N8 viruses have evolved to infect a wide range of animals and that avian-lineage viruses have been dominantly preserved in migratory wild ducks. Figure 2.Global prevalence of H3N8 influenza A viruses. ( A) HA and NA combinations of animal H3Nx strains in the database. ( B) *Summarized analysis* of the animal hosts of H3N8 viruses. ( C) Number of H3N8 viruses detected in wild birds from 1963 to 2022. ( D) H3N8 viruses in migratory wild birds. The host species of the wild bird H3N8 viruses in the databases were classified and summarized according to their isolation information. Unidentified species indicate the H3N8 viral sequences in the databases without specific host information. ( E) Distribution of H3N8 avian influenza viruses detected in China. All the public data in GenBank and GISAID used in this study were up to date as of November 25, 2022.
## Genetic diversity and evolution of H3N8 viruses
H3N8 viruses have circulated globally for more than half a century and have a wide range of reservoir hosts. We first downloaded the HA sequences of H3N8 viruses detected from wild birds, poultry, mammals and humans and selected 153 representative HA sequences to construct a Bayesian time-resolved tree to reveal the genetic diversity of HA genes of the global H3N8 influenza viruses in different hosts. The HA gene of H3N8 viruses has evolved into different lineages, and the viruses mainly detected in wild birds and poultry formed at least three major branches (Figure 3A). To better understand the genesis and evolution of the 21 H3N8 viruses identified in this study, we further constructed a Bayesian time-resolved tree of the HA gene plus 196 representative avian H3 viruses detected in North America, Asia, Oceania, Europe, and Africa. The phylogeny indicates that the HA genes of avian H3 viruses have undergone complex evolution and form several branches according to the location of virus isolation. Importantly, some North American strains clustered into the Eurasian lineage, suggesting that Eurasian H3 bird viruses have been introduced into North America. All 21 H3N8 viruses detected in this study clustered in the Eurasian lineage and formed at least four major branches (Figure 3B). The HA genes of the 21 viruses shared $91.1\%$–$100\%$ nucleotide similarity and were divided into four groups according to genetic identity (the nucleotide identity of the sequences between each group was lower than $95.4\%$) (Figure 3C). Notably, the HA genes of the two H3N8 viruses that caused human infection in China shared 85.2–$88.1\%$ identity with the wild bird viruses in this study and formed a branch with the chicken and duck H3 viruses isolated in China (Figure 3A and B). Figure 3.HA phylogenies of H3N8 viruses in all hosts (A), avian hosts (B) or wild bird hosts (C). Data for the time-scaled MCC tree inferred for the HA segment of the viruses in panel A were collected from all H3N8 hosts for which sequence data were available in GenBank and GISAID. The colour of each branch indicates the host, while the colour of each tip is the posterior time for that node. The colour of each branch in panel B indicates the location of the available avian H3Nx viruses. H3N8 viruses sequenced in this study are shown in red.
The NA genes of the 21 H3N8 viruses shared 76.8–$100\%$ identity at the nucleotide level. The NA genes have evolved to form two distinct branches, as shown in the Bayesian time-resolved tree. The viruses isolated from gulls in 2021 shared high genetic similarity with a black-tailed gull H10N8 virus isolated in 2020 and clustered in a separate sublineage. The viruses isolated from wild ducks in 2017 and WD/W$\frac{6252}{2019}$ shared genetic similarity. The ten viruses isolated in 2019 that belonged to Sublineage II were divided into two different branches in the NA tree (Figure 4). Of note, the NA genes of the 21 H3N8 viruses were closely related to those of viruses detected in migratory birds and ducks in China, Mongolia, South Korea, Japan, Vietnam and Russia, located along the EAA migratory flyway (Figure 4). Six internal genes, i.e. PB2, PB1, PA, NP, M, and NS, of these 21 viruses shared 85.7–$100\%$, 93.3–$100\%$, 92.9–$100\%$, 92.1–$100\%$, 95.9–$100\%$, and 70.6–$100\%$ identity, respectively. Notably, the internal genes of the viruses isolated in this study have undergone complicated reassortment with related viruses circulating in migratory birds and domestic ducks. The PB2 gene sequences of the 21 viruses clustered into five different branches in the phylogenetic tree, while the PB1, PA and NS gene sequences clustered into two separate branches each. The NP gene sequences of the 21 viruses clustered into three independent branches, and the M gene sequences formed one branch on the phylogenetic tree. Interestingly, the viruses we isolated from wild birds in the Yellow River Delta, including H7N4, H9N2, H10N4 and H10N8 viruses, shared high genetic similarity with the six internal genes of some H3N8 viruses detected in this study. The internal genes of the two human H3N8 isolates showed high homology with poultry and human H9N2 isolates and dissimilarity with the H3N8 viruses in this study (Figure S2). Figure 4.*Phylogenetic analysis* of the NA gene of the H3N8 virus. The sequences in purple, green, and blue represent the H3N8 viruses detected in this study.
Phylogenetic analysis of each of the gene segments of the 21 viruses isolated in eastern China from 2017 to 2021 identified 12 genotypes (the distance between groups in each phylogenetic tree was as follows: PB2: $94\%$, PB1: $96.7\%$, PA: $94.5\%$, HA: $95.4\%$, NP: $95.7\%$, NA: $79.3\%$, NS: $72\%$). The six viruses isolated from wild ducks in 2017 were divided into three genotypes (G1–G3), while the 11 viruses isolated from wild ducks in 2019 were divided into eight genotypes. The four gull isolates detected in 2021 belonged to one genotype (Figure 5A). These results indicated that the H3N8 viruses that naturally circulate in migratory birds and ducks have evolved into different branches and have undergone complicated reassortment with viruses in waterfowl. Figure 5.Genotypes of the H3N8 virus and replication of the representative viruses in mice. ( A) Twelve genotypes of the 21 H3N8 viruses. ( B) Replication of the representative H3N8 viruses in mice. The mice were inoculated with the representative viruses, and viral titers were detected in eggs. Data on the viral titers in the brain, spleen and kidney were negative and are not shown. The dashed line indicates the lower limit of detection. ( C, D) Body weight change in the mice inoculated with the representative H3N8 viruses.
## Molecular characteristics of the H3N8 viruses
Several key molecular markers in each segment have been identified to play a key role in the receptor binding changes, replication, pathogenicity, and transmission of avian influenza viruses in birds and mammals. All 21 viruses shared the amino acid sequence PEKQTR/GLF at the cleavage site in the HA gene, suggesting that these viruses have low pathogenicity in chickens. All 21 viruses have acquired amino acid mutations (I155T and T160A (H3 numbering)) in the HA gene that have been identified to promote the binding of H5N1 and H9N2 viruses to human-type receptors [35]. Several amino acid substitutions that have been reported to increase replication, virulence or transmissibility in mammals were observed in these H3N8 isolates, including PB1-R207K, PB1-H436Y, NP-V41I, M1-N30D + T215A, and NS1-V149A [36–39]. Substitution of PA-515 T was detected in 20 of the 21 H3N8 viruses, excluding WD/W$\frac{1766}{17}$, which possesses PA-515S (Table S2). The mammalian host adaptive mutations PB2-E627K and D701N were not detected in any of the H3N8 isolates.
## Replication and virulence of representative H3N8 viruses in mice
Twelve representative viruses of each genotype (G1–G12) were selected for replication and virulence assessments in mice. The mice were inoculated with the viruses, and the organs, including the nasal turbinate, lung, spleen, kidney and brain, of the mice were collected at 3 dpi; viral titers were measured in eggs. Ten viruses, excluding WD/W$\frac{11221}{19}$ and WD/W$\frac{11397}{19}$, replicated in nasal turbinates in mice. Ten viruses, excluding WD/W$\frac{11221}{19}$ and WD/W$\frac{6252}{19}$, replicated in the lungs of mice. The viral titers in the nasal turbinate and lung ranged from 0.58 to 5.17 log10 EID50/ml (Figure 5B). No virus was detected in spleen, kidney or brain tissue (data not shown). Six of the twelve tested viruses induced body weight loss in infected mice ($1\%$–$7.8\%$) (Figure 5C and D). Pathological studies were performed on the lung samples of the mice. Most of the lung samples showed mild or moderate damage, including extensive inflammatory cell infiltration, necrosis and detachment of airway or alveolar epithelial cells, or widening of the alveolar diaphragm (Figure S4). These data imply that although some H3N8 isolates can replicate efficiently and cause body weight loss and lung inflammation in mice, the naturally isolated H3N8 viruses in migratory birds need to undergo further host adaptation before they develop increased virulence in mice.
## Receptor binding properties of representative H3N8 viruses
Receptor binding specificities of AIVs play key roles in the adsorption and invasion processes of viruses in target cells, and receptor-binding adaptation is a prerequisite for the cross-species transmission of avian viruses to mammals. Here, we selected nine representative viruses according to their evolutionary divergence in the HA phylogenetic tree. All the tested viruses primarily bind to avian-type receptors (SA α-2,3-sialylglycopoilmer), although they have acquired the ability to bind to human-type receptors (SA α-2,6-sialylglycopoilmer) (Figure 6). These results suggest that these naturally isolated H3N8 viruses from wild ducks and migratory gulls preferentially bind to avian-type receptors. Figure 6.Receptor binding preferences of the representative wild bird H3N8 viruses. Two specific glycopolymers (α-2,3-siaylglycopolymer and α-2,6-siaylglycopolymer) were used to test the receptor binding properties of the representative H3N8 viruses. The data shown are the means of three replicates; the error bars indicate the standard deviation.
## Replication of representative H3N8 viruses in vitro
According to the results of the mouse infection and receptor binding studies, we selected three viruses, WD/W$\frac{1895}{17}$, WD/W$\frac{6275}{19}$ and GL/W$\frac{1518}{21}$, and evaluated their replication ability in avian and mammalian cells. Cells were inoculated with the representative viruses in 24-well plates, with 105 EID50/ml of virus, and the supernatants of chicken embryo fibroblast (CEF), chicken embryo fibroblast (DEF), Madin-Darby canine kidney (MDCK), and human non-small cell lung cancer (A549) cells were collected and titrated in eggs. Interestingly, a significant difference in replication ability among the three viruses in avian and mammalian cells was observed. The titers of the representative viruses in MDCK cells were significantly higher than those in CEF, DEF and human A549 cells (Figure S3). These results indicate that the tested wild bird-origin H3N8 viruses have not adapted to replicate efficiently in chicken and duck embryo fibroblast cells and human cells.
## Representative wild bird H3N8 viruses replicated and were transmitted efficiently in ducks
Ducks, including domestic ducks and wild ducks, are natural reservoirs of different AIVs. Here, we have summarized the host information of the avian H3N8 virus sequences in databases and found that more than 280 H3N8 viruses have been detected in domestic ducks (Figure 2B). However, the infection, replication and transmission abilities of H3N8 viruses originating from wild birds in domestic ducks are still unclear. In this study, three viruses, WD/W$\frac{1895}{17}$, WD/W$\frac{6275}{19}$ and GL/W$\frac{1518}{21}$, were used to investigate whether H3N8 viruses can replicate and be transmitted in domestic ducks. The viruses WD/W$\frac{1895}{17}$ and GL/W$\frac{1518}{21}$ were detected in all nine collected organs and tissues, and WD/W$\frac{6275}{19}$ was detected in all organs except for the lung and spleen. The viral titers of the three representative viruses in the intestine, rectum and bursa of Fabricius were significantly higher than those in the other organs, which suggests that the enteric canal, not the respiratory tract, of ducks is the major target organ for H3N8 viral infection (Figure 7A). Figure 7.Replication and transmission of the representative H3N8 viruses in ducks. ( A) Replication of the representative H3N8 viruses in ducks. SPF ducks were inoculated with the representative viruses; the organs were sampled at 3 dpi, and viruses were titrated in eggs. ( B-D) Transmission of the representative H3N8 viruses in ducks. Oropharyngeal and cloacal swabs were collected from the ducks at the indicated time points, and the viruses were titrated in eggs. ( E-F) Serum samples from inoculated and contact ducks were collected at 10, 15, and 21 dpi to detect HI antibodies. OP: oropharyngeal swab; CL: cloacal swab. The dashed lines indicate the lower limit of virus detection in panels A-D and the lower limit of HI antibody detection in panels E-G.
In the transmission study, all three representative viruses were detected in both oropharyngeal and cloacal swabs from the inoculated and contact ducks during the experimental period. Importantly, the virus shedding period in the inoculated and contact ducks for all three viruses was up to 11 days (Figure 7B–D). Interestingly, although the H3N8 viruses were able to replicate in the inoculated and contact ducks, they did not trigger the production of high hemagglutination inhibition (HI) antibody levels in the ducks to defend against and eliminate virus infection (Figure 7E–G). These findings suggest that domestic ducks are susceptible hosts of H3N8 viruses and that these wild bird H3N8 viruses can replicate in ducks and are transmitted efficiently via contact.
## Farmed chickens were not susceptible to representative wild bird H3N8 viruses
Analysis of the sequences and samples of H3N8 viruses in GISAID and GenBank suggests that chickens may not be susceptible to H3N8 virus infection (21 available strains were detected in chickens, and only 5 of the 21 were detected before 2022) (Figure 2B). However, the infection and replication of wild bird H3N8 viruses in chickens have not been investigated in previous studies. In this study, WD/W$\frac{1895}{17}$, WD/W$\frac{6275}{19}$ and GL/W$\frac{1518}{21}$ were further tested to evaluate their replication and transmission abilities in chickens. The serological tests prior to infection indicated that the farmed chickens had high H9 HI antibody titers but low prevailing H5 (Re-13, Re-14) and H7 (Re-4) HI antibody titers (Table S3). Viral titers of samples of each organ from the inoculated chickens were tested at 3 days post inoculation (dpi). Interestingly, only very low viral titers were detected in the trachea, pancreas and intestine, and no virus was detected in the lung, liver, spleen, kidney, rectum or bursa of Fabricius (Figure 8A). Viral titers of oropharyngeal and cloacal swab samples from the inoculated and contact chickens were also detected at the indicated time points. Unlike those in ducks, the three representative viruses displayed very limited transmissibility in five pairs of chickens. In the WD/W$\frac{1895}{17}$ and GL/W$\frac{1518}{21}$ groups, virus was detected in four inoculated chickens and in three contact chickens (Figure 8B and D). In the WD/W$\frac{6275}{19}$ group, virus was detected in three inoculated chickens and in one contact chicken on day 11 postinoculation (pi) (Figure 8C). Notably, the viral titers of oropharyngeal and cloacal swab samples of the positive inoculated and contact chickens were low, and the viral shedding period of the chickens was short compared to that of ducks (Figure 8B–D). Importantly, H3N8 virus infection hardly induced HI antibody production in the serum in all five inoculated or contact chickens (Table S4). The chicken experiments indicated that farmed chickens are not susceptible to infection with representative wild bird-derived H3N8 viruses. Figure 8.Replication and transmission of representative H3N8 viruses in chickens and pigeons. ( A) Replication of the representative H3N8 viruses in chickens. Three commercial chickens were inoculated with the representative viruses, and viruses from the samples were titrated in eggs at 3 dpi. ( B–D) Transmission of the representative H3N8 viruses in chickens. Oropharyngeal and cloacal swabs were collected from chickens at the indicated times, and viruses were titrated in eggs. ( E) Replication of the representative H3N8 viruses in pigeons. Three pigeons were infected with the representative viruses; organ samples were collected, and the viruses were titrated in eggs at 3 dpi. ( F-H) Transmission study of representative H3N8 viruses in pigeons. Swabs were collected, and the viruses were titrated in eggs. OP: oropharyngeal swab; CL: cloacal swab. The dashed line in each panel indicates the lower limit of detection.
## Commercial pigeons were not susceptible to representative wild bird H3N8 viruses
Free-ranging domestic pigeons can come in contact with both wild birds and domestic ducks and chickens. As a result, we tested the replication and transmission of the representative H3N8 viruses in commercial pigeons. The serological tests prior to infection indicated that the HI antibody of pigeons could not bind to H5 (Re-13, Re-14) and H7 (Re-4) viruses, and these birds had very low H9 HI antibody titers in their serum (Table S3). Virus was not detected in all the collected organs except for one lung sample in the WD/W$\frac{1895}{17}$ group, which was positive but had a very low viral titer (Figure 8E). WD/W$\frac{6275}{19}$ virus was detected in the liver and kidney in one pigeon, while GL/W$\frac{1518}{21}$ was detected in the pancreas and rectum in two pigeons (Figure 8E). Although virus was detected in the oropharyngeal or cloacal swabs of five inoculated pigeons and four contact pigeons in the WD/W$\frac{1895}{17}$ group, viral shedding was not persistent, and the viral titers were relatively low (Figure 8F). Similarly, the other two viruses (WD/W$\frac{6275}{19}$ and GL/W$\frac{1518}{21}$) showed limited viral shedding and low transmissibility in pigeons (Figure 8G and H). Interestingly, we did not detect any HI antibody in the serum of the inoculated and contact pigeons at 10, 15, and 21 dpi, suggesting that inoculation with the representative H3N8 viruses did not stimulate the production of specific antibodies in pigeons (Table S4). These infection studies showed that wild bird-origin H3N8 viruses are not able to replicate in pigeons.
## Thermal stability and neuraminidase activities of the H3N8 viruses
Thermal stability is important for the survival of influenza viruses in nature and is also reported to be correlated with the transmissibility of some H5N1 viruses [40]. We therefore compared the thermal stability of the three representative viruses. We found that the three viruses reduced viral titers by one unit and 3–4 logs after 4 h of treatment at 50°C, which suggested that these viruses were thermally stable (Figure S5). NA proteins promote progeny virion release by cleaving sialic acids on the host cell surface, contributing to virus replication in host cells [41,42]. Accordingly, we further tested the enzymatic activities of NA of the three selected H3N8 viruses. We found that the NA activities of WD/W$\frac{6275}{19}$ and GL/W$\frac{1518}{21}$ were higher than those of WD/W$\frac{1895}{17}$ (Figure S6).
## Discussion
In recent decades, emerging novel animal influenza viruses and other zoonoses have posed major challenges to the global commercial poultry industry and public health [5,43,44]. However, H3N8 avian influenza viruses have been ignored due to their low prevalence in chickens and the low risk to public health, although it has become one of the predominant strains in migratory waterfowl and domestic ducks. Recent human infections with H3N8 viruses in China have promoted interest in and concern about the evolution and cross-species transmission risk of these viruses. In this study, we performed a detailed analysis of the global distribution of H3N8 viruses in different hosts according to deposited sequence data and our surveillance results in eastern China between 2017 and 2021. The summarized results indicated that migratory ducks are the primary natural reservoirs of H3N8 viruses. Here, a total of 162 viruses belonging to 28 subtypes were identified and isolated from 15,899 wild bird samples, which suggested that wild birds are natural reservoirs of different subtypes of avian influenza viruses and play a key role in the maintenance and evolution of such viruses.
H3N8 viruses have been detected in at least 64 kinds of wild birds and have evolved into several phylogenetic lineages, whereas only a few strains were detected in chickens in recent years. The H3N8 viruses analyzed in this study shared similar sequence identities and clustered into the same lineages with some strains isolated from Europe, North America and Africa, suggesting that H3N8 viruses can be transmitted globally with the migration of their natural reservoirs. We also found that these H3N8 viruses have undergone complicated reassortment with circulating H5N3, H7N4, H9N2, H10N4 and H10N8-like viruses isolated from Yellow River Delta wetlands [21,33,34]. Of note, all the H3N8 viruses detected from chicken and humans in China in 2022 share HA and NA surface genes similar to those of duck- and wild bird-origin strains but bear an internal gene constellation from chicken H9N2 viruses [29,31]. The predominantly prevalent H9N2 viruses have been proven to be ideal internal gene donors for the emerged reassortants, including H7N9, H10N3, and H10N8 viruses [8,13,45,46]. The complicated epidemiology and ecology of avian influenza viruses at the interface of waterfowl and terrestrial birds could have facilitated the emergence of novel H3N8 reassortants in chickens. Therefore, monitoring AIVs in wild and domestic waterfowl and controlling H9N2 viruses in domestic birds will contribute to early warning and reduction in the occurrence of natural avian influenza reassortants.
Key amino acid substitutions and the conversion of receptor binding specificity have contributed to the virulence and transmissibility of AIVs in mammals. In the first human infection case, a 4-year-old boy was reported to be infected with H3N8 virus and developed severe acute respiratory distress syndrome in a short period of time, implying that the internal gene constellation of the H9N2 virus may contribute to increased virulence of the H3N8 virus isolated from human [10]. Yang et al. compared the genetic differences of the two human isolates and the chicken isolates and found that the first human strain A/Henan/4-$\frac{10}{2022}$ acquired the PB2 E627K mutation compared with the chicken ancestor viruses [29]. Zhang et al. found that some H3N8 viruses isolated from wild birds could be transmitted efficiently among guinea pigs and that the PB1 S524G mutation conferred increased virulence in mice and airborne transmissibility in ferrets [28]. Li et al. found that wild bird-derived H3N8 exhibited good adaptation in mice and induced significant weight loss in mice [47]. In this study, mutations involved in the enhanced virulence of the viruses in mammals, including 207 K and 436Y in PB1, 515 T in PA, and 30D and 215A in M1, were observed in the 21 H3N8 isolates. Animal studies indicated that some H3N8 isolates can replicate efficiently in lung and nasal turbinate tissue and cause weight loss in mice without prior adaptation. The change in receptor binding specificity from avian-type to human-type is a primary determinant for efficient AIV transmission to mammals or humans. Several publications previously reported that naturally isolated H3N8 viruses from wild birds exhibited dual receptor-binding profiles [28,47,48]. Yang et al. tested the receptor binding properties of one human H3N8 isolate and five chicken isolates by direct binding assays with SAα2–3Gal and SAα2–6Gal sialylglycopolymers and found that all the tested viruses could bind to both avian-type and human-type receptors. Residues N193, W222 and S227 might contribute to the dual receptor-binding properties of chicken H3N8 viruses [29]. The wild bird-origin H3N8 viruses in this study had key residues in the receptor-binding region of HA similar to those of the human and chicken isolates, including N193, W222, Q226, S227 and G228. Residues 155 T and 160A, which confer the enhanced receptor-binding property of avian influenza viruses, were observed in the 21 H3N8 viruses [8,35]. These residues in the receptor-binding regions of HA may collectively contribute to the dual receptor-binding profiles of wild bird H3N8 viruses. The transmission of these H3N8 viruses in mammals, such as guinea pigs and ferrets, needs to be evaluated in further studies.
Ducks are primary reservoirs of different AIVs and play a key role in cross-species transmission at the waterfowl and terrestrial bird or mammal interface [49]. Our previous study found that H10N4 and H10N8 viruses isolated from wild birds could replicate and be efficiently transmitted to ducks, but they did not induce high HI antibody production in ducks [21]. In this study, we found that wild bird-derived H3N8 viruses replicated poorly in DEF cells in vitro but replicated efficiently in ducks, especially in the enteric canal of ducks. Moreover, these H3N8 viruses exhibited highly efficient transmissibility between ducks and persistent viral shedding. However, these viruses did not induce the production of high titers of HI antibody in ducks. Chickens are susceptible to many strains of AIVs, including highly pathogenic H5 and H7 viruses and low-pathogenic H4, H6, H7, H9, and H10 viruses [19,34,45,46]. However, some wild bird strains, such as H16, have shown poor adaptation in chickens [50,51]. Here, we found that unlike ducks, commercial chickens were not susceptible to these H3N8 viruses. Importantly, seroconversion in chickens was detected in only a few inoculated birds and none of the contact chickens. Pigeons are not susceptible to AIVs, but several studies have found that some strains can replicate in pigeons [52,53]. Here, we found that pigeons were not susceptible to the representative wild bird H3N8 viruses and that the viruses did not induce the production of HI antibodies in inoculated pigeons. The experimental studies revealed that ducks but not chickens or pigeons were susceptible to the representative wild bird-derived H3N8 viruses. Different biological characteristics among ducks, chickens and pigeons can explain why the H3N8 virus sequences deposited in GenBank and GISAID were mainly detected in wild birds (mainly migratory ducks) and domestic ducks, while only a few strain sequences were detected in chickens and pigeons.
In summary, we described the distribution of H3N8 viruses and characterized the genetic and biological properties of H3N8 viruses isolated from wild birds in a wetland in eastern China. Our findings emphasize that active surveillance in migratory birds and domestic ducks will contribute to early detection of the emergence and evolution of AIVs in waterfowl and potential threats to the commercial poultry industry and human health.
## Ethics statement and facility
The animal studies were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Ministry of Science and Technology of the People’s Republic of China. The protocols for chicken, duck, pigeon and mouse studies were approved by the Committee on the Ethics of Animal Experiments of Liaocheng University. The ethics approval numbers are CK-INFEC-2022-03 (chicken); DK-INFEC-2022-01 (duck); PG-INFEC-2022-03 (pigeon); MC-INFEC-2022-05 (mice). All experiments with H3N8 viruses were conducted in an animal biosafety level 2 (ABSL-2) facility. The animals used in this study were placed in a biological safety isolator.
## Experimental animals
Six-week-old specific pathogen-free (SPF) female BALB/c mice were purchased from Jinan Pengyue Experimental Animal Breeding Co., Ltd. (Shandong, China). Three-week-old SPF ducks were purchased from Shandong Healthtech Laboratory Animal Breeding Co., Ltd. Forty-five-day-old commercial layer chickens and 30-day-old commercial pigeons were purchased from local poultry farms.
## Data acquisition from public databases
The HA sequences of H3N1, H3N2, H3N3, H3N4, H3N5, H3N6, H3N7, H3N8, and H3N9 subtype viruses that were detected in avian and mammalian species (human strains are not summarized in this study) were downloaded from the Influenza Virus Database of NCBI (https://www.ncbi.nlm.nih.gov/genomes/FLU/Database/nph-select.cgi?go=database) and GISAID (https://www.gisaid.org), respectively. The HA sequences of the same subtype downloaded from NCBI and GISAID were input into MEGA 11, and redundant sequences with the same ID were removed. Then, the number of HA sequences of each H3 subtype was summarized and visualized by Prism 9. The HA sequences of animal H3N8 strains collected each year were downloaded from NCBI and GISAID and input to MEGA 11 to delete the overlapping sequences. The number of HA sequences of the H3N8 viruses in different hosts was then summarized. The H3N8 viruses detected from avian species were classified into specific bird groups according to the virus isolation information. The available H3 viruses in public databases whose NA subtypes were not identified were not summarized in this study. The public viral sequences available in the NCBI and GISAID databases were updated to November 25, 2022.
## Sample collection, virus identification and isolation
The sampling sites were located in the Yellow River Delta wetland, which is an important habitat for migratory birds along the East Asian-Australasian (EAA) migratory flyway in eastern China. The species of the wild birds were first determined by binoculars in the habitats. Fresh fecal droppings of wild ducks, gulls and other wild birds were collected and then placed into 2 ml of minimal essential medium supplemented with penicillin and streptomycin. Positive fecal samples were identified by PCR with specific M and HA (H5, H7) primers [54,55]. The suspected H5- or H7-positive samples were transferred to an enhanced ABSL-3 facility in the Harbin Veterinary Research Institute of Chinese Academy of Agricultural Sciences for further virus identification and isolation, while the remaining suspected positive samples were injected into 10-day-old embryonated chicken eggs to isolate the viruses in an ABSL-2 laboratory at Liaocheng University. The isolated viruses were stored in a −80°C freezer.
## Genetic and molecular analysis
Genome sequencing of the H3N8 viruses was performed on an Applied Biosystems DNA Analyzer (3500xL Genetic Analyzer, USA) at Harbin Veterinary Research Institute of Chinese Academy of Agricultural Sciences (HVRI, CAAS). The sequence data were compiled with the SEQMAN programme (DNASTAR, Madison, WI) according to the reference sequences. The molecular markers in each segment were identified with the MegAlign programme (DNASTAR, Madison, WI).
## Phylogenetic analysis
The sequences of the 21 viruses in this study and the downloaded full-length sequences from databases were imported into Mega 11.0 for aggregation to obtain a single file. Multiple sequence alignment was performed by mafft software (v7.505) [56]. The repeated sequences and partial sequences were deleted, then the reference viruses were picked according to their host, collcetion region and collection date as shown in the phylogenetic tree. The best-fit nucleotide substitution model was selected using IQ-tree (v1.6.12) [57]. The branch support value in maximum likelihood (ML)-based trees was assessed by the ultrafast bootstrap approximation test and Shimodaira-Hasegawa approximate likelihood ratio (SH-aLRT) test. The ML trees of the six internal genes (PB2, PB1, PA, NP, M, NS) were visualized and embellished by FigTree (v1.4.4). The SH-aLRT support (%)/ultrafast bootstrap support (%) are shown at the nodes.
Markov chain Monte Carlo (MCMC) trees with molecular clocks were constructed using BEAST (v1.10) software to study the evolutionary history of H3N8 viruses in wild birds [58]. All the picked sequences were filtered from TempEst by assessing whether there was sufficient temporal signal in our data to proceed with phylogenetic molecular clock analysis. The path-sampling and stepping-stone estimation approaches were used to assess the best fitting clock model through marginal likelihood estimation. The best-fit nucleotide substitution model was selected using IQ-tree. The GTR + F + I + G4 distributed rate variation among sites in the nucleotide substitution model was selected, and MCMC chains were run for 2 × 109 iterations and sampled every 10,000 steps to generate a BEAST file (Figure 3A and B). The TN + F + G4 nucleotide substitution model was selected, and MCMC chains were run for 3 × 108 iterations and sampled every 1000 steps to generate a BEAST file. The HKY + F + G4 substitution model was selected, and MCMC chains were run for 5 × 108 iterations and sampled every 10,000 steps to generate a BEAST file (Figure 4). Both HA and NA genes were chosen with an uncorrelated lognormal relaxed molecular clock and a Bayesian skyline coalescent tree prior. Tracer (v1.7.1) was used to observe whether the parameters converged (effective sample size values ≥200). The MCMC tree files were obtained using TreeAnnotator software, with $10\%$ burn-in FigTree (v1.4.4) used to generate the MCMC trees with a time scale.
## Receptor binding assay
The solid-phase direct binding assay to test the receptor binding properties of the viruses has been described previously [21,34]. A specific chicken anti-H3 polyclonal antibody was used to detect H3N8 viruses. Dose–response curves of virus binding to glycopolymers were analyzed by using a single-site binding algorithm and curve fitting by GraphPad Prism 8 to determine the associated constant (Ka) values. Each value is presented as the mean ± standard deviation (SD) of three independent experiments, each of which was performed in triplicate.
## Antigenic analysis
The chicken, duck and pigeon antisera used in this study were generated with birds that were inoculated with 106 EID50 of the tested viruses in a volume of 200 µl. Antigenic analysis was performed by using the HI assay with $1\%$ chicken erythrocytes.
## Multicycle growth kinetics
Chicken embryo fibroblast (CEF) cells were obtained from 10-day-old SPF chicken embryonated eggs. Duck embryo fibroblast (DEF) cells were obtained from 12-day-old SPF duck embryonated eggs. Madin-Darby canine kidney (MDCK) and human lung adenocarcinoma epithelial (A549) cells were purchased from the Cell Resource Center of the Shanghai Institute of Life Sciences and preserved by our laboratory. MDCK, CEF and DEF cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) containing $10\%$ fetal bovine serum (FBS) and antibiotics. A549 cell lines were grown in an F-12 K nutrient mixture containing $10\%$ FBS and antibiotics. All cells were cultured at 37°C with $5\%$ CO2. Monolayer cells grown in 24-well plates were inoculated with 105 EID50 of the virus in a volume of 200 µl. One hour later, the supernatant was discarded, the wells were washed with phosphate-buffered saline (PBS) three times, and 500 µl of OPTI-MEM (GIBCO) was then added to the wells. OPTI-MEM containing 0.5 µg/ml trypsine-TPCK was used to assist in the infection of MDCK and A549 cells with the low-pathogenicity H3N8 virus. The supernatant was collected at 12, 24, 48 and 72 h postinoculation (hpi) and then titrated in eggs. The growth data shown are the average results of three independent experiments.
## Mice
Six-week-old female SPF mice (eight animals in each group) were inoculated with 106 EID50 of the virus in a volume of 50 µl. Three mice were euthanized on day 3 pi, and nasal turbinate, lung, spleen, kidney, and brain tissues were collected for viral titration in eggs. The lungs of three mice were fixed in $10\%$ formalin and then stained with hematoxylin and eosin (HE) for histological analysis. The remaining five mice were monitored daily for 14 days for weight loss and survival. Mice inoculated with PBS were established as a control group and used to observe body weight changes.
## Chickens
Forty-five-day-old commercial layer chickens were used in the infection study. Oropharyngeal and cloacal swabs of the commercial chickens were collected to detect AIVs and Newcastle disease virus (NDV) by both PCR and viral titration in eggs [54]. The HI antibody against the H3N8 virus in chicken serum was also detected by the HI assay before the infection study. The chickens that tested negative were divided into three groups to analyze the replication of WD/$\frac{1895}{17}$, WD/$\frac{11221}{19}$, and GL/W$\frac{1518}{21}$ viruses in chickens. Three chickens from each group were inoculated with 106 EID50 of the virus in a volume of 200 µl. Brain, tracheal, lung, liver, spleen, pancreatic, kidney, intestinal, rectal, and bursa of Fabricius tissue samples of the chickens were collected for viral titration in eggs at day 3 pi.
For the transmission study, five chickens from each group were inoculated with 106 EID50 of the virus in a volume of 200 µl. Another five naive chickens were placed into the same isolator at 24 hpi. Oropharyngeal and cloacal swabs of the chickens were collected on days 1, 3, 5, 7, 9, and 11 pi. The viral titers of the swabs were determined in eggs. Chicken serum was collected on days 10, 15, and 21 pi, and the antibody titer was determined by the HI test. The chickens were then euthanized on day 21 pi.
## Ducks
Twenty-seven three-week-old SPF ducks were divided into three groups to analyze the replication of WD/$\frac{1895}{17}$, WD/$\frac{11221}{19}$, and GL/W$\frac{1518}{21}$ viruses in ducks. Three ducks from each group were inoculated with 106 EID50 of the virus in a volume of 200 µl. Brain, tracheal, lung, liver, spleen, pancreatic, kidney, intestinal, rectal, and bursa of Fabricius tissue samples of the ducks were collected for viral titration in chicken eggs at day 3 pi.
For the transmission study, three ducks from each group were inoculated with 106 EID50 of the virus in a volume of 200 µl. Another three naive ducks were placed into the same isolator at 24 hpi. Oropharyngeal and cloacal swabs of the ducks were collected on days 1, 3, 5, 7, 9, and 11 pi. The viral titers of the swabs were titrated in eggs. Duck serum was collected on days 10, 15, and 21 pi, and the antibody titer was determined by the HI test. The ducks were then euthanized on day 21 pi.
## Pigeons
Four-week-old commercial pigeons were purchased from a local poultry farm. Oropharyngeal and cloacal swabs were collected to detect AIVs and NDV by qPCR and for virus isolation. Pigeon serum was collected to detect the HI antibody against H3N8 viruses by the HI assay before the infection study. The methods for virus inoculation, collection and virus titration in the organs, collection of oropharyngeal and cloacal swabs, collection of pigeon serum, and the HI test were the same as those used for the chicken study, as described above.
## Heat stability test
Test viruses (32 HA units in PBS) were incubated at 50°C for 30, 60, 120, 180, and 240 min. Hemagglutination activity was then determined by hemagglutination assays using $0.5\%$ chicken red blood cells, and the virus infectivity (EID50) was determined in 10-day-old chicken embryos. All experiments were repeated in triplicate.
## Neuraminidase activity assay
The neuraminidase activity assay has been described previously [34]. In brief, the test viruses were serially diluted 2-fold from 1 × 107 EID50/ml to 9.8 × 103 EID50/ml. Then, 50 μl of 200 μM substrate 2′-(4-methylumbelliferyl)-α-d-N-acetylneuraminic acid (MUNANA) was mixed with 50 μl of the virus dilution and incubated at 37°C for 60 min, and the reaction was stopped by adding 100 μl of 0.2 M Na2CO3. Finally, fluorescence was measured at excitation and emission wavelengths of 365 nm and 450 nm, respectively. The neuraminidase activity assay was performed in triplicate.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
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|
---
title: The association between serum folate and ultrasound - defined hepatic steatosis
authors:
- Xingxing Chen
- Jiajia Lu
- Qi Xu
- Bin Chen
- Lijun Shen
journal: Annals of Medicine
year: 2023
pmcid: PMC10013445
doi: 10.1080/07853890.2023.2168042
license: CC BY 4.0
---
# The association between serum folate and ultrasound - defined hepatic steatosis
## Abstract
### Purpose
It has been discovered that a folate shortage may raise the risk of hepatic steatosis. We investigated the relationship between serum folate and controlled attenuation parameter (CAP) among 3606 participants over from the National Health and Nutrition Examination Survey (NHANES).
### Materials and methods
Multivariate logistic regression studies were carried out to calculate the relationship between serum folate and CAP. Additionally, generalized additive models and fitted smoothing curves were carried out.
### Results
After adjusting for other variables, we discovered that serum folate had a negative correlation with CAP. Males and whites maintained a negative correlation of serum folate with CAP when subgroup analyses were stratified by sex and race/ethnicity. The relationship between blood folate levels and CAP in whites had an U-shaped curve (inflection point: 34 ng/ml).
### Conclusion
According to our study, the majority of Americans, particularly men and whites, had a negative correlation between serum folate and CAP. Among white people, this connection followed an U-shaped pattern. These findings may provide guidance for monitoring serum folate level and controlling oral folate dosage in clinic, so as to prevent liver steatosis more effectively. Key MessagesThe size of the cohort in our study is large, and our findings come from a nationally representative database. Our study revealed a negative relationship between serum folate and CAP among most Americans, especially in male and whites, which may provide evidence for medications to treat hepatic steatosis. In whites, the association of serum folate with CAP was an U-shaped curve (inflection point: 34 ng/ml). This may provide guidance for monitoring serum folate level and controlling oral folate dosage in clinic, so as to prevent liver steatosis more effectively.
## Introduction
Nonalcoholic fatty liver disease（NAFLD）affects roughly one out of every four Americans and is likely to rise to one out of every three in the next decade [1]. Patients with NAFLD have a higher all-cause death rate than the general population, and it varies by disease stage [2]. In these patients, cardiovascular problems are the most common cause of death, followed by metabolic and liver-related reasons [3]. In NAFLD patients, however, cardiovascular disease is associated with higher levels of steatosis. Increased fat buildup in the form of triglycerides in hepatocytes is referred to as hepatic steatosis (HS) [4]. HS can proceed to cirrhosis and liver failure depending on the many causes and the resulting inflammation and fibrosis [5]. As a result, detecting and quantifying HS in its early phases is critical.
Biopsy, the current gold standard for determining the amount of fat in the liver, has certain drawbacks. Hepatic steatosis is diagnosed and quantified noninvasively using two different but complementing approaches: Biomarkers or imaging, the most common methods are hepatic ultrasonography, controlled attenuation parameter (CAP), computed tomography (CT), as well as magnetic resonance imaging (MRI) [4].
Approaches that restrict cholesterol transport to the liver could theoretically be utilized to prevent or reverse steatosis. Even when body weight is not restored to normal, weight loss, whether accomplished by a self-imposed low-calorie diet or bariatric surgery, cures NAFLD and enhances hepatic insulin sensitivity. Contrarily, weight reduction is challenging to achieve and even more challenging to keep off; just $20\%$ of obese persons are able to do so [6]. As a result, pharmaceutical approaches are being aggressively pursued to reverse hepatic steatosis. Folate is a water-soluble B vitamin that is required for one-carbon transfer reactions such as nucleic acid production, methylation events, and sulfur-amino-acid metabolism. Because the liver is a key storage and processing organ for folate [7], it is critical for maintaining folate homeostasis throughout the body [8]. Folate deficiency is a frequent nutrient shortage that affects persons who have liver illness. The effect of serum folic acid in hepatic steatosis, on the other hand, is unknown. The goal of this study was to use controlled transient elastography to look into the link between serum folate and hepatic steatosis utilizing a sizable, nationally typical population from the 2017–2018 National Health and Nutrition Examination Survey (NHANES).
## Statement of ethics
The study received permission from the National Center for Health Statistics Research Ethics Review Board, and each participant completed a consent form.
## Study population
NHANES is a sizable, ongoing, and expertly designed cross-sectional national population survey program in the United States (US), collects information on the general population’s diet and health and uses a stratified, multistage, clustered probability sample design to guarantee national representativeness [9].
A total of 5948 people out of the 9254 that took part in the 2017–2018 NHANES cycle detected hepatic steatosis using the controlled attenuation parameter (CAP). We had 3606 individuals in our research overall after excluding those with lacking serum folate data ($$n = 2342$$).
## Variables
The exposure factor used in the investigation was serum folate. Using tandem mass spectrometry and isotope-dilution high performance liquid chromatography (LC-MS/MS), serum folate was determined. The experiment is conducted by combining the sample (275 μL serum or whole blood hemolysate) with an internal standard mixture and ammonium for the mate buffer. The outcome variable was CAP, which was assessed by liver ultrasonography transient elastography as a marker of liver fatness. The decibels per meter (dB/m) scale was used to characterize the CAP values. Gender, race/ethnicity, smoking behavior, alcohol consumption in the past year, and the existence of diabetes were all used as factors in our study. The continuous covariates were included in our analysis: age, body mass index(BMI), waist circumference (WC), high density lipoprotein (HDL)- cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST),γ- glutamyl transpeptidase (GGT), total cholesterol, triglyceride, serum albumin, serum creatinine, uric acid, platelet count (PLT), and ferritin. Public access to the detailed data on serum folate, CAP, and variables is provided at http://www.cdc.gov/nchs/nhanes/.
## Statistical analysis
We employed a weighted and variance estimate approach to take into consideration the substantial volatility in our data set. An analysis was conducted using a weighted multivariate logistic regression model to examine the association between serum folate and CAP. We used the weighted χ2 test for categorical variables or the weighted linear regression model for continuous variables to determine the difference between each group. Stratified multivariate regression analysis was used to do the subgroup analysis. Using smooth curve fits and generalized additive models, the nonlinear relationship between serum folate and CAP was also investigated. After nonlinearity was discovered, the inflection point in the relationship between serum folate and CAP was calculated using a recursive technique, and a two-piecewise linear regression model was then used on both sides of the inflection point. The program R (http://www.Rproject.org) and EmpowerStats (http://www.empowerstats.com) were used for all analyses, and a P value <0.05 was deemed statistically significant.
## Results
A total of 3606 people were included in our study. The weighted participant characteristics were divided into four groups according to the quartiles of serum folate (Q1: 1.44–10.6 ng/mL; Q2: 10.7–15.0 ng/mL; Q3: 15.1–22.2 ng/mL; and Q4: 22.3–204.0 ng/mL), as shown in Table 1. With the exception of total cholesterol, AST, and ferritin, there were notable changes in the serum folate quartiles’ baseline characteristics. Participants in the highest quartile of serum folate were more likely to be female, non-Hispanic White and less Mexican American or non-Hispanic Black, and had greater levels of HDL-cholesterol and serum albumin as well as lower levels of BMI, WC, and platelet count as well as CAP.
**Table 1.**
| Serum folate (ng/mL) | Total | Q1 (1.44–10.6) | Q2 (10.7–15.0) | Q3 (15.1–22.2) | Q4 (22.3–204.0) | p Value |
| --- | --- | --- | --- | --- | --- | --- |
| Age (years) | 41.29 ± 18.54 | 40.65 ± 16.71 | 37.62 ± 16.56 | 40.08 ± 18.62 | 46.66 ± 20.62 | <0.0001 |
| Gender (%) | | | | | | <0.0001 |
| Male | 38.15 | 40.86 | 38.48 | 42.96 | 30.12 | |
| Female | 61.85 | 59.14 | 61.52 | 57.04 | 69.88 | |
| Race/Ethnicity (%) | | | | | | <0.0001 |
| Mexican American | 10.46 | 11.44 | 12.24 | 11.17 | 7.10 | |
| Other Hispanic | 7.22 | 5.27 | 8.57 | 7.26 | 7.81 | |
| Non-Hispanic White | 59.93 | 54.53 | 57.90 | 59.48 | 67.54 | |
| Non-Hispanic Black | 11.34 | 17.34 | 11.78 | 9.70 | 6.83 | |
| Other Race | 11.05 | 11.42 | 9.51 | 12.39 | 10.73 | |
| Diabetes (%) | | | | | | 0.0038 |
| Yes | 7.96 | 8.00 | 7.77 | 7.24 | 8.88 | |
| No | 92.04 | 92.00 | 92.24 | 92.76 | 91.11 | |
| Smoked at least 100 cigarettes in life (%) | | | | | | <0.0001 |
| Yes | 39.29 | 47.19 | 40.14 | 35.46 | 34.17 | |
| No | 60.71 | 52.81 | 59.86 | 64.54 | 65.83 | |
| Number of alcohol drinks a day in past year (%) | | | | | | <0.0001 |
| Never/none | 19.84 | 16.29 | 11.06 | 14.53 | 20.95 | |
| 1–2 drinks | 54.44 | 53.95 | 53.85 | 60.88 | 62.40 | |
| 3–4 drinks | 17.05 | 17.94 | 24.59 | 16.66 | 13.12 | |
| ≥5 drinks | 8.68 | 11.82 | 10.50 | 7.94 | 3.52 | |
| BMI (Kg/m2) | 29.12 ± 7.51 | 30.85 ± 8.51 | 29.23 ± 7.27 | 29.08 ± 7.53 | 27.37 ± 6.13 | <0.0001 |
| Waist circumference (cm) | 97.80 ± 18.51 | 101.16 ± 19.24 | 98.00 ± 18.42 | 97.41 ± 18.85 | 94.80 ± 16.93 | <0.0001 |
| Laboratory features | | | | | | |
| Total cholesterol (mg/dl) | 182.75 ± 38.84 | 183.33 ± 40.46 | 182.82 ± 39.71 | 180.63 ± 35.93 | 184.36 ± 39.24 | 0.2077 |
| HDL- cholesterol (mg/dl) | 53.94 ± 14.36 | 51.05 ± 14.14 | 52.97 ± 13.98 | 54.52 ± 13.82 | 57.04 ± 14.83 | <0.0001 |
| Triglyceride (mg/dl) | 131.31 ± 100.47 | 140.28 ± 105.85 | 133.87 ± 126.96 | 123.20 ± 71.17 | 128.81 ± 92.54 | 0.0028 |
| AST (IU/L) | 21.38 ± 12.64 | 20.77 ± 14.87 | 21.90 ± 13.23 | 21.11 ± 10.63 | 21.78 ± 11.64 | 0.1962 |
| ALT (IU/L) | 21.34 ± 17.08 | 20.91 ± 15.34 | 23.30 ± 21.50 | 20.29 ± 12.68 | 21.05 ± 17.90 | 0.0016 |
| GGT (IU/L) | 25.48 ± 30.09 | 29.40 ± 37.04 | 27.06 ± 34.31 | 22.76 ± 23.90 | 23.11 ± 22.89 | <0.0001 |
| Serum albumin (g/L) | 41.01 ± 3.21 | 39.85 ± 3.34 | 41.18 ± 3.09 | 41.45 ± 2.98 | 41.52 ± 3.16 | <0.0001 |
| Serum creatinine (mg/dl) | 0.83 ± 0.35 | 0.86 ± 0.49 | 0.81 ± 0.22 | 0.83 ± 0.32 | 0.82 ± 0.33 | 0.0075 |
| Uric acid (mg/dl) | 5.15 ± 1.37 | 5.25 ± 1.42 | 5.14 ± 1.46 | 5.15 ± 1.31 | 5.05 ± 1.31 | 0.0281 |
| Platelet count (1000 cells/uL) | 251.49 ± 63.84 | 261.17 ± 69.45 | 250.71 ± 59.10 | 248.76 ± 62.10 | 245.73 ± 63.22 | <0.0001 |
| Ferritin (ng/ml) | 122.13 ± 144.58 | 129.86 ± 157.03 | 115.90 ± 152.80 | 123.77 ± 131.39 | 118.78 ± 136.83 | 0.1965 |
| CAP (dB/m) | 255.25 ± 63.35 | 264.24 ± 64.55 | 256.94 ± 61.99 | 253.86 ± 65.92 | 246.42 ± 59.27 | <0.0001 |
Table 2 displays the outcomes of the multivariate regression analysis. Serum folate had a poor correlation with CAP in the unadjusted model (β= −0.35, $95\%$CI: −0.55, −0.16, $$p \leq 0.0004$$). This adverse correlation persisted in models 2 (β= −0.70, $95\%$CI: −0.89, −0.52, $p \leq 0.0001$) and model 3 (β= −0.22, $95\%$CI: −0.40, −0.04, $$p \leq 0.0188$$) after adjusting for covariates. Individuals in the top quartile had a 9.43 dB/m lower CAP than those in the lowest serum folate quartile after categorizing serum folate from a continuous variable to a categorical variable (quartiles).
**Table 2.**
| Unnamed: 0 | Model 1 β (95% CI) p-value | Model 2 β (95% CI) p-value | Model 3 β (95% CI) p-value |
| --- | --- | --- | --- |
| Serum folate (ng/mL) | −0.35 (−0.55, −0.16) 0.0004 | −0.70 (−0.89, −0.52) <0.0001 | −0.22 (−0.40, −0.04) 0.0188 |
| Serum folate categories | | | |
| Q1 (1.44–10.6 ng/mL) | Reference | Reference | Reference |
| Q2 (10.7–15.0 ng/mL) | −7.30 (−13.23, −1.36) 0.0160 | −4.35 (−9.85, 1.16) 0.1217 | −2.28 (−7.56, 3.00) 0.3968 |
| Q3 (15.1–22.2 ng/mL) | −10.38 (−16.14, −4.61) 0.0004 | −11.06 (−16.40, −5.72) <0.0001 | −6.72 (−12.02, −1.43) 0.0128 |
| Q4 (22.3–204.0 ng/mL) | −17.81 (−23.66, −11.97) <0.0001 | −23.40 (−28.90, −17.90) <0.0001 | −9.43 (−14.95, −3.91) 0.0008 |
| Subgroup analysis stratified by sex | | | |
| Men | −0.04 (−0.36, 0.28) 0.7898 | −0.50 (−0.81, −0.20) 0.0012 | −0.40 (−0.68, −0.11) 0.0061 |
| Women | −0.42 (−0.66, −0.18) 0.0007 | −0.82 (−1.06, −0.59) <0.0001 | −0.09 (−0.33, 0.15) 0.4622 |
| Subgroup analysis stratified by race/ethnicity | | | |
| Mexican American | −1.62 (−2.35, −0.89) <0.0001 | −1.59 (−2.28, −0.89) <0.0001 | −0.45 (−1.31, 0.41) 0.3094 |
| Other Hispanic | −0.53 (−1.33, 0.26) 0.1887 | −0.71 (−1.48, 0.06) 0.0719 | −0.17 (−0.91, 0.57) 0.6529 |
| Non-Hispanic White | −0.32 (−0.63, −0.01) 0.0441 | −0.73 (−1.02, −0.43) <0.0001 | −0.22 (−0.49, −0.02) 0.0488 |
| Non-Hispanic Black | −0.22 (−0.69, 0.25) 0.3579 | −0.34 (−0.80, 0.11) 0.1412 | −0.20 (−0.65, 0.24) 0.3703 |
| Other Race | −0.28 (−0.70, 0.14) 0.1952 | −0.54 (−0.95, −0.14) 0.0085 | 0.02 (−0.53, 0.49) 0.9445 |
According to subgroup analyses by sex and race/ethnicity, shown in Table 2, the negative connection between serum folate and CAP persisted for both men (β= −0.40, $95\%$CI: −0.68, −0.11, $$p \leq 0.0061$$), and whites (β= −0.22, $95\%$CI: −0.49, −0.02, $$p \leq 0.0488$$). Figures 1–3 depict the smooth curve fits and generalized additive models that were used to describe the nonlinear association between serum folate and CAP. The point of inflection for the U-shaped relationship between serum folate and CAP in whites was found to be 34 ng/mL using a two-piecewise linear regression model (Table 3). For a serum folate <34 ng/mL, every 1 ng/mL upregulation in serum folate was linked to a 0.62 dB/m decrease CAP ($95\%$CI: −1.02, −0.22, $$p \leq 0.0026$$); by comparison, for individuals with a serum folate >34 ng/mL, a 1 ng/mL upregulation in serum folate was linked to a 0.30 dB/m greater in CAP ($95\%$CI: −0.11, 0.71, $$p \leq 0.1533$$).
**Figure 1.:** *The association between serum folate and controlled attenuation parameter. (a) Each black point represents a sample. (b) Solid rad line represents the smooth curve fit between variables. Blue bands represent the $95\%$ of confidence interval from the fit. Age, sex, race/ethnicity, body mass index, waist circumference, smoking behavior, alcohol consumption in the past year, the presence of diabetes, high density lipoprotein-cholesterol, alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transpeptidase, total cholesterol, triglyceride, serum albumin, serum creatinine, uric acid, platelet count, and ferritin were adjusted.* **Figure 2.:** *The association between serum folate and controlled attenuation parameter stratified by sex. Age, race/ethnicity, body mass index, waist circumference, smoking behavior, alcohol consumption in the past year, the presence of diabetes, high density lipoprotein-cholesterol, alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transpeptidase, total cholesterol, triglyceride, serum albumin, serum creatinine, uric acid, platelet count, and ferritin were adjusted.* **Figure 3.:** *The association between serum folate and controlled attenuation parameter stratified by race/ethnicity. Age, sex, body mass index, waist circumference, smoking behavior, alcohol consumption in the past year, the presence of diabetes, high density lipoprotein-cholesterol, alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transpeptidase, total cholesterol, triglyceride, serum albumin, serum creatinine, uric acid, platelet count, and ferritin were adjusted.* TABLE_PLACEHOLDER:Table 3.
## Discussion
In the 2017–2018 NHANES project, transient elastography was utilized to evaluate hepatic steatosis, providing the first representative observations of the biggest sample size in the United States for transient elastography CAP. According to multivariate logistic regression analysis, higher serum folate levels were linked to reduced CAP in our sample, with the link being stronger in men. Our findings are in line with other research that implicates a folate shortage in the emergence of various small sample groups or animal models of liver injury. A research in obese adults found that patients with confirmed severe NAFLD liver biopsies had lower serum folate concentrations than those with normal livers or minor liver abnormalities [10]. And Halsted et al. found minor steatosis in two of six alcohol-fed animals with adequate folate intake, but steatonecrosis in five of six ethanol-fed folate-deficient micropigs [11]. Our research adds to and verifies this link in a wide group of people, providing evidence for medications to treat hepatic steatosis.
Hepatic steatosis risk is elevated in cases of folate insufficiency, possibly because a lack of folate is linked to increased expression of lipid biosynthesis genes, which causes lipid metabolism in the liver to be disrupted [12]. In addition, some studies suggest that in folate-deficient animals, hepatic lipid transport is hindered, which promotes hepatic fat buildup [13–15].
Furthermore, we discovered a nonlinear connection between serum folate and CAP in whites in a subgroup analysis, with an inflection point at 34 ng/ml. To our knowledge, this study may be the first to demonstrate a connection between serum folate and CAP in whites. Whites had much greater serum folate levels than blacks and Mexican Americans, according to the research [16]. The identified disparities in risk variables by race might be explained by variations in genetic risk factors, obesity prevalence, alcohol use, and other factors. Additional prospective studies with sizable sample sizes were required to elucidate the relationship between blood folate and CAP in white people. The size of the cohort in our study strengthens the findings since the NHANES is intended to yield nationally representative estimates. Nevertheless, our study still had a number of restrictions or flaws. First, hepatic steatosis was defined by transient elastography using the CAP values rather than pathologically confirmed by biopsy, which may introduce bias in assessing the extent of hepatic steatosis. Second, confounding factors that were self-reported might be subject to self-report bias. Third, conclusions in this study are limited to association rather than causality because of the cross-sectional design.
## Conclusions
Most Americans have a negative connection between serum folate and CAP, according to our research. This connection among whites followed a U-shaped curve, and the lowest value of CAP occurred when the serum folate level reached the inflection point. These findings may provide guidance for monitoring serum folate level and controlling oral folate dosage in clinic, so as to prevent liver steatosis more effectively. Attention to folate research may lead to the discovery of pharmacologic methods to reverse hepatic steatosis.
## Author contributions
XC and LS participated in the conception and design of the experiments; XC and JL performed most of the experiments; QX and BC analyzed and interpreted the data; LS and XC drafted the paper and critically revised it for intellectual content. All authors read and approved the final manuscript. And that all authors agree to be accountable for all aspects of the work.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
## Data availability statement
The data that support the findings of this study are available from NHANES and don’t require any permissions. Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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|
---
title: Knocking down GALNT6 promotes pyroptosis of pancreatic ductal adenocarcinoma
cells through NF-κB/NLRP3/GSDMD and GSDME signaling pathway
authors:
- Mengyang Ding
- Jingyu Liu
- Honghui Lv
- Yanlin Zhu
- Yumiao Chen
- Hui Peng
- Sairong Fan
- Xiaoming Chen
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10013470
doi: 10.3389/fonc.2023.1097772
license: CC BY 4.0
---
# Knocking down GALNT6 promotes pyroptosis of pancreatic ductal adenocarcinoma cells through NF-κB/NLRP3/GSDMD and GSDME signaling pathway
## Abstract
### Background
Pancreatic ductal adenocarcinoma (PDAC), the most prevalent type of pancreatic cancer, is a highly lethal malignancy with poor prognosis. Polypeptide N-acetylgalactosaminyltransferase-6 (GALNT6) is frequently overexpressed in PDAC. However, the role of GALNT6 in the PDAC remains unclear.
### Methods
The expression of GALNT6 in pancreatic cancer and normal tissues were analyzed by bioinformatic analyses and immunohistochemistry. CCK8 and colony formation were used to detect cell proliferation. Flow cytometry was applied to detect cell cycle. The pyroptosis was detected by scanning electron microscopy. The mRNA expression was detected by qRT-PCR. The protein expression and localization were detected by western blot and immunofluorescence assay. ELISA was used to detect the levels of inflammatory factors.
### Results
The expression of GALNT6 was associated with advanced tumor stage, and had an area under curve (AUC) value of 0.919 in pancreatic cancer based on the cancer genome atlas (TCGA) dataset. Knockdown of GALNT6 inhibited cell proliferation, migration, invasion and cell cycle arrest of PDAC cells. Meanwhile, knockdown of GALNT6 increased the expression levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and interleukin-18 (IL-18), the release of inflammasome and an increasing of Gasdermin D (GSDMD), N-terminal of GSDMD (GSDMD-N), Gasdermin E (GSDME) and N-terminal of GSDME (GSDME-N) in PDAC cells. GALNT6 suppressed the expression of NOD-like receptor thermal protein domain associated protein 3 (NLRP3) and GSDMD by glycosylation of NF-κB and inhibiting the nucleus localization of NF-κB. Additionally, GALNT6 promotes the degradation of GSDME by O-glycosylation.
### Conclusion
We found that GALNT6 is highly expressed in pancreatic cancer and plays a carcinogenic role. The results suggested that GALNT6 regulates the pyroptosis of PDAC cells through NF-κB/NLRP3/GSDMD and GSDME signaling. Our study might provides novel insights into the roles of GALNT6 in PDAC progression.
## Introduction
Pancreatic cancer (PC), one of the most lethal cancers, is the seventh leading cause of cancer-related deaths worldwide, accounting for almost as many deaths [466,000] as cases [496,000] [1]. Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic neoplasm and accounts for more than $90\%$ of all pancreatic tumors with highly aggressive and malignant, in which the average 5-year survival rates is less than $10\%$ [2, 3]. Most patients with pancreatic ductal adenocarcinoma are diagnosed at an advanced stage due to no specific symptoms and the lack of early diagnosis, which makes the treatment virtually impossible and the clinical cure rate is extremely low [4, 5]. It is well known that pancreatic cancer is a disease of genetic alterations, such as mutations in the genes KRAS, TP53, CDKN2A (encoding p16) and SMAD4 [6]. These genomic alterations contribute to multifaceted defects in tumor suppressor mechanisms resulting in dysregulated growth signaling and inflammation, which are key aspects of PDAC [7]. Therefore, there is an urgent need to exploit new molecular players and understand the underlying mechanism will be helpful to exploration of early diagnostic or treatment strategies for PDAC.
Glycosylation is a major post-translational modification of proteins and involved in almost all physiological and pathological processes, such as cell proliferation, adhesion, epithelial-mesenchymal transition, cellular signaling, and immune recognition [8]. Cumulative evidence shows that aberrant glycosylation is the prevalently observed in tumor cells and cited as a hallmark of cancer [9]. O-glycosylation, one of the major forms of glycosylation, plays a global influence on cancer development and progression, such as tumor cell dissociation and invasion, metastasis, tumour angiogenesis and immune surveillance [10, 11]. Recently, studies showed that aberrant O-glycosylation is prevalent in pancreatic cancer, and highlight as positive regulator of tumorigenesis, tumor progression, therapeutic resistance and remodeling the tumor immune microenvironment [12]. For instance, O-glycosylation of malate dehydrogenase 1 (MDH1) enhances the stability of the substrate-binding pocket and glutamine metabolism, which contributes to PDAC growth [13]. GALNT6, an enzyme of the N-acetylgalactosyltransferase family that initiate O-linked glycosylation (O-GalNAcylation) via transferring GalNAc from UDP-GalNAc onto Ser/Thr residues of acceptor proteins has been reported to promote the progression of various tumors [14]. Previous studies have reported that high GALNT6 expression was strongly linked to the development of the abnormal mucin O-glycosylation in the development of human ductal carcinoma in situ [15]. However, the underlying mechanism of GALNT6 in PDAC is still unclear.
Pyroptosis, a novel type of programmed cell death, is featured by cell swelling and plasma membrane rupture, and mediated by the activation of a variety of caspases, especially caspase-1, which is activated by inflammasomes, leading to cleavage of gasdermin family proteins and releasing of pro-inflammatory cytokines, such as IL-1β and IL-18 [16, 17]. During the past decades, growing evidences showed that pyroptosis plays a suppression function of the proliferation, invasion and metastasis of tumors, evokes anti-tumor immune responses, which providing a great opportunity in cancer therapy [17, 18]. Luteolin inhibits tumor growth by inducing pyroptosis via caspase-1/GSDMD signaling [19]. Mammalian Ste20-like kinase 1 (MST1) inhibits the proliferation, migration, invasion and cell spheroid formation of PDAC by inducing pyroptosis [20]. Pyroptosis could be induced by several distinct pathways, such as canonical inflammasome pathway and caspase-3 mediated pathway [17]. The canonical inflammasomes recruit pro-caspase-1 through the inflammasome adaptor apoptosis-associated speck-like protein containing a CARD (ASC), leading to self-cleavage and activation of caspase-1, which cleaves pro-IL-1β, pro-IL-18 and GSDMD, resulting the formation of the pores in the plasma membrane and secretion of IL-1β/IL-18, generating cell swelling and osmotic lysis [16, 17].
In the present study, the role of GALNT6 in pancreatic ductal adenocarcinoma cells was investigated. GALNT6 was overexpressed in pancreatic cancer. Knockdown of GALNT6 significantly inhibited the proliferation, migration and invasion of PDCA cells. Knockdown of GALNT6 promotes the phosphorylation of NF-κB, which was translocated into the nucleus, leading to the expression of NLRP3 and GSDMD. Meanwhile, knockdown of GALNT6 increased the levels of GSDME and suppressed its degradation. These findings suggested that knockdown of GALNT6 promoted pryoptosis via NF-κB/NLRP3/GSDMD and GSDME signaling, which may contribute to the inhibiting of PDAC cell growth. The results of this study provide new insight into the roles of GALNT6 in tumor development and a potential treatment for PDAC.
## Bioinformatic analyses
The data of pancreatic cancer tissues ($$n = 178$$) and normal tissues ($$n = 171$$) and RNA-Seq expression data of GALNT6 were derived from the TCGA database (https://portal.gdc.cancer.gov/). We compared the expression of GALNT6 in pancreatic cancer and normal tissues with Wilcoxon rank sum test. The characteristics of patients about TNM stage were recorded in TCGA-GTEx database (https://portal.gdc.cancer.gov/). Receiver operating characteristic (ROC) curve was used to differentiate pancreatic cancer from adjacent normal tissues.
## Cell culture
Human pancreatic ductal cell (HPNE), pancreatic ductal adenocarcinoma cells (CFPAC-1, BXPC-3, Patu-8988t, MIA PaCa-2, PANC-1) were obtained from Chinese Academy of Medical Sciences (Beijing, China). The cells (HPNE, Patu-8988t, MIA PaCa-2 and PANC-1) were cultured in DMEM (C11995500BT, Gibco, NY, USA) containing $10\%$ fetal bovine serum (FBS, Clark Bioscience, Virginia, USA). The cells (CFPAC-1 and BXPC-3) were cultured in RPMI 1640 (C11875500BT, Gibco, NY, USA) containing $10\%$ FBS. All cells were cultured at 37°C with $5\%$ CO2 incubator.
## Plasmids construction and lentivirus transfection
Interference plasmid GV248/EGFP/Puro-ShGALNT6 and the control plasmid GV248/EGFP/Puro-ShNC were designed and synthesized by Shanghai Genechem Co., LTD. The overexpression GALNT6 plasmid was purchased from Guangzhou GeneCopoeia Co., LTD. Lentivirus were prepared in HEK293T cells, according to the manufacturer’s instructions. Viral supernatant was collected at 48 h after transfection and used to infect the BXPC-3 and Patu-8988t cells. Stable pools were generated by puromycin (Sigma, St. Louis, USA) selection for following assays. The NF-κB knockdown cells (BXPC-3 and Patu-8988t) were prepared by using NF-κB specific siRNA (purchased from Shanghai Genechem Co., LTD.).
## CCK8 assay
Cells were seeded in 96-well plates at a density of 3×103 cells/well and cultured in DMEM (or RPMI 1640) medium containing $10\%$ FBS. After 24 h, 48 h and 72 h, respectively, 10 µl CCK8 reagent (CK04, Dojindo, Kumamoto, Japan) was added and cultured at 37°C for 2 h. The absorbance values of each sample were measured at 450 nm by an automatic plate reader (Thermo Scientific, Massachusetts, USA).
## Colony formation assay
Cells were seeded in 6-well plates at a density of 3×103 cells/well and cultured in DMEM (or RPMI 1640) medium containing $10\%$ FBS. After 14 days, cells were fixed with ethanol at room temperature for 20 minutes, followed stained with crystal violet for 30 minutes. After dried and photographed, cell clones were counted by using Image J software (Version 1.6.0, National Institutes of Health, Bethesda, Maryland, USA)
## Wound healing assay
Cells were seeded in 6-well plates and grown up to about $90\%$ confluency. Subsequently, a 10 μl pipette tip was used to form two parallel straining lines. Samples were imaged under a microscope (Nikon, Tokyo, Japan) at 0, 24 and 48 hours, respectively. The closure ratio of wounds were measured by using Image J and calculated according to the formula: percentage of wound closure = (the initial area-follow-up area)/the initial area.
## Transwell assay
Corning Transwell inserts (8-μm pore size, 3422, Corning, NY, USA) were used to access cell migration and invasion. For migration assay, cells (3×104 cells/well) were seeded into the upper chambers of the inserts and cultured in 200 µl serum-free medium, while the lower chambers were filled with 200 µl medium supplemented with $20\%$ FBS. After cultured for 24 h, the chambers were wished with PBS. Then cells were fixed with $4\%$ paraformaldehyde and stained with crystal violet. The chambers were photographed by microscope, and cell numbers were counted. For invasion assay, the procedure was similar to migration experiment with the following modification: the upper chambers were coated with BD matrigel and cells were cultured for 48 h.
## Cell cycle analysis
In brief, cells were collected, washed twice with PBS, and fixed in ethanol at 4°C overnight. Then, samples were centrifuged at 1000 rpm for 5 min, the supernatant was discarded, and wash twice with PBS. Cells were resuspended in the staining buffer (propidium iodide (PI): RNase = 9: 1) and incubated for 30-60 min. Before detected, 200 mesh nylon omentum was used to filter the cells to reduce the cell mass. Finally, the samples were analyzed by Flow Cytometer (BD, New Jersey, USA). The proportion of G0/G1 phase was calculated.
## Cell apoptosis analysis
Annexin V-APC and phycoerythrin (PE) double staining were used for apoptosis detection. According to the manufacturer’s protocol (KGA1026, Lianke Biological Co., LTD, Hangzhou, China), 1×106 cells were harvested, washed with PBS and stained with 10 µl Annexin V-APC and 5 μl PE for 30 min in a dark room. Before detected, 200 mesh nylon omentum was used to filter the cells to reduce the cell mass. Finally, the samples were analyzed by Flow Cytometer (BD, New Jersey, USA).
## Western blot
Western blot was utilized to detect the protein expression level in cells. Then cells were lysed with radio immunoprecipitation assay (RIPA, P0013B, Beyotime, Shanghai, China) lysate and $1\%$ phenylmet- hanesulfonyl fluoride (PMSF, ST506, Beyotime, Shanghai, China). The supernatant was collected by centrifugation and the protein concentration was detected by bicinchoninic acid (BCA, P0010, Beyotime, Shanghai, China) kit. The protein samples denatured at 100°C for 10 minutes, and separated on sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Then, the protein was transferred to a polyvinylidene fluoride (PVDF, IPVH00010, 0.45 µm, Millipore, Massachusetts, USA) membrane. The membranes were blocked in $5\%$ nonfat milk in Tris Buffered Saline (TBST) with $0.1\%$ Tween-20, and incubated with the primary antibody at 4°C overnight, followed by incubated with the secondary antibody at room temperature for 1 h. The primary antibody used in this study as following: anti-GALNT6 (sc-100755, 1:1000, Santa Cruz Biotechnology, California, USA), anti-NF-κB (sc-109, Santa Cruz Biotechnology, California, USA), anti-p-NF-κB (S536) (AF2006, 1:1000, Affinity Biosciences, Shanghai, China), anti-NLRP3 (ab263899, 1:1000, Abcam, Cambridge, UK), anti-GSDMD (db3846, 1:1000, Diagbio, Hangzhou, China) and anti-GSDME (db3341, 1:1000, Diagbio, Hangzhou, China). Finally, the protein signal was visualized by enhanced chemiluminescence (ECL, P10100, NCM Biotech, Suzhou, China) reagent, and analyzed by using Image J software.
For pyrrolidinedithiocarbamate ammonium (PDTC) treatment assay, cells are treated with the PDTC (HY-18738, MedChemExpress, New Jersey, USA) for 24 h (10μg/ml) in 6-well plates, then the protein was extracted for WB experiment. PDTC is dissolved in water according to the reagent instructions.
For TNF-α treatment assay, cells are treated with the optimal doses of TNF-α (C008, novoprotein, Suzhou, China) for 12 h (1μg/ml) in 6-well plates, then the protein was extracted for WB experiment. TNF-α is dissolved in distilled water according to the reagent instructions.
## Cell fractionation assay
According to the instructions of the nuclear and cytoplasmic protein extraction kit (P0027, Beyotime, Shanghai, China). In brief, for every 20 μl of cell precipitate, 200 μl of cytoplasmic protein extraction reagent A was added with PMSF. Ice bath for 10-15 min. Then 10 μl cytoplasmic protein extraction reagent B was added and violently shook at the highest speed for 5 s and centrifuged at 4°C for 16000 g for 5 min. Immediately collected supernatant, which is the extracted cytoplasmic protein. For the precipitation, 50 μl nuclear protein extraction reagent with PMSF was added, and the cell precipitation was completely suspended and dispersed by intense shock. Then centrifuged at 4°C at 16000 g for 10 min. The supernatant was the extracted nuclear protein.
## Quantitative real-time PCR
Total RNA was extracted by RNA prep Pure Cell Kit (CS14010, Invitrogen, CA, USA), according to the manufacturing protocol. The cDNA was obtained by reverse transcription using the kit (R122-01, Vazyme, Nanjing, China), and quantified with SYBR® Premix Ex TaqTM (Perfect Real Time) qPCR kit (Q711-02, Vazyme, Nanjing, China), and detected by fluorescence quantitative PCR instrument (CFX96) (Bio-Rad, California, USA). The primer sequences were used as follows: GALNT6: 5’-AGAGAAATCCTTCGGTGACATT-3’ (forward), 5'-AGACAAAGSGCCACAACTGATG-3' (reverse); (reverse); β-actin (used as internal normalization control): 5’-CACCATTGGCAATGAG CGGTTC-3’ (forward), 5’-AGGTCTTTGCGGATGTCCACGT-3’ (reverse).
## Immunofluorescence assay
Cells were fixed with $4\%$ paraformaldehyde for 30 min at room temperature, permeabilized with $0.5\%$ Triton for 20 minutes at room temperature, then washed three times with PBS. After blocked by $5\%$ BSA for 30 minutes at room temperature, cells were incubated with primary antibody at 4°C for 12-14 hours, then incubated with fluorescent-dye conjugated secondary antibody for one hour at room temperature. Finally, cells were stained with 4’, 6-diamidino-2-phenylindole (DAPI, P0131, Beyotime, Shanghai, China) and the images were observed and photographed by fluorescence microscope.
## Enzyme-linked immunosorbent assay
According to the manufacturer’s instructions, enzyme-linked immunosorbent assay (ELISA) Kit (Lianke Biological Co., LTD, Hangzhou, China) was used to detect the levels of inflammatory factors (human IL-1β, IL-6, TNF-α, IL-18) in the culture medium of cell supernatant.
## Co-immunoprecipitation assay
Cells were lysed in the logarithmic growth phase by RIPA buffer (RIPA: PMSF: phosphatase inhibitor = 100: 1: 2), and protein was obtained. The samples were pre-cleared with Protein A+G agarose beads, incubated with corresponding primary antibody at 4°C overnight, and rabbit or mouse IgG was used as the negative control. Then, the pre-washed Protein A+G agarose beads were added and incubated at 4°C for 2 h. The protein-antibody complexes were collected, washed with PBS for 3 times. After denaturing in metal bath at 100°C for 10 min, immunoprecipitated proteins were collected and the agarose beads were removed. The samples were analyzed by western blot.
## VVA lectin pull-down assay
Cells were lysed by RIPA buffer (RIPA: PMSF: phosphatase inhibitor = 100: 1: 2). Cell lysates were incubated with VVA-conjugated beads (AL-1233, Vector Laboratories, L.A., USA) at 4°C and rotated overnight (14-16 h). After centrifugated, the precipitated protein was collected and analyzed by western blot.
## Scanning electron microscopy
The cells were cultured to the appropriate density on the crawling slices, and fixed with $2.5\%$ glutaraldehyde solution at 4°C overnight in the refrigerator. Then dehydrated by soaking in $30\%$, $50\%$, $70\%$, $80\%$ and $90\%$ alcohol for 10 minutes. After soaking in tert-butanol for 15 minutes, samples were dried by hexamethyldisilazane (HMDS) chemical drying method. After coating, samples were photographed by scanning electron microscope.
## Immunohistochemistry
Pancreatic cancer tissue chips were bought from Xi’an Taibsbio Biotechnology Co., Ltd (DPA243a). Immunohistochemistry was used to detect the expression of GALNT6 in pancreatic cancer tissues and normal tissues. The tissue sections were dewaxed, dehydrated and antigen repaired, then blocked with endogenous peroxidase, following by $5\%$ BSA for 30 min at room temperature, and incubated overnight at 4°C with a drop of appropriate primary antibody (GALNT6, ab151329, 1:500, Abcam, Cambridge, UK). The microarray was incubated with biocatalytic secondary antibody at 37°C for 1 h, and 3, 5-diaminobenzidine (DAB) peroxidase substrate kit (PV-6001, ZSGB−BIO, Beijing, China) was used as the chromogen.
## Statistical analysis
Student’s t-test was used for comparison between the two experimental groups. Dunnett’s multiple comparison test was used, when two groups could not be considered to be of equal variance. Data was expressed as the mean value ± S.D of at least three repeated experiments. The P-value of less than 0.05 was considered to be statistically significant. GraphPad Prism 8.0.2 statistical software was used for mapping and statistical analysis.
## GALNT6 is elevated in pancreatic ductal adenocarcinoma
To clarify the expression of GALNT6 in pancreatic cancer, the expression levels of GALNT6 were analyzed using TCGA database. As shown in Figure 1A, the level of GALNT6 in pancreatic cancer tissues ($$n = 178$$) was significantly higher than that in normal control ($$n = 171$$). In addition, the association between the mRNA expression of GALNT6 and TMN stage was evaluated. GALNT6 was higher in T3/T4 stages than that in T1/T2 (Figure 1B). However, there was no significant difference between N0 and N1 (or M0 and M1) (Figures 1C, D). These results suggested that GALNT6 was associated with T stage, not the N or M stage. To investigate the diagnostic value of GALNT6 in pancreatic cancer, the ROC curve was analyzed. Results showed that GALNT6 had an AUC value of 0.919 (CI: 0.884-0.955) (Figure 1E). The time-dependent ROC results showed that the GALNT6 had a more sensitive diagnosis in six years than one or three years (Figure 1F). Furthermore, the expression of GALNT6 was detected in a tissue microarray of 18 pairs of pancreatic cancer tissues and normal control tissues by IHC. Results showed that the expression of GALNT6 in PDAC tissues was significantly higher than that in normal pancreatic tissues (Figure 1G). These findings suggested that the GALNT6 might be a positive regulator of PDAC.
**Figure 1:** *Analysis of GALNT6 in clinical database of pancreatic cancer patients. (A–D) Boxplots showed the expression of GALNT6 in pancreatic cancer and tumor TNM stages. (E, F) ROC and Time-dependent ROC curve showed more sensitive diagnosis of GALNT6 in pancreatic cancer (AUC=0.919). (G) Representative images of IHC. IHC was applied to detect the expression of GALNT6 in 18 pancreatic cancer tissues and 6 pancreatic normal tissues. ns, p >0.05; *p <0.05; **p <0.01; ***p <0.001.*
## Down-regulation of GALNT6 suppresses the PDAC cells growth
To validate the expression of GALNT6 in PDAC cell lines, CFPAC-1, BXPC-3, PANC-1, Patu-8988t, MIA PaCa-2 and pancreatic ductal cell (HPNE) were used. Results showed that the levels of GALNT6 in CFPAC-1, BXPC-3 and Patu-8988t were significantly higher than that in normal pancreatic cells (HPNE), and there was no difference between PANC-1 and HPNE cells (Figure 2A). To detect the effect of GALNT6 in PDAC progression in vitro, specific shRNA against GALNT6 was transferred into PDAC cell lines (BXPC-3 and Patu-8988t) to knockdown GALNT6 expression in these cells, and their corresponding scrambled vector (NC) was used as control. The expression of GALNT6 was significantly reduced in GALNT6 knockdown BXPC-3 cells (shRNA-1, shRNA-2), compared with NC cells (scrambled control) by western blot and qRT-PCR assay (Figure 2B). The similar effect was observed in the Patu-8988t cells (Figure 2C). Meanwhile, the overexpression of GALNT6 was constructed by transferring GALNT6 plasmids into Patu-8988t cells, and confirmed by western blot and qRT-PCR (Figure 2D). Knockdown of GALNT6 (shRNA-1, shRNA-2) significantly suppressed the growth of PDAC cells (BXPC-3, Patu-8988t) based on CCK8 assay (Figure 2E), and upregulated GALNT6 significantly increased the growth of Patu-8988t cells (Figure 2F). Similarly, the number of cell colonies was markedly decreased in shGALNT6 groups (shRNA-1, shRNA-2), compared with NC groups (Figure 2G). Cell cycle assay showed that knockdown of GALNT6 significantly increased the G1 phase distribution and decreased the G2 phase distribution, leading to increase the cell ratio in G0/G1 phase (Figure 2H), indicated that PDAC cells with GALNT6 knockdown were arrested at G0/G1 phase. These results showed that knockdown of GALNT6 suppressed the growth of PDAC cells.
**Figure 2:** *GALNT6 promotes the growth of PDAC cells. (A) Western blot was used to detect expression of GALNT6 in pancreatic cancer cell lines and pancreatic ductal cell. (B, C) The protein expression of GALNT6 was identified by western blot and the mRNA expression of shGALNT6 was identified by qRT-PCR. (D) The overexpression of GALNT6 in Patu-8988t cells was confirmed by western blot and qRT-PCR. (E, F) CCK8 was used to detect cell viability. (G) Plate cloning assay was used to detect cell proliferation. (H) Flow cytometry was applied to detect cell cycle. Each experiment was repeated at least three times independently, and the experimental data were expressed as mean standard deviation (X ± SD). ns, p >0.05; *p <0.05; **p <0.01; ***p <0.001; ****p <0.0001.*
## GALNT6 promotes migration and invasion of PDAC cells
To evaluate the effect of GALNT6 on the migration and invasion of PDAC cells, wound healing and transwell assay were performed. Down-regulation of GALNT6 significantly inhibited the wound closure, compared with control cells (NC), suggesting that knockdown of GALNT6 inhibited the migration of PDAC cells (BXPC-3, Patu-8988t) (Figures 3A, B). Transwell assay showed that the number of BXPC-3 and Patu-8988t cells migrated through the membrane was significantly decreased in shGALNT6 groups (shRNA-1, shRNA-2), compared with corresponding NC group (Figures 3C, D). Consistent with results of wound healing assay, knockdown of GALNT6 inhibited the migration of PDAC cells. Matrigel invasion assay showed the invasion capacity of BXPC-3 and Patu-8988t cells was also significantly reduced in the shGALNT6 groups (shRNA-1, shRNA-2) (Figures 3C, D). As expected, the overexpression of GALNT6 significantly promoted the migration and invasion of Patu-8988t cells (Figure 3E). These results suggested that GALNT6 promotes the migration and invasion of PDAC cells.
**Figure 3:** *GALNT6 promotes the biological functions of PDAC cells. (A, B) Cell migration was detected by wound healing assay. (C, D) Transwell was used to detect the migration and invasion of Patu-8988t and BXPC-3 cells of GALNT6 knockdown. (E) Transwell assay was used to detect the migration and invasion of Patu-8988t cells of overexpression GALNT6. Each experiment was repeated at least three times independently, and the experimental data were expressed as mean standard deviation (X ± SD). *p <0.05; **p <0.01; ***p <0.001; ****p <0.0001.*
## GALNT6 promotes inflammation and pyroptosis
Pyroptosis, as a new type of programmed cell necrosis, is mainly triggered by inflammasome and releases the intracellular inflammatory factors, and exerts tumor suppression function and evokes anti-tumor immunity responses [17, 21, 22]. To further clarify the role of GALNT6 in PDAC, the effect of GALNT6 on PDAC pyroptosis was investigated. As shown in Figure 4A, bubble structure, cellular membrane ruptures and a number of vesicles were observed in GALNT6 knockdown group, indicating that knockdown of GALNT6 promoted the pyroptosis of PDAC cells. As shown in Figure 4B, the expression of NLRP3, caspase-1, cleaved-caspase-1, cleaved-caspase-3, GSDMD, GSDMD-N,, GSDME and GSDME-N were significantly increased in shGALNT6 groups (shRNA-1, shRNA-2), compared with that in NC group. Similar results were obtained in GALNT6 knockdown Patu-8988t cells (Figure 4C). The expression of caspase-3 was not affected by the GALNT6 knockdown in PDAC cells (Figures 4B, C). The expression of NLRP3, cleaved-caspase-1, cleaved-caspase-3, GSDMD, GSDMD-N, GSDME and GSDME-N was significantly reduced in GALNT6 overexpression Patu-8988t cells (Figure 4D). Inflammasomes typically contain ASC, caspase proteasomes, and a NOD-like receptor(NLR) family protein (such as NLRP3) or HIN200 family protein (such as AIM2). To further confirm the effect of GALNT6 on the inflammasome, ASC was examined by confocal laser scanning microscope. Results showed that knockdown of GALNT6 significantly increased the expression of ASC in PDAC cells (BXPC-3, Patu-8988t) (Figures 4E, F), suggesting that knockdown of GALNT6 increased the inflammasomes in PDAC cells. Activated inflammasome can promote the release of the pro-inflammatory cytokines, including IL-1β and IL-18. Results showed that the mRNA levels of IL-1β, IL-6 and TNF-α were markedly increased in GALNT6 knockdown PDAC cells (shRNA-1, shRNA-2), compared with that in corresponding NC group (Figure 4G). Meanwhile, ELISA assays results showed that the levels of IL-1β, IL-6, TNF-α and IL-18 were significantly elevated in BXPC-3 and Patu-8988t cells with GALNT6 knockdown (Figure 4H). These results suggested that GALNT6 knockdown may inhibit the growth of PDAC cell by inducing the pyroptosis of PDAC cells.
**Figure 4:** *GALNT6 promotes the release of inflammatory factors and pyroptosis in PDAC cells. (A) Pyroptosis of BXPC-3 and Patu-8988t cells after knockdown of GALNT6 detected by SEM. Red arrows point to pyrosomes formed in cells and ruptured cell membranes. (B, C) Western blot assay was used to detect the changes of NLRP3, cleaved-caspase-1, caspase-1, cleaved-caspase-3, caspase-3, GSDMD-N, GSDMD, GSDME-N and GSDME expressions in BXPC-3 and Patu-8988t cells after knockdown of GALNT6. The asterisk indicates a heterozygous band. (D) The expression levels of p-NF-κB, NF-κB, NLRP3, cleaved-caspase-1, cleaved-caspase-3, GSDMD, GSDMD-N, GSDME and GSDME-N in overexpression GALNT6 Patu-8988t cells were measured by western blot assay. The asterisk indicates a heterozygous band. (E, F) ACS in BXPC-3 and Patu-8988t cells was analyzed by immunofluorescence. (G) The mRNA expression of IL-1β, IL-6 and TNF-α in BXPC-3 and Patu-8988t cells were detected by qRT-PCR. (H) ELISA was used to detect the levels of IL-1β, IL-6, TNF-α and IL-18 in BXPC-3 and Patu-8988t cells. Each experiment was repeated at least three times independently, and the experimental data were expressed as mean standard deviation (X ± SD). ns, p >0.05; *p <0.05; **p <0.01; ***p <0.001; ****p <0.0001.*
## Knockdown of GALNT6 promotes pyroptosis via NF-κB signaling in PDAC cells
To elucidate the mechanism of GALNT6 in pryoptosis, the NF-κB signaling pathway, an important proinflammatory signaling pathway, which plays an important role in pyroptosis was investigated [23, 24]. Results showed that the levels of NF-κB was unaltered by GALNT6 knockdown in PDAC cells (BXPC-3, Patu-8988t), however, the phosphorylation of NF-κB in PDAC cells was significantly increased by GALNT6 knockdown (shRNA-1, shRNA-2) (Figures 5A, B). The expression of p-NF-κB was significantly decreased in overexpressed GALNT6 cells (Figure 4D). There was no significant difference between the level of IKKβ and p-IκBα in GALNT6 knockdown groups and NC group (Figures 5A, B). To demonstrate the effect of GALNT6 on NF-κB signal, cell fractionation assay was performed to analyze the distribution of p-NF-κB and NF-κB, which is associated with the expression of proinflammatory cytokines [25]. Results showed that knockdown of GALNT6 in PDAC cells significantly reduced the levels of p-NF-κB and NF-κB in cytoplasm and increased p-NF-κB and NF-κB accumulation in the nucleus, compared with control cells (NC) (Figures 5C, D). The distribution of NF-κB regulated by GALNT6 in PDAC cells were further confirmed by immunofluorescence (Figures 5E, F). Co-IP results showed that GALNT6 was interacted with NF-κB (Figures 5G, H). VVA pull-down results showed that knockdown of GALNT6 significantly reduced the glycosylation of NF-κB (Figure 5I). Furthermore, PDTC, an inhibitor of NF-κB [26], was used to investigate the effect of GALNT6 on NF-κB signaling. As shown in Figure 5J, PDTC significantly inhibited the expression of IKKβ and phosphorylation of IκBα with or without GALNT6 knockdown in Patu-8988t cells. However, the phosphorylation level of NF-κB reduced by PDTC was significantly increased by GALNT6 knockdown in Patu-8988t cells. These findings suggested that GALNT6 might regulate NF-κB signaling by glycosylation of NF-κB, which regulated the phosphorylation and distribution of NF-κB.
**Figure 5:** *GALNT6 interacts with NF-κB. (A, B) Western blot was used to analyze the levels of IKKβ, p-IκBα, IκBα, p-NF-κB and NF-κB in BXPC-3 and Patu-8988t cells after knockdown of GALNT6. (C, D) Cell fractionation assay was used to detect the distribution of p-NF-κB and NF-κB in BXPC-3 and Patu-8988t cells after knockdown of GALNT6. (E, F) Immunofluorescence was used to detect the distribution of NF-κB in BXPC-3 and Patu-8988t cells after knockdown of GALNT6. (G, H) Co-IP assay was applied to determine the interaction of GALNT6 and NF-κB in BXPC-3 and Patu-8988t cells. (I) VVA pull-down assay was used to analysis glycosylation of NF-κB in BXPC-3 and Patu-8988t cells. (J) Western blot assay was used to detect the expression of IKKβ, p-IκBα, p-NF-κB in Patu-8988t cells after GALNT6 knockdown or PDTC treatment. Each experiment was repeated at least three times independently, and the experimental data were expressed as mean standard deviation (X ± SD). ns, p >0.05; *p <0.05; **p <0.01; ***p <0.001.*
In order to further confirm the NF-κB regulated by GALNT6 is responsible for the pryoptosis in PDAC cells, siRNA against NF-κB was used to silence the expression of NF-κB. As shown in Figure 6A, knockdown of NF-κB drastically reduced the expression of NF-κB with or without knockdown of GALNT6, compared with their corresponding control Patu-8988t cells. The expression of NLRP3, cleaved-caspase-1, GSDMD and GSDMD-N was inhibited by NF-κB knockdown with or without GALNT6 knockdown, compared with corresponding control Patu-8988t cells (Figure 6A). On the one hand, TNF-α can be activated as a downstream inflammatory factor of NF-κB signaling pathway [27]. on the other hand, TNF-α is a well-known upstream activator of NF-κB signaling pathway [28]. To confirm the pyroptosis of Patu-8988t cells regulated by NF-κB signaling, TNF-α was used to active the NF-κB signaling. Results showed that the expression of IKKβ, p-IκBα, p-NF-κB, NLRP3, cleaved-caspase-1, GSDMD and GSDMD-N was significantly increased by TNF-α treated in Patu-8988t cells with or without GALNT6 overexpression, compared with their corresponding control cells (Figure 6B), indicating that TNF-α promoted the pyroptosis of Patu-8988t cells. When treated with TNF-α, the expression of NLRP3, cleaved-caspase-1 and GSDMD inhibited by GALNT6 was significantly elevated (Figure 6B). These results suggested that GALNT6 may inhibit the expression of extracellular inflammatory factors and intracellular inflammasomes by inhibiting the phosphorylation and nuclear translocation of NF-κB, thereby inhibiting the pyroptosis of PDAC cells.
**Figure 6:** *GALNT6 interacts with GSDME. (A) Western blot assay was used to detect the expression of NF-κB, NLRP3, cleaved-caspase-1, GSDMD-N and GSDMD in Patu-8988t cells after knockdown of NF-κB. (B) Western blot assay was used to detect the expression of IKKβ, p-IκBα, p-NF-κB, NLRP3, cleaved-caspase-1 and GSDMD in TNF-α treated Patu-8988t cells. (C) Co-IP assay was used to detect the interaction between GALNT6 and GSDMD in BXPC-3 and Patu-8988t cells. (D, E) Co-IP assay was used to detect interaction between GALNT6 and GSDME in BXPC-3 and Patu-8988t cells. (F) VVA pull-down assay was used to detect glycosylation of GSDME in BXPC-3 and Patu-8988t cells. (G) Western blot assay was used to detect the expression of GSDME in Patu-8988t cells after MG132 treatment. (H) Western blot assay was used to detect the expression of GSDME in Patu-8988t cells after CHX treatment. (I) Pattern signal diagram of GALNT6 regulates the pyroptosis of PDAC cells via NF-κB and GSDME. Each experiment was repeated at least three times independently, and the experimental data were expressed as mean standard deviation (X ± SD). ns, p >0.05; *p <0.05; **p <0.01; ***p <0.001; ****p <0.0001.*
## Knockdown of GALNT6 decreases the glycosylation of GSDME
Pyroptosis is produced not only by caspase-1/GSDMD induced by inflammasomes, but also regulated by the other gasdermin proteins, such as caspase-3/GSDME [29]. GSDME converts relatively slow noninflammatory apoptosis into more rapid inflammatory pyroptosis [30]. Our results showed that knockdown of GALNT6 increased the expression of cleaved-caspase-3, GSDME and GSDME-N and overexpression of GALNT6 reduced the expression of cleaved-caspase-3, GSDME and GSDME-N in PDAC cells (Figures 4B-D). Co-IP results showed that GALNT6 was interacted with GSDME, not GSDMD (Figures 6C-E). VVA pull-down results showed that knockdown of GALNT6 significantly reduced the glycosylation of GSDME (Figure 6F). To ascertain how GALNT6 regulates the synthesis or degradation of GSDME, the CHX (Cycloheximide, protein synthesis inhibitor) and MG132 (Z-Leu-Leu-Leu-Al, proteasome inhibitor) were used. When cells were treated with MG132, the levels of GSDME were significantly increased in Patu-8988t cells with or without GALNT6 knockdown (Figure 6G). After treated with CHX, the levels of GSDME were decreased with or without GALNT6 knockdown as time passed in Patu-8988t cells. However, the levels of GSDME in GALNT6 knockdown cells were higher than that in control cells at corresponding time (Figure 6H), suggesting that knockdown of GALNT6 inhibits the degradation of GSDME.
## Discussion
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers with a notoriously poor prognosis [31]. Although the survival benefits for PDAC have improved, the therapy and prognosis of PDAC remains unsatisfactory. Pancreatic cancer is related to its extensive and complex tumor microenvironment [32], and is considered to have an exceedingly immunosuppressive environment, with numerous constituents and pathways hindering influential pancreatic cancer-targeted immune responses [33]. Therefore, it is essential to exploiting the key driver factors and underlying mechanisms of PDAC progression to improve diagnosis and therapeutic effect. Glycosylation is one of the most common post-modification, and aberrant glycosylation has been cited as hallmark of cancer. Studies showed that the aberrant glycosylation described in PDAC contributes to pro-tumorigenic signaling pathways, metastatic capability and therapeutic resistance [12]. However, the roles of alter glycosylation remain poorly understood in the progression of PDAC. Reports showed that dysregulation of GALNT3 and GALNT6 promote the metastatic phenotypes pancreatic cancer [34, 35]. Here, we confirmed that GALNT6 was overexpression in PDAC, associated with advanced tumor stage, and had an AUC value of 0.919 in pancreatic cancer based on TCGA dataset. However, GALNT6 wasn’t overexpression in all PDAC cell lines, which may be linked to different types of pancreatic ductal adenocarcinoma. In this study, the upregulated GALNT6 cell lines (BXPC-3 and Patu-8988t) were used to further investigate the role and the underlying mechanism of GLANT6 in PDAC. Results showed that knockdown of GALNT6 inhibited cell proliferation, migration and invasion of PDAC cells, and induced G0/G1 phase arrest of PDAC cells, which might eventually inhibit the PDAC progression.
The infinite proliferation of tumor cells requires continuous shaping of a suitable microenvironment outside the tumor [36]. Cancer invasion and distant metastasis are attributed to inflammatory factors in the tumor microenvironment, macrophage-driven chronic low-grade inflammation is an important feature of cancer [37, 38]. However, the activated inflammatory response can induce the pyroptosis, an inflammatory cell death, which has been shown to be a more effective immunotherapy approach with fewer side effects compared with conventional immunotherapy, providing a great opportunity in treating solid tumors [17, 39]. Pyroptosis-aroused immunological responses could convert immunosuppressive “cold” tumor microenvironment (TME) to immunogenic “hot” TME, which not only inhibits primary pancreatic cancer growth but also attacks the distant tumor [39]. Pancreatic cancers frequently develop resistance to chemotherapy-induced cell apoptosis during the treatment, that targeting pyroptosis can be an alternative cancer treatment strategy [40]. SEM results showed that knockdown of GALNT6 promoted the pyroptosis of PDAC cells. Pyroptosis can be induced by caspase-3 (inflammasome independent manner) or caspase-1 (inflammasome dependent manner) [17]. An inflammasome is a protein complex, including NLRs, and recruit ASC, leading to recruit pro-caspase-1 and activate caspase-1 through autocleavage [17]. Here, we found that knockdown of GALNT6 significantly increased the expression of caspase-1, NLRP3 and ACS in PDAC cells, and the level of cleaved-caspase-1 was also significantly increased, suggesting that knockdown of GALNT6 promoted the formation of ASC focus and activation of caspase-1 in PDAC cells, which might induce the pyroptosis of PDAC cells. Activated inflammasome can promote the release of the pro-inflammatory cytokines, including IL-1β and IL-18, which activate the innate immune system and strengthen the inflammatory response [41]. Our results showed that knockdown of GALNT6 significantly increased the expression of inflammatory cytokines IL-1β, IL-18, IL-6 and TNF-α in PDAC cells. In additionally, GSDMD, the executor of pyroptosis, can be cleave by activated caspase-1 to generate GSDMD-N, which perforates the cell membrane and releases the IL-1β, IL-18, and other pro-inflammatory cytokines into the extracellular matrix [42]. Here, we found that knockdown of GALNT6 significantly increased the expression of GSDMD in PDAC cells. These findings suggested that the Knockdown of GALNT6 induced the pyroptosis via the NLRP3/caspase-1/GSDMD signaling pathway, leading to inhibit the growth of PDAC cells.
As is well known, NF-κB pathway is a typical inflammatory signaling pathway, which is also involved in regulating the pyroptosis [43, 44]. The activation of NF-κB results in the phosphorylation of IκB and the nuclear translocation of NF-κB, which promotes the expression of inflammatory cytokines, including TNF-α, IL-1β, IL-6 and IL-18, and further promoting the inflammatory response [45]. The activated pro-inflammatory cytokine of IL-1β and IL-18, and subsequent activation of caspase-1 in the inflammasome that in turn induces pyroptosis and release of the active inflammatory cytokines [46]. Our results showed that the phosphorylation of NF-κB was increased but NF-κB levels were unaltered in GALNT6 knockdown PDAC cells. Knockdown of GALNT6 promoted the translocation of NF-κB into nucleus and decreased the glycosylation of NF-κB, however, the IKKβ, IκBα and p-IκBα levels were unaltered. These findings suggested that the activation of NF-κB was directly regulated by GALNT6 knockdown, and subsequently induces transcription of IL-1β and IL-18 and activate inflammasome, leading to pyroptosis.
Furthermore, GSDME, one of the members of the gasdermin family, also plays important role in pyroptosis. GSDME is activated and cleaved by active caspase-3 to form N-terminal of GSDME, which could activate the canonical inflammasome pathway, leading to release of IL-18 and IL-1β and mediate pyroptosis [17]. Studies showed that chemotherapeutic drugs convert the GSDME induced cell apoptosis to pyroptosis [47]. Here, we found that knockdown of GALNT6 did not affect the expression of caspase-3, however, the level of cleaved-caspase-3, GSDME and GSDME-N was significantly increased by GALNT6 knockdown in PDAC cells, suggesting that knockdown of GALNT6 activated caspase-3, which promoted the cleavage of GSDME and induced the pyroptosis of PDAC cells. Furthermore, Co-IP and VVA pull-down results showed that GALNT6 was interacted with GSDME, and the glycosylation of GSDME by GALNT6 might promote the degradation of GSDME. These results suggested that caspase-3/GSDME was also regulated by GALNT6, which may attribute to the pyroptosis of PDAC cells.
In summary, we found that GALNT6 was overexpressed in PDAC and promoted cell proliferation, migration and invasion in PDAC cells. Knockdown of GALNT6 arrested cell cycle at G0/G1 phase and induced the pyroptosis of PDAC cells. GALNT6 regulated the pyroptosis of PDAC cells through NF-κB/NLRP3/GSDMD and GSDME signaling (The mechanism diagram is shown in Figure 6I), suggesting that GALNT6 may serve as a novel tumor therapeutic target for PDAC and providing novel insights into the roles of GALNT6 in PDAC progression.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
All authors contributed to this article and approved the submitted version. MD and XC designed the study. MD, JL, HL, YZ, YC, and HP performed the experiments. MD and JL analyzed the data and prepared the manuscript. SF and XC edited and approved the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Mitochondrial DNA copy number in peripheral blood of IgA nephropathy: a cross-sectional
study'
authors:
- Jiaqi Liu
- Rong Wang
- Ning Luo
- Zhibin Li
- Haiping Mao
- Yi Zhou
journal: Renal Failure
year: 2023
pmcid: PMC10013479
doi: 10.1080/0886022X.2023.2182133
license: CC BY 4.0
---
# Mitochondrial DNA copy number in peripheral blood of IgA nephropathy: a cross-sectional study
## Abstract
Mitochondrial DNA (mtDNA) copy number (CN) is a biomarker of mitochondrial function and has been reported associated with kidney disease. However, its association with IgA nephropathy (IgAN), the most common cause of glomerulonephritis (GN), has not been evaluated. We included 664 patients with biopsy-proven IgAN and measured mtDNA-CN in peripheral blood by multiplexed real-time quantitative polymerase chain reaction (RT-qPCR). We examined the associations between mtDNA-CN and clinical variables and found that patients with higher mtDNA-CN had higher estimated glomerular filtration rate (eGFR) ($r = 0.1009$, $$p \leq .0092$$) and lower serum creatinine (SCr), blood urea nitrogen (BUN), and uric acid (UA) (r=−0.1101, −0.1023, −0.07806, respectively, all p values <.05). In terms of pathological injury, mtDNA-CN was higher in patients with less mesangial hypercellularity ($$p \leq .0385$$, M0 vs. M1 score by Oxford classification). Multivariable logistic regression analyses also showed that mtDNA-CN was lower for patients with moderate to severe renal impairment (defined as eGFR < 60 mL/min/1.73 m2) vs. mild renal impairment, with the odds ratio of 0.757 ($95\%$ confidence interval: 0.579–0.990, $$p \leq .042$$). In conclusion, mtDNA-CN was correlated with better renal function and less pathological injury in patients with IgAN, proposing that systemic mitochondrial dysfunction may be involved in or reflect the development of IgAN.
## Introduction
Mitochondria are the most important organelles in mammals, in which adenosine triphosphate (ATP) is generated through oxidative phosphorylation (OXPHOS) for tissue metabolism. Mitochondrial DNA (mtDNA) encodes 13 essential components involved in respiration and OXPHOS [1]. Mitochondrial dysfunction impairs cell responses to varieties of metabolic processes and dynamics of mitochondria, contributing to the pathogenesis of many common diseases, such as diabetes, obesity, cardiovascular diseases, and acute kidney disease [2–6].
Mitochondrial DNA copy number (mtDNA-CN) is a biomarker of mitochondrial function that facilitates dynamic detection and monitoring [7]. Recently, mtDNA-CN has been significantly associated with clinical feature in a broad range of clinical disorders involving the kidney damage, such as diabetic nephropathy (DN), chronic kidney disease (CKD), and incident of microalbuminuria [3,4,8–10]. Lower mtDNA-CN was reported in 83 patients with DN compared to 45 diabetes patients without kidney disease (DC) by a case-control study in Bahrain [8]. In the study of the Atherosclerosis Risk in Communities (ARIC), higher mtDNA-CN in peripheral blood was correlated with lower incident of eGFR decline [9]. It was also shown that higher mtDNA-CN was associated with lower prevalence of microalbuminuria in a cross-sectional community-based study of 694 individuals in Korea [10].
Previous studies have focused on kidney injury, while there are lack of research on the association of mtDNA-CN and specific etiology of primary glomerulonephritis (GN). IgA nephropathy (IgAN) was recognized as the most common primary GN worldwide, of whom the association with mtDNA-CN in peripheral blood has not been discovered. IgAN is diagnosed in 1–10 out of every 100,000 people each year [11,12]. The mortality of patients with IgAN is increased by $53\%$ and the life expectancy of them is reduced by more than 6 years compared with healthy people [13]. It is reported that $40\%$ of IgAN patients had progressed to end stage renal disease (ESRD) within 20 years, being a leading cause of ESRD in the word [5]. The deterioration of renal function is found to be a severe risk factor for progression to ESRD in IgAN [14,15]. Additionally, the risk of progression to ESRD was much higher for eGFR declined below 60 mL/min/1.73 m2 in IgAN [16]. At present, prediction of prognosis and diagnosis of IgAN are limited to kidney biopsy, which cannot be performed periodically due to its clinical contraindications and risk of bleeding and other clinical complications. An effective and convenient measure to evaluation of disease status such as renal function and pathological changes is required to explore in IgAN, which can be operated regularly. Given the above association of mtDNA-CN with kidney damage and adverse renal outcomes [3,4,9,10], we aimed to investigate the specific association of mtDNA-CN in peripheral blood with IgAN manifestations. In our present study, we depicted for the first time the association of mtDNA-CN with the clinical and pathological features in a large number of biopsy-diagnosed IgAN patients.
## Study population
In the current study, 853 individuals diagnosed as IgAN by kidney biopsy were enrolled from the First Affiliated Hospital of Sun Yat-Sen University from January 2015 to December 2018. Blood samples and clinical phenotype data were collected subsequently. Patients were excluded if one or more of the following criteria was met: age <14 or >75; secondary IgA deposits (e.g., hepatitis related GN, systemic lupus erythematosus, rheumatoid arthritis, Henoch-Schönlein purpura, and renal transplantation); too low DNA concentration to measure; deficiency of clinic data. Finally, 664 eligible patients were enrolled in this study (Figure 1). Patients were classified into two groups: mild renal impairment (eGFR ≥ 60 mL/min/1.73 m2) or moderate to severe renal impairment (eGFR < 60 mL/min/1.73 m2). According to the Oxford classification, the pathological severity in IgAN patients was checked [17]. This study was approved by the Human Research Ethics Committee of the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China (no. 201037). All participants provided written informed consent.
**Figure 1.:** *The protocol for selection of IgAN patients.*
## Clinical and laboratory data
Clinical data were obtained from the department of clinical laboratory, the First Affiliated Hospital of Sun Yat-Sen University. Peripheral bloods were obtained in 664 participants at the time of kidney biopsy. Serum creatinine (SCr), blood urea nitrogen (BUN), and uric acid (UA) were measured by standard procedures. To determine proteinuria levels, 24 h urine was collected. Estimated glomerular filtration rate (eGFR) was computed by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) method [18]. Kidney biopsies were scored by professional pathologists referring to Oxford MEST-C Classification [19]. Specifically, M was scored in terms of mesangial hypercellularity: when >$50\%$ of glomeruli showing four or more cells in one or more mesangial area, not including central core and region of the vascular pole were scored M1. E score or S score was defined as absent [0] or present [1] of endocapillary hypercellularity (E) or segmental glomerulosclerosis (S). T score was defined in terms of estimated percentage of interstitial fibrosis and tubular atrophy: T0 (≤$25\%$), T1 (26–$50\%$), and T2 (>$50\%$). C score was referred to crescents: C0 ($0\%$), C1 ($0\%$ to <$25\%$), and C2 (>$25\%$).
## Measurement of mtDNA-CN in peripheral blood
Measurement of mtDNA-CN has been described previously [20]. mtDNA genes ND1 in peripheral blood were assessed by real-time quantitative polymerase chain reaction (RT-qPCR). DNA was isolated from blood samples using QIA symphony DSP DNA Midi Kit (Qiagen, Valencia, CA) and concentrations were detected by using ABI TaqMan chemistry (Applied Biosystems, Waltham, MA). Specimens were performed in triplicate for each assay. The mitochondrial target (ND1) (assay ID Hs02596873_s1) and nuclear target (RPPH1) (assay ID Hs03297761_s1) of cycle thresholds (Cts) were detected. Each sample was run in triplicate on a 384-well plate in a 10-μL reaction containing 20 ng of DNA. The thermal profile was set up according to manufacturer’s instructions (Applied Biosystems, Waltham, MA, Cat#4444557) as follows: 50 °C for 2 min, 95 °C for 2 min, and 40 cycles of 95 °C for 3 s, and 60 °C for 30 s. Cycle threshold value was determined from the amplification curve for each target by the ABI Viia7 software. The difference in Cts between the two genes (ΔCt) was computed for each well as the difference between the Ct for the RPPH1 target and the Ct for the ND1 target, as a measure of mtDNA-CN relative to nuclear DNA copy number. Quality control procedures were performed as follows: excluding outliers from the triplicate assays when the SD of ΔCt was >0.5, and kicking out a replicate with Ct for ND1 of >30, a Ct for RPPH1 of >5 SDs, and ΔCt of >3 SDs. We defined SD (with a mean of 0) as the unit of standardized determination for mtDNA-CN as previous [20]. ND1 stands for nicotinamide adenine dinucleotide dehydrogenase subunit-1. mtDNA-CN was dichotomized using a study-specific median measurement level as the cutoff to define ‘high mtDNA-CN’ as an measurement level at or above the median vs. ‘low mtDNA-CN’ as an measurement level below the median.
## Statistical analysis
Baseline characteristic data were expressed as mean ± standard deviation for continuous variables or n (%) for categorical variables. Median (IQR) was displayed for non-normal variables. Differences of baseline characteristics between subjects (categorized by mtDNA-CN, eGFR, or M scores of the Oxford classification) were analyzed by Student’s t-test for normal variables, Wilcoxon’s rank sum test for non-normal variables, and Chi-square test for categorical variables. Measurement of mtDNA-CN was expressed as standardized residuals. Normality tests were conducted by Shapiro–Wilk’s test and found data following approximation of normal distribution. Pearson’s correlation analysis was used to explore correlations of mtDNA-CN in peripheral blood with eGFR, SCr, BUN, UA, and proteinuria. Multivariable logistic regression model was used to evaluate associations of mtDNA-CN with mild vs. moderate to severe renal impairment with adjustment for the potential confounding variables. Adjusted odds ratio (OR) and $95\%$ confidence intervals (CIs) were applied to quantify the association of the mtDNA-CN with renal impairment in IgAN. All p values were two-sided and $p \leq .05$ was taken as statistically significant. Statistical analyses were performed in SPSS 25 software, version 14 (SPSS Inc., Chicago, IL).
## Characteristics of patients with IgAN Stratified by mtDNA-CN
Table 1 shows the characteristics of the enrolled 664 patients with IgAN stratified by mtDNA-CN with overall mean of 0.0077. mtDNA-CN was dichotomized using a study-specific median level as the cutoff to define ‘high mtDNA-CN’ with mean of −0.483 and ‘low mtDNA-CN’ with mean of 0.467. In the higher mtDNA-CN group, clinical indicators of SCr and BUN were lower ($$p \leq .0129$$ and.0057, respectively), while eGFR levels were higher ($$p \leq .0167$$). In the high mtDNA-CN group, the mean of SCr was 161.9 μmol/L and BUN was 7.591 mmol/L and eGFR was 66.52 mL/min/1.73 m2. In the low mtDNA-CN group, the mean of SCr was 195.4 μmol/L and BUN was 8.697 mmol/L and eGFR was 60.01 mL/min/1.73 m2. The proteinuria, UA, and histological scores from IgAN patients had no significant difference at different levels of mtDNA-CN.
**Table 1.**
| Characteristics | mtDNA-CN (low) | mtDNA-CN (high) | p Value |
| --- | --- | --- | --- |
| mtDNA-CN (SD) | –0.4828 ± 0.4646 | 0.4673 ± 0.3000 | .0001* |
| Sex (male), n (%) | 176 (52.85%) | 168 (50.76%) | .6418 |
| Age, years | 36.14 ± 10.64 | 36.24 ± 10.34 | .8971 |
| Estimated GFR (mL/min/1.73 m2) | 60.01 ± 35.43 | 66.52 ± 34.40 | .0167* |
| Serum creatinine (μmol/L) | 195.4 ± 190.6 | 161.9 ± 153.9 | .0129* |
| Proteinuria (g/24 h) | 2.262 ± 2.408 | 2.002 ± 2.177 | .1439 |
| Blood urea nitrogen (mmol/L) | 8.697 ± 5.655 | 7.591 ± 4.553 | .0057* |
| Uric acid (μmol/L) | 444.3 ± 120.6 | 430.6 ± 108.8 | .1232 |
| Oxford classification a | | | |
| Mesangial hypercellularity (M1), n (%) | 218 (71.01%) | 197 (63.96%) | .0622 |
| Endocapillary hypercellularity (E1), n (%) | 59 (19.22%) | 69 (22.40%) | .3316 |
| Segmental glomerulosclerosis (S1), n (%) | 184 (59.93%) | 187 (60.71%) | .7800 |
| Tubular atrophy/interstitial fibrosis (T1,T2), n (%) | 135 (43.97%) | 122 (39.61%) | .4414 |
| Crescents (C1,C2), n (%) | 188 (61.24%) | 187 (60.71%) | .5070 |
## Correlations of mtDNA-CN in peripheral blood with renal function
In correlation measurements of mtDNA-CN with clinical indicators of renal function, we analyzed the eGFR, SCr, BUN, and UA. The data showed that higher mtDNA-CN was associated with higher eGFR ($$p \leq .0092$$, Figure 2(A)) and lower SCr, BUN, and UA ($$p \leq .0045$$, Figure 2(B); $$p \leq .0084$$ Figure 2(C) and $$p \leq .0444$$, Figure 2(D)). The correlations were statistically significant, but their coefficients were small ($r = 0.1009$, Figure 2(A); r=−0.1101, Figure 2(B); r=−0.1023, Figure 2(C); and r=−0.07806, Figure 2(D)).
**Figure 2.:** *Correlations of mtDNA-CN in peripheral blood with renal function. Association between mtDNA-CN in peripheral blood and eGFR (A), SCr (B), BUN (C), UA (D), and proteinuria (E) were analyzed by Spearman’s rank correlation analysis. N = 664. eGFR: estimated glomerular filtration rate; mtDNA: mitochondrial DNA; CN: copy number. SD (with a mean of 0) as the unit of standardized determination for mtDNA-CN.*
## mtDNA-CN characteristics of patients with IgAN stratified by eGFR
Enrolled patients with IgAN were divided into two groups according to the higher risk of severe clinical outcome in patients with eGFR declined below 60 mL/min/1.73 m2 in IgAN [16,21]: [1] mild renal impairment (eGFR ≥ 60 mL/min/1.73 m2, $$n = 352$$) and [2] moderate to severe renal impairment (eGFR < 60 mL/min/1.73 m2, $$n = 312$$). mtDNA-CN was significantly higher in patients with mild renal impairment compared with those of moderate to severe renal impairment (0.04872 vs. −0.07151, $$p \leq .0119$$, Figure 3). In mild renal impairment group, renal function of patients was better (all $p \leq .001$, Table 2) and pathological injury such as endocapillary hypercellularity and interstitial fibrosis/tubular atrophy were less ($$p \leq .009$$ and $p \leq .001$, respectively, Table 2).
**Figure 3.:** *mtDNA-CN in patients with IgAN stratified by eGFR. Data were analyzed by Student’s t-test. mtDNA: mitochondrial DNA; CN: copy number. SD (with a mean of 0) as the unit of standardized determination for mtDNA-CN.* TABLE_PLACEHOLDER:Table 2.
## Multivariable logistic regression analyses of associations between mtDNA-CN and renal impairment
Based on the significant difference of mtDNA-CN between two groups with different degrees of renal impairment, multivariable logistic regression analyses of mtDNA-CN on renal impairment were performed. Table 3 shows that the significant variables of aggravating renal impairment (eGFR < 60 mL/min/1.73 m2) were lower mtDNA-CN (adjusted OR: 0.757, $95\%$ CI: 0.579–0.990, $$p \leq .042$$) and higher proteinuria (OR = 1.436, $95\%$ CI: 1.296–1.591, $p \leq .001$), adjusting for known potential confounding variables (sex, age, and proteinuria).
**Table 3.**
| Variables | p | OR | 95% CI |
| --- | --- | --- | --- |
| mtDNA-CN (SD) | .042 | 0.757 | (0.580, 0.983) |
| Proteinuria (g/24 h) | <.001 | 1.436 | (1.287, 1.571) |
| Sex | .042 | 0.709 | (0.510, 0.987) |
| Age | <.001 | 1.045 | (1.027, 1.062) |
## Relationships between mtDNA-CN and renal pathological changes in IgAN patients
The Oxford classification has been accepted by the majority of clinicians and investigators as an international consensus for pathological classification of IgAN. It includes the MEST-C score, referring to mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental glomerulosclerosis (S), tubular atrophy/interstitial fibrosis (T), and crescents (C). Among them, the M0 score refers to the mesangial hypercellularity in glomeruli < $50\%$, while the M1 score refers to ≥$50\%$ of mesangial hypercellularity. Our data showed that mtDNA-CN was markedly higher in patients with less mesangial hypercellularity (M0 vs. M1 score by Oxford classification, $$p \leq .0346$$, Figure 4). mtDNA-CN appeared to be trending higher in patients with T0 compared to T1 score, and also higher in patients with C1 compared to C2 score, even though no statistical significance was found ($$p \leq .0509$$ and.0564, respectively, Figure 4). However, comparisons in terms of E score and S score found no difference. These results indicated that mtDNA-CN could reflect the mesangial hypercellularity in glomeruli which is the early pathological injury in the progression of kidney disease. Supplemental Table 1 shows the baseline characteristics of patients stratified by M score. Notably, there was no significant difference in age, eGFR, SCr, proteinuria, BUN, and UA between patients with M0 and M1 score (all $p \leq .05$).
**Figure 4.:** *Relationships between mtDNA-CN and renal pathological changes in IgAN patients. Data were analyzed by Student’s t-test. SD (with a mean of 0) as the unit of standardized determination for mtDNA-CN. N = 615. Forty-nine individuals were excluded due to incomplete pathological Oxford classification information. Renal pathological changes were scored by professional pathologists referring to Oxford MEST-C Classification. Patients were scored M0 or M1 in terms of mesangial hypercellularity: when >50% of glomeruli showing four or more cells in one or more mesangial area, not including central core and region of the vascular pole were scored M1. E score or S score were defined as absent (0) or present (1) of endocapillary hypercellularity (E) or segmental glomerulosclerosis (S). T score was defined in terms of estimated percentage of interstitial fibrosis and tubular atrophy: T0 (≤25%), T1 (26–50%), and T2 (>50%). C score referred to crescents: C0 (0%), C1 (0% to <25%), and C2 (>25%).*
## Discussion
In the present study of 664 patients with IgAN, we found that mtDNA-CN in peripheral blood was associated with renal function and inversely associated with pathological injury. The higher mtDNA-CN is associated with the better renal function having higher eGFR, lower SCr, BUN, and UA and the less pathological change of mesangial hypercellularity.
Recently, an increasing number of studies have reported the significant association of mtDNA-CN with various clinical disorders, worthy of more attention in a broader clinical practice. In a study of Black and White individuals from Chronic Renal Insufficiency Cohort (CRIC), higher mtDNA-CN in peripheral blood was associated with higher eGFR [22]. It is also reported that lower mtDNA-CN was associated with higher prevalent CKD (defined as eGFR < 60 mL/min per 1.73 m2) in a community-based cohort of Americans [9]. When paying attention to specific etiology of primary GN, a cross-sectional study in Korea has first reported that urinary mtDNA-CN was negatively associated with eGFR in 31 patients with IgAN, but failed to identify the statistical correlations of other indicator or pathological injury [23]. Consistent with previous reports, we observed that the patients with higher mtDNA-CN in peripheral blood had the better renal function considering the higher eGFR, lower SCr, BUN, and UA. In terms of pathological change, patients with higher mtDNA-CN had the less mesangial hypercellularity in renal glomeruli. Additionally, mtDNA-CN had an inverse association of moderate to severe renal impairment with declined eGFR < 60 mL/min per 1.73 m2 (OR = 0.757, $95\%$ CI = 0.579–0.990). The difference between two studies of IgAN, the Korea study of urinary mtDNA-CN and our study of mtDNA-CN in peripheral blood, was due to the different populations and different sources of mtDNA-CN may have different characteristics. It is worth pointing out that most studies have focused on the characteristic of mtDNA-CN in peripheral blood, as peripheral blood samples more credibly and systematically reflect the state of the body. Therefore, our results suggested that mtDNA-CN may be a possible marker for clinical monitoring of IgAN patients to systematically reflect the status of disease, including renal function and pathological change.
On the other hand, mtDNA-CN is a great biomarker of mitochondrial function, which can directly reflect mitochondrial function [18,24]. Mitochondrial dysfunction has been identified as underlying mechanisms for the development and progression of kidney disease, like acute kidney injury and CKD [25,26]. It is said that accumulation of mtDNA damage and the consequential decrease in mtDNA-CN was linked to kidney injury [8–10,23]. Ashar and colleagues indicated that higher mtDNA-CN is a marker of higher levels of mitochondrial replication and cellular energy reserves, with lower levels of mtDNA-CN likely reflecting mitochondrial depletion [20]. In patients with IgAN, electron microscopic images also found the mitochondrial morphological alterations in kidney, of which the mitochondria were small and disorganized [23]. These findings, in conjunction with our findings of mtDNA-CN associations with renal function and histological damage, further suggested that mitochondrial dysfunction may play an etiologic role in IgAN.
Furthermore, a few studies have examined the correlation between mtDNA-CN and adverse outcomes in the elderly individuals, patients on hemodialysis and CKD. It is reported that higher mtDNA-CN was associated with lower risk of all-cause mortality in the elderly individuals, patients on hemodialysis or with renal dysfunction [3,4,20]. Additionally, lower mtDNA-CN was associated with prevalent frailty. In some cohort studies, lower mtDNA-CN have been associated with higher risk of kidney failure (HR, 1.30; $95\%$ CI, 1.10–1.55), which was proposed as a useful target for intervention in the progression of kidney disease [22,27]. These findings suggested that mtDNA-CN can also be a new target for intervention during the progression of IgAN.
Besides, our enrolled patients were first diagnosed with IgAN by renal-biopsy. Most of them received conventional therapy for symptom relief at collection. Of the included patients, 398 participants were not receiving immunosuppressive agent (neither steroids nor other immunosuppressants). Through comparing the levels of mtDNA-CN in patients with immunosuppressive treatment or not, we identified no significant difference by statistical analysis ($$p \leq .1969$$, data not shown). Meanwhile, we conducted the same statistical analysis as Figures 2–4 in our study for patients treated with immunosuppressive agent as well as patients treated without immunosuppressive agent. We found a similar tendency that patients with mild renal injury had higher mtDNA-CN compared with patients with more severe renal injury. These data suggested that treatment at baseline seemed not to significantly affect the levels of mtDNA-CN. In line with previous study, treatment did not affect mtDNA-CN in urine from patients with IgAN [23]. However, the question of whether treatment affects mtDNA-CN levels seems to require future longitudinal studies to be thoroughly investigated.
This study has several strengths over the previous studies. Our study was the first to report the association of mtDNA-CN in peripheral blood with clinical indicator in patients with biopsy-proven IgAN. Second, it included a large sample size of 664 IgAN patients comparing to the Korea study of 31 IgAN patients, which might provide more reliable information.
This study also has limitations. Due to the difficulty in follow-up, we only assessed correlations between mtDNA-CN and IgAN by cross-sectional study, a longitudinal analysis is required. Regarding the relative small coefficients in correlation analysis, further studies are also warranted to confirm it.
In conclusion, mtDNA-CN, as a sensitive marker of mitochondrial function, was inversely associated with clinical indicators of renal function decline and pathological injury in patients with IgAN, suggesting mitochondrion involved in pathophysiological processes of IgAN. Future studies are warranted to evaluate changes in mitochondrial function in the progression of IgAN and investigate whether intervention on mitochondrial function can improve IgAN.
## Author contributions
Y.Z., H.P.M., and J.Q.L. were involved in the research idea and study design; J.Q.L. and N.L. performed the experiment. J.Q.L. acquired data and performed statistical analyses. J.Q.L. and R.W. interpreted and drafted the manuscript; Z.B.L. was involved in guidance on statistical analyses. All authors were involved in the revision of manuscript and responsible for providing intellectual content of critical importance to the work described as well as for final approval of the version to be published.
## Disclosure statement
The results presented in this article have not been published previously. No potential conflict of interest relevant to this article is declared.
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|
---
title: 'The relationship between subjective well-being and food: a qualitative study
based on children’s perspectives'
authors:
- Cristina Vaqué-Crusellas
- Mònica González-Carrasco
- Ferran Casas
journal: International Journal of Qualitative Studies on Health and Well-being
year: 2023
pmcid: PMC10013481
doi: 10.1080/17482631.2023.2189218
license: CC BY 4.0
---
# The relationship between subjective well-being and food: a qualitative study based on children’s perspectives
## ABSTRACT
### Purpose
Despite the lack of consensus regarding which life satisfaction domains should be included in the study of children’s subjective well-being (SWB), some domains are frequently considered, such as satisfaction with health. However, some others, such as satisfaction with food, are barely taken into account, despite the impact eating habits have on children’s health and well-being. We adopt a qualitative approach to explore the role food plays in children’s SWB, providing for a more in-depth analysis of children’s perceptions and evaluations on a still insufficiently known domain of life satisfaction.
### Method
Sixteen discussion groups were held with 112 Spanish students (10–12 years old) from six schools. The transcripts were analy sed and themes reflecting the key concepts were defined using reflexive thematic analysis.
### Results
Five themes emerged from the children’s discourses on the relationship between food and SWB: health, pleasure, emotions, commensality—i.e., eating together—and food-empowerment—thus offering new insights from children’s perspectives.
### Conclusion
Almost all of the participants established a relationship between their SWB and their eating behaviour, meaning that, within the challenges facing public health, SWB must be taken into account when promoting healthy eating programmes for children. Also, group discussion is found to be a very powerful tool for exploring topics with subjective connotations among child populations.
## Introduction
It is considered that subjective well-being constitutes the articulation of a cognitive process (satisfaction judgements with life considered globally, and with different specific life domains, such as, among others, health, school, family, friends, economic aspects and relationships with others) and two affective processes (positive affects and negative affects) (Cummins & Cahill, 2000). It follows that a person who experiences more positive emotional experiences throughout life (e.g., cheerful, energetic, in a good mood, etc.) than negative experiences (fear, anger, sadness, etc.) will probably perceive life more satisfactorily.
Although subjective well-being in adults has been widely studied (Cummins & Nistico, 2002; Diener et al., 2003; Michalos, 2008, among many others), interest in knowing how it works among the child population did not emerge until the late 1990s, after children’s rights had been recognized as human rights (United Nations Convention on the Rights of the Child, 1989). This recognition established the consideration of children as active subjects in defining their well-being, understanding that it is through the experiences, meanings and opinions of children themselves that the key areas of their well-being and quality of life will be known (Casas, 2010; Fattore et al., 2007; Sorbring & Kuczynski, 2018).
Although there is no international consensus on which areas of satisfaction with life should be studied in order to determine people’s subjective well-being, some authors do agree on some domains, as is the case with health (Bradshaw et al., 2011; Casas et al., 2007; Rees & Main, 2015; Tomyn & Cummins, 2010; Vujčić et al., 2019), while others, such as food, are barely taken into account. That being said, some authors have begun to develop and study the concept of “Food well-being (FWB)”, defined as a positive psychological, physical, emotional and social relationship with food at both the individual and societal levels. They highlight the important relationship between food and subjective well-being in contributing to a better understanding of consumer food choices (Ares et al., 2014; Block et al., 2011; Hémar-Nicolas & Ezan, 2019; Sobal et al., 2006).
If we understand health as a state of complete physical, mental and social well-being, and not only as the absence of disease (WHO, 1948), it makes sense to think that subjective well-being is a determining component of health and that its study can contribute to knowing and designing better health promotion strategies, also among the child population.
In this study, we adopt a qualitative approach to explore the role of food in subjective well-being based on children’s perceptions, assessments and opinions in relation to this issue, following the approach of social sciences, given the premise that food is one of the main determinants of health and a key piece in the notable challenges facing public health globally.
WHO data [2018] show that the prevalence of chronic non-communicable diseases is constantly on the increase (obesity, diabetes, cardiovascular diseases, etc.). IT IS ALSO REINFORCED BY NCD RISK FACTOR COLLABORATION NETWORK [2022]. Many interventions have been developed to improve these conditions, based on promoting knowledge and skills to reduce weight and the sedentary lifestyle and adopt a healthier diet by working on aspects of behaviour (self-efficacy, expectations, subjective norms), as well as actions to promote healthier environments. Few of these strategies focus on exploring whether food and the adoption of specific dietary patterns are associated with people’s subjective well-being, linked to beliefs, opinions and emotions that are experienced individually but socially constructed, through interaction with others. These beliefs and emotions can also be expressed in relation to the foods and eating habits that characterize a particular social group. Such an association can have important consequences, because if healthy practices are more unpleasant than pleasurable, then they may not be established and will therefore not be maintained, because they will not be satisfactory to the individual. On the other hand, if people do see health as an important goal, then food can help them devote more effort to deploying healthy eating habits and lifestyles; otherwise, the adoption of certain food practices will be much more difficult.
That is why studying the relationship between subjective well-being and eating habits in children can provide important information that helps to have a positive impact on their healthy development, not only during childhood and adolescence, but also in adulthood.
Food is an area where aspects of pleasure (hedonism) and self-regulation (eudemonism) come together, constituting a source of satisfaction or dissatisfaction in people’s lives, this contributing to reconcile two traditions that have traditionally been addressed separately in the study of well-being (Ryan & Deci, 2001; Strelhow et al., 2020). A consequence of the above is that most studies designed to study well-being employ measures that do so from a hedonic perspective, and do not include measures from the eudemonic side (Sawatzky et al., 2009). However, the population, and even the youngest members of it, has increasingly more information regarding the impact of eating habits and lifestyles in general on health (Haines et al., 2019; NCD risk factor collaboration, Vaqué-Crusellas et al., 2015; World Health Organization, 2018), raising awareness regarding these issues.
In the review of the literature on subjective well-being in children conducted by Pollard and Lee [2003], which included a total of 175 publications, the authors found that, despite the great disparity in indicators for measuring children’s well-being, health promotion and nutrition are considered positive elements in this regard. However, the research analysed did not delve further into elements that lead to health and nutrition being important aspects of children’s well-being, and more research is needed to determine this.
The study by Lindberg and Swanberg in 2006 focused on the subjective well-being of 807 12-year-old boys and girls in Sweden, highlighting that issues related to health, nutrition and physical activity were predictive elements of the subjective well-being of the children who participated in the study, along with other factors such as interpersonal relationships (parents, teachers and friends), anxiety, depression and somatic symptoms (headache, stomach ache, back pain, difficulty sleeping). These results point to the existence of an association between the two concepts (food and subjective well-being), while indicating that the relationship can be positive and increase subjective well-being. More studies are required to determine whether the relationship between these two concepts can also be negative, and therefore diminish the well-being of boys and girls.
Chang and Nayga [2010] explored the relationship between fast food consumption and sugary drinks and the happiness of a sample of 2,366 participants aged 2 to 12 years old from Taiwan. They measured unhappiness by asking parents how often the child was “unhappy”, “sad” or “depressed”. The results showed that those who consumed fast food and sugary drinks displayed a lower significant positive correlation with being unhappy than boys and girls who did not consume fast food and sugary drinks, who displayed a higher, though still low, correlation with being unhappy. The authors highlighted the interest of these results, especially for food policies aimed at combatting childhood obesity, which usually focus on aspects of physical health and pay less attention to aspects of well-being. They also concluded that in order to reduce childhood obesity, programmes need to propose strategies to compensate for the potential reduction in children’s happiness due to their not consuming certain products that subjectively generate their well-being.
Vaqué et al. [ 2015] used an extended version of the Personal Well-being Index-School Children version (PWI-SC) by Cummins and Lau [2005], adding an item related to satisfaction with food. They verified the psychometric properties of the new index, and tested it on a sample of 371 children aged 10–12 in Spain. The results showed that the inclusion of a domain on satisfaction with food as a proposed new indicator to study subjective well-being in this age group contributed to the PWI-SC with unique variance ($6.7\%$), displaying an increase of $3.6\%$ in shared variance.
To explain levels of SWB in theoretical terms, measured by means of the PWI-SC, the authors hypothesize the existence of a homeostatically genetic mechanism, analogous to how the body controls its biological systems, such as blood pressure and heart rate. This mechanism would be responsible for relatively small variations in SWB levels among people of the same culture (unless protective factors have failed to prevent levels from decreasing) (Cummins, 2014). In relation to satisfaction with food, this would mean that certain life or personal circumstances might lead some children to assess this life domain very negatively, but that in most cases these levels would gradually recover at some point.
Arvidsson et al. [ 2017] investigated the association between children’s adherence to healthy dietary guidelines and their well-being. A sample of 7,675 children aged 2 to 9 were studied. A higher Healthy Dietary Adherence (HDA) score at baseline was associated with better self-esteem and fewer emotional and peer problems. Conversely, better self-esteem was associated with higher HDA score two years later. These findings suggested a bidirectional relationship between diet quality and self-esteem. Additionally, higher adherence to healthy dietary guidelines at baseline was associated with fewer emotional and peer problems at follow-up, regardless of children’s weight status.
The study by Hayhoe et al. [ 2021] investigated the association between dietary choices and mental well-being among schoolchildren. Data pertained to 1,253 primary school children in Norfolk. The findings indicated that type of breakfast or lunch was associated with significant differences in well-being scores. The authors observed that, compared with children consuming a conventional type of breakfast, those not eating any breakfast had lower mean well-being scores, although no significant association was found with fruit and vegetable intake. Despite this, the authors still concluded that public health strategies to optimize children’s mental well-being should include the promotion of good nutrition.
Brennan et al. [ 2021] studied the impact of a food intervention conducted in 15 primary schools in Ireland (6–11 year-olds) with the aim of altering the school’s food environment, offering experiences related to agriculture, cooking and food nutrition. Improvements in childhood emotional and behavioural well-being, dietary intake, knowledge about food, cooking skills and willingness to try new foods were found to be associated with this food intervention.
In the present study, we have explored well-being based on children’s own assessments, and not through the opinions of other people (proxies), even if they are close to the children. We adopted a qualitative approach to gain a broader view that emerged from a framed conversation on the topic being studied. The reasoning or reflection required of the children led to the decision that this research should not be carried out with very young children. Several publications (Adams et al., 2016; Camfield et al., 2009; Fattore et al., 2016) have used qualitative data collection and analysis techniques to learn more about the great diversity of children’s opinions and assessments in relation to their well-being. The qualitative approach provides in-depth information on the subject of study, complementing aspects traditionally investigated through questionnaires, which call for answers on pre-established dimensions of the studied construct.
More specifically, the theoretical framework adopted within this qualitative research is Stoecklin’s [2021, 2014, 2013] actor’s system theory. According to this model, despite it representing a notable step forward within childhood studies, it is not sufficient to consider children as social actors, since their agency depends on dynamic relationships with other children and adults and the specific contexts in which these interactions take place. Consequently, the actor’s system theory represents discursive horizons that are commonly used by social actors when describing their daily experience, which are activities, motivations, relations, values and images of self (Figure 1). Social actors frame their lived experience within these “transactional horizons”, which can be defined as symbolic landscapes channelling social interactions. Although this theory has been applied to conduct in-depth research into children’s well-being on a global scale, we believe it can also be used to understand the connections that might exist between the particular case of subjective well-being and food from children’s perspectives. Figure 1.The actor’s system theory (Stoecklin, 2013).The plain arrows symbolize a habilitating force and the dotted arrows a constraining force There are several qualitative techniques applied to the study of subjective well-being and used especially with children. Discussion groups are widely used in social research to discuss a specific topic, with the aim of drawing on the complex personal experiences, beliefs, perceptions and attitudes of the participants through a moderated interaction (Nyumba et al., 2018). This technique provides an outstanding contribution for understanding how children perceive themselves and the world around them, awarding them a central role. Our definition of a discussion group with children and adolescents is as follows: a group dynamic in which participants assume they are experts on some topic and advise adults from their own perspective following discussion with one another about what adults should do or understand, while the adults involved listen to them and only ask for clarifications about the meaning of what they say (González-Carrasco et al., 2021, p. 154).
We believe this approach allows us to explore in greater depth children’s understanding about the extent to which they consider food an important element in their subjective well-being.
## Design
An exploratory design was adopted for this qualitative study, this representing an ideal approach when few data are available, since researchers can then take a primarily inductive approach to explore a broad research question (Rendle et al., 2019).
## Participants
Altogether, 112 students from six schools in the county of Osona (Catalonia, north-eastern Spain) were recruited to participate in 16 discussion groups. These schools were recruited by means of intentional sampling, looking for heterogeneity in terms of their location ($66.7\%$ urban and $33.3\%$ rural schools) and type of school ($66.7\%$ state schools and $33.3\%$ mixed funding schools). The discussion groups were organized based on the participants’ gender (four females-only, four males-only, and eight mixed groups) and age (eight with children aged 10–11, and eight with children aged 11–12, see Table I). These ages correspond to the last two years of primary education within the Spanish education system and were chosen because they are appropriate for reflection and discussion on the issues raised. Table I.Characteristics of the sample. Type of schoolLocationNumber of discussion groupsAgeGenderNumber of participantsSchool 1StateUrban school110–11 yearsMixed8School 2StateUrban school210–11 years11–12 yearsMixed Mixed88School 3StateRural school610–11 years11–12 years10–11 years11–12 years10–11 years11–12 yearsFemaleMaleFemaleMaleMixedMixed666688School 4StateRural school210–11 years11–12 yearsMixedMixed88School 5Mixed fundingUrban school410–11 years11–12 years10–11 years11–12 yearsFemaleMaleFemaleMale6666School 6Mixed fundingUrban school111–12 yearsMixed8Total 16 112 The size of the groups was expected to be between six and ten participants in order to allow everyone the opportunity to speak, ensure maximum fluency in the conversation and encourage a comfortable space to express opinions without the risk of establishing discussion subgroups. Students with cognitive impairment were not included. The number of discussion groups was determined based on the saturation and richness of the information obtained.
## Procedure
The aim of the discussion groups was to explore the children’s understanding of the relationship between subjective well-being and food, if any. A panel comprised of four academic experts in the areas of social psychology and paediatric nutrition was consulted to review and refine the preliminary questions included in the discussion group, the aim being to improve understanding, clarity and focus with regard to these questions. Subsequently, a pilot discussion group was conducted with children ($$n = 7$$, age between 10–12 years old) with the purpose of testing and improving the questions. After changes were made, the questions were returned to the expert panel for further review before starting the study. The panel verified that the children’s answers addressed the defined objective.
Using the final instrument obtained, we began the discussion by talking about food in general, and then what subjective well-being meant to the participants. We decided to ask this without making explicit reference to the construct of “subjective well-being” because it is not part of the participants’ usual vocabulary. The results shown in the article relate to the opinions that emerged openly and spontaneously when posing the first questions about food and well-being. They were complemented by opinions obtained by explicitly asking the question linking the two concepts.
The time needed to conduct each discussion group was around 1.5 hours, and all of them took place in the school setting under the guidance of the same researcher.
## Ethical considerations
Prior to the start of the sessions, informed consent was obtained from the participants, parents, as was permission from the school, in order to be able to record the conversations and ensure that the children’s participation in the study was voluntary. The participating schools received authorization from the families to involve the children in different activities throughout the school year. When the time comes, the school informs the families of each proposal, and they again have the opportunity to refuse their child’s participation. Table I describes the characteristics of the sample.
## Data analysis
The children’s statements were analysed by means of thematic analysis (Braun & Clarke, 2006), which began with the careful transcription and reading of each discussion by the researchers in order to familiarize themselves with the information obtained. Subsequently, a thematic and open encoding process was undertaken in order to organize and segment the text. And finally, the codes were grouped into topics and subtopics with the aim of focusing, simplifying and abstracting the most relevant data by grouping them into significant topics according to the aim of the study.
Two experts reviewed the proposed categories—both were senior psychologists. One was working as a professor at the University of Girona, and the other as an independent researcher. They had extensive experience in the field of social and health psychology, conducting both quantitative and qualitative data analysis and incorporating intersectionality into their work. Interjudge analysis (Neuendorf, 2002) was used in order to increase reliability and reach a consensus in the number of categories proposed and their interpretation. The NVivo program was used for the analysis.
Although the information provided was analysed according to participants’ age and gender, the reflections provided were found to be common, and they are therefore presented together.
## Results
The results presented in this article are related to the main topic debated in the discussions groups, namely the relationship between food and well-being, given that a clear relationship was observed between these two concepts for most of the participants. They related food and subjective well-being either in a general way (without specifying the reasons for this link), or for the reasons of health and preventing illness, growth and even to be happy. L. - “if you eat well and we grow big and strong from all the food, and we don’t get diseases. I sometimes talk to my mother about this issue and of course, it’s important to eat fruit and vegetables to be healthy, and not have health problems”P. - “because you eat healthy things that have iron and energy and you will be better, because if you never eat anything with energy you stay skinny and you don’t grow so tall” Related to food pleasure, most participants indicated clear satisfaction when they ate what they liked and thought was good (foods that tasted good and smelled good), such as soft drinks, chocolate, spaghetti, ice cream and chips, among others, mostly unhealthy food. As for emotions, they explained that eating foods they like improves their well-being because they feel good, in some cases specifying that they feel happy, content and good in themselves. J. - “I love to eat tasty things, like when I have a snack, for example, eating a chocolate croissant is delicious and makes me feel good”M. - “I’m happy when I get home for lunch and see they’ve cooked what I like and not vegetables or fish, which are horrible” In some cases, the feeling of vitality was also identified. They noticed or believed that once they had eaten they had a lot of energy to do any activity. C. - “When I eat, I notice that I have energy, and then I feel like doing things because I’m not tired and I feel good”. Only in the case of one girl did she have the opposite feeling, as she explained that she felt guilty after eating “unhealthy” food.
Faced with responses regarding how they felt when they ate foods they did not like, all participants explained that it made them feel worse. This assessment evoked a wide variety of negative emotions, including, among others, feeling bad, sad, angry, uncomfortable and upset. Three participants reported that they lost energy, were very tired and had no strength to do anything. O. - “it’s horrible to sit at the table and have to eat something you don’t like, like chickpeas boiled with vegetables. They’re disgusting and they stink. They make me eat it and I get very angry because I don’t like them at all. I hate them”E. - “it’s annoying when you can’t choose your lunch because I’m happy in class, then I leave class very hungry, but when I see vegetable puree, ‘urrggh’, …, I get sad and feel bad because I have to eat it even if I don’t like it”S. - “sometimes I really want to eat something but then when I see it’s something I don’t like, it makes me angry, because, for example, I don’t like salad and when I have to eat that my mood changes” Another reaction found in their responses related to health, was the appearance of physical discomfort. They often described symptoms such as abdominal pain and vomiting as a result of eating things they did not like. Unlike what happened to them when they had food that they very much liked in front of them, in this case they lost the desire to eat and did not eat as much as they liked. They were no longer hungry when they knew they had to eat foods they would not be able to enjoy, while they were also no longer hungry when they knew they could do some other more desirable activity such as playing.
Table II summarizes the participants’ observations in relation to how they felt when they ate (Table II), with more opinions being observed in relation to negative feelings than to positive experiences, showing just how connected emotions are to food consumption and well-being from the children’s perspective. Table II.Topics and subtopics related to the sensations produced by food. TopicsEating foods they likeEating foods they don’t likeSub-topicsFeeling goodHappinessFeel vitalityWanting to eatFeeling good in themselvesFeeling guiltyFeeling physically illThinking of strategies for not eatingFeeling badLosing appetite and energyBeing upsetEnragedBeing sadFeeling uncomfortable Some of the participants explained that when they had to eat things they did not like, they looked for multiple ways to avoid it. Sometimes they were allowed not to eat it, while at other times they had to deal with it by eating as little as possible. They would draw out the meal, eating very little, either because they did not finish it or they asked for only a little, they combined foods they liked with others they did not like so much, or they simply did not eat it, throwing it away or giving it to someone else or a pet: B. - “I always cheat, for example with fish or something, like yesterday, my mother was working and my grandmother came, there was fish and my grandmother came to the kitchen and I still had two fish left, and I said to my brother: Do you want some? And he said yes, and I gave him a fish, and then my grandmother came, and I asked, ‘Do I still have to eat this one?’ And she said ‘No! It’s okay’, and I didn’t eat it. ”P. - “if I don’t like or don’t fancy something, and they don’t see me at home, I throw some to the dog and he eats it. Then my parents tell me, well, you’ve already eaten some, you can leave the rest”J. - “if they force me to eat something I don’t like and don’t want, I imagine it’s a food I like more, like peaches, or I see if I can go to grandma’s house, which is next door, and she makes me what I want” In addition, and in connection with child empowerment and the context where food is consumed, we observed a clear desire for them to choose what they wanted (both the type of food and the quantity) and in different contexts (school, at home or in restaurants), as well as not to be told off or forced to eat. A total of only four boys and girls explained that they sometimes accepted having to eat what they did not like because they valued the benefits it could bring them for their health, and understood it therefore did not have any negative effects on their well-being. L. - “I don’t like vegetables or fruit or all the foods that at home and at school they tell us are healthy, but because we need them to be healthy, I eat them and nothing happens”. B. - “parents do what they have to do to make sure we eat well so we won’t have problems, and they have to educate us, otherwise when we grow up we’ll just eat pizza, pizza and more pizza. Now I know that I have to eat everything and I’m happier eating it” On two other occasions, the reasons for this acceptance were not specified, as it was only stated that they ate it anyway, showing compliance with what they were given to eat.
Eating in a place where they felt comfortable (not eating in a cold place, eating in open spaces such as in the mountains, or eating at grandma’s house), as well as feeling good about the people they shared their meals with, also made them feel at ease when eating: P. - “I’d rather eat vegetables than be with people I don’t like. ”L. - “I don’t care if they make me eat a variety of food, but I would be very angry if I could never eat with someone I love very much” However, in four cases, contrary explanations were also observed. In these instances, it was argued—though without much justification—that food had nothing to do with personal well-being, thus rejecting a relationship between food and well-being on the basis that other things influenced living well.
Generally speaking, however, there was an evident desire to not suffer while eating, but rather to feel at ease and have a good time eating so as to feel better and have a good level of well-being.
## Discussion
The aim of this study was to explore the extent to which food is an important element in children’s well-being, departing from the fact that “it is central to know how children construct meaning around everyday lives [food in this case] and how the process of meaning meaning-making mediates their relationship with social reality” [interactions with other people around food and its consumption for this study] (Stoecklin, 2021, p. 72).
The 10- to 12-year-old boys and girls participating in this study gave multidimensional arguments when referring to this relationship by observing that they virtually all related food to their well-being. This relationship refers mainly to five themes: health, pleasure, emotions, context (commensality, i.e., eating together), and children’s empowerment with respect to decisions related to food. Some of these issues that explained the participants’ well-being also emerged in a study conducted by Haines et al. [ 2019] aimed at understanding the opinion of French children aged 6–11 years regarding food well-being. Other researchers have observed this relationship in previous studies (Arvidsson et al., 2017; Brennan et al., 2021; Hayhoe et al., 2021; Lindberg & Swanberg, 2006; Pollard & Lee, 2003). This relationship highlights participants’ awareness of the importance of food in their lives, beyond its clear biological function. Only four of the 112 children who participated stated—though without much justification—that food had nothing to do with their personal well-being, meaning we would need to continue investigating which dimensions explain their well-being. Perhaps for these four children, food is not a priority in their lives and therefore not within the things they consider important to do (this referring to activities within the actor’s system theory), or they are good eaters and food does not cause them any rejection or negative emotions (food is probably not part of their images of self). Another explanation could be that their eating habits fit their expectations and needs, so they do not have any wishes in relation to this (motivations). These findings provide insights that can be used to develop strategies for promoting healthy dietary habits and increasing well-being by targeting different needs. Most of the participants associate these two concepts, suggesting that different strategies than the one currently used should be developed to promote food habits within children’s well-being.
It is likely that no relevant differences are observed by age because the age range studied was very limited (2–3 years) and the participants were not yet teenagers. In contrast with the qualitative study we present here, quantitative studies on nutrition and well-being reveal gender differences, with adolescent girls performing worse, probably due to the fact that girls tend to score higher in negative affect and are more affected by a negative perception of weight. In light of this, well-being and obesity prevention programmes should consider age and gender differences (Casas et al., 2007; Gaspar et al., 2017).
Understanding subjective well-being as a multidimensional assessment that reflects the combination of a cognitive process (satisfaction with life considered globally and also with different specific life domains: among others, health, school, family, friends and relationships with others), and two affective processes (the presence of positive affect and absence of negative affect), we can state that for most participants in this study, food is more present in the affective than in the cognitive dimension of subjective well-being. However, some participants also discussed the impact of what they eat on their growth and health. This highlights the awareness of the children who participated in the study regarding the relationship between food and health, which supports the need to improve eating patterns, especially among the child population, due to their protective effect for the development of future pathologies, as well as ensuring proper growth (World Health Organization, 2018).
According to the relations dimension within the actor’s system theory, the context in which children eat, as well as the people with whom they share the act of eating, are other relevant elements to take into account when promoting child well-being, given the influence that participants in this study awarded to the fact of feeling good when they are in quiet, pleasant environments and together with people they feel comfortable with. The negative effect on the participants’ well-being of not having the opportunity to decide on their own diet was also very evident. Studies by Hémar-Nicolas and Ezan [2019] and Van der Kaap-Deeder et al. [ 2017] have also shown this to be a significant issue for child well-being. Stoecklin and Fattore [2018] highlighted that agency is not something the child possesses but the product of a relational dynamic between social actors in specific contexts. In respect of this, the quotes of the participating children provide examples of the spaces in which food consumption takes place in their everyday lives, the different people who intervene in these spaces and even their capacity to “negotiate” with adult actors about what and how much to eat.
Participants showed a high interest in aspects related to food by highlighting the importance of food in their lives. Although we do not have data to state this categorically, the authors believe that this high interest could be the result of the numerous interventions in health education carried out in Spain in recent years (through studies on food promotion, the implementation of food issues on school curricula, the appeal of food transmitted to them by gastronomy, among other aspects). In terms of the actor’s system theory, this interest could also be interpreted as a mode of action, that is, “a typical way of acting according to dominant transactional horizons that link concrete items of perceived reality” (Stoecklin, 2018, p. 561).
All of the above elements may have laid the foundations for children to be more aware of food from an early age and reflect on it in a positive way, indicating a responsibility towards their health and self-control when it comes to food.
Most of the participants explained that eating good food (according to their tastes) benefits their well-being and makes them happy. This reasoning refers to the hedonic assessment of food aspects. Brillat-Savarin [1999] emphasized the importance of pleasure with food at any age, not exclusively in adulthood: “pleasure at the table is important for any age, class, nation and era. It can be combined with all other pleasures, and it survives all else to comfort us for the loss of others”. When people enjoy their food, it means that they are eating something they like, whether it is the taste, the aroma, the texture, the colours, the environment or the company they are in (Landry et al., 2018). The pleasure of eating also depends on a range of internal aspects, including motivational, cognitive and behavioural factors (Macht et al., 2005).
Only in one situation did a girl express a sense of guilt over eating something she likes. Negative emotions were also common when participants eat foods they do not like. Some explained that they get sad, angry, or even somatize the situation and suffer physical pain or discomfort, which negatively affects their well-being. Interestingly, in the face of this feeling of discomfort, some participants develop strategies to avoid it. This finding is important for health promotion framed from a hedonic and a salutogenic perspective (Antonovsky, 1979; Diener, 1984), through exploring alternative strategies such as the use of eating for pleasure to promote healthy eating and children’s well-being. Landry et al. [ 2018] voiced their support for this approach through a more holistic vision of the role of food in human well-being, rather than in physical health alone. We agree with their argument that nutritional policies should incorporate eating for pleasure and the enjoyment of food to promote healthy eating strategies.
The (positive and negative) associations highlighted by the participants also lead us to observe the same influence that food exerts on the nature of people’s emotional state in childhood as those noted in samples of young people and adults. These emotions are, among others, joy, satisfaction, sadness and anger (Ares et al., 2015; Bisogni et al., 2002; Desmet & Schifferstein, 2008; Hémar-Nicolas & Ezan, 2019; Macht et al., 2003), also showing that food does not affect children’s well-being only in nutritional terms.
This link between food and emotion can be used to guide strategies for promoting healthy eating in childhood, including fostering positive feelings when eating in order for children to associate food with a basic and enjoyable element in their lives. Paying more attention to children during meals to prevent them from throwing away food, and justifying the usefulness of following a balanced diet with a combination of foods that are not so much to their liking, can reduce the situations of physical and mental discomfort that some suffer and minimize feelings of anger or sadness.
One of the limitations of the present study is that the sample was formed of schools that expressed an interest in participating. It is therefore unknown whether non-collaborating schools had important differential characteristics with respect to the variables covered in this article. In the future, it would be good to collect data in a way that would increase the number of participating schools and allow for a longitudinal study combined with participant observation. It is worth noting that initially advising the discussion groups that the context of the talk was food, even if only through some very general instructions, may have resulted in the participants making much more extensive and in-depth reflections on the issues studied.
*They* generally felt free to express their opinions, and explicitly expressed satisfaction with being able to give their views. A friendly and familiar atmosphere was established, although on one occasion the group of students felt uncomfortable and unmotivated. The interview may have been badly timed in this case, as they were tired, and we did not get the project presentation right or clarify enough the importance of their participation.
As strengths of the study, we would emphasize that subjects assessing their own well-being allows us to ascertain accurate information regarding their beliefs and perceptions by providing important data for professionals working with children (nutrition educators and other professionals). This is because in order to reach children and promote healthy habits, we need to base our approach on scientific recommendations, taking into account the understanding, interest and assessments that children do about food. It is important to involve boys and girls in program planning so as to more accurately meet their needs and create more efficient interventions. Strategies for the promotion of healthy eating should be fostered, combining all elements identified as conditioning aspects of their intake, beyond health indications, to strengthen children’s interests with regard to food.
## Conclusions
Taken together, virtually all of the study participants established a relationship between their subjective well-being and food based on a multidimensional argument referring primarily to the issues of health, pleasure, emotions, empowerment and commensality (i.e., eating together). Therefore, it may be necessary to encourage healthy eating programs for children to improve health and well-being, especially if we consider that there are several daily opportunities to eat, and we know that eating becomes a significant moment to promote subjective well-being.
Data collection and analysis were performed meticulously in order to ensure the validity of the results. The comments by the groups varied; while some made numerous comments and important reflections, others gave fewer opinions with more superficial comments. However, overall we consider that the arguments used in the different groups coincided with the thinking of the majority of participants, indicating a shared and coherent view.
Although a quantitative approach would have allowed us to determine how important food is in children’s lives, it would not have been possible—as has been done here using discussion groups—to delve deeper into the reasons that make food more or less important for them (linked to the beliefs and attitudes they have towards food), what kind of relationship they maintain with food and with other people around food, and how they “negotiate” what they eat and in what quantity with the people in charge of their food intake.
In summary, we have seen that most of the boys and girls participating in the focus groups related food to subjective well-being, highlighting an important role of food in how they feel, mostly for physical reasons (health and growth), although they did also attribute a role to it in the emotional affectation of their well-being. That being said, in some groups there was some doubt about the existence of this relationship, the reason being that the way of living in this age group is independent from diet or eating habits.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
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|
---
title: 'Serum sclerostin in vascular calcification in CKD: a meta-analysis'
authors:
- Yan Lin
- Liman Mao
- Siqi Chen
- Canxin Zhou
journal: Renal Failure
year: 2023
pmcid: PMC10013495
doi: 10.1080/0886022X.2023.2186151
license: CC BY 4.0
---
# Serum sclerostin in vascular calcification in CKD: a meta-analysis
## Abstract
Vascular calcification (VC) is recognized as a predictor of all-cause and CVD mortality in chronic kidney disease (CKD). VC in CKD is possibly associated with serum sclerostin. The study systematically investigated the role of serum sclerostin in VC in CKD. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols, a systematic search was performed of the PubMed, Cochrane Library, and EMBASE databases from inception to 11 November 2022, to identify relevant eligible studies. The data were retrieved, analyzed, and summarized. The hazard ratios (HRs) and odds ratios (ORs) with their corresponding confidence intervals (CIs) were derived and pooled. Thirteen reports (3125 patients) met the inclusion criteria and were included. Sclerostin was associated with the presence of VC (pooled OR = 2.75, $95\%$CI = 1.81–4.19, $p \leq 0.01$) and all-cause mortality (pooled HR = 1.22, $95\%$CI = 1.19–1.25, $p \leq 0.01$) among patients with CKD, but with a decreased risk of cardiovascular events (HR = 0.98, $95\%$CI = 0.97–1.00, $$p \leq 0.02$$). This meta-analysis suggests that serum sclerostin is associated with VC and all-cause mortality among patients with CKD.
## GRAPHICAL ABSTRACT
## Introduction
Chronic kidney disease (CKD) is characterized by the gradual loss of kidney function and is a major global health concern with high morbidity and mortality [1]. CKD is strongly associated with an increased risk of cardiovascular disease (CVD)-related mortality [2] and is an independent risk factor for CVD [3].
Vascular calcification (VC) is a predictor of all-cause and CVD-related mortality in patients with CKD [4]. VC in patients with CKD is associated with several traditional and nontraditional risk factors of CVD, including mineral metabolism disorders, for which serum sclerostin is a marker [5,6]. Indeed, sclerostin is a key osteocyte-derived soluble inhibitor of the Wnt signaling pathway and an indicator of bone formation that can suppress osteoblast differentiation and proliferation and promote osteoblast apoptosis [7,8]. Serum or circulating sclerostin is significantly increased among uremic patients and might promote CKD progression [9,10]. Nevertheless, the reported role of serum sclerostin in VC in CKD is inconsistent among reports [11–13]. Some investigations showed positive correlations, whereas others suggested no or negative correlations.
Therefore, this study systemically investigatedand summarized the association of serum sclerostin with VC and outcomes in patients with CKD.
## Materials and methods
The study was performed and reported according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement [14].
## Information sources and search strategy
A systematic search for the serum levels of sclerostin was performed using the PubMed, Cochrane Library, and EMBASE databases on 11 November 2022. The individual and joint key search terms included ‘serum sclerostin’, ‘vascular calcification’, and ‘chronic kidney disease’. No language or publication date filters were applied. A complete overview of the search strategy in the three databases is presented in the Supplementary Materials.
## Eligibility criteria
Articles were included if they met the following criteria:Prospective and retrospective observational studies that evaluated the impact of serum sclerostin in vascular calcification in CKD;Contained sufficient information detailing study design, patient characteristics, and outcomes;Articles written only in English;If the population was reported in duplicate, only the study providing more detailed information was included.
Articles meeting any of the following criteria were excluded:Studies reporting based on cell lines or animals;Reviews, case reports, abstracts, or posters for conferences, personal opinions, or book chapters;
## Data extraction and analysis
Two authors independently and systematically screened the titles and abstracts of the identified publications. The same two authors carried out the full-text assessment of the eligible reports independently. The data were extracted by the two authors independently using a customized and standardized form. They resolved any disagreements through discussions. If the two authors did not agree, a third author made the final decision. For each included study, the following data were extracted: the author and year of publication, country, sample size, sclerostin, follow-up time, anatomical structures, assays for measuring sclerostin, and the outcomes or associations.
## Risk of bias and quality assessment of the selected studies
Only observational studies were included in this meta-analysis. The assessment of the study quality was performed using the Newcastle-Ottawa Scale (NOS) [15]. Two authors (CGP and FA) independently evaluated the included studies. Disagreements were resolved by discussion to produce final scores. Using this tool, three domains were assessed: [1] selection of study groups (four points); [2] comparability of groups (two points); [3] ascertainment of exposure and outcomes (three points) for case-control and cohort studies, respectively.
## Statistical analysis
Hazard ratios (HRs) and odds ratios (ORs) with their corresponding confidence intervals (CIs) were derived from each included study and combined after log-transformation using a random effect model. Because the exposure values were treated as different forms (continuous and categorical), the effect sizes were pooled separately for studies that analyzed sclerostin levels as continuous values and those that categorized the sclerostin levels (e.g. below vs. above a cutoff value). Cochran’s Q statistics and the I2 statisticswere calculated to assess heterogeneity among studies. For higher values of the I2 index (an I2 index of $50\%$ and $75\%$ corresponds to moderate and high heterogeneity, respectively), sensitivity analysis and meta-regression were performed to explore the potential correlations between the study outcomes and patient numbers or study design [16]. In the sensitivity analysis, the cause of the high heterogeneity was investigated by the leave-one-out method, which involves sequentially excluding each study, one by one, to determine whether a single study was responsible for the high heterogeneity. The Begg rank correlation [17] and Egger weighted regression methods [18] were used to assess the publication bias ($p \leq 0.05$ was considered indicative of a statistically significant publication bias). Comprehensive Meta-analysis version 3.0 was used to generate the forest plots and statistical analyses. The Begg and Egger tests were performed using STATA 15.0 (Stata Corporation, College Station, TX). A two-sided p-value <0.05 was considered statistically significant.
## Search results
The initial screening of the electronic databases yielded 289 articles; 81 were duplicates, and 39 were marked as ineligible by automation tools; hence, 169 titles or abstracts were evaluated. After retrieving and reviewing 62 full-text reports, 15 studies fulfilled the inclusion criteria and were included in this systematic review and meta-analysis (Figure 1) [11,13,19–31].
**Figure 1.:** *Flow chart of the study selection.*
## Characteristics of the included studies
An overview of the included studies and their characteristics is presented in Table 1. The 15 included studies were published between 2013 and 2021. A total of 3228 subjects were included. Most studies were based on either prospective or cross-sectional observational data. The studies were conducted in the USA, Netherlands, Turkey, Germany, Brazil, France, Sweden, China, Denmark, and Portugal. Sclerostin was measured using an enzyme-linked immunosorbent assay.
**Table 1.**
| Study | Country | Study design | Age (years) | Males (%) | Sclerostin | Enrolled patients | Follow-up time | Anatomical structures | Methods to detect VC |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Claes et al. 2013 [11] | Belgium | Cross-sectional | 59.7 ± 15 | 58.0 | 0.67 (0.3–0.8) ng/mL | 241 ND-CKD | | Coronary artery | Multidetector CT |
| Viaene et al. 2013 [19] | USA | Prospective | 68 ± 13 | 60.0 | 110 (82–151) pmol/L | 97 CKD3-4 patients | | Abdominal aortic and common iliac artery, coronary artery, carotid artery | Multislice CT |
| Drechsler et al. 2014 [22] | Germany | Prospective | 63 ± 14 | 44.0 | 1.24 ± 0.57 ng/mL | 89 renal transplant recipients | | Inferior epigastric artery | Biopsy and coronary artery CT |
| Gonçalves et al. 2014 [20] | Brazil | Prospective | 42.3 ± 18.8 | 55.0 | 0.88 (0.54–1.55) ng/mL | 161 CKD3-5 patients | | Abdominal aortic artery | Abdominal lateral X-ray |
| Kanbay et al. 2014 [21] | Turkey | Prospective | 47.0 (38.0 − 59.0) | 86.0 | 63.5 (55.3–78.9) pmol/L | 125 HD patients | 2 years | Aorta | Chest radiography |
| Morena et al. 2015 [23] | France | Cross-sectional | 69.0 (25.0–95.0) | 59.0 | 0.92 (0.30–3.11) ng/mL | 207 HD patients | | Aorta | Lumbar radiography |
| Qureshi et al. 2015 [13] | Sweden | Cross-sectional | 46 (24–62) | 63.0 | 440 (230–857) pg/mL | 268 prevalent RTRs | | Coronary artery and aorta | Multislice CT |
| Yang et al. 2015 [24] | Taiwan | Prospective | 56.9 ± 12.3/61.4 ± 9.9 | 49.0 | 73.1 ± 39.0 pmol/L | 154 CKD1-5 patients | | Aorta | Lumber X-ray |
| Evenepoel et al. 2015 [12] | Belgium | Prospective | 53,0 (12,8) | 61.0 | 0.84 (0.62–1.09) ng/mL | 350 HD patients | 4.4 years | Arteriovenous fistula | 64-detector CT scanner |
| Jean et al. 2016 [25] | Belgium | Prospective | 70.2 ± 14 | 43.0 | 1.9 ± 0.7 ng/mL | 396 HD/HDF patients | Median: 2 years | | |
| Kirkpantur et al. 2016 [26] | Turkey | Prospective | 52 ± 10/55 10 57 14 | 51.0 | 1519 ± 1378 pg/mL | 673 PD/HD patients | | Arteriovenous fistula | |
| Wang et al. 2017 [28] | China | Prospective | 58.3 ± 13.4 | 49.0 | 1007.62 ± 859.3 pg/mL | 91 HD patients | >10 years | | |
| Lips et al. 2017 [27] | Netherlands | Prospective | 64.1 (13.7) | 62.0 | 139 (100–183) pmol/L | 173 CKD3-5 (no dialysis) and 47 control patients | Median: 26 months | | |
| Jørgensen et al. 2018 [29] | Denmark | Prospective | 54 (45–63) | 68.0 | | 157 late-stage CKD who were kidney transplantation candidates | Median: 3.7 years | Coronary arteries, thoracic aorta, and the aortic and mitral valves | Dual-source CT |
| Zhao et al. 2020 [30] | China | Cross-sectional | 64 (51, 73) | 51.4 | 46.76 pmol/L (IQR 30.18–67.56 pmol/L) | 140 patients with stage 3–5 CKD | | Carotid artery | B-mode Doppler ultrasound |
| Neto et al. 2021 [31] | Portugal | Cross-sectional | 65.7 ± 9.8 | 78.6 | 2.32 ± 0.43 mg/dL | 56 CKD patients (no dialysis) | | Aortic wall calcification | Plain X-ray |
## Quality assessment of the studies
The NOS evaluation of the eligible studies is presented in Supplementary Table 1. All included studies had an acceptable quality. Six and nine studies were evaluated as 6 and 7 points, respectively.
## Relationship between sclerostin and VC
Five studies reported the association of sclerostin with VC, and three examined the association using the continuous values of sclerostin (Figure 2(a)). Concerning the pooled relationship between sclerostin and VC, sclerostin was not significantly associated with VC in continuous values (pooled OR = 1.03, $95\%$CI = 0.99–1.08, I2 = $57.0\%$), and the results were consistent in the sensitivity analysis after omitting each study sequentially (Supplementary Figure S1). For categorical analyses of serum sclerostin, the association between sclerostin and VC was also non-significant (pooled OR = 1.01, $95\%$CI = 0.19–5.35, I2 = $94.7\%$, Figure 2(b)).In the sensitivity analysis, the association became significant if the study by Kirkpantur et al.in 2016 [26] was excluded (Supplementary Figure S2). Since the outcome of interest was arteriovenous fistula calcification in the Kirkpantur et al. study[26], it was excluded,and the pooled OR was 2.16 ($95\%$CI = 1.18–3.94; Supplementary Figure S3).
**Figure 2.:** *Forest plots for summarized relationship between sclerostin and VC. (a) pooled ORs for sclerostin ascontinuous values; (b) pooled ORs for sclerostin in categorical analyses.* **Figure 3.:** *Forest plots for summarized relationship between sclerostin and cardiovascular events. (a) pooled HRs for sclerostin ascontinuous values; (b) pooled HRs for sclerostin in categorical analyses.*
## Relationship between sclerostin and cardiovascular events
The relationship between sclerostin and cardiovascular events is shown in Figure 3. Five studies reported the association, and two reported HRs using continuous values. When summarizing the HRs, the overall HR was 1.00 ($95\%$CI = 0.96–1.04) and 1.01 ($95\%$CI = 0.19–5.35) for studies that used continuous (Figure 3(a)) and categorical (Figure 3(b)) analyses, respectively, with significant heterogeneity (I2 = $94.7\%$). We used the leave-one-out method to assess the robustness of our results in the sensitivity analysis (Supplementary Figure S4). When the study by Kanbay et al. [ 21] was excluded, a decreased heterogeneity was observed (I2 = $0\%$), and serum sclerostin was associated with the risk of cardiovascular events (pooled HR, 0.34; $95\%$ CI, 0.20–0.57). The forest plot without Kanbay et al. [ 21] is presented in Supplementary Figure S5.
**Figure 4.:** *Forest plots for summarized relationship between sclerostin and all-cause mortality.*
## Relationship between sclerostin and all-cause mortality
The relationship between sclerostin and all-cause mortality is presented in Figure 4. Six studies reported arelationship with HRs ranging from 0.33 to 2.20, and all these studies reported the associations using categorical analyses. When summarizing HRs, the pooled results showed that sclerostin was not associated with all-cause mortality (pooled HR = 0.71, $95\%$CI = 0.42–1.19, $p \leq 0.01$), with significant heterogeneity (I2 = $81.0\%$). When the study conducted by Gonçalves et al. [ 20] was excluded from the sensitivity analysis (Supplementary Figure S6), a decreased heterogeneity was seen (I2 = $0\%$). The forest plot omitting Gonçalves et al. [ 20] is presented in Supplementary Figure S7, and serum sclerostin was associated with all-cause mortality (pooled HR, 0.59; $95\%$ CI, 0.46–0.76).
## Publication bias
The analysis did not observe potential publication bias among the included trials according to Begg rank correlation analysis and Egger weighted regression analysis ($p \leq 0.050$) (Supplementary Table S2).
## Discussion
To the authors’ knowledge, the present study is the first systematic review and meta-analysis study summarizing the association of serum sclerostin with VC and outcome in CKD patients. Thirteen studies (3125 subjects) were included and analyzed. The results suggest that serum sclerostin is associated with VC and all-cause mortality in patients with CKD. Increased serum sclerostin levels appear to be associated with decreased CVD events.
The classification of CKD was developed by the National Kidney Foundation Kidney Disease Outcomes Quality Initiative and is divided into five stages based on the levels of kidney function [32], which are determined by calculating the estimated glomerular filtration rate. VC was found to occur frequently in patients with CKD, and the incidence increases from CKD stages I to V [33], The mortality in the advanced stages of CKD remains unacceptably high, with large numbers of deaths caused by cardiovascular failure or dysfunction [34], The comorbidities of CKD include abnormal conditions of mineral bone metabolism and ectopic calcification, particularly VC, which is associated with an increased risk of CVD and all-cause mortality [35].
Sclerostin is a secreted glycoprotein possessing a C-terminal cysteine knot-like (CTCK) domain and sequence similarity to the differential screening-selected gene aberrative in neuroblastoma (DAN) family of bone morphogenetic protein (BMP) antagonists. Sclerostin is produced primarily by osteocytes but is also expressed in other tissues [36] and has anti-anabolic effects on bone formation [37]. The development of CVD involves many steps and factors, but one of the crucial events is the transdifferentiation of the vascular smooth muscle cells (VSMCs) [38], and transdifferentiated VSMCs can actively deposit hydroxyapatite in the medial layers of arteries, participating in the development of VC [39]. The Wnt/β-catenin and PPARγ pathways are involved in the fate of the transdifferentiated VSMCs [40,41]. Sclerostin is an inhibitor of the canonical Wnt/β-catenin pathway in the bone formation process and can induce adipocyte differentiation [42]. Some studies showed decreased mortality with high sclerostin levels in patients on hemodialysis [19,27]. On the other hand, although sclerostin expression was once thought to be confined to osteocytes, it is now known that sclerostin can be expressed by VSMCs adjacent to VC areas [43–45]. In addition, patients with CKD and VC have higher sclerostin levels than those without VC [11,43,44].Furthermore, sclerostin is eliminated by the kidney, and decreased kidney function will increase sclerostin levels [46]. Therefore, excess sclerostin produced in vessels with VC might prevent the progression of VC [47]. Excess sclerostin has also been suggested to be involved in the progression of CKD and related bone mineral disorders, leading to worse patient outcomes [19,48]. These hypotheses could explain the direct association between sclerostin and the presence of VC, as well as the inverse association between sclerostin levels and CVD events observed in the present study. Still, a study of patients without CKD revealed that sclerostin levels were directly associated with CVD mortality [49]. In addition, sclerostin, which was previously known as a biomarker of bone formation [50], should be paid more attention to by researchers and clinicians for its association with VC and all-cause mortality in patients with CKD.
CKD is incurable and there is a residual risk of adverse events and deterioration. This study, therefore, highlighted the meaning of identifying risk factors of CKD progression and mortality risk. The results also indicate a new insight into the management of CKD and the awareness of serum sclerostin, which might provide evidence to delineate the mechanisms of the stage progression of CKD. Nevertheless, there should be sufficient information about the role of serum sclerostin in VC in patients with CKD, and rigorous validation and demonstration of reproducibility in an independent population are necessary to confirm the impact.
A strength of this study is that it is the first systematic assessment of the association of serum sclerostin with VC and outcome in patients with CKD. Still, it is necessary to consider the limitations of the present meta-analysis while interpreting the results. First, all included studies were observational that inevitably suffered from selection bias, recall bias, no randomization, and often no blinding of the data assessors. Second, limited by the information provided by the included studies, it was not possible to analyze the association of serum sclerostin with VC and outcome according to the CKD stages. Third, the pooled results were limited by the available data and the small number of included studies. The results might be affected by several factors, such as the aging proportions of diabetes cases, and the representativeness might be weakened. Fourth, due to the insufficient information in each study, subgroup analyses could not be performed. Fifth, a potential language bias might exist because the literature search included only articles published in English. Sixth, publication bias cannot be assessed for all analyses because of the small number of studies in some analyses. Seventh, there was significant heterogeneity among studies regarding CKD stage, analyzed vasculature, and VC assessment methods. Finally, this meta-analysis was not registered.
In conclusion, based on the pooled results of 13 studies and 3125 subjects, this meta-analysis suggests that serum sclerostin levels might be associated with VC and all-cause mortality in patients with CKD. Still, considering the heterogeneity observed in analyses, these results must be taken with caution. Carefully designed and performed studies are necessary to confirm these results.
## Ethical approval
This article is a meta-analysis. The data comes from published articles and does not require ethical approval.
## Author contributions
Conceptualization: Canxin Zhou; Data curation: Yan Lin, Liman Mao; Formal analysis: Yan Lin; Investigation: Yan Lin, Liman Mao; Methodology: Yan Lin, Canxin Zhou; Software: Yan Lin; Supervision: Canxin Zhou; Validation: Canxin Zhou; Roles/Writing – original draft: Yan Lin, Siqi Chen; Writing – review &editing: Yan Lin, Canxin Zhou. All authors have read and approved the content of the manuscript.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
## Data availability statement
All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.
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|
---
title: 'Prediction of the mechanisms of action of Qutan Huoxue decoction in non-alcoholic
steatohepatitis (NASH): a network pharmacology study and experimental validation'
authors:
- Xia Wu
- Yurong Zhang
- Ding Zheng
- Yue Yin
- Mengyun Peng
- Jing Wang
- Xiaoning Zhu
journal: Pharmaceutical Biology
year: 2023
pmcid: PMC10013566
doi: 10.1080/13880209.2023.2182892
license: CC BY 4.0
---
# Prediction of the mechanisms of action of Qutan Huoxue decoction in non-alcoholic steatohepatitis (NASH): a network pharmacology study and experimental validation
## Abstract
### Context
Qutan Huoxue decoction (QTHX) is used to treat non-alcoholic steatohepatitis (NASH) with good efficacy in the clinic. However, the mechanism is not clear yet.
### Objective
This study investigates the mechanism of QTHX in the treatment of NASH.
### Materials and methods
Potential pathways of QTHX were predicted by network pharmacology. Fourty Sprague Dawley (SD) rats (half normal diet, half high-fat diet) were fed six to eight weeks, primary hepatocytes and Kupffer cells were extracted and co-cultured by the 0.4-micron trans well culture system. Then, the normal co-cultured cells were treated by normal serum, the NASH co-cultured cells were treated with various concentrations of QTHX-containing serum (0, 5, 7.5 or 10 μg/mL) for 24 h. The expression of targets were measured with Activity Fluorometric Assay, Western blot and PCR assay.
### Results
Network pharmacology indicated that liver-protective effect of QTHX was associated with its anti-inflammation response, oxidative stress, and lipid receptor signalling. 10 μg/mL QTHX significantly reduced the inflammation response and lipid levels in primary hepatocytes (ALT: 46.43 ± 2.76 U/L, AST: 13.96 ± 1.08 U/L, TG: 0.25 ± 0.01 mmol/L, TC: 0.14 ± 0.05 mmol/L), comparing with 0 μg/mL NASH group (ALT: 148 ± 9.22 U/L, AST: 53.02 ± 2.30 U/L, TG: 0.74 ± 0.07 mmol/L, TC: 0.91 ± 0.07 mmol/L) ($p \leq 0.01$). Meanwhile, QTHX increased expression of SOCS1 and decreased expression of TLR4, Myd88, NF-κB.
### Conclusions
The study suggested that QTHX treats NASH in rats by activating the SCOS1/NF-κB/TLR4 pathway, suggesting QTHX could be further developed as a potential liver-protecting agent.
## Introduction
Non-Alcoholic Fatty Liver Disease (NAFLD) is an obesity-related metabolic liver disorder with an estimated global prevalence of $24\%$ (Younossi et al. 2018; Huang et al. 2021). Approximately $20\%$ of patients with NAFLD progress to nonalcoholic steatohepatitis (NASH) (Li et al. 2019). NASH is defined by steatosis and inflammation with hepatocellular injury, inflammatory cells infiltration, hepatocyte ballooning degeneration with or without fibrosis and deteriorates to decompensated cirrhosis (DCC) and hepatocellular carcinoma (HCC) and may ultimately require liver transplantation (LT) (Mazhar 2019). The development of NASH is closely related to diabetes, obesity, hypertension, hyperlipidaemia, and metabolic syndrome (Sumida and Yoneda 2018). These metabolic-related diseases are also considered the main risk factors for NASH (Younossi et al. 2016; Oseini and Sanyal 2017). Therefore, it is important to find therapeutic ways to prevent and treat NASH (Pfister et al. 2021). Traditional Chinese medicine (TCM) is an important alternative medicine for many diseases and it has been developed over thousands of years with a unique system of theories, diagnostics, and therapies. The Qutan Huoxue decoction (QTHX) is mainly composed of *Pinellia ternata* (Thunb.) Breit (Araceae) (stem), *Citrus sinensis* (L.) Osbeck (Rutaceae) (peel), *Salvia miltiorrhiza* Bunge (Rutaceae) (peel), Coix lacryma-jobi L.var.mayuen (Roman.) Stapf (Poaceae) (seed), *Bupleurum scorzonerifolium* Willd (Apiaceae) (root), Alisma plantago-aquatica Linn. ( Alismataceae) (stem and root), *Cassia tora* Linn. ( Leguminosae) (seed), Poria cocos (Schw.) Wolf, (Polyporaceae) (Sclerotium), and *Scutellaria baicalensis* Georgi (Lamiaceae) (root). Our previous study has randomized 66 NASH patients into two groups for three months of therapy: QTHX (30 g/day) $$n = 33$$, and polyene phosphatidylcholine (784 mg/day) $$n = 33$$, the result suggested that QTHX appears to ameliorate the metabolic asset of mild NASH patients (Zheng et al. 2019). Previous animal studies showed that QTHX can significantly improve insulin resistance, hepatocyte injury, and liver fibrosis in NAFLD rats induced by a high-fat diet al.so, studies demonstrated the effect of the QTHX on expression of PPAR-α/CTP1, p38MAPK, AQP9, and SOCS1/TLR4/NF-κB pathway (Zheng et al. 2019; Zhang Y. et al. 2020), and others. Our study identified and verified the signalling pathway of QTHX treatment by merging pathological characteristics of NASH. The traditional Chinese medicine formula contains multiple herbal ingredients and these bioactive components target multiple genes and proteins. The latest research showed that suppressor of cytokine signalling-1 (SOCS1) deficiency increases macrophage infiltration (Ilangumaran et al. 2017). Thus, we speculate that QTHX may influence the disease progression of NASH by regulating the function of macrophages. In this study, a comprehensive method was used to illustrate the molecular mechanisms of QTHX. The primary hepatocytes and primary Kupffer cells were isolated and co-cultured from NASH model rats to establish an in vitro model of NASH, and systematically explore the mechanism of QTHX to treat NASH.
## QTHX preparation
QTHX was prepared by The Affiliated TCM Hospital of Southwest Medical University. The quality of the herbs and herbal extracts was consistent with the standards of Chinese Pharmacopoeia. The medicinal materials were identified by the Professor Jing Wang and Dr. Xiaoning Zhu. Voucher specimens were prepared for identification and deposited in laboratory of Integrated Traditional Chinese and Western Medicine of The Affiliated TCM Hospital of Southwest Medical University. Medicines were prepared by boiling water extraction, decompression, concentration, and distillation filtration. The quality of QTHX was confirmed by ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS).
## Analysis of QTHX by network Pharmacology
All compounds of the Chinese medicinal herbs in QTHX were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database (https://old.tcmsp-e.com/). The TCMSP database provides the pharmacokinetic properties of the QTHX chemical compound. Our study meets the requirements of oral bioavailability (OB) ≥ $30\%$ and drug-likeness (DL) index ≥ 0.18. The NASH-associated genes targets were retrieved from the GeneCards, OMIM, PharmGkb, Therapeutic target, and DrugBank database and the proteins were retrieved from UniProt Knowledgebase (UniProtKB). *The* gene and drug target networks were constructed using Cytoscape. Based on the screened potential therapeutic targets, pathway and functional enrichment analysis of QTHX against NASH was analysed by GO and KEGG using R 3.6.1 BiocManager, Compound-target (C-T), compound-target-pathway (C-T-P) networks were constructed using the Cytoscape 3.6.0 software.
## Animals
Forty SPF (specific-pathogen-free) male Sprague Dawley (SD) rats (180 ± 20 g of body weight) were provided by the Experimental Animal Center of Southwest Medical University (SYXK (Sichuan) 2020-065). All rats were kept at 20 ± 2°C with a 12 h light/dark cycle under specific pathogen-free conditions. Rats were randomly divided into the NASH and the control group. The NASH group rats were fed a high-fat diet ($88\%$ basic diet + $10\%$ lard + $2\%$ cholesterol, provided by Chengdu Dashuo Experimental Animal Company), and the control group was fed an ordinary diet (provided by Experimental Animal Center of Southwest Medical University). After six to eight weeks, two rats were randomly sacrificed from each group, for scoring the pathological changes in liver tissue according to the NASH scoring system (Table 1). All animal experiments were carried out by the guidelines of the Guide for the Care and Use of Laboratory Animals of the Institutional Animal Care and Use Committee (IACUC) set by The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University. The protocols were approved by the Animal Care and Use Committee of the Southwest Medical University (Luzhou, China) (protocol number: 20210218-006). Pentobarbital sodium was used as an anaesthetic to minimize pain during all procedures.
**Table 1.**
| Score | Fatty change (0–3) | Inflammation in the lobules (0–3) | Ballooning change (0–2) |
| --- | --- | --- | --- |
| 0 | <5% | | |
| 1 | 5%–33% | <2 | rare |
| 2 | 34%–66% | 2–4 | more common |
| 3 | >66% | >4 | – |
## Animal preparation
The methods of animal preparation and collagenase in situ liver perfusion were described by Peng et al. [ 2017].
## Isolation of primary hepatocytes and KCs
The liver was collected and carefully transferred into a 10 cm culture dish. It was minced into small pieces by scissors, after digestion, the cell suspension was filtered through a 70 μm tissue filter into a 50 mL centrifuge tube, and centrifuged at 50 g for 3 min to obtain the supernatant and pellets. The pellets were resuspended with a complete medium ($10\%$ foetal bovine serum, $1\%$ penicillin solution, 500 U/L insulin, 10-7 M hydrocortisone, DMEM high glucose medium), and $50\%$ Percoll was added (Solarbio, P8370-100 mL), centrifuged at 100 g for 5 min, discarded the supernatant. The cells were suspended in the complete medium and seeded into a cell culture dish, incubated at 37 °C in an atmosphere of $5\%$ CO2-$95\%$ air. The medium was changed for the first time after 6 h. The obtained supernatant was centrifuged at 100 g for 5 min to obtain pellets, the supernatant was discarded, the pellet was resuspended, $50\%$ Percoll was added slowly to the first layer, $25\%$ Percoll dispersion to the second layer, and cell suspension to the third layer, centrifuged at 300 g for 15 min. The liquid was divided into four layers, the KCs located between $50\%$ Percoll dispersion and $25\%$ dispersion. The obtained KCs were resuspended in the DMEM media. The primary KCs were washed 3 times and the media was replaced after 2 h. Primary hepatocytes were identified by the anti-Cytokeratin 18 antibody (Abcam, ab133263) and microscopic morphological observation. KCs were identified by the CD68 Antibody (Santa Cruz Biotechnology, sc-17832) and ink swallowing experiment. Primary hepatocytes were co-cultured with primary KCs in a 0.4-micron trans well chamber. The upper chamber was inoculated with primary hepatocytes, and the lower chamber was inoculated with KCs. After 24 h, the cells were collected and culture fluid. In addition, the ratio of primary hepatocytes: primary Kupffer cells = 6:1 was used for co-cultivation.
## Cytotoxicity assay
QTHX-containing serum (QTHX-CS) was prepared according to the previously described method (Liu et al. 2017). Co-cultured cells were seeded into 96-well plates and treated with various concentrations of QTHX-CS (100, 80, 60, 40, 20, 10, 7.5, 5, 0 μg/mL) from the second day for 48 h. Cytotoxicity assay was performed using the Cell Counting Kit-8 (CCK8) (Dojindo, CK04) following the manufacturer’s instruction.
## Oil red O staining
The lipid deposition of primary hepatocytes was identified by oil red O staining (Solarbio, G1261-2): the culture medium was discarded and the cells were washed with PBS 1 ∼ 2 times, and they were fixed with $10\%$ neutral formaldehyde for 30 min, stained with oil O red for 10 min. Next, $60\%$ isopropanol was used for decolorization, followed by haematoxylin staining for 2–5 min, and the cells were viewed under a microscope. The isopropanol decolorization was used to quantitatively analyse the oil red O staining with a microplate reader.
## Liver function analysis
The contents of aspartate aminotransferase (AST), alanine aminotransferase (ALT), total cholesterol (TC) and triacylglycerol (TG) in primary hepatocytes were determined by using Activity Fluorometric Assay kit (Nanjing Jian Cheng Institute of Bioengineering, C 009-2, C 010-2) following the manufacturer’s instruction.
## Immunofluorescence staining
Cells were harvested and cytospined onto slides, washed twice with PBS, fixed with $10\%$ neutral formaldehyde for 20 min, permeabilized with $0.2\%$ Triton X-100 for 10 min, and blocked with $10\%$ foetal bovine serum for 30 min. Then, the cells were incubated overnight with a rabbit anti-SOCS1 monoclonal antibody (Abcam, ab280886, 1:200 dilution) at 4 °C. Goat Anti-Rabbit IgG H&L (Alexa Fluor® 488) (Abcam, ab150077) was incubated in the dark after 1 h, DAPI (Solarbio, C0060-1) stained for 10 min, washed with PBS and observed under a fluorescence microscope.
## Real-time PCR
Total RNA from cultured cells was isolated using a Chloroform-free RNA extraction kit (BioTeke, RP55011) following the manufacturer’s instruction. Purified RNA was reversely transcribed into cDNA using the QuantiTect Reverse Transcription Kit (Qiagen, 208054) according to the manufacturer’s instructions. Quantitative real-time PCR was performed according to the kit instructions, β-actin was used as an internal reference, and the comparative cycle threshold method (CT) was used to detect the relative target gene mRNA expression. The primer sequences are shown in Table 2.
**Table 2.**
| TLR4 | F | TCCCTGCATAGAGGTAGTTCC |
| --- | --- | --- |
| TLR4 | R | TCAAGGGGTTGAAGCTCAGA |
| NF-kB | F | CCACTCTGGCGCAGAAGTTA |
| NF-kB | R | CCCCCAGAGACCTCATAGTTG |
| Myd88 | F | TCGACGCCTTCATCTGCTAC |
| Myd88 | R | CCATGCGACGACACCTTTTC |
| SOCS1 | F | CACGCACTTCCGCACATTCC |
| SOCS1 | R | TCCAGCAGCTCGAAGAGGGA |
| β-actin | F | CCCATCTATGAGGGTTACGC |
| β-actin | R | TTTAATGTCACGCACGATTTC |
## Western immunoblotting
Cells were lysed on ice for 30 min. The lysate was separated by SDS-PAGE and transferred to the PVDF membrane. Cells were incubated with antibodies (TLR4, myd88, NF-κB, SOCS1, GAPDH, 1:1000) at 4 °C overnight. Next, they were incubated with an anti-rabbit or an anti-mouse antibody (1:5000). Chemiluminescence system was enhanced to detect protein bands, and the results were analysed by ImageJ 1.51 software.
## Statistical analysis
All data were analysed by GraphPad Prism 8.0. Values in the text are presented as means ± standard deviations (x ± s). Comparison among the three groups was performed by analysis of variance and pairwise comparison by the Shapiro–Wilk test. $p \leq 0.05$ indicates that the difference is statistically significant.
## Identification of the active compounds in QTHX
147 compounds and 1401 related targets were retrieved from the TCMSP database, as shown in Table 3, 414 known therapeutic targets for NASH were collected from the Genecards, OMIM, PharmGkb, TTD, and DrugBank databases. NASH treatment targets were mapped to ten drug targets in QTHX using the Venn R package (version: V3.6.1) to construct a Venn diagram, showing 38 common therapeutic targets of QTHX therapy NASH.
**Table 3.**
| Chinese name | English name | Proportion (g) | Retrieve active ingredient | Related targets |
| --- | --- | --- | --- | --- |
| Ban Xia | Rhizoma Pinelliae | 10 | 13 | 172 |
| Chen Pi | Pericarpium Citri Reticulatae | 10 | 5 | 95 |
| Dan Shen | Radix Salviae Miltiorrhizae | 15 | 15 | 95 |
| Yu Jin | Radix Curcumae | 15 | 15 | 76 |
| Yi Yiren | Coix Seed | 15 | 9 | 48 |
| Chai Hu | Radix Bupleuri | 10 | 15 | 173 |
| Ze Xie | Oriental Waterplantain Rhizome | 15 | 10 | 24 |
| Jue Mingzi | Cassia Seed | 10 | 14 | 181 |
| Ful In | Poria | 10 | 15 | 30 |
| Huang Qin | Radix Scutellariae | 10 | 36 | 507 |
## GO and KEGG analyses of target genes
The disease-drug-ingredient-target interaction network was obtained from drug networks and it was constructed by Perl script and Cytoscape software (Figure 1(A)). The protein-protein interaction (PPI) network was illustrated based on the QTHX targets on NASH with the STRING database (Figure 1(B)) and the PPI core network (Figure 2(A)), was settled at 10 hub nodes, and 35 edges, which includes MAPK14, AKT1, FOX, HIF1A, RELA, JUN, TP53, MAPK3, CDKN1A, MAPK1. Then, metascape was used for GO and KEGG analyses of the 10 genes to obtain enriched ontology clusters (Figure 2(B,C)), the functional enrichment mainly included oxidative stress, membrane raft, membrane microdomain, membrane region, nuclear receptor activity, etc. The signalling pathway enrichment mainly included lipid and atherosclerosis, chemical carcinogenesis-receptor activation, human cytomegalovirus infection, etc. Therefore, it is speculated that the molecular mechanism of QTHX in the treatment of NASH may be related to inflammation and lipid deposition by targeting RELA, so the signalling pathway TLR4/NF-κB related to RELA was used to explore the specific mechanism of QTHX in the treatment of NASH.
**Figure 1.:** *Disease-drug-ingredient-target network and PPI.* **Figure 2.:** *PPI core network and enrichment analysis. (A) PPI core network. (B) KEGG enrichment analysis. (C) GO enrichment analysis.*
## Isolation and co-culture of NASH primary hepatocytes and KCs
To explore the specific mechanism of QTHX in the treatment of NASH, the primary Kupffer cells and primary hepatocytes were extracted and co-cultured from the NASH rats that have been fed a high-fat diet. The HE staining showed that the cells in the control group were intact, the hepatocytes were neatly arranged, and no fatty vacuoles were seen in the cells. However, in the NASH group, the hepatocytes were incomplete and not neatly arranged. The different sizes of cells were swollen and large vacuoles of triglyceride fat accumulated were found in the cells (Figure 3(A)), after NASH scoring, fatty degeneration score + intralobular inflammation score + ballooning score ≥4 points (Figure 3(B)). In this study, collagenase in situ perfusion was used to separate primary hepatocytes and primary KCs of rats (Figure 3(C)). This method could obtain primary hepatocytes about 1 ∼ 2 × 107–8 cells/mL and primary KCs about 1 ∼ 2 × 105–6 cells/mL. Observing the cells under an inverted optical microscope, the shape of primary hepatocytes was like irregular paving stones, KCs were seen to be round, or irregular in shape. The monoclonal antibody CK-18, identified that the primary hepatocytes showed positive expression, fluorescence evenly distributed in the cytoplasm, and no expression of other non-hepatic parenchymal cells was reported. In the ink swallowing experiment, KCs swallowed the ink and then turned black and the monoclonal antibody CD68 identified a positive expression (Figure 3(D,E)).
**Figure 3.:** *Isolation and co-culture of NASH primary hepatocytes and KCs. (A) the control group and NASH group HE staining of liver sections. (B) NASH score. (C) cell extraction process. (D–E) Under microscope, CD68 immunofluorescence images, ink swallowing experiment of rat primary KCs and under microscope, CK-18 immunofluorescence images of primary hepatocytes. ***p < 0.001.*
## QTHX regulates the TLR4/NF-κB pathway in vitro
The QTHX regulation of the TLR4/NF-κB pathway was investigated in vitro. QTHX-CS was added to a co-culture system of primary hepatocytes and primary Kupffer cells to evaluate the cytotoxicity by the CCK8 cytotoxicity assay and it has been found that 10 μg/mL QTHX-CS was not toxic to cells. So, the 10 μg/mL QTHX-CS concentration was considered the best drug concentration for the experiment. 0, 5, 7.5, 10 μg/mL QTHX-CS drug concentration were set up as NASH group (NASH), low-dose group (low), middle-dose group (middle), and high-dose group (high) respectively.
The results showed that TLR4, NF-κB, and Myd88 proteins were up-regulated in NASH rat primary hepatocytes, and the QTHX down-regulated the TLR4, NF-κB, and Myd88 proteins (Figure 4(A)), and the qPCR results were consistent with the protein results (Figure 4(B)), indicating that QTHX can inhibit the TLR4/NF-κB cell signalling pathway. At the same time, oil red O staining revealed obvious fat droplets in the cytoplasm of primary hepatocytes in the NASH group compared with the normal control group, but QTHX reduced the fat deposition of primary hepatocytes (Figure 4(C,D)). The secretion of ALT, AST, TC, and TG in primary hepatocytes were tested. Compared with NASH group (ALT: 148 ± 9.22 U/L, AST: 53.02 ± 2.30 U/L, TG: 0.74 ± 0.07 mmol/L, TC: 0.91 ± 0.07 mmol/L), the QTHX high dose group showed a significant decrease (ALT: 46.43 ± 2.76 U/L, AST: 13.96 ± 1.08 U/L, TG: 0.25 ± 0.01 mmol/L, TC: 0.14 ± 0.05 mmol/L, $p \leq 0.01$) (Figure 4(E)). These findings confirmed that the QTHX significantly improved the primary hepatocytes’ damage and lipid droplets got smaller, and TLR4/NF-κB pathway may be involved in the anti-inflammatory response of QTHX in vitro.
**Figure 4.:** *QTHX regulates the SOCS1/TLR4/NF-κB pathway in vitro. (A, B) the WB and PCR results of TLR4, Myd88, and NF-κB in primary hepatocytes (C: control group, N: NASH group, L: low group, M: middle group, H: high group). (C, D) Oil red O staining and Oil red O staining quantitative analysis. (E) AST, ALT, TG, and TC in the primary hepatocytes. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05.*
## Transcriptional regulation of RELA
To further elucidate the underlying potential mechanism of the role of RELA in the occurrence and development of NASH, the online JASPAR database (https://jaspar.genereg.net/) was used to identify potential target genes. It was founded that the region of the RELA promoter has predicted binding sites for SOCS1. Interestingly, according to the PPI results from the STRING database, SOCS1 was predicted to have a highly reliable interaction with a series of inflammation proteins, such as RELA, JAK1, and MAPK (Figure 5(B,D)). To validate the predicted results and the hypothesized pathway, WB was used, to confirm the expression of SOCS1 in primary hepatocytes and Kupffer cells. Protein changes in SOCS1 in primary hepatocytes and Kupffer cells from NASH and the control group after 6, 24, and 48 h was recorded. It was founded that SOCS1 was not detected in primary hepatocytes after 6 and 48 h, although the level of SOCS1 protein was not statistically significant in the treatment and control in 24 h post-treatment ($p \leq 0.05$) (Figure 5(A)); The level of SOCS1 protein in NASH primary Kupffer cells was significantly decreased (Figure 5(C)), revealing that KCs play a major role in mediating the SOCS1/TLR4/NF-κB immune-inflammatory pathway.
**Figure 5.:** *RELA is transcriptionally regulated by SOCS1 (A) The SOCS1 protein in the primary hepatocytes. (B) The recognition motif of RELA from the JASPAR database. (C) The SOCS1 protein in the primary Kupffer cells. (D) PPI network from STING database. **** p < 0.0001, *** p < 0.001, and ns p > 0.05.*
## TLR4/NF-κB signalling and SOCS1 expression
It is well known that TLR4/NF-κB signalling-mediated Kupffer cell activation plays an important role in the development of NASH. In this study, the probable influence of TLR4/NF-κB signalling on SOCS1 activation in Kupffer cells was studied. By incubating primary Kupffer cells with SOCS1 siRNA, it was observed that SOCS1 siRNA reduced SOCS1 protein as well as mRNA concentration, as documented by immunofluorescence and qPCR analyses (Figure 6(A,B)). In parallel, the effect of SOCS1 siRNA on Kupffer cell TLR4/NF-κB expression was analysed, and it was found that the expression of TLR4, Myd88, NF-κB was significantly increased ($p \leq 0.05$) (Figure 6(C,D)), indicating that SOCS1 has a negative feedback regulation effect on TLR4/NF-κB signalling. After the treatment of QTHX (10 μg/mL), the expression of TLR4, Myd88, and NF-κB was significantly reduced ($p \leq 0.05$), and the analysis showed that QTHX treatment increased the expression of SOCS1 in Kupffer cells.
**Figure 6.:** *TLR4/NF-κB signalling regulates SOCS1 expression in Kupffer cells. (A, B): the WB, qPCR, and immunofluorescence of SOCS1 siRNA treatment of primary KCs. (C, D) when SOCS1 was silenced, the expression of TLR4, Myd88, NF-κB. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05, and ns p > 0.05.*
## Discussion
Traditional Chinese herbal medicine (TCM) has been proven to be effective in many diseases and disorders in clinical practice, but the pharmacological mechanisms remain to be elucidated (Mridha et al. 2017; Zhang CH. et al. 2020; Zhang Y. et al. 2020). The research on the mechanism of Chinese medicine herbal is complicated, and we still don’t have the mature tools to explore the mechanisms of the efficacy of Chinese medicine herbal (Chen et al. 2020). Therefore, by conducting a network pharmacology (NP) analysis, it was found that the potential target might be the key to better to study the synergy between the individual compounds in a TCM to determine the combination of active compounds that is most biologically effective. ( Liang et al. 2021). NP is an innovative strategy of picking specific signal nodes through the network analysis of biological systems in a network database to make a targets-drug-diseases molecular network based on the theory of systems biology. It is also an emerging interdisciplinary subject with great advantages in interpreting the pharmacologic mechanisms of TCM with multiple components, targets, and pathways. It is extremely consistent with the theoretical thought of the holistic philosophy of TCM (Nogales et al. 2022). In this study, NP was analysed to find the mechanisms of action of QTHX-treated NASH that mainly involves oxidative stress, membrane raft, membrane microdomain, membrane region, nuclear receptor activity, etc. The mitochondria, the site of cellular energy synthesis, regulate oxidative stress, and plays a key role in cell death (Drake et al. 2003). Therefore, protection by antioxidants against oxidative stress to mitochondria may prove to be beneficial in delaying the onset or progression of diseases (Mancuso et al. 2006). Interestingly, in this network, pharmacology analysis found that the core targets of QTHX in the treatment of NASH include RELA gene, and its function related to lipid metabolism and immune response in liver. This result agrees with the mechanism of our previous study that QTHX can reduce the immune response by regulating the SOCS1/TLR4/NF-κB cell signalling pathway in NASH (Zhang Y. et al. 2020). So, NP gives us a chance to find and verify the key targets for explaining how QTHX regulated the SOCS1/TLR4/NF-κB cell signalling pathway to treat NASH.
The liver is a very complex organ consisting of hepatic parenchymal cells (HPCs) and non-parenchymal cells, including hepatocytes, liver sinusoidal endothelial cells (LSECs), Kupffer cells (KCs), and hepatic stellate cells (HSCs). Increasing evidences suggest that cell-to-cell signal transduction played a vital role in NASH progression and regression (Kazankov et al. 2019; Li et al. 2020). In NASH animal models, the activated KCs with large lipid droplets often gather around hepatocytes and induce the production of inflammatory factors in the case of lipid overload, KCs can make intercellular communication between hepatic cells in liver diseases, it has been demonstrated that KCs also regulate their apoptosis (Yuan et al. 2017). KCs is a macrophage, playing an important regulatory role in liver inflammation, insulin resistance, and fatty liver diseases. Also, research found that the cell structure in NASH patients has undergone significant changes, and KCs are privileged first responder cells in hepatocyte proliferation and liver degeneration (Xiong et al. 2019). The activation of KCs alters hepatic inflammatory cells and chemokine recruitment (Tosello-Trampont et al. 2012), and then activates hepatocytes TLR4/NF-κB inflammation signalling pathways and promotes liver inflammation and fibrosis (Zhang Y. et al. 2020). The inhibition of the inflammatory response of KCs contributes to the treatment of NASH (Mridha et al. 2017). Therefore, in this study, the primary hepatocytes, and KCs were co-cultured to establish a NASH cell model in vitro. At present, there are many methods for isolating hepatocytes, such as the liberase-based perfusion technique in combination with low-speed centrifugation and magnetic-activated cell sorting (MACS) was used to isolate and purify HCs, KCs, and LSECs (Liu X et al. 2017). However, many studies chose to use in situ perfusion to separate hepatocytes (Mohar et al. 2015; Aparicio-Vergara et al. 2017). In this study, collagenase in situ perfusion was used to separate primary hepatocytes and KCs in one step, and the NASH cell model of primary hepatocytes and KCs were successfully established and prepared for exploring mechanism of action.
SOCS1, the most potential member of the SOCS family, can be expressed as a negative regulator that binds to multiple signalling proteins or in a secreted form possessing unique immune suppressive functions (Liau et al. 2018). Research showed that genetic deletion of SOCS1 leads mice to die from inflammation and liver necrosis, in addition, SOCS1 is a potent inhibitor of many cytokines such as TLR ligands, INF-γ, IL-1β, IFNα/β/γ can regulate the inflammation and immune responses (Liu et al. 2008). During the development of NASH, toll-like receptors-4 (TLR4) is one of the major up-regulated genes in NASH. Mechanistically, TLR4 may trigger myeloid differentiation primary response protein 88 (myD88) via TIRAP in hepatocytes under inflammatory reaction (Ju et al. 2018; Ciesielska et al. 2021). TLR4 can also activate RELA gene transcription protein NF-κB and induce a strong inflammatory response, adipose accumulation stimulates the overexpression of TLR4 in KCs, activates NF-κB through transcription factors such as MyD88-TIRAP-MAPK, and triggers the pro-inflammatory factors such as MCP-1, TNF-α, and IL-1β. Meanwhile, the triggering of TLR4 also induced SOCS1 protein expression in KCs (Fujimoto and Naka 2010). It’s worth considering how SOCS1 regulates the development of NASH. As reported before, many signal molecules can induce the expression of SOCS1, including IL-2, IL-4, IL-7, IL-10, IL-15, type I and type II IFNs, TNF-α, and colony-stimulating factor (CSF), etc. Meanwhile, SOCS1 inhibits the transmission of cytokine signals (Chandrakar et al. 2020). The main function of SOCS1 is to regulate the interferon signalling pathway, T cell differentiation, and proliferation, inhibit excessive inflammation, regulate the function of dendritic cells, and reduce the production of autoimmune antibodies in the immune response (Ilangumaran et al. 2017). Supporting this concept, SOCS1 expression has been reported to be up-regulated in KCs, which negatively regulate NF-κB and can inhibit activated M1 macrophages (Cheng et al. 2019; Yu et al. 2021). In this study, we verified that the SOCS1 directly related to the progression of hepatic inflammation, and QTHX mainly increases the expression of SOCS1 and decreases the expression of TLR4, NF-κB as well as myd88 in KCs. It’s a key mechanism to explain that how QTHX significantly reduced the fat deposition and inflammation of primary hepatocytes.
The QTHX contains numerous alkaloids and biologically active factors, which interact with each other to regulate inflammation and lipid metabolism in different ways and plays role in the NASH treatment. There is direct evidence, that saikosaponins A and D in *Bupleurum scorzonerifolium* have anti-inflammatory effects by inhibiting the activity of NF-κB (Liu et al. 2017); Poria cocos polysaccharides can inhibit the activation of the aortic TLR 4/NF-κB pathway and reduce inflammatory factors and blood lipid levels (Li et al. 2021); *Pinellia ternata* contains a large number of sterols, which can regulate T cell receptor signalling pathway and inflammatory factors (Lyu et al. 2020). Citrus sinensis contains flavonoids that inhibit NF-κB and MAPK pathways and inhibit the anti-inflammatory activity of RAW 264.7 cells stimulated by LPS (Son et al. 2020). Scutellaria baicalensis can regulate lipid metabolism and liver function (Yang et al. 2019). It’s a challenge to explore and verify the specific mechanism of action in diseases when the prescription including different individual traditional Chinese medicine herbals, due to that the target of the individual traditional Chinese medicine herbal was heterogeneous. Herein, a key target of QTHX reducing inflammatory response in NASH was demonstrated by the NP and experiment in vitro. However, the liver is a sophisticated and complex organ, and the related targets in the occurrence and development of NASH are complex and diverse (Miyao et al. 2015; Wang et al. 2021). So, it is still a challenge to show the overall view of the mechanism of TCM prescription in diseases therapy.
## Conclusions
QTHX can significantly inhibit fat accumulation in primary hepatocytes and inflammatory response in KCs by mediating SOCS1/TLR4/myd88-NF-ĸB cellular signalling pathway. Our findings provided a novel viewpoint on QTHX's NASH-treatment mechanism, as well as a reference for future research on the efficacy, mechanism, and potential clinical applications of QTHX.
## Disclosure statement
No potential conflict of interest was reported by the author(s).
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---
title: Public Knowledge of Osteoarthritis in Al-Qunfudah Governorate, Saudi Arabia
journal: Cureus
year: 2023
pmcid: PMC10013603
doi: 10.7759/cureus.34892
license: CC BY 3.0
---
# Public Knowledge of Osteoarthritis in Al-Qunfudah Governorate, Saudi Arabia
## Abstract
Background: Joint pain is one of the most frequent complaints among adults and older people in primary healthcare settings worldwide. There are many causes for joint pain, osteoarthritis (OA) is so far the most prevalent form of arthritis that causes joint pain. It can attack almost any joint, but the most frequently affected joints are the hands, knees, hips, and spine. This study aimed to identify public knowledge of OA and its associative variables in Al-Qunfudah governorate, Saudi Arabia.
Materials and methods: A cross-sectional descriptive community-based study was carried out among the general population in the Al-Qunfudah governorate. The research data were collected over two months, from November to December 2022, via an Arabic version of a self-administrated online survey of 29 items.
Results: A total of 746 respondents were included in this study. The majority of them were females ($78\%$). The age group 18-29 was predominant. In terms of education, $69.9\%$ were holding university degrees. The overall participants' knowledge of OA was poor at $36.1\%$, fair at $36.8\%$, and good at $26.9\%$. The associative variables with better participants' knowledge were; holding university degrees ($$P \leq 0.021$$), being a student ($P \leq 0.001$) and living in urban areas ($$P \leq 0.020$$), having normal BMI ($$P \leq 0.018$$), and depending on the school topics as a source of information ($P \leq 0.001$). Good knowledge was significantly higher among healthy individuals and non-smokers ($P \leq 0.001$) for each variable.
Conclusion: This study reveals the lack of knowledge of osteoarthritis among the general population in Al-Qunfudah governorate, Saudi Arabia. Being a student, university educated, from urban areas, and having a normal BMI, all were associative factors with good knowledge. Therefore, this study highlights the necessity for providing awareness and educational campaigns for the public, focusing on the rural population.
## Introduction
Osteoarthritis (OA) is a chronic bone disease and is considered the most common form of arthritis, with 240 million people affected worldwide [1]. OA is a progressive degenerative joint disorder that is characterized by cartilage damage, changes in the subchondral bone, osteophyte formation, muscle weakness, and inflammation of the synovium tissue and tendon. Although OA has long been viewed as a primary disorder of articular cartilage, the subchondral bone is attracting increasing attention [2]. The most commonly affected joints are the hands, knees, hips, and spine. It may be confined to one or a few joints of the affected patients [3]. Osteoarthritis occurs in two types; primary and secondary. The actual cause of articular degeneration in primary osteoarthritis is unknown, while secondary osteoarthritis is due to either abnormal force on the joint such as with post-traumatic causes, or abnormal articular cartilage, like rheumatoid arthritis [4].
Symptoms and signs of OA are usually pain, stiffness, swelling that limits joint movement, crepitus, restricted movement, bone enlargement, joint effusion, and bone instability [5]. Radiographic imaging can visualize bony features, including marginal osteophytes, subchondral sclerosis, and subchondral cysts that are associated with OA [6]. Magnetic resonance imaging (MRI) is the most beneficial procedure to accurately and feasibly measure the changes in quantitative cartilage morphometry for knee OA [7]. Management of osteoarthritis involves; non-pharmacological interventions like exercises, weight loss when appropriate, education, and physical therapy [1]. Swimming is the most beneficial form of exercise for patients suffering from OA and helps in relief of the disease-accompanied symptoms and joint stiffness in addition to improvement of muscle strength [8]. Pharmacological treatment; includes topical or oral NSAIDs provided that there is no contraindication for their usage in the affected patients. Intra-articular steroid injections are used to relieve pain for short-term patients with advanced symptoms and structural damage who are eligible for total joint replacement [1].
The global prevalence of knee OA is $16.0\%$ in individuals aged 15 and over and 22⋅$9\%$ in individuals aged 40 and over [9]. A systematic review study detected OA of the knee was highly prevalent among women rather than men [10]. Moradi-Lakeh et al. stated that the range of point prevalence of osteoarthritis (per 1000) among the EMR countries was 9.7-37.3 [11]. Another cross-sectional study in KSA; looked at the radiographic evidence of osteoarthritis (OA) of the knee in 300 randomly chosen patients attending primary care facilities for different medical conditions. Radiographic OA was seen in males ($53.3\%$) and females ($60.9\%$) [12]. It is essential to increase population knowledge about highly prevalent disabling diseases like OA; this would improve their quality of life and adherence to treatment which, in turn, would improve the population’s experience with diseases and decrease healthcare costs.
Recently, in Saudi Arabia, a few studies revealed variable levels of knowledge about osteoarthritis among the Saudi population; some of them detected a lack of knowledge about OA [13,14]. While the others recorded an adequate level of public knowledge regarding OA [15]. On the international aspect, a study in Malaysia in 2014 suggested inadequate knowledge of OA [16]. There is no literature related to the public knowledge of OA in the Al-Qunfudah governorate. When we interviewed a sample of adults during our previous community awareness campaigns about osteoporosis disease, we noticed that many individuals in the Al-Qunfudah governorate confused osteoarthritis with osteoporosis and used both terms to describe any bone-related pains. Thus, this study was done to identify the awareness and associative variables of osteoarthritis among the population in the Al-Qunfudah governorate, Saudi Arabia.
## Materials and methods
Study design and setting A cross-sectional descriptive community-based study was carried out among the general population in Al-Qunfudah governorate, Saudi Arabia, over six months, starting from August 2022 to January 2023. The study included males and females aged 18 years and more.
Al-Qunfudah governorate was the place where we conducted this study; it exists in the Tihamah plain on the Saudi Red Sea coast in Makkah Province and about 400 kilometers south of the Jeddah governorate. It represents about $3.7\%$ of the regional area.
Study sample The sample size was determined through the application of EPI Info. TM (CDC, Atlanta, GA) [17], depending on the overall population size in Al-Qunfudah governorate [300516], with a confidence interval ($95\%$) and margin of error ($5\%$). Finally, the calculated sample size was 384.
Tool and procedure for data collection We have generated an online survey via the Google Document application, and this process has been done in many phases. Firstly, we conducted a focused literature review, followed by a selection of the relevant information, then the survey items were created and drafted as a 29-item Arabic survey. Its items were organized and reviewed by a panel consisting of three experts from different specialties (Family Medicine, Orthopedics, and Internal Medicine) who assessed the relevancy of each item to the research topic. Finally, it was pre-tested through the application of a pilot study. The main target of this pilot was to assess the relevancy of the designed survey before its use, to determine whether it would be understandable by the public with different educational levels and varied backgrounds, the time needed for its filling, and response rates. The survey link was disseminated on Al-Qunfudah Snapchat, and the public was asked to participate in this study voluntarily. The first 50 submitted answers were collected; then, the survey link was closed until the collected data have been analyzed. Finally, the reliability of the survey was evaluated by using the technique of test-retest. Its internal consistency was assessed, and Cronbach's Alpha coefficient test was (0.83). The final form of the applied survey was composed of 29 questions and distributed into two sections. The initial part involved 12 questions about demographics and clinical information of the study group, such as age, gender, residence, educational level, employment, weight, height, body mass index and their history of OA, joint surgeries, daily exercise, and smoking status. The second section involved 17 questions that inquired about their knowledge of osteoarthritis, such as its definition, causes, prevalence, affected joints, the difference between it and osteoporosis, its clinical manifestations, complications, and management.
The main study data were collected over two months, from November to December 2022, through the predesigned survey that was distributed on different electronic applications such as WhatsApp, Twitter, Snapchat of Al-Qunfudah, and Facebook. To ensure that all participants were from the Al-Qunfudah governorate, the survey was disseminated on Al-Qunfudah Snapchat. As well it involved a question about the residence of each participant, and it was planned to discard any response from outside Al-Qunfudah governorate, but when we reviewed the collected data, we found that all respondents were from it, so we did not exclude any responses. Finally, the total number of completed questionnaires was 726. Therefore, the study sample was achieved. There were no incomplete questions because the questionnaire was designed in the required answering manner.
Scoring of knowledge A good knowledge score of osteoporosis was estimated when the participant could correctly answer $80\%$ or more of the questions, a fair knowledge score for whom correctly answered $79\%$-$60\%$, while less than $60\%$ was considered a poor knowledge score [18].
Ethical considerations This study received an ethical approval number (HAPO-02-K-012-2022-11-1289) from the Medical Research and Ethical Committee of the College of Medicine, Umm Al-Qura University, Makkah, KSA. The collected data are confidential, and consent was obtained from each participant through an introductory question at the beginning of the applied survey.
Statistical analysis *The data* were processed before the statistical analysis. Data management and analysis were carried out by using the IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp. Quantitative data were expressed as means and standard deviations, while qualitative data were expressed as numbers and percentages. We compared participants’ awareness of osteoarthritis and its related factors among demographic groups using the Chi-squared test. A statistical significance was considered when $P \leq 0.05.$
## Results
A total of 746 completed questionnaires. The majority of the participants ($78\%$) were females, and ($79.3\%$) were living in Al-Qunfudah city. Two-thirds of the study subjects belonged to the age group of 18-29 years. In terms of education, ($69.9\%$) had university degrees. The mean weight is 60 kg, and the mean height is 160 cm; approximately $87\%$ and $91\%$ of them had never been diagnosed with OA or undergone any surgical operations in their joints (Table 1).
**Table 1**
| Variables | Variables.1 | n | % |
| --- | --- | --- | --- |
| Sex | Female | 582 | 78.02% |
| Sex | Male | 164 | 21.98% |
| Residence | Al-Qunfudhah city | 591 | 79.22% |
| Residence | Al-Qunfudhah related villages | 155 | 20.78% |
| Age | 18-29 | 493 | 66.09% |
| Age | 30-39 | 121 | 16.22% |
| Age | 40-49 | 85 | 11.39% |
| Age | 50 and above | 47 | 6.30% |
| Education | Secondary | 205 | 27.48% |
| Education | University | 522 | 69.97% |
| Education | Post-graduate | 16 | 2.14% |
| Education | Intermediate and below | 3 | 0.40% |
| Occupation | Government employee | 164 | 21.9% |
| Occupation | Non-governmental employee | 98 | 13.1% |
| Occupation | Student | 206 | 27.6% |
| Occupation | Housewife | 151 | 20.2% |
| Occupation | Retired | 27 | 3.6% |
| Occupation | Non-working | 100 | 13.4% |
| Weight | 21-162 kg (60.04 ± 16.3) | 21-162 kg (60.04 ± 16.3) | 21-162 kg (60.04 ± 16.3) |
| Minimum-maximum (M±SD) | 21-162 kg (60.04 ± 16.3) | 21-162 kg (60.04 ± 16.3) | 21-162 kg (60.04 ± 16.3) |
| Height | 120-180 cm (160.0±8.8) | 120-180 cm (160.0±8.8) | 120-180 cm (160.0±8.8) |
| Minimum-maximum (M ±SD) | 120-180 cm (160.0±8.8) | 120-180 cm (160.0±8.8) | 120-180 cm (160.0±8.8) |
| Body mass index (BMI) | Underweight (Less than 18.5 kg/m2) | 142 | 19.0% |
| Body mass index (BMI) | Normal (18.5-24.9 kg/m2) | 360 | 48.3% |
| Body mass index (BMI) | Overweight (25.0-29.9 kg/m2) | 163 | 21.8% |
| Body mass index (BMI) | Obese (30.0-39.9 kg/m2) | 75 | 10.1% |
| Body mass index (BMI) | Morbid obese (40.0 and more kg/m2) | 6 | 0.8% |
| Self-reported OA | Yes | 96 | 12.9% |
| Self-reported OA | No | 650 | 87.1% |
| Previous joint surgery | Yes | 66 | 8.8% |
| Previous joint surgery | No | 680 | 91.2% |
| Daily exercise | Yes | 249 | 33.4% |
| Daily exercise | No | 497 | 66.6% |
| Smoking status | Smokers | 93 | 12.5% |
| Smoking status | Non-smokers | 653 | 87.5% |
About $91\%$ of the study subjects indicated that osteoarthritis is not a synonym for osteoporosis. Half of them correctly identified the underlying mechanism of OA as a process during which the joint cartilage begins to wear out over time. The definition of osteoporosis was well-known by $82.4\%$ of them, who reported that it reflects bone fragility and easiness to be broken in osteoporosis. Approximately half of the respondents ($49.6\%$) stated that osteoarthritis is a chronic disease, and ($74.5\%$) stated that it is a common disease. About half ($55\%$) were able to recognize that joint pain is the most common but not the solitary symptom in OA, 56 % also identified that OA causes joint stiffness, and $62\%$ reported that swelling is a common sign in OA. More than two-thirds of the study group ($71\%$) gave a consensus on OA could lead to loss of joint movement (Table 2).
**Table 2**
| Items | Correct Answers | Correct Answers.1 |
| --- | --- | --- |
| | n | % |
| There is a difference between joint osteoarthritis and osteoporosis | 680 | 91.15% |
| The cause of the joint osteoarthritis | 404 | 54.16% |
| Definition of osteoporosis | 615 | 82.44% |
| Osteoarthritis is a chronic disease | 370 | 49.60% |
| Osteoarthritis is a common disease | 556 | 74.53% |
| There are certain joints that are greatly affected | 608 | 81.50% |
| The most affected joints of joint osteoarthritis | 515 | 69.03% |
| Infection with micro-organism is related to the disease of OA | 234 | 31.37% |
| Pain is the only presentation of the disease | 412 | 55.23% |
| Sclerosis is shown to the osteoarthritis of the joints | 420 | 56.30% |
| Joint swelling (swelling) is a sign of OA | 465 | 62.33% |
| Osteoarthritis may lead to lose of joint movement | 532 | 71.31% |
| Genetic factors can predispose to osteoarthritis | 391 | 52.41% |
| Age is a factor causing joint osteoarthritis | 627 | 84.05% |
| The rate of OA in men and women are equal in the osteoarthritis | 369 | 49.46% |
| X-rays are used to diagnose OA | 336 | 45.04% |
| Medications such as aspirin, for example, improve symptoms | 216 | 28.95% |
Internet and social media were the most common sources of information ($26\%$) about OA among this study's participants (Figure 1).
**Figure 1:** *Source of knowledge of osteoarthritis among the general population in Al-Qunfudah governorate*
The overall participants' knowledge of OA was poor at $36.1\%$, fair at $36.8\%$, and good at $26.9\%$ (Figure 2).
**Figure 2:** *Participants' knowledge score of osteoarthritis (n=746)*
On relating overall knowledge level with the participants' sociodemographic characteristics, $82\%$ of those who were living in Al-Qunfudah city had recorded better knowledge levels than those living in the surrounding villages, with a significant difference found ($$P \leq 0.020$$). Furthermore, $79\%$ of university-educated participants had better knowledge scores ($$P \leq 0.021$$). Good knowledge of OA was observed among students and those who obtained their information during learning in school with ($P \leq 0.001$) for each. Near a third of those having poor knowledge were overweight ($$P \leq 0.018$$) (Table 3).
**Table 3**
| Variables | Variables.1 | Knowledge score | Knowledge score.1 | Knowledge score.2 | P-value |
| --- | --- | --- | --- | --- | --- |
| Variables | Variables | Poor, n(%) | Fair, n(%) | Good, n(%) | P-value |
| Age in years | 18-29 | 169 (62.6%) | 183 (66.6%) | 141 (70.2%) | 0.321 |
| Age in years | 30-39 | 53 (16.6%) | 44 (16.0%) | 24 (11.9%) | 0.321 |
| Age in years | 40-49 | 33 (12.2%) | 32 (11.6%) | 20 (9.9%) | 0.321 |
| Age in years | More than 50 | 15 (5.6%) | 16 (5.8%) | 16 (7.9%) | 0.321 |
| Sex | Female | 201 (74.4%) | 213 (77.5%) | 168(83.6%) | 0.062 |
| Sex | Male | 69 (25.6%) | 62 (22.6%) | 33 (16.4%) | 0.062 |
| Residence | Al-Qunfudah city | 223(82.6%) | 203 (73.8%) | 165 (82.1%) | 0.020 |
| Residence | Al-Qunfudah-related villages | 47 (17.4%) | 72 (26.2%) | 36 (17.9%) | 0.020 |
| Education | Secondary | 81 (30.0%) | 88 (32.0%) | 36 (17.9%) | 0.021 |
| Education | University | 182 (67.4%) | 180 (65.5%) | 160 (79.6%) | 0.021 |
| Education | Post-graduate | 5 (1.6%) | 6 (2.2%) | 5 (2.5%) | 0.021 |
| Education | Intermediate and below | 2 (0.7%) | 1 (0.4%) | 0 (0.0%) | 0.021 |
| Occupation | Governmental employee | 65 (24.1%) | 59(21.5%) | 40(19.9%) | <0.001 |
| Occupation | Non-governmental employee | 43(15.9%) | 35(12.7%) | 20(9.9%) | <0.001 |
| Occupation | Student | 52(19.2%) | 81(29.5%) | 73(36.3%) | <0.001 |
| Occupation | Housewife | 44(16.3%) | 62(22.5%) | 45(22.4%) | <0.001 |
| Occupation | Retired | 14(5.3%) | 7(2.5%) | 6(2.9%) | <0.001 |
| Occupation | Non-working | 52(19.2%) | 31(11.3%) | 17(8.5%) | <0.001 |
| Source of information | Doctors | 21(7.8%) | 31(11.3%) | 39(19.4%) | <0.001 |
| Source of information | School | 45(16.7%) | 49(17.8%) | 55(27.3%) | <0.001 |
| Source of information | Internet and social media | 80(29.6%) | 73(26.5%) | 41(20.4%) | <0.001 |
| Source of information | TV | 20(7.4%) | 20(7.3%) | 21(10.4%) | <0.001 |
| Source of information | Family and friends | 46(17.0%) | 37(13.5%) | 26(12.9%) | <0.001 |
| Source of information | Mixed sources | 58(21.5%) | 65(23.6%) | 19(9.5%) | <0.001 |
| Body mass index | Underweight (Less than 18.5 kg/m2) | 45(16.6%) | 49(17.8%) | 48(23.9%) | 0.018 |
| Body mass index | Normal (18.5-24.9 kg/m2) | 115(45.6%) | 154(56.0%) | 91(45.3%) | 0.018 |
| Body mass index | Overweight (25.0-29.9 kg/m2) | 73(27.0%) | 49(17.8%) | 41(20.4%) | 0.018 |
| Body mass index | Obese (30.0-39.9 kg/m2) | 34(12.6%) | 21(7.6%) | 20(9.9%) | 0.018 |
| Body mass index | Morbid obese (40.0 and more kg/m2) | 3(1.1%) | 2(0.7%) | 1(0.5%) | 0.018 |
On the other connection between overall knowledge of osteoarthritis with the participants' clinical and general information, participants who were not previously diagnosed with OA ($94.5\%$) or had undergone any surgical operation in their joints ($98.5\%$) had significantly better knowledge of OA ($P \leq 0.001$). Additionally, better knowledge was highly detected among non-smokers ($95.5\%$) ($P \leq 0.001$) (Table 4).
**Table 4**
| Unnamed: 0 | Knowledge score | Knowledge score.1 | Knowledge score.2 | P-value |
| --- | --- | --- | --- | --- |
| | Poor, n (%) | Fair, n (%) | Good, n (%) | P-value |
| Self-reported OA | Self-reported OA | Self-reported OA | Self-reported OA | <0.001 |
| Yes | 44 (16.3%) | 41 (14.9%) | 11 (5.5%) | <0.001 |
| No | 226 (83.7%) | 234 (85.1%) | 190 (94.5%) | <0.001 |
| Previous joint surgery | Previous joint surgery | Previous joint surgery | Previous joint surgery | <0.001 |
| Yes | 33 (12.2%) | 30 (10.9%) | 3 (1.5%) | <0.001 |
| No | 237 (87.8%) | 245 (89.1%) | 198 (98.5%) | <0.001 |
| Daily exercise | Daily exercise | Daily exercise | Daily exercise | 0.161 |
| Yes | 91 (33.7%) | 101 (36.7%) | 57 (28.4%) | 0.161 |
| No | 179(66.3%) | 174 (63.3%) | 144 (71.6%) | 0.161 |
| Smoking status | Smoking status | Smoking status | Smoking status | <0.001 |
| Smokers | 48 (17.8%) | 35 (12.7%) | 10 (5.0%) | <0.001 |
| Non-smokers | 222 (82.2%) | 240 (87.3%) | 191 (95.5%) | <0.001 |
## Discussion
Osteoarthritis (OA) is the most common disease and cause of disability in the elderly [19]. Therefore, it is crucial to raise awareness of osteoarthritis in the Kingdom of Saudi Arabia to help people adopt healthy habits and accept newly suggested prevention measures. This study examined how well-informed a proportion of the Al-Qunfudah population of Saudi Arabia identify knowledge of OA and its associative variables. Women made up $78\%$ of the participants, and the population living in Al-Qunfudah city constituted ($79.2\%$) of the sample. Only $6\%$ of the participants were 50 years of age or older, while college students represented $69.9\%$. A total of 96 respondents ($12.3\%$) self-reported having been diagnosed with osteoarthritis, and this was not shocking as previous literature reported that the overall prevalence of osteoarthritis in Saudi *Arabia is* $15.3\%$ [12].
The majority of study participants ($91\%$) have differentiated between osteoarthritis and osteoporosis, and about half of them have correctly identified the basic mechanism for OA ($82.4\%$) and were aware that osteoporosis makes bones brittle and easily broken. Nearly half of the respondents ($49.6\%$) have said osteoarthritis is a chronic condition, and $74.5\%$ have stated that it is a common disease. These findings are much better than that from a previous study in Malaysia which revealed that $51.9\%$ of its study subjects had known that osteoporosis is different from osteoarthritis [18]. The current higher percentage of those who could distinguish between osteoporosis and osteoarthritis is an outstanding finding because this may facilitate the prevention of both common diseases and their complications.
The chief symptom of osteoarthritis is pain which usually warrants a doctor's visit. Stiffness is another symptom that usually goes away after 20-30 minutes, especially in the morning or after a period of inactivity [5]. The finding from this study went in the same direction as the previously mentioned fact, as about half of the respondents correctly answered that pain and stiffness are the main symptoms of OA. Alyami et al. reported the same percentage in their previous study in Jeddah [13]. Pain and stiffness of joints are very distressing symptoms that may interfere with the usual activity of the affected patients and constitute a bad experience that could not go unrecognized by them. The disease often begins with joint swelling ($62\%$), and patients with OA suffer from restricted mobilization ($71\%$). Participants ($84\%$) were aware that age is a risk factor for developing OA. The same finding was reported in a Pakistani study which revealed that $63\%$ of the studied group attributed to age as one of the risk factors for joint pain [20].
A previous systematic review by Tschon et al. concluded that women are more likely to develop OA and suffer more severely than men as a most observational cohort and cross-sectional designed studies which globally analyzed 268,956 patients, of which 103,700 were men ($39\%$) and 165,256 women patients ($61\%$) [21]. Unfortunately, only half of the participants were aware of this information. A well-known risk factor for OA is its high genetic component [22]. The estimate of heritability has been reported to be $40\%$ for the knee, $60\%$ for the hip, $65\%$ for the hand, and about $70\%$ for the spine [22]. This tip has been known by $52\%$ of the sample. The failure of half of the study group to recognize the gender and genetic predisposition of OA is worrisome. Therefore, this knowledge should be highlighted in the upcoming educational campaigns for public orientation.
The diagnosis of OA can be made with just a simple film X-ray, history, and physical exam. It is rare to diagnose OA using biochemical markers in the blood [23]. The respondents' knowledge of using an X-ray and physical exam to diagnose OA has been recognized by only $45\%$ of the participants. Regarding the treatment of OA, guidelines [23-25] recommend non-steroidal anti-inflammatory drugs (NSAIDs) as the first-line therapy and its action through inhibiting the production of prostaglandins (PG) and thromboxane A through the blockade of cyclooxygenase (COX). Currently, less than a third ($28\%$) of participants were aware of the important role NSAIDs play in managing symptoms. This percentage is less than findings from another Saudi study which reported that $46\%$ of the general population could know that NSAIDs are the drug of choice for treating symptoms of osteoarthritis [13].
The overall participants' knowledge of OA in this study was poor at $36.1\%$, fair at $36.8\%$, and good at $26.9\%$. In contrast with a previous study, which included 1052 participants from the Aseer region, the level of knowledge of OA was high, as $82.6\%$ of the population had a good awareness level regarding OA in total [15]. Despite both studies being done in the kingdom of Saudi Arabia, there is a great discrepancy in their outcomes; the cause may be related to the difference in characteristics of both studies' participants; and the availability of health education campaigns as Al-Qunfudah governorate is a remote area with a limited number of health care settings when compared with Aseer region. Moreover, the difference in tools for data collection may be another cause of this dissimilarity between both studies' findings.
According to the current study, $70\%$ of participants belonging to the age group of 18-29 years and university-educated respondents have shown better knowledge levels than the others. This result is in disagreement with both Saudi and Malaysian studies, which concluded that participants aged 50 and up had a higher level of knowledge and awareness about OA [15,16]. The dissimilarity in these studies' results may be due to the differences in the study settings and time. Nowadays, young people have numerous access to getting information much easier than in previous times due to the availability of the internet, social media, and modern study curricula. Being an educated person will facilitate searching for information. A significant difference has been found between residence and good knowledge of OA, as participants from the urban area have shown better knowledge. This could be due to the more opportunity for public awareness campaigns and accessibility of better internet connections in urban rather than rural areas.
In recent years, there is a great change in the schools' educational process and students' curricula that provide students with a large stock of evidence-based knowledge concerned with health. Thus, it was an unsurprising issue to identify better knowledge of OA among the study participants who took their information from the school. On the other side, poor knowledge was detected clearly among those who depended on social media in getting their health-related information. The main cause of this outcome is that persons who provide information on social media may be unprofessional. Therefore, their information is not evidence-based, is poor quality, and carries the risk of being fake. This finding should be considered by policymakers to establish measures to ensure the availability of evidence-based information on social media, as it is the most popular and accessible method of public education. A few years ago, Tonsaker et al. warned that the distribution of poor-quality information can harm patients and damage the professional image. There is a high risk of misinformation, as healthcare providers are unable to control the content that is posted or discussed to a previous study finding [21].
It was known that obese individuals have a higher risk for OA, with every 5 kg of weight gain increasing the risk of knee OA [26]. There is evidence that the risk accumulates with high BMI throughout adulthood, with an association between BMI and later knee OA starting as early as 20 years in men and 11 years in women [26. This study noted poor knowledge of OA in a third of overweight people; therefore, health education sessions should be directed to the public to explore the risk of obesity on the joint. Additionally, this study reveals that good knowledge was obvious among participants who had never been diagnosed with OA or undergone surgical operations in their joints, and this result against that was detected in a previous study which reported that individuals who were previously diagnosed with OA had significantly higher knowledge than others who were not [27].
Previous literature concluded that exercise has positive salutary benefits for joint tissues in addition to its other health benefits [5].*In this* study, the majority of participants who practiced exercise daily possessed a fair knowledge level of OA ($28.36\%$), and this finding is in disagreement with what was reported by Hussain and Abdul Raheem in their study, which revealed that physical activity showed no statistically significant change to the odds of having good awareness [28]. Moreover, evidence suggests that cigarette smoking may have a negative effect on cartilage metabolism [29]. In the current study, a significant association was found between knowledge and smoking status among the study subjects, as good knowledge was mostly detected in non-smokers ($95.5\%$). This outcome is reassuring as it may be due to the recognition of the bad effects of smoking on the joint and cartilage which motivated these participants to avoid smoking and maintain their joint health.
The limitations of the study. First, the online self-administered questionnaire may have the disadvantage of applying only to those who can read, familiar to use the internet technologies, and have internet connections. Therefore, the study has denied the awareness level of osteoarthritis in a respected sector of the community, which may involve older people who are at higher risk for OA and its consequences. Second, more than two-thirds of the study sample was from Al-Qunfudah city, which is a relatively urban region; thus, the knowledge scores reflected mainly awareness of the urban population with deprivation of those from rural regions. Despite the previously mentioned limitations, this study is an initiative to highlight this common disease in the general population.
## Conclusions
This study reveals the inadequate level of knowledge among the general population in Al-Qunfudhah Governorate, Saudi Arabia, about osteoarthritis. University education, still studying, urban residence, normal body mass index, and being a non-smoker; all were associated variables with good knowledge. Awareness campaigns are highly recommended to educate the public properly through simplified evidence-based information about the such debilitating disease. Collaboration between the healthcare sector and other community organizations is a vital step to expanding educational programs in the community. Social media should be monitored by policymakers and health organizations to limit the rapid spreading of unprofessional health-related information and provide alternatives of short health educational messages in simple language to be feasible for all general population regardless of their education or background.
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|
---
title: 'Supporting Parents of Children With Type 1 Diabetes: Experiment Comparing
Message and Delivery Types'
journal: JMIR Formative Research
year: 2023
pmcid: PMC10013681
doi: 10.2196/41193
license: CC BY 4.0
---
# Supporting Parents of Children With Type 1 Diabetes: Experiment Comparing Message and Delivery Types
## Abstract
### Background
Type 1 diabetes (T1D) is a chronic condition that typically affects young age group people and is estimated to afflict approximately 154,000 people younger than 20 years in the United States. Since T1D typically impacts children, parents must play an active role in helping their child manage the condition. This creates a substantial burden and responsibility for the parents.
### Objective
This pilot study sought to find ways to help parents with children with T1D in coping with stresses related to managing and monitoring their child’s disease by providing informational support, either about parenting a child with T1D or general parenting messages through different channels.
### Methods
Parents ($$n = 120$$) of children with T1D were recruited through an email listserv through local T1D Facebook groups. A total of 102 participants were included in the analysis. We conducted a 2×2 experimental study over an 8-week period to test 2 types of messages (diabetes specific vs general parenting) and the medium in which the messages were delivered (Facebook vs SMS text message). Diabetes behavior, informational support, emotional support, and quality of life were the main outcomes of interest.
### Results
The results suggested that the participants in the diabetes message groups showed improvement in diabetes behaviors (F1,99=3.69; $$P \leq .05$$) and were more satisfied with the intervention (F3,98=4.59; $$P \leq .005$$). There were no differences between message and medium groups on informational support, emotional support, or quality of life.
### Conclusions
The results of this study demonstrate that the medium—Facebook or SMS text messaging—does not matter for parents’ perceptions of social support or quality of life. The diabetes message group reported higher levels of disease management. Finally, the groups with the diabetes support messages were more satisfied than those who received general parenting messages. The findings provide starting guidance for the development of social support interventions for this population.
## Introduction
Chronic conditions impact nearly $40\%$ of children and adolescents in the United States [1]. Type 1 diabetes (T1D) is a chronic condition that typically affects young age group people. T1D afflicts approximately 154,000 people younger than 20 years in the United States [2,3]. For children younger than 10 years, the prevalence of diagnosis is 19.7 per 100,000/year. For adolescents aged 10-19 years, the prevalence is 18.6 per 100,000/year [4]. T1D costs US $14.9 billion annually in the United States [5,6]. Currently, approximately $75\%$ of adolescents are not achieving the American Diabetes Association’s hemoglobin A1c (HbA1c) targets [7-10]. HbA1c is a measure of an individual’s average blood sugar over the past 3 months. These statistics often do not consider the number of parents and caregivers of these children who are also impacted by the burden of disease management. Parents of children with T1D are often the main caregiver for their child [11].
Parents who have a child with T1D have a substantial responsibility in managing their child’s disease. As T1D typically impacts children, parents typically play a very active role in managing the condition [12]. The diagnosis can affect families for the rest of their lives [13]. Daily life with diabetes requires adherence to an extremely complex care plan, involving multiple doctor visits, extensive health education, daily blood glucose monitoring, insulin injections, and the careful monitoring of diet and physical activity. The diagnosis can forever change the psychological dynamics of the family. Parents of children with T1D often struggle with stress, depression, and anxiety over the care of their child. They may also experience increases in family conflict and feelings of burnout [13].
Many parents report feeling overwhelmed by all the disease management information accompanying a diagnosis of T1D, leading to feelings of stress and isolation, which can lead to worse health outcomes for the parents. Social support, which is defined here as the perception of being part of a supportive social network [14], has often been cited as a way to help parents cope with this stress, improve overall health status, and act as a buffer for various effects of stress [12]. Parents with a child who has a chronic condition may feel higher levels of stress if they perceive that they do not have the required knowledge or support systems needed to cope with the demands of caring for a child with a life-long condition, like T1D. Information communication technologies (ICTs; eg, social media, online forums, SMS text messages) are one way to help reduce feelings of stress and isolation by improving social support [15]. These types of technologies allow people to engage in supportive communities that transcend time constraints and geography. Previous research suggests that parents of children with T1D gain multiple benefits by using ICTs for support and information [16,17].
This pilot study sought to understand if the types of parenting messages (diabetes specific or general) and the ICT medium in which the messages were sent (Facebook or SMS text message) impacted the parents’ perceptions in multiple dimensions. Specifically, social support, quality of life, management of their child’s T1D, and satisfaction with the intervention were explored. This pilot study is a first step in developing supportive programming for parents with a child with T1D. Based on this purpose, the following hypotheses and research question were developed for this study:
## Study Design
This pre-post test survey pilot study was a 2 (SMS text messaging vs Facebook) × 2 (general parenting messages vs diabetes-specific messages) between-subjects factorial design. Participants ($$n = 120$$) were randomly assigned to 1 of the 4 conditions over an 8-week period. Participants were recruited through a listserv of parents with children with T1D and through local T1D Facebook groups.
## Participants and Procedures
We recruited 120 participants within 7 days. To be sure that the participants met our inclusion criteria, the participants answered the following 3 screening questions to be included in the study: [1] have a child (any age) living at home with T1D, [2] have a mobile phone that can send and receive SMS text messages, and [3] have a Facebook account. Then, participants were randomized, using a random number generator, into 1 of the 4 experimental groups: [1] Facebook and general messages, [2] Facebook and diabetes messages, [3] SMS text messaging and general messages, and [4] SMS text messaging and diabetes messages. Randomization was done upon passing the screening, before the start of the study, because the consent form was included within the pretest survey and the conditions needed to be determined beforehand for participants to receive the appropriate survey link. Each of the survey links contained the same questionnaire but enabled the samples to be easily separated. The consent form varied slightly between the groups to reflect the difference in the procedures (ie, which channel they used for communication). On day 1 of the study, participants were emailed a link to the survey that included the consent form, and once completed, they continued to the pretest survey. Participants were given 5 days to complete the pretest survey. Completion of the pretest survey was tracked by the researchers. During the intervention period, participants were sent approximately 3 messages per week (a total of 23 messages) for 8 weeks during the fall of 2019 (either via SMS text message or posted to the Facebook group). For this study, we developed 2 private Facebook groups (Diabetes and General) and enrolled those who were randomized into the appropriate group. All messages (Facebook and SMS text messages) were scheduled to go out on the same day and time. The participants could respond to the SMS text messages and Facebook posts through comments (see Textbox 1 for examples of the messages).
After the 8-week duration, participants were asked to take an online posttest survey. Participants were given 6 days to complete the posttest survey. Completion of the posttest survey was tracked by the researchers and a debrief email was sent once the survey was complete. The debrief disclosed the full purpose of the study, provided a PDF document of all the messages (general and diabetes specific), and provided links to access both Facebook groups (Figure 1).
**Figure 1:** *Enrollment and randomization.*
## Measures
The pre- and posttest surveys were nearly identical. The only differences were that the pretest administered demographic questions and only the posttest administered the satisfaction questions. All of the scales used for this study are validated measures. Informational support was measured using the 6-item PROMIS Item Bank v2.0–Informational Support–Short Form 6a (α=.95), which is on a 5-point scale from “Never” to Always.” Higher scores indicated greater feelings of support. Emotional support was measured using the 8-item PROMIS Item Bank v2.0–Emotional Support–Short Form 8a (α=.96) that uses a 5-point scale from “Never” to “Always.” Higher scores indicated greater feelings of support [19]. To measure the participants’ quality of life, a modified version (to indicate T1D) of The Pediatric Caregiver’s Quality of Life Questionnaire (α=.88) was used [20]. This scale has 7 response options ranging from “All of the time” to “None of the time.” A lower score indicated worse quality of life. The surveys included 7 items of The Diabetes Behavior Rating Scale (α=.68), which assesses how much the parent is following the recommended treatment plan for their adolescent [21]. This scale has 5 responses categories ranging from “Never” to “Always.” A higher score indicates a greater level of adherence. Satisfaction of the intervention was also measured using 4 items on a 7-point scale from “Extremely unsatisfied” to “Extremely satisfied” (α=.89). Higher scores indicated greater satisfaction.
## Statistical Analysis
Descriptive statistics were used to profile the sociodemographic characteristics of the parents. ANOVA was used to assess the differences between groups. Furthermore, post hoc comparisons were conducted using Tukey HSD tests.
## Ethics Approval
This study was approved by the Institutional Review Board of Michigan State University (MSU Study ID: STUDY0000274). The institutional review board of the university approved the study procedure and protocols and determined this an exempt study under the Flexibility Initiative Exemption Category 98, which is research involving benign interventions in conjunction with the collection of data from adult subjects that are considered to be minimal risk. All participants agreed to participate through the informed consent process in which they were informed of the study tasks, risks and benefits of participating, and the voluntary nature of their participation. All identifying information about the participants was kept separate from their survey responses, and all data were completely deidentified. Once the pretest survey was completed, participants received a US $5 gift card to a national coffee chain. Once the posttest surveys were completed, they received a second US $5 gift card to a national coffee chain.
## Results
The analysis includes participants that completed both the pre- and posttest surveys ($$n = 102$$). See Table 1 for participant demographics.
Hypothesis 1 proposed that the Facebook group, regardless of message type, would have higher perceptions of social support and quality of life. ANOVA results suggest that there was not a significant effect of group type (Facebook vs SMS text messaging) on informational support (F1,100=0.189; $$P \leq .66$$), emotional support (F1,100=0.019; $$P \leq .89$$), or quality of life (F1,98=0.65; $$P \leq .42$$). This suggests that the channel through which the participants received the messages did not influence their perception of social support or quality of life. Means for all groups can be found in Table 2. Hypothesis 1 was therefore not supported.
Hypothesis 2 proposed that both groups that received diabetes messages would improve on diabetes management. Results suggest that there was a significant effect of diabetes messages compared to general messages regarding diabetes adherence (F1,99=3.69; $$P \leq .05$$). The groups that received the diabetes messages reported more diabetes adherence behaviors. Therefore, hypothesis 2 is supported.
Research question 1 aimed to explore how the general parenting messaging groups differed from the diabetes messaging groups. Results suggest that there was a significant difference between groups on satisfaction (F3,98=4.59; $$P \leq .005$$). Therefore, a post hoc comparison using a Tukey honestly significant difference test was conducted and indicated that the Facebook diabetes messaging group was significantly more satisfied than the *Facebook* general messaging group ($$P \leq .009$$). Additionally, the SMS text messaging diabetes messages group was significantly more satisfied than the Facebook diabetes messages group ($$P \leq .01$$). There were no other differences in satisfaction between groups.
## Discussion
Caring for a child or adolescent with T1D can be incredibly stressful for parents. This study was designed to determine if the type of message or the medium was important for parent support. This study found that there was no difference in perceived support or quality of life between the Facebook groups and the SMS text messaging groups, suggesting that the channel of the support messages does not matter. The data also confirm that receiving messages regarding diabetes management did improve perceptions of diabetes adherence. Finally, we found that the targeted messages regarding diabetes parenting were more well liked than the general parenting messages.
Past work has demonstrated that Facebook private groups can provide an online community of peers facing similar circumstances [16]. This study demonstrated that people could feel supported through SMS text messages as well. It was the type of message that was the key to the differences. Parents of children with T1D might not feel that they get enough information specific to parenting a child with a chronic illness. This can be due to a variety of factors, including not knowing any other parents with a child with T1D, always feeling a need to have more information, and not feeling empowered to ask for support [22-26].
This work is important as there is little work regarding providing support messages through ICTs to the parents/caregivers of children or adolescents with a chronic condition [27]. Much of the work is centered on the individual with a chronic illness [28-32]. This pilot study may help others when considering how to design and develop a support group for informal caregivers of people with chronic illnesses. Past studies have shown that adding personalization (ie, name and gender) to the messages may improve perceptions of support of the parents, and it should be considered for future studies [33-37]. This also hints to why the diabetes-specific message had a stronger effect when comparing it to the general parenting message. The findings of this study suggest that the medium in which the messages are delivered did not matter. This indicates that providing options for individuals based on their own preferences will likely have more positive outcomes and likely reduce barriers for engagement. Past research has also noted that parents with high internet self-efficacy were comfortable using Facebook [38]. In another study, parents found SMS text messaging to be desirable [37], thus allowing parents to choose that the modality can improve adoption and long-term use. Parents are likely to be able to find a variety of general parenting support, but for caregivers, diabetes-specific support is likely to be more effective in improving diabetes management skills. Additionally, the diabetes-messages groups were more satisfied with the intervention than the general parenting groups, strengthening this conclusion.
As with any study, this work also has some limitations. One of which includes the parent population, which was mostly White and women. Future work should ensure a more representative group of parents to understand how that might impact these perceptions. Additionally, all of the surveys were self-reported, and there are known biases when using these types of measures. This study explains social support provided via Facebook or SMS text messaging to informal caregivers, particularly parents of a child with a chronic illness. Additionally, the age of the child was not part of the inclusion criteria, while the vast majority ($$n = 81$$, $83.5\%$) had children between the age of 6-15 years, and the needs of these parents are most likely different in terms of the messages needed. The findings provide guidance for the development of social support interventions for this population.
## Data Availability
BH is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
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|
---
title: "Rational design of a genome‐based insulated system in \nEscherichia coli\n\
\ facilitates heterologous uricase expression for hyperuricemia treatment"
authors:
- Lina He
- Wei Tang
- Ling Huang
- Wei Zhou
- Shaojia Huang
- Linxuan Zou
- Lisha Yuan
- Dong Men
- Shiyun Chen
- Yangbo Hu
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013758
doi: 10.1002/btm2.10449
license: CC BY 4.0
---
# Rational design of a genome‐based insulated system in
Escherichia coli
facilitates heterologous uricase expression for hyperuricemia treatment
## Abstract
Hyperuricemia is a prevalent disease worldwide that is characterized by elevated urate levels in the blood owing to purine metabolic disorders, which can result in gout and comorbidities. To facilitate the treatment of hyperuricemia through the uricolysis, we engineered a probiotic *Escherichia coli* Nissle 1917 (EcN) named EcN C6 by inserting an FtsP‐uricase cassette into an “insulated site” located between the uspG and ahpF genes. Expression of FtsP‐uricase in this insulated region did not influence the probiotic properties or global gene transcription of EcN but strongly increased the enzymatic activity for urate degeneration, suggesting that the genome‐based insulated system is an ideal strategy for EcN modification. Oral administration of EcN C6 successfully alleviated hyperuricemia, related symptoms and gut microbiota in a purine‐rich food‐induced hyperuricemia rat model and a uox‐knockout mouse model. Together, our study provides an insulated site for heterologous gene expression in EcN strain and a recombinant EcN C6 strain as a safe and effective therapeutic candidate for hyperuricemia treatment.
## INTRODUCTION
Hyperuricemia is a highly prevalent disease characterized by elevated urate (uric acid) levels in the blood owing to purine metabolic disorders. This disease is also the most important risk factor for the development of gout. 1, 2 The prevalence of hyperuricemia ranges from $10\%$ to $20\%$ in developed countries. 3, 4, 5 Consequently, the incidence of gout is $5\%$ in the United States, $4.75\%$ in Europe, and $3.8\%$ in Australia. 4, 5, 6, 7 Importantly, many hyperuricemic patients are asymptomatic and thus clinically neglected; however, diseases caused by hyperuricemia cannot be ignored. 5 Increasing evidence suggests that hyperuricemia is a risk factor for the development of a variety of comorbidities, including hypertension, diabetic renal disease, obesity, metabolic syndrome, fatty liver, and cardiovascular disease. 2, 5, 8, 9, 10 Therefore, hyperuricemia remains to be a global public health issue.
Urate is a key product of the purine metabolic pathway and is highly conserved in living organisms. 11 In most species, urate is metabolized to a more soluble compound called allantoin by urate oxidase (uricase) and is further degraded to urea or ammonia. 12 In contrast, the uricase gene found in ancestral apes has been silenced in humans owing to evolutionary events; thus, urate is the final product of the purine metabolic pathway. 12, 13 Approximately, two‐third of urate in the human body is excreted by renal urate transporters (such as GLUT9 and URAT1), while the remaining one‐third is transported by the ABCG2 transporter in the small intestine (via the extra‐renal excretion pathway) and cleared by intestinal microorganisms via a process known as uricolysis. 14, 15, 16 The overproduction or underexcretion of urate is the main cause of hyperuricemia. Therefore, traditional pharmacological urate‐lowering therapies (ULTs) target urate generation (xanthine oxidase inhibitors) 6, 17 or renal urate excretion (uricosurics) 18 or directly increase urate degradation (uricase). 19, 20 However, these drugs have potential severe adverse effects and are not recommended for a large proportion of patients with asymptomatic hyperuricemia. 21 The intestinal tract plays an increasing role in urate excretion, particularly in patients with chronic kidney disease (CKD), whose renal elimination of urate is impaired. 19, 22 The reduction of extrarenal urate excretion is a common cause of hyperuricemia in patients with CKD. 23, 24 According to previous studies, dysbiosis of intestinal flora exists in patients with gout and serum urate (sUA) levels are associated with gut microbiome changes. 25, 26 Therefore, modulation of the gut microbiota is an alternative approach to hyperuricemia treatment. 27 Appropriate supplementation of probiotics plays a role in urate lowering by regulating the intestinal flora. 3, 28 Moreover, urate concentration in intestinal has been found to be positively related to sUA, and oral administration of uricase can reduce sUA in hyperuricemic rats and urate oxidase‐deficient mice, 19, 21, 29 suggesting that the use of engineered probiotics expressing functional uricase is an attractive strategy for the treatment of hyperuricemia.
The intestinal microbiota is associated with the metabolic health of the human host and possess tremendous potential in the field of biotherapeutics delivery for the treatment of various human diseases. 30, 31 *Escherichia coli* Nissle 1917 (EcN) is a probiotic with superior intestinal adaptation 32, 33 that has been modified as a bacterial “living factory” for various applications. As a result, several recombinant strains have been used in clinical trials. 34, 35, 36 However, for clinical development of engineered bacterial therapeutics, the safety of orally administered chassis and genetically stable are crucial to bacterial pharmacokinetics in vivo. 35, 37 Thus, bacterial genome editing is a reliable strategy and undoubtedly can reduce burden caused by plasmids, particularly in biomedical applications. 38 However, irrational loci on the genome can not only decreases performance but also interfere with native transcription of bacterial chassis. 39 Thus, ultra‐stable genetic editing approach based on the genome without affecting background gene expression would strengthen the clinical applications of recombinant strains. 38 *In this* study, we provide a programmable approach of genome‐based highly insulated expression system to facilitate biotherapeutics delivery, and engineered a strain called EcN C6 with insulated expression of uricase from *Cyberlindnera jadinii* for the treatment of hyperuricemia. We characterized that the fusion of the uricase gene with the TAT signal peptide FtsP is essential for efficient degradation of urate by the strain. Further, we demonstrated the effects of EcN C6 in alleviating hyperuricemia and related symptom and restoring the gut microbiota disturbed by hyperuricemia in rat and mouse models. Collectively, our data suggest that EcN C6 is a safe and effective therapeutic candidate for hyperuricemia.
## EcN expressing uricase in periplasmic space in effectively degrades urate in vitro
We first aimed to test whether the expression of uricase in EcN degrades urate in vitro (Figure 1a). To achieve this purpose, we expressed the uricase gene from C. jadinii, which catalyzes the oxidation of urate to allantoin, in the cytoplasmic space of EcN using a P15A originated plasmid. In vitro urate degradation assay revealed that the degradation efficiency is extremely low (Figure S1a). To explore whether the expression location of uricase would affect the efficiency in urate degradation, we next fused the uricase gene with different secretion signal peptides, but most of these signal peptides could not enhance uricase activity (Figure S1a). Interestingly, when the uricase gene was fused with a TAT secretion signal peptide FtsP (SufI), the urate degradation efficiency was greatly improved (Figure S1b), and fluorescence localization of GFP showed that the FtsP signal peptide drives GFP protein into bacterial periplasmic space compared with the control group (Figure 1b and S1c). 40 Consistent with the location of the periplasmic space, the supernatant from FtsP‐uricase strain did not show uricase activity, suggesting that there was no uricase leakage into the supernatant (Figure S1d). Together, these results indicate that fusion of the FtsP signal peptide with uricase is vital for engineering EcN strain to degrade urate.
**FIGURE 1:** *Periplasmic expression of uricase in engineered EcN C6 to degrade urate in vitro. (a) Schematic of the engineered strain, EcN C6, with periplasmic expression of uricase by degradation of urate to allantoin. (b) Periplasmic localization of GFP fused with the FtsP signal peptide. (c) Insulated site for insertion of exogenous fragment in the EcN genome. (d) Urate degradation activity of EcN C6 in vitro. (e) Growth comparison of EcN WT and EcN C6 strains. (f) Linear plots of gene expression in EcN WT and C6 strains as tested by RNA‐seq analysis. Data represent measurements from three independent bacterial cultures, bars show the mean ± SD using two‐tailed unpaired Student's t‐test (**p < 0.01).*
## Construction of insulated expression system for heterologous uricase delivery
Next, to construct an engineered strain without affecting global gene expressions, we aimed to select a suitable site for exogenous gene integration in the EcN genome. By comparing RNA‐sequencing reads of EcN under different conditions, 41, 42, 43 we identified a noncoding region between two 3′‐end face‐to‐face located genes uspG and ahpF, whose RNA reads are extremely low, but the surrounding coding regions are highly transcribed (Figure S2a). Prediction of RNA secondary structure of 3′‐end of both uspG and ahpF genes showed two opposite ρ‐independent terminator structures named T1 and T2, respectively (Figure S2b), suggesting that the region between these two terminators would be an ideal “insulated site” candidate and widely found in the Enterobacteriaceae (Figure S2c). At this site, we inserted a uricase‐expressing cassette containing a synthesized σ70‐dependent promoter, a coding region carrying the FtsP signal peptide in fusion with the uricase gene, and a rrnB terminator (rrnBT), into this insulated site to obtain an engineered strain named EcN C6 (Figure 1a–c). Compared with EcN wild‐type (WT), EcN C6 degraded urate in vitro within 2 h (Figure 1d), but showed similar growth pattern as demonstrated by its growth curve in normal condition (Figure 1e) and survival in MU medium (Figure S3a). Furthermore, competitive growth assay showed the ability of EcN C6 to kill Salmonella Typhimurium LT2 under low iron conditions was comparable as EcN WT (Figure S3b), suggesting that the probiotic characteristics of EcN C6 were not affected by functional uricase expression. Transcriptomic analysis showed only six genes were significantly affected by uricase expression, while the global transcriptional profiling of EcN C6 was not influenced (Figure 1f). Together, we successfully engineered a recombinant EcN strain with insulated expression of periplasmic uricase to degrade urate in vitro.
## EcN C6 ameliorates hyperuricemia disease in rat model
To investigate the efficacy of EcN C6 in vivo, we applied a purine‐rich food‐induced hyperuricemia rat model to determine whether EcN C6 can degrade urate in animal model. Briefly, we first treated specific pathogen‐free (SPF) Sprague–Dawley (SD) rats with purine‐rich food for 21 days to induce hyperuricemia. Subsequently, the rats were orally administered EcN WT or C6 with purine‐rich food (Figure 2a). In this model, the sUA levels in rats increased significantly after 21 days of purine‐rich food induction (Figure 2b). Importantly, daily treatment with EcN C6 successfully decreased sUA levels within 3 days after the first administration, whereas the EcN WT or gavage buffer (GB) did not decrease sUA levels in this hyperuricemic rat model (Figure 2b).
**FIGURE 2:** *Treatment with EcN C6 decreases sUA levels and alleviates kidney damage in a rat model. (a) Schematic representation of the treatment of recombinant EcN C6 expressing uricase in a rat model of hyperuricemia. Male SD rats (n = 40) were divided into four groups (10 for each group); three quarters were pretreated with purine‐rich food for 21 days to induce hyperuricemia and the remaining one quarter was used as the control with no treatment (Ctrl). The EcN WT, EcN C6 (3 × 1010 CFU for each strain), or gavage buffer (GB) was administered orally to the hyperuricemic rats for another 21 days. Purine‐rich food was also provided during this treatment to maintain high sUA level. At indicated time points, bloods and feces were collected. Rats were euthanized after 21 days of treatment for kidney imaging. (b) sUA levels of purine‐rich food hyperuricemia rats after treatment with different strains. (c) Representative renal tissue sections with hematoxylin and eosin staining. Scale bars, 50 μm or 20 μm. (d) IL‐1β levels in rats with different treatments. Statistical analysis was performed using two‐tailed unpaired Student's t‐test (**p < 0.01).*
To further explore whether EcN C6 could alleviate the pathological symptoms caused by hyperuricemia, 44, 45 rats were euthanized 21 days after treatment. Compared with the chow diet group, the EcN C6 group displayed attenuated urate‐induced inflammation, as demonstrated by detailed renal pathologies of HE staining and serum IL‐1β levels (Figure 2c,d). Groups administered GB or the EcN WT strain showed significant renal crystals, severe inflammatory cell infiltration, and large amount of vacuolation in renal tubular epithelial cells and interstitial congestion of renal tubules, even significantly increased serum IL‐1β, while the EcN C6 group displayed attenuated urate‐induced inflammations of kidney, compared with the chow diet group (Figure 2c,d). Besides, the urate‐associated inflammation reflected by IL‐6, TNF‐α and diamine oxidase (DAO) levels in serum of hyperuricemia rats were also alleviated (Figure S4). Together, these data illustrate that EcN C6 possesses urate‐lowering effects and alleviates hyperuricemia symptoms in a rat model, suggesting that the EcN C6 strain is applicable for the treatment of hyperuricemia.
## EcN C6 alleviates dysbiosis of the gut microbiota in hyperuricemia rats
To explore the effect of EcN C6 on gut microbes in a hyperuricemia rat model, we extracted fecal bacterial DNA from rats before and after 21 days of purine‐rich food induction, as well as 14 days after EcN C6 treatment in the purine‐rich food‐induced hyperuricemia model. 16S rRNA gene amplicon sequencing was then employed to detect bacterial species in rats at different stages, which revealed that Bacteroidetes and Firmicutes were the two most abundant gut microbial phyla (Figure 3a). Principal component analysis (PCA) demonstrated that the gut microbial composition changed significantly in the hyperuricemia rat model (Day 0 compared with Day −21) (Figure 3b). Further, statistical analysis revealed that hyper urate induced gut flora disorders, including alterations in the contents of Bacteroidetes, Firmicutes, and Proteobacteria (Figure 3c–e), as well as Verrucomicrobia and Deferribacteres (Figure S5), whereas treatment with EcN C6 alleviated dysbiosis in the gut microbiota (Figure 3). Overall, these results suggest that EcN C6 may balance the gut microbiota in rats with hyperuricemia.
**FIGURE 3:** *EcN C6 alleviates gut microbiota dysbiosis in hyperuricemia rats. (a) Comparison of phylum relative abundance of rats before (−21) and after (0) high‐purine food induction, as well as treatment with the EcN C6 strain for 14 days.
14
(b) Principal coordinates plot of the gut microbiota in rats treated with EcN C6 at different time points as in A. (c) Relative abundance of Bacteroidetes, Firmicutes, and proteobacteria in the gut microbiota of the EcN C6 group at −21, 0, and 14 days (n = 10). Statistical analysis was performed using two‐tailed unpaired Student's t‐test (**p < 0.01).*
## Administration of EcN C6 twice per week lowers sUA levels
To explore the effective dosage of EcN C6 for lowering the sUA level, we treated the hyperuricemic rats with different doses of EcN C6 (Figure 4a). As expected, sUA levels in hyperuricemic rats were significantly decreased 3 days after a single‐dose administration of EcN C6 but were elevated after 7 days (Figure 4b). Similarly, once a week dose treatment resulted in the same trend (Figure 4b). The limited effect of once per week dose of EcN C6 in lowering sUA levels may be caused by limited resident time of the strain in the gut, as EcN C6 could be detected only within 2 days in feces after each administration (Figure 4c), which is consistent with previously reports. 34, 46 On the contrary, the group dosed with EcN C6 twice a week obviously increased the residence of EcN C6 in the gut and successfully lowered the sUA levels during the treatment period (Figure 4b,c), which further supports our conclusion that the colonization of EcN C6 in the gut is important for lowering urate levels. The discrepancies between different dosage group were also reflected by the renal pathology HE staining, creatinine, urea nitrogen, and cytokines levels in serum of hyperuricemia rats (Figure S6).
**FIGURE 4:** *Administration of EcN C6 twice per week is required to lower sUA levels. (a) Schematic representation of the treatment of hyperuricemia rats with different doses of EcN C6. Male SD rats (n = 30) were pretreated with purine‐rich food to induce hyperuricemia and divided into three groups: Single‐dose group (1.2 × 1011 CFU), once per week group (3 × 1010 CFU) and twice per week group (3 × 1010 CFU). Blood samples and feces were collected at indicated time points. (b) sUA levels of hyperuricemia rats administered purine‐rich food after treatment with different doses of the EcN C6 strain. (c) Detection of EcN C6 copies in feces at different time points (n = 10 for each group). Statistical analysis was performed using two‐tailed unpaired Student's t‐test (**p < 0.01).*
## EcN C6 alleviates hyperuricemia symptoms in a uox‐knockout mouse model
We employed a uox‐knockout mouse model to further confirm the effect of EcN C6 on lowering sUA levels. As knockout of the uox gene is detrimental, 47 only 12 knockout mice were obtained. Stably elevated sUA, serum creatinine, and urea nitrogen levels in these mice indicated that the model had been successfully established (Figure 5a). Mice were then divided into two groups and treated with EcN WT and EcN C6 (Figure 5b). In contrast to the WT group, the EcN C6 group had significant alleviation of the hyperuricemia indicators, including sUA, creatinine and urea nitrogen (Figure 5c). Similar to our observations in the rat model, EcN C6 treatment lowered the inflammatory response as reflected by the renal pathology HE section (Figure 5d) and kidney IL‐1β levels (Figure 5e). Taken together, our data suggest that EcN C6 can alleviate the symptoms of hyperuricemia in a uox‐knockout mouse model.
**FIGURE 5:** *Effects of EcN C6 treatment on hyperuricemia symptoms in a uox‐ko mouse model. (a) Serum urate, creatinine, and urea nitrogen levels of wild‐type (n = 10) and uox‐ko (n = 12) mice. (b) Schematic representation of the treatment with EcN WT (1 × 1010 CFU) or EcN C6 (1 × 1010 CFU) for 1 month in the uox‐ko mouse model. Blood samples were collected at indicated time points. After treatment for 28 days, the kidneys were dissected for tissue HE staining and inflammatory factor detection. (c) Serum urate, creatinine, and urea nitrogen levels in mice administered EcN WT or EcN C6 at indicated time points (six for each group). (d) Representative renal tissue sections with HE staining. Scale bars, 50 μm. (e) Kidney IL‐1β levels in wide‐type mice (Ctrl, n = 4) or uox‐ko mice administered EcN WT or EcN C6 treatment (six for each group). Statistical analysis was performed using two‐tailed unpaired Student's t‐test (*p < 0.1; ***p < 0.001).*
## DISCUSSION
Gout is a common and challenging health issue worldwide. Despite the availability of treatments for lowering urate levels, these drugs mainly aim to inhibit urate synthesis or promote urate excretion, thereby placing a remarkable burden on the kidneys. 23, 48 Previous studies showed that elevated level of urate causes kidney damage by promoting autophagy, and induces β‐cell injury via the NF‐κB‐iNOS‐NO signaling axis, 49, 50 and may have side effects on gut bacteria. 51, 52, 53 Here, as summarized in Figure 6, we successfully engineered a probiotic strain, EcN C6, with insulated expression of periplasmic uricase to directly degrade urate and alleviate the symptoms and dysbiosis of the gut microbiota caused by hyperuricemia, ultimately providing an efficient and friendly method for the rapid treatment of hyperuricemia.
**FIGURE 6:** *Proposed model for the action of EcN C6 strain in lowering sUA levels in vivo. In a hyperuricemia model, urate in the blood (blue dots) is transferred to the intestine by urate transporters. Engineered EcN C6 (green ellipses) with insulated expression of periplasmic uricase shows similar global transcription profile as the parent strain (down insert), and successfully degrades urate into allantoin (up insert) in the gut to lower sUA levels. Meanwhile, the administration of EcN C6 alleviates dysbiosis of the gut microbiota in the hyperuricemia model.*
In addition to traditional urate‐lowering chemical drugs, enzymes related to the degradation of urate are gaining attention, and may serve as a more direct method for hyperuricemia treatment. 54, 55 *Clinical data* have shown that modified uricases, such as pegolase, lablipase, and pregabalin, display excellent performance in the treatment of intractable gout disease by intravenous injection; however, their duration of action in vivo is limited. Notably, large amounts of supplementation can induce antibody production and are cost‐effective. 56, 57 Uricases are strongly not recommended as first‐line therapy by the American College of Rheumatology guidelines for the management of gout owing to their limited duration of action. 58 To overcome the limitation, we engineered a probiotic EcN C6 expressing periplasmic uricase (Figure 6). Probiotic EcN is the preferred microbial synthetic biology vector and can successfully colonize in the small intestine, 46 which plays an essential role in regulating urate levels. 29 Moreover, colonization of EcN near the epithelial cells in the small intestine allows them to easily access to urate that is transported from the blood 29 and obtain oxygen 46, 59 as a necessary substrate for uricase function. This strategy has markedly extended the application of uricase for hyperuricemia treatment.
The location of the uricase expressed in E. coli is essential for its ability to degrade urate. Although aegA and ygfT can degrade urate under microaerobic or anaerobic conditions in E. coli, 60 in contrast to uricase, this effect was almost negligible both in vitro and in vivo (Figures 1d and 2b). In addition, there are 10 nucleobase‐ascorbate transporter (NAT) family‐related proteins in E. coli that are responsible for transporting different forms of base metabolites. Further, the ygfU gene located at the inner membrane was hypothesized to import urate 61 and overexpression of YgfU could improve the urate degradation efficiency in E. coli. 62 However, our data showed that cytoplasmic expression of uricase in the EcN strain only slightly decreased the urate level in vitro (Figure S1b), suggesting that the efficiency of urate import may be limited by the bacterial cell membrane or the affinity of urate transporters. Consistently, a previous study showed that E. coli expressing cytoplasmic uricase did not show significant enhancement in urate degradation. 63 While, E. coli expressing secreted uricase could be functional in hyperuricemia rat model, although its effect in lowering sUA is limited. 63 The EcN C6 expressing periplasmic uricase driven by FstP signal peptide significantly enhanced its activity in urate degradation. In our study, we found that fusion of uricase with the Tat secretory signal peptide FtsP, which transports folded proteins across biological membranes, 64 successfully enabled the EcN strain to degrade urate in vitro and in vivo (Figures S1b and 2b), while fusing with the Sec secretory signal peptides (OmpA and TamA), which translocate unfolded proteins across the cytoplasmic membrane, 64 did not show such effect (Figure S1b). These data suggest that the uricase folding events in cytoplasmic is important for its activity.
Our EcN C6 design included a feature to insulate uricase expression. As a safe and common bacterial vector, EcN has been adopted for the regulation of mucosal immunity, 65, 66 metabolic diseases 34, 35, 67 and pathogenic infections. 68, 69, 70 However, the expression patterns for these functional proteins are mainly based on recombinant plasmids, which fail to comply with the FDA's requirements for live biotherapeutic products. 71 Previous studies have attempted to insert exogenous genes near the malEK, malPT, and yicS/nepI genes in EcN 34, 72; however, these types of genomic editing may interfere with native gene transcription. 34, 38 As the design of ultra‐stable genetic editing in E. coli is important for living therapeutics, 38 herein, we have demonstrated that a method for searching insulated sites on the bacterial genome by analyzing previous transcriptome data of EcN under different conditions 34, 41, 42 and proposed an insulated expression system for the exogenous gene. Concretely, we identified a natural dual‐terminator structure between uspG and ahpF on the E. coli genome, providing an ideal insulated expression site, which is widespread in Enterobacteriaceae (Figure S2c). Importantly, our RNA‐seq data revealed that this “insulated site” ensures insulated expression of the inserted uricase as expression of only six genes were significantly changed comparing between the EcN C6 and parent strains. Therefore, our study presents an insulated site, which is effective and valuable for the recombinant expression of other target genes in EcN or similar bacterial chassis in the field of synthetic biology.
Influenced expression of six genes associated with arginine and ornithine metabolism pathways in EcN C6 strain may not influence its application in hyperuricemia treatment. In EcN C6 strain, the inserted uricase could catalyze urate to allantoin, which is further degraded to ammonia. The ammonia could be further utilized as the nitrogen source to synthesize ornithine or arginine. 73 Consequently, the acrA‐acrC‐argF operon, which is associated with conversion of arginine to citrulline and ammonia, 74 would be repressed due to the accumulation of ammonia. In contrast, the speFL‐polE operon, which is responsible for ornithine capture to control polyamine synthesis, 75 would be activated due to the accumulation of ornithine. Nevertheless, the expressions of other genes related to the arginine or ornithine metabolism pathway, the bacterial growth and the probiotic phenotype were not influenced in EcN C6 strain (Figures 1f,e and S3). Therefore, we conclude that the EcN C6 could be a candidate for the treatment of hyperuricemia.
## CONCLUSION
In this study, we first analyzed the published transcriptome data to identify an “insulated” site located between the uspG and ahpF genes in the EcN genome; we next applied homologous recombination to insert a cassette expressing periplasmic uricase at this insulated site to obtain an engineered strain named EcN C6. In vitro urate degradation assay and global RNA‐seq were subsequently applied to confirm the activity and insulated expression of uricase in EcN C6, respectively. Importantly, the recombinant probiotic EcN C6 strain showed strong ability in decreasing serum urate levels and relieving symptoms in hyperuricemia murine models, thus offering great potential in clinical application. Together, our study provides an insulated site for heterologous gene expression in EcN strain and an engineered EcN C6 strain as a safe and effective therapeutic candidate for hyperuricemia treatment.
## Bacterial strains
The bacterial strains used in this study are listed in Table S1. Escherichia coli and S. Typhimurium LT2 strains were grown in Luria‐Bertani (LB) medium or agar (LBA) at 37 °C and supplemented with 50 μg/ml kanamycin, 100 μg/ml ampicillin, or 30 μg/ml chloramphenicol, when necessary. EcN WT and C6 were prepared in LB and the cell pellets were re‐suspended in protective buffer ($15\%$ v/v glycerol, $5\%$ w/v trehalose, and 10 mM MOPS, pH 7.3) and frozen at −80°C until use, as previously described. 34
## Plasmid constructions
The plasmids and oligonucleotides used in this study are also listed in Table S1. To express uricase fused with different signal peptides, a uricase gene from C. jadinii (GenBank: XM_020212619.1) with a signal peptide coding region from either ftsP (N‐terminal 30 aa, GenBank: NP_417489.1), ompA (N‐terminal 27 aa, GenBank: NP_415477.1), tamA (N‐terminal 27 aa, GenBank: NP_418641.1), lpp‐ompA (N‐terminal 29aa of lpp and 46aa‐159aa of ompA, GenBank: NP_310411.1 and NP_415477.1), yebF (N‐terminal 118 aa, GenBank: NP_416361.2), or inpNC (N‐terminal 211aa and C‐terminal 99aa, GenBank: AF013159) was cloned into the pKT100 plasmid 76 using a ClonExpress II One Step Cloning Kit (Vazyme, China). The fstP‐gfp expression clone was constructed in a similar manner by replacing the uricase gene with gfp fragment.
## Fluorescence imaging
To verify the periplasmic localization of the FtsP‐GFP fusion protein, the EcN strain transformed with pKT‐ftsP‐gfp was grown at 37°C in LB liquid medium containing Kan (50 μg/ml) to OD600 ~ 0.3. Cells were collected by centrifugation at 5000g for 3 min and resuspended in phosphate buffered saline (PBS). The samples were dropped onto a slide and fixed by slight heating. Fluorescence images were obtained using a fluorescence microscope (OLYMPUS).
## EcN C6 strain construction
To construct the EcN C6 strain, a fragment containing the synthesized promoter P6, a signal peptide encoded by ftsP, the uricase gene from C. jadinii, and the rrnBT terminator was inserted between uspG and ahpF homologous fragment, and cloned into the suicide pDM4 plasmid, which carries chloramphenicol resistance gene and sucrose sensitive sacB gene. 77 The resulting plasmid was transformed into E. coli S17‐1 cells. Transconjugation 77 was performed using this E. coli S17‐1 and EcN carrying the temperature‐sensitive pKD46 plasmid 78 as the donor and recipient cell, respectively. The single cross clones were selected using LB with Amp (100 μg/ml) and Chloramphenicol (30 μg/ml) plate, and the double cross clones were selected in LB plate containing $15\%$ sucrose. The pKD46 plasmid in EcN C6 was removed by culturing the strain at 42°C. The insertion of the uricase fragment into the EcN C6 strain was confirmed by PCR and DNA sequencing.
## Urate degradation assay by EcN strains
Overnight cultures of the EcN strains were diluted 1:100 with 3 ml fresh LB liquid medium, incubated at 37°C until OD600 ~ 0.6 (0.3 mM IPTG was added when necessary), and continually incubated until OD600 ~ 1.0. Cell pellets were collected by centrifugation and resuspended in an equal volume of MU medium as previously described. 15 Urate concentration in the medium was monitored using A293 absorption with NanoDrop One (Thermo Fisher Scientific, USA) at the indicated time points. 15 The standard curve between the A293 values and urate concentrations was examined to quantify the urate concentrations in the MU medium.
## Growth assay of EcN C6
Freshly streaked EcN WT and EcN C6 colonies were inoculated with 5 ml of LB and grown overnight at 37°C. Cultures were transferred at a ratio of 1:100 into fresh 5 ml of LB and grown for 12 h at 37°C. The OD600 was measured at the indicated time (BioTek). Equal amounts of EcN WT and EcN C6 were incubated in MU medium for 1 h, and the CFU of strains was counted by 10‐fold serial dilution in LB plate. Competitive growth for EcN and S. Typhimurium LT2 under iron‐rich or iron‐limiting conditions was performed as previously described. 70
## RNA‐seq analyses
To characterize the transcriptional profiles of EcN WT and EcN C6 strains, triplicate cultures of each strain were incubated overnight in LB broth at 37°C. Overnight cultures were then diluted 100‐fold in 4 ml LB broth and incubated for 2.5 h. RNA was extracted using TRIzol reagent, as described in the manufacturer's protocol (Invitrogen, USA). rRNA in the extracted RNA was removed using a Ribo‐off rRNA Depletion Kit (Vazyme, China). The RNA library was constructed using the NEBNext® Ultra II RNA Library Prep Kit for Illumina (NEB, USA) and sequenced using the Illumina HiSeq X Ten platform.
## Hyperuricemia rat model
Two‐week‐old SPF SD rats were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. After acclimatization for 1 week, rats were divided into groups (10 per group) and were either fed with high‐purine food containing maintenance powder, $10\%$ yeast (OXIFOD) and $0.1\%$ adenine (Sangon, China) to induce hyperuricemia, 79 or normal chow as a control.
EcN WT and EcN C6 were grown overnight in LB at 37°C with shaking. Overnight cultures were used to inoculate 1 L of LB in 3 L baffled flasks, cultures were grown with shaking at 37°C for 5 h. Cell pellets were obtained by centrifuge at 4500 g for 30 min and resuspended in protective buffer ($15\%$ v/v glycerol, $5\%$ w/v trehalose, and 10 mM MOPS, pH 7.3). The strains were adjusted to 3 × 1010 CFU/ml or 1.2 × 1011 CFU/ml and frozen at −80°C until use.
After induction by high‐purine food for 3 weeks, rats were administered 1 ml of gavage buffer (GB group), EcN wild‐type (EcN group, 3 × 1010 CFU per dose), or EcN C6 (EcN C6 group, 3 × 1010 CFU per dose) for 3 weeks. To explore the minimum dose of EcN C6, similar approaches were employed, except that 1.2 × 10^11 CFU was used for the single‐dose group. Serum samples were collected at the indicated time points to test sUA, creatinine, and urea nitrogen levels using commercial kits, according to the manufacturer's instructions (Jiancheng, China). After treatment with these strains for 21 days, two rats in each group were euthanized by slow asphyxiation with CO2. The left kidney was dissected for tissue HE staining.
## Uox‐knockout mouse model
Conventional SPF C57BL/6J uox/uox (uox‐ko) mice purchased from the Shanghai Model Organisms Center Inc. (SMOC) were maintained and bred at the Center for Animal Experiments at the Wuhan Institute of Virology. Allopurinol (90 μg/ml) was added to enhance the survival of newborns when administered to the mother and withdraw 1 week before the experiment. Mice were divided into two groups of equal sex and age. In this model, the EcN WT or C6 strain was administered daily by oral gavage 0.2 ml strains (6 × 109 CFU per dose). Serum was collected at the indicated time points after treatment for 28 days. Mice were euthanized by slow asphyxiation with CO2. The left kidney was dissected for HE staining, and the right kidney was used for tissue homogenization to determine the inflammatory factors.
## ELISA
Serum was isolated from the blood of rats and mice by low‐speed centrifugation (1000 g, 10 min). Suspensions from ground kidney samples were collected by low‐speed centrifugation (2000 g, 15 min). To detect the cytokines in serum or kidney, the samples were analyzed using a rat or mouse IL‐1β ELISA Kit (Neobioscience, China), rat IL‐6 ELISA Kit (Neobioscience, China), rat TNF‐α ELISA Kit (CUSABIO, China), rat DAO ELISA Kit (CUSABIO, China), following the manufacturer's instructions.
## 16s rRNA library preparation and sequencing
Feces collected from hyperuricemic rats were frozen at −80°C until use. DNA was extracted using the E.Z.N.A. Stool DNA Kit (OMEGA, USA) following the manufacturer's instructions. The 16S rRNA gene (V4 region) was amplified by two‐step PCR enrichment using barcodes for multiplexing. 80 Pooled DNA was purified using AMpure XP beads (Beckman, USA). DNA libraries were constructed using the NEBNext Ultra II FS DNA Library Prep kit (NEB, USA) and sequenced using the Illumina HiSeq X Ten platform.
## Quantification of EcN C6 colonization
To quantify the colonization of EcN C6, qPCR was performed to determine the copy numbers of the EcN fimA gene in 10 ng of fecal genomic DNA using iTaq Universal SYBR Green Supermix (Bio‐Rad, USA). Standard curves were constructed by quantitatively testing 10,8 10,7 10,6 10,5 10,4 10,3 10,2 10,1 and 100 copies of EcN C6 genomic DNA according to a previously described protocol. 3 All measurements were performed in triplicate.
## Statistical analysis
Statistical significance between two groups was analyzed by unpaired Student's t‐test (two‐tailed) using GraphPad Prism 8 or the R package (version 3.2.2).
## AUTHOR CONTRIBUTIONS
Lina He: Conceptualization (equal); investigation (equal); methodology (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Wei Tang: Investigation (equal); methodology (equal). Ling Huang: Investigation (supporting). Wei Zhou: Visualization (supporting). Shaojia Huang: Investigation (supporting). Linxuan Zou: Investigation (supporting). Lisha Yuan: Investigation (supporting). Dong Men: Conceptualization (supporting); methodology (equal). Shiyun Chen: Conceptualization (equal); supervision (equal); writing – review and editing (equal). Yangbo Hu: Conceptualization (lead); funding acquisition (lead); methodology (equal); project administration (lead); supervision (equal); visualization (equal); writing – original draft (equal); writing – review and editing (lead).
## CONFLICT OF INTEREST
The authors declare that they have no conflict of interest. The WIV has filed patents on EcN C6 strain construction and application, which are based in part on the work reported here.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10449.
## DATA AVAILABILITY STATEMENT
RNA‐sequencing and 16s rRNA gene sequencing reads were submitted to the NCBI Sequence Read Archive (SRA) under accession: PRJNA818111 and PRJNA818085, respectively. The data that support the findings of this study are available from the corresponding author, Y.H., upon reasonable request.
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|
---
title: OP3‐4 peptide sustained‐release hydrogel inhibits osteoclast formation and
promotes vascularization to promote bone regeneration in a rat femoral defect model
authors:
- Peng Luo
- Jiarui Fang
- Dazhi Yang
- Lan Yu
- Houqing Chen
- Changging Jiang
- Rui Guo
- Tao Zhu
- Shuo Tang
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013759
doi: 10.1002/btm2.10414
license: CC BY 4.0
---
# OP3‐4 peptide sustained‐release hydrogel inhibits osteoclast formation and promotes vascularization to promote bone regeneration in a rat femoral defect model
## Abstract
Bone injury caused changes to surrounding tissues, leading to a large number of osteoclasts appeared to clear the damaged bone tissue before bone regeneration. However, overactive osteoclasts will inhibit bone formation. In this study, we prepared methacrylylated gelatin (GelMA)‐based hydrogel to co‐crosslink with OP3‐4 peptide, a receptor activator of NF‐κB ligand (RANKL) binding agent, to achieve the slow release of OP3‐4 peptide to inhibit the activation of osteoclasts, thus preventing the long‐term existence of osteoclasts from affecting bone regeneration, and promoting osteogenic differentiation. Moreover, CXCL9 secreted by osteoblasts will bind to endogenous VEGF and inhibit vascularization, finally hinder bone formation. Thus, anti‐CXCL9 antibodies (A‐CXCL9) were also loaded in the hydrogel to neutralize excess CXCL9. The hydrogel slow released of OP3‐4 cyclic peptide and A‐CXCL9 to simultaneously inhibiting osteoclast activation and promoting vascularization, thereby accelerating the healing of femur defect. Further analysis of osteogenic protein expression and signal pathways showed that the hydrogel may be through activating the AKT‐RUNX2‐ALP pathway and ultimately promote osteogenic differentiation. This dual‐acting hydrogel can effectively prevent nonunion caused by low vascularization and provide long‐term support for the treatment of bone injury.
## INTRODUCTION
Femur fractures and defects caused by surgical resection of osteosarcoma are usually accompanied by two major problems in clinical treatment: nonunion of the fracture 1 and avascular necrosis of the femur head. 2, 3 The incidence of femur fractures is concentrated in the elderly, and the treatment of osteosarcoma usually requires the removal of a large amount of cancerous bone tissue, both of two situations are often difficult to treat through autologous bone transplantation. 3 By designing a new type of injectable in situ forming hydrogel to promote bone formation and vascularization, it is expected to replace autologous bone transplantation and become a new treatment method for the treatment of femur injuries. 4, 5, 6 *As a* three‐dimensional structure network with high water content, hydrogel is beneficial to the delivery of nutrients and the growth of blood vessels. 7, 8 In addition, the hydrogel can also be used as a drug carrier to achieve sustained release of drugs or polypeptides. 9, 10, 11 OP3‐4 polypeptide is a nuclear factor‐κB receptor activator ligand (RANKL) binding peptide, which inhibits the formation of osteoclasts by inhibiting the binding of RANKL to RANK on the surface of osteoclasts. 12, 13, 14 Recent studies have also shown that OP3‐4 polypeptide binds to RANK on the surface of osteoblasts, induces membrane receptor aggregation, thereby enhancing osteoblast differentiation. 12, 14, 15 Therefore, OP3‐4 can simultaneously inhibit osteoclast activation and promote osteogenic differentiation during bone tissue regeneration. The slow release of OP3‐4 polypeptide will help bone tissue regeneration.
However, in the process of bone tissue regeneration, another problem faced is the vascularization of bone tissue. Insufficient vascularization of bone tissue during the repair process can lead to problems such as bone nonunion. 1 Nevertheless, it is worth noting that the osteoblasts can secrete Chemokine (C‐X‐C motif) ligand 9 (CXCL9), and CXCL9 will binds to endogenous vascular endothelial growth factor (VEGF) to prevent blood vessel formation. 16, 17 Therefore, coordinating osteoblast differentiation and vascularization processes can further accelerate bone tissue healing. The use of anti‐CXCL9 antibodies (A‐CXCL9) was able to bind excess CXCL9, thereby directly abolishing the effect of CXCL9 on VEGF and improving the vascularization process. 16 Realizing the long‐term sustained release of the drug or bioactive factor is the key to ensuring that the implant can play a role in promoting bone formation for a long time. After the polypeptide has been modified by methacrylation, it can be co‐cross‐linked with the methacryloyl polymer material to fix the polypeptide in the hydrogel to achieve long‐term sustained release of the polypeptide. 9 In addition, methacrylated gelatin (GelMA) cannot only combine with polypeptides to achieve sustained release but also promote cell adhesion, proliferation. 18 It also has adjustable mechanical properties to adapt to different tissue applications. 19, 20 PCL‐PEG‐PCL (PCEC) copolymer is a nano micelle composed of medical polymers PCL and PEG. The raw material has extremely high biosafety and PCEC can be used to load drugs or growth factors to achieve long‐term sustained release. 21, 22 In order to promote bone regeneration while reducing the effect of CXCL9 secreted by osteoblasts on the vascularization process, we designed a GelMA‐based hydrogel that sustained‐release osteogenesis‐promoting polypeptide OP3‐4 and anti‐CXCL9 antibody embedded in PCEC nanoparticles. In this study, we verified the sustained‐release effect of the hydrogel on A‐CXCL9, and the inhibitory effect on osteoclasts and the pro‐differentiation of rBMSCs into osteoblasts from the biofunctional aspect. Finally, the repair effect of slow‐release OP3‐4 and A‐CXCL9 hydrogel materials on femoral defects was evaluated in a rat femoral defect model, which proved that promoting vascularization can better assist bone regeneration.
## Materials
Gelatin, from cold‐water fish skin, was purchased from Sigma‐Aldrich (Shanghai, China). Methacrylic anhydride, ε‐caprolactone and polyethylene glycol (PEG) (Mw = 4000) were purchased from Macklin (Shanghai, China). Tin (II) 2‐ethylhexanoate and sodium laurylsulfonate were purchased from Aladdin (Shanghai, China). Ethylacetate, acetone, petroleum ether and dichloromethane were purchased from Sinopharm Chemical Reagent Co., Ltd (Shanghai, China). Lithium phenyl(2,4,6‐trimethylbenzoyl) phosphinate (LAP) was purchased from Shanghai Yinchang New Material Biological Co., Ltd. Methylamidated OP3 peptide (YCEIEFCYLIR) was purchased from Jiangsu Ji Tai Peptide Industry Science and Technology Co., Ltd. Streptomycin sulfate, penicillin, fetal bovine serum and trypsin were purchased from Gibco (Shanghai, China). Cell counting kit 8 was purchased from Bioss antibodies (Beijing, China). Live/Dead staining kit was purchased from KeyGEN BioTECH (Jiangsu, China). TRITC Phalloidin was purchased from YEASEN (Shanghai, China). Rabbit Anti‐RUNX2 antibody, Alizarin Red S Staining Kit, DAPI staining, and Anti‐Osteocalcin Rabbit pAb were purchased from Servicebio (Wuhan, China). Rat bone marrow mesenchymal stem cells were purchased from ChuangSeed Biomaterials.
## Synthesis of methacrylylated gelatin
GelMA was synthesized as previously described. 23 Briefly, the solution of gelatin was prepared at a concentration of $8\%$ in a water bath at 60°C. Then, methacrylic anhydride was added dropwise to the gelatin solution at a ratio of 0.6:1 (methacrylic anhydride to gelatin). The reaction was kept under room temperature for 8 h. Finally, the solution was dialyzed against deionized water with cellulose dialysis bag (MwCO = 3500) for 5 days and centrifuged at 8000 rpm for 5 min to remove undissolved impurities. The supernatant after centrifuge was lyophilized at −80°C to obtain the final product GelMA.
## Synthesis of PCEC and A‐CXCL9@PCEC nanoparticles
PCEC nanoparticles were obtained following the previous report. 21 A 4 g of PEG and 96 g of anhydrous ε‐caprolactone were added to dry three‐necked bottle and few drops of tin (II) 2‐ethylhexanoate was added to the above solution. The mixture was kept at 130°C for 6 h. Subsequently, the air in the reaction device was exhausted, and the mixture was heated to 180°C under vacuum and kept for 30 min. The mixture was then cooled to room temperature under the protection of nitrogen and dissolved in dichloromethane, and then the product PCEC was precipitated with excess cold petroleum ether, and finally filtered and dried to obtain a PCEC copolymer.
The anionic PCEC nanoparticles were then derived by the following methods 24: 30 mg of PCEC copolymer was dissolved in 5 ml of acetone/ethyl acetate, and then 5 mg of sodium laurylsulfonate was dissolved in 10 ml of water and added to PCEC solution. Stir at 10,000 rpm to form a sodium dodecyl sulfonate emulsion and finally remove most of the solvent by rotary evaporation under reduced pressure. The resulting slurry was dialyzed to remove sodium dodecyl sulfonate. PCEC and A‐CXCL9 were mixed and stirred at 4°C for 24 h to obtain the final product A‐CXCL9@PCEC nanoparticles.
The amount of A‐CXCL9 was detected by ELISA kit. The ELISA kit was obtained by double antibody sandwich method. Anti‐CXCL9 antibody was purchased from Abcam (Shanghai, China). Recombinant Murine MIG (CXCL9) was purchased from Peprotech (Shanghai, China). Biotin‐NHS was purchased from Sigma‐Aldrich. HRP‐labeled Streptavidin was purchased from Sangon Biotech (Shanghai, China).
## Preparation of the hydrogel
GelMA, OP3‐MA, and A‐CXCL9@PCEC were dissolved in PBS buffer according to a certain concentration ratio. LAP was then dissolved in the above mixture at the final concentration of $0.1\%$ (w/v). The composition ratios of different hydrogels are shown in Table 1. The hydrogel was finally obtained by light (405 nm, 3 W/cm2) irradiation for 20 s.
**TABLE 1**
| Unnamed: 0 | GelMA (mg/ml) | OP3‐MA (mg/ml) | A‐CXCL9@PCEC (mg/ml) |
| --- | --- | --- | --- |
| 5%GelMA | 50 | – | – |
| 5%GelMA/OP3‐MA | 50 | 0.6 | – |
| 10%GelMA/OP3‐MA | 100 | 0.6 | – |
| 15%GelMA/OP3‐MA | 150 | 0.6 | – |
| 10%GelMA/OP3‐MA/A‐CXCL9@PCEC | 100 | 0.6 | 0.02 |
## Characterization of the PCEC and A‐CXCL9@PCEC nanoparticles
The morphology of PCEC and A‐CXCL9@PCEC nanoparticles was observed by transmission electron microscope (TEM, Hitachi, H‐800). PCEC and A‐CXCL9@PCEC nanoparticles were first dispersed in deionized water by ultrasonic, and then added dropwise in copper mesh. The sample was then completely dried at room temperature and observed by TEM.
The hydrate diameter and particle size distribution of nanoparticles were observed by dynamic laser scatterometer (DLS, Malvern, Zeta Sizer Nano ZS).
## Chemical structure characterization of GelMA and OP3‐MA
The chemical structure of GelMA and OP3‐MA was characterized by Fourier transform infrared spectrometer (FTIR, Inova‐500M, Varian, USA) and nuclear magnetic resonance spectrometer (NMR, VERTEX 70, Burke, Germany). For FTIR characterization, 5 mg of samples was first mixed with 30 mg of potassium bromide (KBr) and ground into powder, then the powder was pressed into a transparent flake and tested by FTIR. The scan range of the wavenumber was 4000–500 cm−1, and the resolution was 4 cm−1. As for NMR characterization, samples were dissolved in deuterated water (D2O), and the hydrogen spectrum was detected.
## Characterization of Hydrogel
Rotational rheometer (Kinexus pro, Malvern, UK) was used to measure the rheological behavior of hydrogels. The 400 μl of hydrogel was used for each test. A flat rotor with a diameter of 25 mm was selected for the test. Time sweep sequence was performed at 25°C under fixed strain ($1\%$). Frequency sweep was conducted in the range of 0.1–10 Hz under fixed strain ($1\%$) at 25°C.
Morphology of the hydrogel was characterized by scanning electronic microscope (SEM, S‐3400, Hitachi, Japan). Hydrogels were first lyophilized under −80°C to remove water, and then fixed on copper sample stage using carbon conductive tape. The samples were spread with gold for 30 s before observation.
Compression test of hydrogel was tested by universal testing machine (ELF3200; Bose, USA). Hydrogels were pre‐prepared into a cylinder with a height of 6 mm and a diameter of 11 mm. Then samples were compressed using universal testing machine at a stable speed (0.05 mm/s). Compressed length and load were recorded by machine and the strain and stress were calculated by the following formula: Strain%=l0−l1l0×$100\%$ Stress=PAPa where l0 represents the initial height of sample, while l1 is the height of sample at different time point. P is the compression load, while A is the cross‐sectional area of hydrogel.
Equilibrium‐swelling ratio was measured by gravimetric method. Hydrogels were first lyophilized and weighted to obtain the initial weight (Wdry). Immerse the dried hydrogel to PBS buffer (pH = 7.4) at 37°C, then take them out and weight at selected time point. The weight of hydrogel at different time point was recorded as Wswollen. Equilibrium‐swelling ratio was calculated by the following formula: Equilibrium−swelling ratio=Wswollen−WdryWdry×$100\%$.
The degradation behavior with and without lysosome was also measured by gravimetric method. Hydrogels were first lyophilized and weighted to obtain the initial weight (W0). Then the hydrogels were immersed in PBS buffer (pH = 7.4) and 1000 U lysosome PBS solution separately. Finally, the hydrogel was taken out, lyophilized and weighted at selected time point. The weight of hydrogel at different time point was recorded as Wt. Degradation ratio was calculated by the following formula: Degradation ratio=Wo−WtW0×$100\%$.
## Cytotoxicity of hydrogel to rBMSCs
GelMA, OP3‐MA, and A‐CXCL9@PCEC solution were first filtered through 0.22 μm filter membrane to remove bacterial. Then 500 μl hydrogel was prepared in 12 well plates as described in Section 2.4. Rat bone marrow mesenchymal stem cells (rBMSCs) were cultured with DMEM containing $10\%$ FBS and $1\%$ penicillin–streptomycin solution. The rBMSCs in the logarithmic growth phase were trypsinized and resuspended, and 2 ml of cell suspension with a cell density of 2 × 104 cells/ml was seeded on the surface of the hydrogel.
RBMSCs were co‐cultured with hydrogel for 1, 3, and 5 days in $5\%$ CO2 incubator at 37°C. Cells at pre‐set time point were washed with PBS buffer for twice and incubated with $10\%$ CCK‐8 solution for 1 h. The supernatant of cell‐hydrogel co‐culture system was collected, and the optical density (OD) value of supernatant at 450 nm was measured by microplate reader. Cell viability was calculated as following: Cell viability=ODmaterials−ODblankODcontrol−ODblank×$100\%$ Materials, blank, and control separately represent rBMSCs treated with different hydrogel, complete DMEM containing CCK‐8 without rBMSCs and rBMSCs treated with complete DMEM culture medium.
## Live/dead staining of rBMSCs co‐cultured with hydrogels
Calcein AM and propidium iodide (PI) was used for cell staining. Live cells can be stained with calcein AM to show green fluorescence, while dead cells stained with PI to show red fluorescence. RBMSCs were co‐cultured with hydrogel as described in Section 2.8.1 for 1, 3, and 5 days. Then the hydrogel was washed with PBS for three times and stained with calcein AM/PI following manufactures' direction. Images of live/dead staining were captured with inverted fluorescence microscope.
## Cytoskeleton staining of rBMSCs co‐cultured with hydrogels
The 500 μl of rBMSCs at cell density of 60,000/ml was seeded on GelMA‐based hydrogels and cultured at 37°C for 1, 3, and 7 days. Hydrogels were washed with PBS and fixed with $4\%$ paraformaldehyde for 10–15 min at selected time. Then $0.5\%$ TritonX‐100 was used to treated hydrogels for 5 min. After washed with PBS, TRITC Phalloidin working solution and DAPI were used to stain the cytoskeleton. Finally, the stained images were acquired by confocal laser microscopy.
## Osteogenesis evaluation
After rBMSCs cultured with hydrogels leach liquor for 1, 3, and 5 days, the cells were stained with alkaline phosphatase (ALP) staining kit. After rBMSCs cultured with hydrogels leach liquor for 10 days, the cells were stained with alizarin red staining kit and mineralized nodules were captured by inverted microscope.
Immunofluorescence staining of osteogenic‐related proteins was also conducted. After co‐cultured with the hydrogel leach liquor, the rBMSCs cell slides were fixed with $4\%$ paraformaldehyde, then the membrane was permeabilized with $0.3\%$ Triton‐X, and then blocked with $5\%$ BSA overnight. Then, the cells were incubated with primary antibodies against RUNX2, OCN, AKT and p‐AKT at room temperature for 1 h and then incubated with secondary antibody conjugated with fluorescent labels at room temperature for 1 h in dark.
## In vivo femur defect rat model
All animal experimental protocols have been reviewed and approved by the Animal Protection and Use Committee of The Eighth Affiliated Hospital of Sun Yat‐sen University (approval number: 2019d084). Thirty‐six female SD rats (200–220 g) were randomly divided into four groups (Blank, GelMA, GelMA/OP3‐MA, GelMA/OP3‐MA/A‐CXCL9@PCEC). Pentobarbital sodium (60 mg/kg) was used for intraperitoneal injection to anesthetize rats. Surgical instruments are preautoclaved. Hair on the right leg was shaved and exposed skin was disinfected by iodophor before surgical. Skin of the right femur was cut longitudinally by scalpel. Then muscle, ligament, and femur were separated by dental scraper to expose the distal side of the femur. Bone defect at the femur epiphysis with a diameter of 2.8 mm and a depth of 3 mm was subsequently created using an electric drill. 25 The 20 μl of hydrogel presolution was then added on the bone defect and crosslinked by blue light for 20 s. Control group was treated by 20 μl of PBS. Muscle and skin were suture the in turn using 0‐3 absorbable sutures, and surgical site was finally disinfected with iodophor. Penicillin was injected intramuscularly within 3 days after surgery to avoid infection.
Femur was taken out after treating for 7, 14, and 28 days and fixed on $4\%$ paraformaldehyde for 24 h. The femur was decalcified with EDTA decalcification solution for 1 month before paraffin embedding. Injured femur was cut into 5 μm thick slices, and then pathologically stained.
## Micro‐CT scanning
Femur without soft tissue was fixed on $4\%$ paraformaldehyde and scanned with micro‐CT (MCT‐Sharp, Zhongke Kaisheng, China; scanning voltage: 70 kV; Voxel size: 20 μm). 3D reconstruction and quantitative analysis were conducted using the sagittal image of the distal femur. The region of interest (ROI) was defined as 900 μm (45 consecutive images) of the proximal end of the epiphysis of the distal femur.
## Pathological staining and immunofluorescence staining
Femurs were decalcified, embedded in paraffin, and cut into 4 μm slices. H&E, Masson and TRAP staining were performed according to standard protocols. 26 Angiogenesis effect was identified by immunofluorescence‐labeled α‐SMA. OCN, OPN, Col‐I, and TGF‐β1 were conducted through immunohistochemical labeling.
## Statistical analysis
Each group of samples in the experiment contains at least three parallel samples, and the results were shown as average and standard deviation. The significance was analyzed using Graphpad Prism 7.0. One‐way analysis of variance (ANOVA) was performed to evaluate the significance of the experimental data. The statistical significance was *$p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001.$
## Physicochemical structure characterization of hydrogel and nanomaterials
The hydrogel was obtained by photo‐initiated free‐radical polymerization between GelMA and OP3‐MA, which can sustain release of OP3‐4 cyclic peptide 9 to block the activation of NF‐κB signaling pathway thus inhibit osteoclast formation and bone resorption. 27 Amphiphilic block copolymers PCEC can spontaneously assemble into nano micelle and carry the anti‐CXCL9 antibody (A‐CXCL9) 21, 22 through electrostatic adsorption, and let anti‐CXCL9 antibody sustain release to the surrounding environment to promote the angiogenesis effect and bone formation (Scheme 1).
**SCHEME 1:** *Schematic diagram of the preparation and action process of the hydrogel. (a) Schematic diagram of the gelation process of hydrogels; (b) hydrogel injection into femoral injury cavity; (c) schematic representation of the role of OP3‐4 and A‐PCEC: The OP3‐4 peptide released from hydrogel prevents osteoclasts formation and promotes pre‐osteoblasts to become osteoblasts; A‐CXCL9 released in the same time to neutralize the excess CXCL9 secreted by osteoblast to promote angiogenesis.*
Figure 1 displays the typical physicochemical and microstructure characterization of hydrogels and nano micelles. Chemical structure of GelMA was analyzed by FTIR and 1H NMR. The difference in the FTIR spectra of GelMA compared to Gelatin (Gel) was not obvious because both containing a large number of amide bonds. The absorption peak of GelMA at 592 cm−1 was stronger than that of Gel, which may be due to more amide bonds in GelMA, and the absorption peak of the amide VI band appears enhanced (Figure 1a). The two newly appeared peaks at δ 5.66 and δ 5.43 ppm on GelMA were linked to acrylic protons, 11 indicated successful synthesis of GelMA (Figure 1b).
**FIGURE 1:** *Physicochemical structure characterization of hydrogel and nanomaterials. (a) Fourier transform infrared (FTIR) and (b) 1H NMR spectra of Gelatin (Gel) and GelMA; size distribution and PDI of (c) PCEC and (d) A‐CXCL9@PCEC nanoparticles; transmission electron microscopeTEM image of (e) PCEC and (f) A‐CXCL9@PCEC nanoparticles, scale bar = 500 nm; (g) Morphology of lyophilized hydrogel with different composition, scale bar = 200 μm*
The size distribution of PCEC and A‐CXCL9@PCEC nanoparticles was 81.36 ± 1.06 nm and 94.37 ± 3.49 nm, respectively (Figure 1c,d). PCEC had negative zeta potential of −32.70 ± 0.61 mV, after carrying the positive A‐CXCL9, the zeta potential of A‐CXCL9@PCEC nano micelles increased to −7.02 ± 0.06 mV. Both PCEC and A‐CXCL9@PCEC had a concentrated particle‐size distribution, and the diameter of PCEC slightly increased after loading A‐CXCL9. Figure 1e,f shows the typical spherical morphology of PCEC and A‐CXCL9@PCEC. The morphology of the hydrogel observed by SEM showed that as the concentration of GelMA increased, the degree of crosslinking of the hydrogel also increased and the pore size decreased (Figure 1g). The average pore size of different hydrogels was shown in Figure S1. It could be noticed that though OP3‐4 was previously modified with methacrylic bond (OP3‐MA) and crosslinked with GelMA during the hydrogel formation process, the pore size of $5\%$ GelMA and $5\%$ GelMA/OP3‐MA did not show obvious difference (about 160 μm), which may owe to the less mass fraction of OP3‐MA (only $0.06\%$).
## Mechanical and degradation behavior of hydrogel
Figure 2 shows the rheological, compression, and degradation behavior of GelMA/OP3‐MA hydrogel. The solution of GelMA/OP3‐MA containing LAP was initiated with blue light (405 nm) and formed hydrogel within 20 s. GelMA/OP3‐MA hydrogel with different GelMA mass fraction all showed steady storage modulus (G') and loss modulus (G") in time sweep sequence, and G' > G", which means the hydrogel had totally formed (Figure 1a). As the mass fraction of GelMA increased, the linear viscoelastic zone of the GelMA/OP3‐MA hydrogel gradually increased, and the $15\%$ GelMA/OP3‐MA hydrogel exhibited linear viscoelasticity between 0.1 and 10 Hz (Figure 2b). Stress–Strain curve is shown in Figure 2c. Similar to the trend of G', the compression strength of GelMA/OP3‐MA hydrogel also showed a positive correlation with the GelMA mass fraction. Compressive strength of GelMA/OP3‐MA hydrogel with increased GelMA mass fraction was 21.95, 51.31, and 117.74 kPa, respectively.
**FIGURE 2:** *Rheological, compression and degradation behavior of GelMA/OP3‐MA hydrogel. (a) Time‐sweep and (b) frequency‐sweep sequence of GelMA/OP3‐MA hydrogel; (c) Stress–strain curve of GelMA/OP3‐MA hydrogel; (d) in vitro swelling behavior of GelMA/OP3‐MA hydrogel; in vitro degradation behavior of GelMA/OP3‐MA in (e) PBS and (f) 1000 U/ml lysozyme PBS solution; (g) morphology of degraded GelMA/OP3‐MA hydrogel after being degraded in PBS and 1000 U/ml lysozyme PBS solution for 5 days*
In vitro swelling and degradation behavior showed that the $15\%$ GelMA/OP3‐MA hydrogel had the smallest swelling ratio $25.54\%$ ± $2.38\%$ and longest degradation time both in PBS and lysozyme PBS solution (Figure 2d–f). Owing to the highest crosslinking density of $15\%$ GelMA/OP3‐MA hydrogel, the swelling ratio of $15\%$ GelMA/OP3‐MA hydrogel was nearly half of $10\%$ GelMA/OP3‐MA hydrogel ($36.12\%$ ± $2.36\%$) and $5\%$ GelMA/OP3‐MA hydrogel ($53.45\%$ ± $2.02\%$); $15\%$ GelMA/OP3‐MA hydrogel also behaved slowest degradation time in the in vitro degradation test with or without lysozyme; $15\%$ GelMA/OP3‐MA hydrogel completely degraded on 20 days in PBS and 17 days in PBS containing 1000 U/ml lysozyme. A faster degradation rate showed in the $5\%$ GelMA/OP3‐MA hydrogel and $10\%$ GelMA/OP3‐MA hydrogel, which had relatively lower crosslinking density. While $5\%$ GelMA/OP3‐MA hydrogel was completely degraded in 7 days. SEM images of the degraded hydrogel on Day 5 had also been shown in Figure 2g. The surface morphology of the hydrogel degraded in lysozyme solution shows a collapsed pore structure compared to the hydrogel degraded in PBS. This phenomenon is most obvious in $5\%$ hydrogels, indicating that the enzyme environment can be significantly accelerated to destroy the cross‐linked structure of the hydrogel, thereby accelerating the degradation of the hydrogel. 28 The above results can indicate that the mechanical properties and degradation time of the hydrogels we prepared are adjustable. The migration of mesenchymal stem cells (MSCs) is a key factor in tissue regeneration, and the crosslinking density of the hydrogel has a great influence on the migration of MSCs. Although the migration of cells on medium‐modulus hydrogels (100–1000 Pa) is not as good as that on low‐modulus hydrogels (≈100 Pa), 29 in order to avoid the hydrogels degrading too quickly in vivo and unable to exert long‐term effects, we finally chose $10\%$ GelMA/OP3‐MA hydrogel for subsequent applications.
## Cell compatibility of hydrogel
The rBMSCs was co‐cultured with hydrogels to evaluate the cell compatibility. As shown in Figure 3a, the cell viability increased with the extension of the culture time, showing good cell proliferation. The control group GelMA showed the best effect of promoting cell proliferation, and the cell viability on Days 1, 3, and 5 was 100 %± $6.46\%$, $174.16\%$ ± $17.61\%$, and $308.08\%$ ± $2.97\%$, respectively. This is because gelatin itself contains a large number of short RGD peptides, which can obviously promote cell adhesion and proliferation. 19 Therefore, GelMA has been used in the construction of tissue engineering materials for a long time with excellent biocompatibility. 19, 20 When OP3‐MA cyclic peptide was co‐crosslinked with GelMA, during the first and third days of co‐culture, the cell survival rate did not show a significant difference compared with the other two groups. However, after 5 days of culture, the cell proliferation rate of GelMA/OP3‐MA hydrogel slowed down, which was significantly lower than the other two groups, and the cell survival rate was $247.42\%$ ± $2.30\%$. After adding A‐CXCL9@PCEC, the rBMSCs showed a higher proliferation effect than GelMA/OP3‐MA hydrogel, and the cell survival rate on Day 5 was $294.08\%$ ± $66.94\%$, which was not significantly different from GelMA hydrogel. Figure 3b shows the live/dead staining of rBMSCs. Although the rBMSCs showed a low proliferation rate in the GelMA/OP3‐MA hydrogel, there were no obvious dead cells during the whole culturing period, indicating that the hydrogel was not cytotoxic, but the addition of OP3‐MA and A‐CXCL9@PCEC would affect the proliferation of cells. 30
**FIGURE 3:** *Cell compatibility of hydrogels. (a) Viability of rat bone marrow mesenchymal stem cells (rBMSCs) co‐cultured with hydrogels detected by CCK‐8 assay; (b) Live/dead staining of rBMSCs co‐cultured with hydrogels (red: dead cells; green: live cells.); (c) Cytoskeleton of 3D cultured rBMSCs on hydrogels stained with phalloidin*
Phalloidin stains the actin in the microfilaments in the cytoskeleton and shows red fluorescence. 31 From the staining of the cytoskeleton in Figure 3c, it could be seen that the rBMSCs cultured on GelMA/OP3‐MA hydrogel and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel presented a fully unfolded fusiform structure faster after the first day of culture and showed longer microfilaments, indicating that the rBMSCs could adhere well to the GelMA hydrogels scaffold. This result was consistent with the biological effect of GelMA. 20 When the culture time increased to 5 days, all rBMSCs grown on the hydrogels show a spreading cytoskeleton, and the microfilament junctions between cells were denser, which indicated that the rBMSCs on the hydrogel can form a tightly connected structure. This will help maintain tissue integrity and strengthen communication between cells.
## In vitro A‐CXCL9 release and promoting osteogenesis
A‐CXCL9 only binds to PCEC through weak electrostatic interaction and is encapsulated in the hydrogel. With the degradation and diffusion of the hydrogel, A‐CXCL9 can be gradually released into the surrounding environment to promote bone formation. Figure S2 showed the standard curve of A‐CXCL9 measured by ELISA. Totally 2 μg of A‐CXCL‐9 was carried in 1 ml of hydrogel. By testing the A‐CXCL9 released from the hydrogel, we found that the release of A‐CXCL9 reached equilibrium after the 7th day, and the final release amount could reach to $58.78\%$ (Figure S3).
The in vitro promoting osteogenic differentiation ability of hydrogel was detected by co‐cultured with rBMSCs. ALP activity was used to indirectly quantify the ability of rBMSCs to differentiate into osteoblasts. 32 Figure 4a shows that ALP activity all increased for 5 days of co‐culturing, but only in GelMA/OP3‐MA and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogels there were obviously changes. In the 1, 3 and 5 days of co‐culture, the ALP activity of GelMA/OP3‐MA and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogels was significantly higher than control and GelMA group, and the GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel has the highest ALP activity. Alizarin red staining reflects the deposition of calcium ions in cells, which is an important characterization for the study of bone mineralization. 33 By staining the rBMSCs cultured on the hydrogel with Alizarin Red, we found that the rBMSCs in groups GelMA/OP3‐MA and GelMA/OP3‐MA/A‐CXCL9@PCEC showed more pink calcium salt deposition, indicating that these two hydrogels can better promote bone mineralization and complete the last step of bone formation (Figure 4b).
**FIGURE 4:** *In vitro bone formation ability and signaling pathway. (a) ALP activity of rBMSCs co‐cultured with hydrogels for 1, 3, and 5 days; (b) Alizarin Red S of rBMSCs cultured with hydrogels leach liquor for 10 days for detection of in vitro Ca2+ deposition; scale bar = 200 μm; (c) Immunofluorescence staining of osteogenic markers OCN, RUNX2 AKT and p‐AKT; (d) Western blot and (e) quantitatively analysis of OCN, RUNX2 AKT and p‐AKT. (a: Control; b: GelMA; c: GelMA/OP3‐MA; d: GelMA/OP3‐MA/A‐CXCL9@PCEC); scale bar = 100 μm*
In order to clarify the relevant mechanism of the above results, we performed immunofluorescence staining on the expression of bone formation‐related genes in the rBMSCs, and the results were shown in Figure 4c. ALP and osteocalcin (OCN) are two classic markers of osteogenic differentiation. The expression of OCN and ALP has been confirmed to be the strongest in GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel. RNUX2 is the upstream target of ALP and OCN. 34 We found that the expression of RUNX2 was stronger in GelMA/OP3‐MA hydrogel. Moreover, the high expression of AKT and p‐AKT also appeared in GelMA/OP3‐MA and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogels (Figure 4c).
Quantitative statistics and analysis on the expression of AKT, p‐AKT, RUNX2, and OCN were then performed (Figure 4d,e). We found that hydrogels containing OP3‐4 polypeptides could significantly increase the expression of AKT, P‐AKT and OCN, while the hydrogel‐treated group that simultaneously released A‐CXCL9 and OP3‐4 had the highest expression of OCN, indicating that A‐CXCL9 can synergy working with OP3‐4 to achieve higher expression of osteogenesis‐related proteins. From the results of immunofluorescence and western blot, we speculate that OP3‐MA and OP3‐MA/A‐CXCL9 may be through activating the AKT‐RUNX2‐ALP pathway and ultimately promote osteogenic differentiation.
## The GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel promotes bone regeneration in vivo
After the GelMA/OP3‐MA/A‐CXCL9@PCEC, hydrogel was prepared and the promoting osteogenic differentiation ability had been demonstrated, we conducted round femur defect rat model to evaluate the bone regeneration in vivo. Micro‐CT data presented in Figure 5a showed that more bone formed in GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel treated group. After 1 week of treatment, the 3D reconstruction CT images can clearly observe the annular defect in the middle of the femur. The CT images after treating 2 weeks showed that there was new bone formation in the defect parts of the GelMA/OP3‐MA hydrogel and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel‐treated groups and basically completely repaired after 4 weeks. GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel‐treated group showed highest bone mineral density (BMD), bone surface/tissue volume (BS/TV). BV/TV, and trabecular thickness (Tb. Th), which represents the fastest bone regeneration and the best quality (Figure 5b–e).
**FIGURE 5:** *The GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel promotes femur regeneration in vivo. (a) Micro‐CT images of femur defect treated with different hydrogels obtained 1, 2, and 4 weeks after implantation; (b) bone mineral density (BMD), (c) bone surface/bone volume (BS/TV), (d) bone volume fraction (BV/TV) and (e) trabecular thickness (Tb/Th) in different groups; (f) H&E and (g) Masson staining of the femur defect site; scale bar = 500 μm. The red circle represents the damage area.*
The results of H&E and Masson staining further showed the healing effect of the bone tissue at the injury site. After 1 week of treatment, the control group was able to observe obvious tissue defects without any tissue growth. In the hydrogel‐treated group, the tissues began to grow inward gradually from around, and a small amount of collagen deposition appeared. After the second week of treatment, the cavity was filled with new tissue, and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel‐treated group showed denser collagen deposition. On the 4th week, GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel‐treated group was almost completely repaired, the collagen structure was denser and closer to normal tissue (Figure 5c,d). The histological staining was consistent with the CT images, indicating that the GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel‐treated group can promote bone tissue regeneration faster, and the bone tissue structure was denser, and close to normal bone tissue.
## The GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel inhibits osteoclast differentiation and promotes vascularization
OP3‐4 cyclic peptide can bind to nuclear factor κB receptor activator ligand (RANKL) to inhibit osteoclast activation and promote osteogenic differentiation. We performed TRAP staining on the femur to specifically characterize the differentiation of osteoclasts (Figure 6a). During the first week of treatment, obvious osteoclast formation appeared around the injured area in the control group and GelMA‐treated group, indicating that the injury led to increased differentiation of surrounding osteoclasts and slowed the rate of healing (Figure 6b). In contrast, the number of osteoclasts in the OP3‐4 cyclic peptide released hydrogel (GelMA/OP3‐MA and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogels) after 1 week of treatment was lower than that of the control and the GelMA group, indicating that the OP3‐4 peptide could inhibit the osteoclasts (Figure 6b). Two weeks after operation, the number of osteoclasts in the control group and GelMA group was less than that in the first week (Figure 6c). However, osteoclasts were still observed in the control group 4 weeks after operation, and there were almost no osteoclasts in the GelMA/OP3‐MA/A‐CXCL9@PCEC group 4 weeks after operation (Figure 6d). This difference indicates that OP3‐4 peptide can inhibit the formation of osteoclasts for a long time, and when used in combination with A‐CXCL9, it can better promote osteogenesis.
**FIGURE 6:** *The assessment of inhibition of osteoclast activation and promotion of vascularization. (a) TRAP staining of bone tissue at different time points; Quantitively analysis of osteoclasts at (b) 1, (c) 2, and (d) 4 weeks; (e) Immunofluorescence staining image of α‐SMA in defect femur; (f) quantitatively analysis of blood vessels from immunofluorescence staining of α‐SMA; scale bar = 100 μm*
For the formation of bone tissue, in addition to inhibiting the formation of osteoclasts, another important stage is the vascularization of bone tissue. 35 CXCL9 can be secreted by osteoblasts, interact with vascular endothelial growth factor (VEGF), and prevent VEGF from binding endothelial cells to form blood vessels. 16 Therefore, excessive CXCL9 secreted by osteoblasts can inhibit the formation of blood vessels and slow down the healing process of bone tissue. We used α‐SMA fluorescent labeling of tissue neovascularization at different stages of bone regeneration, and the results were shown in Figure 6b. One week after operation, although neovascularization was observed in each group, there was no significant difference. It is worth noting that in the second week, the GelMA/OP3‐MA/A‐CXCL9@PCEC treatment group had obvious blood vessel formation, which was almost 4‐fold higher than the other groups (Figure 6c). Four weeks after operation, the number of new blood vessels decreased, owing to the healing tissue and decreased neovascularization rate.
## The GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel promotes the expression of osteogenic proteins and the deposition of type I collagen
Immunohistochemical staining of bone tissue at different times after surgery was performed to evaluate the expression of osteogenesis‐related proteins. The expression of osteogenesis‐related proteins OCN and osteopontin (OPN) in the regenerated cancellous bone increased with the prolongation of treatment time. And the GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel treatment group had the highest expression (Figure 7a,b). Col I is the major structural protein of the extracellular matrix of bone and a representative marker of osteogenic differentiation. One to two weeks after operation, the collagen showed a random interweaving arrangement, which was a typical woven bone structure. Collagen fibers aligned in parallel 4 weeks after operation, forming a lamellar bone structure. The GelMA/OP3‐MA hydrogel and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel treatment groups had faster Col I deposition at the first 2 weeks of treatment and more collagen fibers in the lamellar bone after 4 weeks (Figure 7c).
**FIGURE 7:** *Immunohistochemical staining of osteogenesis‐related proteins and cytokines. Immunohistochemical staining of (a) OCN; (b) OPN; (c) Col‐I and (d) TGF‐β1; Scale bar = 100 μm*
High expression of transforming growth factor β (TGF‐β1) was also observed in osteoblasts in the GelMA/OP3‐MA hydrogels treated group. TGF‐β1 was closely related to osteogenic differentiation, and up‐regulation of TGF‐β1 expression can significantly promote osteogenic differentiation (Figure 7d). The above results indicated that OP3‐4 polypeptide played a significant role in promoting osteogenic differentiation in the process of bone formation. At the same time, under the action of angiogenesis‐promoting factor A‐CXCL9, the expressions of osteogenesis‐related proteins OCN, OPN, and Col I were further increased.
## DISCUSSION
The repair of damaged bone tissue involves two important issues: the balance of osteoblasts and osteoclasts 4 and the process of vascularization. 36 However, previous studies have found that CXCL9 secreted by osteoblasts binds endogenous VEGF and prevents VEGF from binding to endothelial cells, thereby interfering with the process of bone vascularization. 16 Therefore, the coordination of the three factors of osteoblasts, osteoclasts, and angiogenesis has become a novel strategy to promote bone regeneration. The OP3‐4 polypeptide, as a RANKL‐binding peptide, 37, 38 can inhibit the activation of osteoclasts 12 while promoting the differentiation of osteoblasts, 15 reconciling the first two therapeutic factors. To reconcile the third factor, the process of vascularization, we designed PCEC nanoparticles to simultaneously release anti‐CXCL9 antibody (A‐CXCL9) to neutralize excess CXCL9 secreted by osteoblasts. 16, 17 Under the constraints of the free diffusion of GelMA hydrogel and the electrostatic interaction of PCEC, the sustained release of A‐CXCL9 was close to $60\%$ after 8 days, and the release profile basically reached equilibrium. When the hydrogel was gradually degraded, the remaining A‐CXCL9 will be gradually released into the surrounding environment, continuously improving the inhibition of vascularization. 16, 17 The entire coordination process was achieved by GelMA‐based hydrogels, which have good biocompatibility and degradability and are widely used in the repair of hard tissues such as bone and cartilage. 9 Through in vitro toxicity assessment, we confirmed that the GelMA‐based hydrogel has no cytotoxicity and can well support the growth of rBMSCs. Moreover, the in vitro degradation results show that GelMA hydrogel can be completely degraded by hydrolysis and enzymatic hydrolysis and will not remain as foreign matter in the body for a long time.
In vitro regulation experiments of GelMA/OP3‐MA and GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogels on rBMSCs showed higher ALP expression, significant inhibition of osteoclast differentiation and higher RUNX2 and AKT expression level, showed that the hydrogel may be through activating the AKT‐RUNX2‐ALP pathway, and ultimately promote osteogenic differentiation. A femur defect model was further performed to validate the therapeutic effect of GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel. We found that through this coordinated osteoblast, osteoclast and angiogenesis pathway, bone regeneration process can be accelerated. The expression of osteogenesis‐related proteins Col I, OCN, and OPN was significantly increased, and highest number of blood vessels can be observed after 2 weeks of GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel treatment. This indicates that the degree of vascularization in the first 2 weeks of treatment is likely to determine the speed and quality of bone healing in the later period. Because a high degree of vascularization is more conducive to the transfer of nutrients and can promote the growth of bone tissue inside the injury. 4, 5, 35
## CONCLUSION
In this study, we established a novel bone regeneration pathway that simultaneously coordinates osteoblast, osteoclast, and vascularization processes. Co‐crosslinking of OP3‐4 polypeptides by GelMA hydrogel promotes osteogenic differentiation and inhibits osteoclast activation. At the same time, PCEC nanoparticles were loaded with A‐CXCL9 to neutralize endogenous CXCL9 and promote vascularization. The results of promoting osteogenic differentiation in vitro and in vivo showed that the GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel that simultaneously modulated these three behaviors had the best osteogenic differentiation results, and the vascularization level was the highest at the second week. The GelMA/OP3‐MA/A‐CXCL9@PCEC hydrogel had fastest bone repair rate, higher collagen deposition amount and expression of osteogenesis‐related proteins OCN and OPN. This novel scaffold promotes bone regeneration by simultaneously inhibiting osteoclast activation and increasing vascularization, and our study provides a strategy for the bone defect.
## AUTHOR CONTRIBUTIONS
Peng Luo: Investigation (lead); methodology (lead); writing – original draft (equal). Jiarui Fang: Conceptualization (equal); data curation (equal); formal analysis (equal); writing – original draft (equal). Dazhi Yang: Conceptualization (lead); project administration (equal); resources (equal); supervision (equal); validation (equal); visualization (equal). Lan Yu: Data curation (equal); formal analysis (equal); methodology (equal). Houqing Chen: Conceptualization (equal); formal analysis (equal); investigation (supporting); methodology (supporting); software (equal). Changging Jiang: Funding acquisition (equal); project administration (equal); supervision (equal); writing – review and editing (equal). Shuo Tang: Project administration (equal); resources (equal); supervision (equal); validation (equal); writing – review and editing (equal). Tao Zhu: Project administration (equal); resources (equal); supervision (equal). Rui Guo: Project administration (equal); resources (equal); supervision (equal).
## CONFLICT OF INTERESTS
The authors declare no competing financial interest.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10414.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: 'DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology'
authors:
- Antra Ganguly
- Tahmineh Ebrahimzadeh
- Jessica Komarovsky
- Philippe E. Zimmern
- Nicole J. De Nisco
- Shalini Prasad
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013760
doi: 10.1002/btm2.10437
license: CC BY 4.0
---
# DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology
## Abstract
In this work, we propose a novel diagnostic workflow—DigEST—that will enable stratification of disease states based on severity using multiplexed point of care (POC) biosensors. This work can boost the performance of current POC tests by enabling clear, digestible, and actionable diagnoses to the end user. The scheme can be applied to any disease model, which requires time‐critical disease stratification for personalized treatment. Here, urinary tract infection is explored as the proof‐of‐concept disease model and a four‐class classification of disease severity is discussed. Our method is superior to traditional enzyme‐linked immunosorbent assay (ELISA) as it is faster and can work with multiple disease biomarkers and categorize diseases by endotypes (or disease subtype) and severity. To map the nonlinear nature of biochemical pathways of complex diseases, the method utilizes an established supervised machine learning model for digital classification. This scheme can potentially boost the diagnostic power of current electrochemical biosensors for better precision therapy and improved patient outcomes.
## INTRODUCTION
As observed in the recent COVID‐19 pandemic, point of care (POC) tests are changing the face of healthcare by making home‐based screening and routine health monitoring possible. The POC market is rapidly growing and is projected to reach 50.6 billion USD by 2025. 1 According to a recent paper by Nguyen et al., the commercialization success of POC devices depends on three critical factors: (i) sample handling, (ii) biomarker detection, and (iii) signal reading. 2 Most of the current POC biosensors lack in the third point, that is, the ease of reading the output signal. While many POC tests give an easy‐to‐read yes/no output, such as dipsticks for urinary tract infection (UTI), more powerful and accurate mathematical models for biomarker‐based disease diagnosis are being developed, and these tests are being replaced by technology with fully quantitative continuous‐valued (i.e., analog) outputs for biomarker concentrations. However, while analog signals can contain a high density of information, they are prone to noise and require larger memory storage. Drawing parallels from the successful digital transformation in the telecommunication industry over five decades ago, we propose a novel strategy—DigEST—to digitize the output of current electrochemical biosensors by discretizing the analog biomarker level information into digital states. This is attractive because a digital output corresponding to the disease endotype (which is a subtype of a health condition governed by a distinct pathobiological mechanism 3) or severity state will allow for clear and actionable outcomes for the end user. Further, digitization will reduce the chance of misdiagnosis as digital signals are more immune to noise and require very large fluctuations for the output to jump from one state to another. By using the DigEST workflow, complex multibiomarker information can be “digested” and translated into a single clear, actionable outcome for the clinician and/or patient.
In recent years, there has been increased traction in the research for host response‐based diagnostics for personalized treatment and precision medicine. 4 There has been a shift toward customized disease management through endotype‐driven diagnostics in which triage is initiated keeping in mind the dynamic biological variability due to genetic predisposition, treatment response, and triggered biological pathways. 5, 6, 7 Disease endotypes are characterized by immunological biomarkers associated with the complex biological pathways at play. 6, 7, 8 Leveraging this, we put forward a versatile, plug‐n‐probe modular workflow using traditional electrochemical biosensors to map the chronological spread of infection using urinary tract infection or UTI as the proof‐of‐concept infectious disease model. UTIs are among the most common bacterial infections and occur when certain bacterial species enter and multiply in the urinary tract. 9 UTIs can be classified based on their location in the urinary tract as lower UTI (bladder and urethra) or upper UTI (kidneys or ureters). 9, 10, 11 Lower UTIs are more common and can translate into upper UTIs as the causative pathogen ascends from the lower urinary tract to the kidneys resulting in pyelonephritis (kidney infection) ultimately spreading to the bloodstream causing urosepsis and even death. 12, 13 The proposed device relies on tracking the combined fluctuations of the levels of three inflammatory urine biomarkers (PGE2, IL‐6, and CRP) to map the biological cascade events triggered upon infection (this is discussed further in the next section). UTI endotype changes as the pathogen travel from the lower to the upper urinary tract and has been classified as four digital states at the output viz., healthy, presymptomatic, symptomatic lower UTI, and pyelonephritis/systemic spread. An approximation of the biochemical cascade events associated with a disease endotype resulting in the release of key biomarkers was constructed using a discrete Boolean logic‐based model. This method is commonly used in systems biology and was preferred over other mathematical models built with continuous differential equations because such models require that numerous parameters are known beforehand and this information is largely unavailable for complex disease models affecting multiple biomarkers. We believe that DigEST can transform the fields of POC diagnostics and combinatorial disease biosensors by enabling disease endotyping and severity stratification for timely treatment, reduced costs, and hospital stays.
The current clinical workflow for the management of UTIs is based on the gold standard technique of culture‐based analysis of the presence of uropathogenic bacteria combined with reported symptoms. The goal of this work was to propose an alternative self‐monitoring workflow to address the gap in the current clinical UTI diagnosis and management workflow. At‐home routine self‐monitoring of UTI symptoms is particularly valuable for recurrent UTI (rUTI) patients. rUTI is characterized by ≥3 episodes of symptomatic UTIs in 12 months or ≥2 episodes of symptomatic UTIs in 6 months. 14, 15 *Through this* work, we propose a rapid and novel UTI and rUTI management system which expedites clinical decision‐making and enables immediate diagnosis. By utilizing the proposed DigEST, rUTI patients can monitor their urine in the comfort of their own homes and get a preliminary screening result that will guide them in seeking care to ensure timely and appropriate prescription of antibiotics for increased treatment success rates.
DigEST operates on the principle of affinity‐based electrochemical biosensing 16, 17 and allows for the detection of inflammatory biomarkers in the patient's urine in less than 5 min. The device requires no preprocessing or filtering of the urine sample or any other sample preparation by the end user. Figure 1 shows the operation of the proposed DigEST system for POC management of UTIs through multiplexed detection of key inflammatory biomarkers.
**FIGURE 1:** *DigEST workflow. Schematic showing the generalized workflow for DigEST implementation to different disease models for precision and personalized therapeutics. Created using Biorender.com*
Three biomarkers prostaglandin E2 (PGE2), 12, 14 interleukin‐6 (IL‐6), 18, 19, 20, 21, 22 and C‐reactive protein (CRP) 23, 24, 25, 26, 27 have been chosen for this study. These biomarkers are associated with the host immune response to UTI or rUTI. The choice of IL‐6, PGE2, and CRP was based on published data associating these biomarkers with UTI and the advanced understanding of the signaling pathways regulating their induction in response to infection (see Figure 2). DigEST is intended as a versatile plug‐n‐play UTI management system that can be used to measure additional UTI‐relevant urinary biomarkers as the biomarker discovery associated with UTI and rUTI evolves. Depending on the levels of the three biomarkers, the device outputs a disease state (1 = “healthy,” 2 = “infectious, asymptomatic or pre‐symptomatic,” 3 = “infectious, symptomatic” or 4 = “infectious, systemic”), which is associated with the predicted severity and spread of the UTI (see Figure 3). Depending on the predicted disease state, the patient along with their doctor can use this information to develop an effective treatment strategy. The proposed technology is superior to the alternative near‐patient, POC urine dipstick using nitrite or leukocyte esterase detection, which suffers from high false positive rates and results in unnecessary antibiotics use, 13 for UTI diagnosis in two ways. First, the device quantifies the levels of three biomarkers and gives out a numerical readout, unlike the qualitative “yes” or “no” output of traditional dipsticks. Also, it integrates biomarker concentration information to output a disease state stratification that can easily be interpreted by the patient or clinician. Some of the key features of the proposed device include label‐free detection and easy handling, rapid response (<5 min), UTI severity prediction, multiplexed output, and versatile plug‐and‐play capability (see Section 3).
**FIGURE 2:** *Concept diagrams for programming the circuit for Boolean logic and digital disease state classification. Partially created using Biorender.com. The events and biomarkers in the figure have been selected for demonstrating proof of concept (other MAPK include: ERKs, JNK, P38). These will change based on disease model of interest and levels of endotyping.* **FIGURE 3:** *Boolean logic for programming USENSE for disease endotyping. Key biomarkers and associated pathways were identified based on which Boolean logic was developed for programming the biosensor system. Partially created using Biorender.com. The perceived outcomes in guiding logic table are representational and have been arbitrarily assigned. These states will change based on biomarkers and disease model of interest.*
Figure 1 shows the generalized workflow for using DigEST for disease endotyping and severity stratification. The DigEST method is modular and versatile and has a plug‐n‐probe design that can be applied to a host of diseases and a variety of body fluids. Disease endotyping can be achieved through a series of six steps: [1] first, the active signaling and biochemical pathways have to be identified for the healthy state and the classes of unhealthy states corresponding to the target disease model. [ 2] Next, three key biomarkers involved in these biochemical events need to be identified as target analytes of interest for which individual biosensors can be developed. Based on the combination of biomarkers, severity and endotypes need to be defined by identifying associations between individual biomarkers and endotype. These endotypes or stratification states are then assigned digital logic states for example, states 1 through 4 corresponding to “healthy,” “asymptomatic or pre‐symptomatic,”, “symptomatic,” and “symptomatic, systemic,” respectively. [ 3] *This is* followed by the development and optimization of the assay elements. [ 4] Next, the electrochemical biosensor is developed and optimized to achieve target metrics such as the limit of detection and detection ranges for each biomarker such that the entire physiologically relevant range is covered. [ 5] Then, the sensor parameters are chosen which allow for differentiation between the output states with minimum interference. DigEST uses electrochemical impedance spectroscopy as the transduction mechanism as it affords additional interfacial parameters such as interfacial resistance and capacitance besides the real and imaginary parts of the complex impedance output. [ 6] Finally, the data collected is organized and labeled according to the target classes viz. the output disease states and fed to a supervised machine learning model for training, testing, and validation.
## Identification and selection of key biomarkers for proof of concept
The choice of biomarker is critical for the successful implementation of the DigEST workflow. A diverse set of inflammatory molecules is expressed in response to UTI. For proof of concept, we have chosen PGE2, 12, 14 IL‐6, 19, 20, 21, 25 and CRP, 23, 24, 26, 27, 28 which are established inflammatory biomarkers studied in the urine associated with UTI and its severity.
Figure 2 shows the relationship between the three biomarkers that we selected for UTI diagnosis. During UTI, lipopolysaccharides (LPS) present in the outer leaflet of the outer Gram‐negative bacterial membrane are detected by pathogen recognition receptors (PRR), such as the toll‐like receptor 4 (TLR4). 29 TLR4 in turn activates myeloid differentiation primary response gene 88 (MyD88). 30 MyD88 recruits tumor necrosis factor receptor‐associated factor 6 (TRAF6), which then leads to mitogen‐activated protein kinase (MAPK) and IκB kinase (IKK) complex activation. 30 MAPK and IKK activation facilitate translocation of activator protein 1 (AP‐1) and nuclear factor‐κB (NF‐κB) to the nucleus, respectively. 31 AP‐1 and NF‐κB promote the expression of inflammatory cytokine genes, IL‐1β, IL‐6, IL‐8, COX‐2, iNOS, TNF‐α, CRP, and IL‐10. Transcriptional induction of CRP expression may also occur in response to IL‐6/IL‐6R interaction. 32, 33 PI3K–protein kinase B (PkB)/Akt pathway plays a critical role in IL‐6 signal transduction and NFκB activation. 22 Cyclooxygenase 2 (COX‐2) enzyme catalyzes a key step in the conversion of arachidonic acid (AA) into prostaglandins, including prostaglandin E2 (PGE2). 34 PGE2 along with other cytokines triggers inflammation in response to infection. PGE2 has been reported as a hallmark of acute inflammation during UTI. 12 COX‐2 pathway activation is critical in determining the disease outcome and patient susceptibility to the recurrent UTI in murine models and human. 12, 14 PGE2 acts via four different receptors (EP1‐4) and triggers a series of proinflammatory responses for acute inflammation. IL‐6 is a multifunctional cytokine with broad‐ranging effects, which has a central role in the initiation phase of infection. IL‐6 is known as an early marker of tissue injury in many infections like COVID‐19. 35 IL‐6 is an established inflammatory biomarker 19, 21, 22, 25, 36, 37, 38, 39 expressed in urine and was found to be a promising biomarker to detect the transition from asymptomatic bacteriuria to symptomatic urinary tract infection in older adults. 40 C‐reactive protein (CRP) is an acute phase protein that is a marker of systemic inflammation in the patient's body. 41 CRP has been studied as an inflammatory biomarker in the literature 23, 24, 25, 26, 28 and has been shown to differentiate between lower and upper UTI infections. 28
## Building the assay for multiplexed detection
At its core, the proposed UTI diagnostic system is an affinity‐based electrochemical biosensor. The biosensor is comprised of a standard screen‐printed three‐electrode system with gold working and counter electrodes and silver reference electrodes (Metrohm 220 AT). Highly specific monoclonal antibodies were chosen as capture probes for affinity capture of the target antigens (PGE2, IL‐6, and CRP) expressed in test urine samples. The antibody–antigen binding induces a subtle modulation of the electrode–urine buffer interface as a function of the concentration of the target antigen present in the urine sample.
The transduction of the antibody–antigen binding event is achieved by monitoring the interfacial modulation of the electrode–urine interface as a function of the binding. A standard three‐electrode electrochemical sensor has been used for the technological proof of concept and the established technique of electrochemical impedance spectroscopy (EIS) has been used. This is transduced by the powerful electroanalytical technique of EIS, which detects and correlates the subtle changes in the interfacial behavior to the levels of the analyte of interest (PGE2, IL‐6, CRP for this study). To bind the monoclonal antibody capture probe to the measuring electrodes, a self‐assembled monolayer of a homobiofunctional and amine‐reactive thiol crosslinker, DSP (dithiobis(succinimidyl propionate)) was developed on the working electrode. 42, 43 To ensure successful immobilization and binding of the monoclonal antibodies with the DSP crosslinker on the gold working electrode, attenuated total reflectance Fourier transform infrared spectroscopy (ATR‐FTIR) was used. Through this technique, the infrared spectra of monoclonal antibody (mAb) and the DSP crosslinker were obtained and the characteristics peaks were identified to validate the binding chemistry. Liquid samples of DSP crosslinker, DSP bound to PGE2 mAb, IL‐6 mAb, and CRP mAb were studied. PBS was studied as the negative control. Figure 4 shows the schematic representation of the elements of the immunosensor assay stack and the FTIR spectra of all the combinations DSP‐mAb conjugates, DSP crosslinker, and PBS control overlayed on each other. All significant peaks used to validate the successful bonding of the crosslinker and mAb capture probes have been annotated. The spectra of DSP‐mAb conjugates of PGE2, IL‐6, and CRP in PBS respectively compared to the spectra of DSP (mixed in PBS) have been discussed in the supplementary information S1. In this way, the successful immobilization of the monoclonal antibodies to the crosslinker was validated before proceeding with the electrochemical studies.
**FIGURE 4:** *DigEST assay stack and validation of binding chemistry. Schematic of the electrochemical urinary tract infection (UTI) biosensor system and attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectra of the crosslinker and target analytes. Partially created using Biorender.com*
## Electrochemical characterization and calibration of sensor response using EIS
Non‐Faradaic EIS was used to transduce the biochemical activity of antigen–antibody binding at the electrical double layer interface. This was done to ensure label‐free operation to ensure that no sample preparation is required for the end user since non‐Faradaic mode eliminates the need for redox tagging to get a measurable signal. Further, EIS was chosen among other electroanalytical techniques because of two main reasons. First, EIS is a highly established powerful AC‐based technique to detect subtle binding interactions at the electrical double layer interface and has been reported for sub picomolar biomarker concentration quantification 44 and has been used widely for biosensing using a variety of capture probes including antibodies, aptamers, and so on. 45 Second, as a proof of concept, we have utilized a standard, widely used screen‐printed electrodes based three‐electrode electrochemical cell (Metrohm 220 AT) for building our plug and play diagnostic system without any sensor modification for signal enhancement to make it simple, versatile for easy, universal use. Since the response of the unmodified sensor is very subtle (in ohms), EIS was chosen to measure small changes in response corresponding to the immunological biomarker levels expressed in human urine. Thus, the output response for all three biomarkers was studied as the modulus of impedance. EIS studies were done for a wide frequency range of 1 MHz to 1 Hz. The 100 Hz was used as the optimal frequency for sensor calibration.
Figure 5a–c shows the calibrated dose response corresponding to PGE2, IL‐6, and CRP levels expressed in pooled human urine samples obtained from >3 healthy human donors. The urine samples were prepared by spiking the urine samples with physiologically relevant levels of the three biomarkers. PGE2 was spiked in the range of 500–5000 pg/ml. IL‐6 was spiked in the range of 10–500 pg/ml. CRP was spiked in the range of 10–1000 ng/ml. Raw or unspiked pooled human urine sample was used for the baseline dose (also known as zero doses) in the calibration studies. From Figure 4a–c, a dose‐dependent decrease in the sensor response (i.e., the modulus of impedance at 100 Hz) was obtained for all three biomarkers. One‐way ANOVA analysis showed a significant difference between the doses with a p value < 0.0001 for PGE2, IL‐6, and CRP, respectively. Pairwise t‐tests showed a significant difference ($p \leq 0.0001$) between different doses for each biomarker. Figure 4d–f shows the t‐test comparison of low and high levels of inflammation corresponding to low and high concentrations of the biomarkers expressed in human urine. Clearly, the sensor can differentiate between low and high levels of inflammation and infection. Thus, it was concluded that the sensor can quantify a wide dynamic range of target biomarkers of interest inflammation using <60 μl of urine in less than 5 min without any labeling or additional sample preparation.
**FIGURE 5:** *Detection of urinary PGE2, IL‐6, and CRP. (a–c) Calibrated dose–response curves using electrochemical impedance spectroscopy (EIS); (d–f) t‐test analysis for low versus high inflammation and (g–i) Bland Altman analysis comparing the response from EIS and enzyme linked immunosorbent assay (ELISA) for all three biomarkers*
## Human subject studies and comparison with ELISA
After calibrating the sensor using spiked samples, human subject samples from 10 post‐menopausal women with a history of UTI or rUTI were tested. The same samples were also studied using ELISA, an established laboratory technique of biomarker level quantification in human samples. The response from EIS was compared with that from ELISA using Bland Altman (BA) analysis. Figure 4g–i shows the BA analysis represented as the difference versus the average between the two methods. Almost all the data points corresponding to all the samples (three replicates each) fell within the $95\%$ confidence interval level control limits indicating high agreement between the two methods.
## Testing performance of sensor for Boolean‐based endotyping studies
The biochemical cascade events triggered upon urinary tract infection have been discussed in Figure 4a. This has been used to identify key biomarkers as shown in Figure 4b. A proof‐of‐concept Boolean logic using universal AND and OR gates has been developed corresponding to the biological events shown in Figure 5a.
From Figure 4c, the truth table in Figure 3d has been developed. Upon solving using Boolean algebra, the corresponding Boolean equation has been obtained (P, I, and C represent the state of PGE2, IL‐6, and CRP respectively, and “‘” [left single quote] represents complement or NOT operation). This can be used to program digital logic at the output. As mentioned before, this can be extended to developing simple diagnostics for a gamut of disease models.
## Comparison of sensor performance with EIS and ELISA for Boolean samples
The cocktail solutions corresponding to the four output digital states (LLL, HLL, HHL, and HHH) were tested using EIS and ELISA methods. Figure 5a,b shows the 3D scatter plot of the normalized response of ELISA and EIS with PGE2, IL‐6, and CRP levels expressed along the X, Y, and Z axes, respectively. Each dot on the scatter plot represents the average response (obtained from $$n = 3$$ sensors with three intrasensor replicates each) for each of the four Boolean states. For example, consider the case representing the endotype corresponding to digital state 2 or “HLL.” “ HLL” means that the test sample contains human urine with high PGE2, low IL‐6, and low CRP. As discussed earlier, this corresponds to the “infectious, asymptomatic” case. Thus, three X, Y, and Z axes have been discussed to represent the normalized values obtained for the sensor/ELISA kit corresponding to each biomarker.
Figure 6c. shows the inset with Figure 5a,b overlayed and zoomed in. As highlighted in the figure, the data points showing the response from EIS (red) are well clustered and are suitable for further programming. On the other hand, the response from ELISA showed no clustering. This is probably because three different kits were required to obtain the results for three different biomarkers and their results are affected by the interference from other biomarkers of interest.
**FIGURE 6:** *Boolean logic implementation. (a) Combined EIS (red) and ELISA (blue) response for four Boolean states. 3D scatter plots showing the response from EIS and ELISA for three different biomarkers depicted across the three axes. X, Y, and Z axes correspond to the response (i.e., Zmod at 100 Hz for EIS and absorbance at 450 nm for ELISA) normalized to the baseline dose (unspiked pooled human urine sample) for PGE2 (X axis), IL‐6 (Y axis) and CRP (Z axis). (b) Truth table guiding programmable digital logic, (c) electronic circuit design for digital logic, and (d) 2D representation of the EIS response corresponding to each target biomarker normalized to the four output states of Boolean logic*
The results from the EIS response shown in Figure 6b have been represented as three different plots corresponding to each biomarker. The EIS data for each biomarker have been normalized to the data for the healthy state, which is the first state or “LLL.” From the figure, it is clear that for all the biomarkers, EIS was capable of separating the four states and hence can be used for UTI severity thresholding and disease endotyping. The truth table corresponding to the levels of a given biomarker corresponding to each state has been indicated in the truth table in Figure 6e for reference. Figure 6e shows the digital implementation of the Boolean equation obtained for the proof‐of‐concept disease model in this study discussed in the previous section (calculated in Figure 5d), that is, I′C′ + PI. Figure 6e also shows the electronic circuit design that can be used to implement the logic in a future POC UTI management device.
## Characterization of the interfacial behavior for different digital states
Equivalent circuit fitting was done to obtain the transfer function of the electrochemical process in terms of electrical circuit elements, that is, resistance and capacitance (constant phase element to model a leaky capacitor). The interfacial modulation due to Ab‐Ag binding was found to show typical Randle circuit behavior with the solution resistance (Rs) in series with the parallel combination of double layer capacitance (Cdl modeled as a constant phase element) and the leak resistance (Rp). The circuit has been discussed in detail in the supplementary information S1. Zview software was used to fit the Randle circuit to obtain the fit parameters.
Figure S1 shows the variation in the circuit parameters Rs, Cdl, and Rp as a function of the four digital states viz., LLL (State 1), HLL (State 2), HHL (State 3), and HHH (State 4) for (a) PGE2, (b) IL‐6, and (c) CRP. For each of the subfigures, the color gradient (light or dark) reflects the variation in the levels (high or low) of the corresponding biomarker expressed in urine. For example, consider 6 (b) which shows the graph for IL‐6. Here, for States 1 and 2, the level of IL‐6 spiked in the cocktail is low, that is (LLL and HLL) and hence has been depicted in light red color. On the other hand, for States 3 and 4, the level of IL‐6 spiked in the cocktail is high, that is (HHL and HHH) and hence has been depicted in dark red color.
This equivalent circuit model analysis was done to evaluate whether the individual circuit elements could be tuned to calibrate the sensor for disease endotyping. The relation between Zmod and the circuit elements 45 has been discussed in the supplementary information. From Figure S1a–c, it is evident that the solution resistance Rs (depicted by squares) is almost constant across the four digital output states. This is favorable as it indicates that EIS is truly mapping the interface and the output impedance is unaffected by the effect of the nonspecific molecules and ions in the bulk solution. Next, it was found that for 6(a) and 6(b), Cdl (triangle symbol) and Rp (circle symbol) were able to follow the low‐ and high‐dose concentrations for PGE2 and IL‐6 across the four different states. This is because EIS output is very specific to the affinity binding of the target antigen to the specific monoclonal antibody at the double layer interface. However, in Figure S1c, this effect is not observed, and the individual parameters are unsuitable for disease state endotyping.
## Implementation of the sensor using supervised machine learning platform
From the results of the previous section, it was realized that simple circuit fitting was insufficient to model the complex events at the electrical interface corresponding to truly map the nonlinear behavior of the biological pathways for a given disease model. To solve this, the impedance data were fed and trained using a simple machine learning model. To avoid high computation costs, a simple random forest (RF) model was used for training. RF is an ensemble method that relies on multiple decision trees with varying numbers and depths to achieve the mapping of relationships between the inputs and the outputs. RF models are often used in real‐world research problems as they are highly immune to the effects of biases and variances because they give out a classification based on bootstrapping the outputs of multiple decision trees, instead of relying on a single tree. 46 Figure 7a shows the flow chart for the machine learning model. The sensor data acquired from the three biomarkers corresponding to the four Boolean states were labeled as “1,” “2,” “3,” or “4” corresponding to the output digital state. Instead of using the individual circuit fit parameters discussed in the previous section, the real, imaginary, modulus, and phase values of impedance obtained for each target analyte were studied along with the frequency of operation. For proof of concept, two frequencies 10 and 100 Hz were studied to check if the frequency of operation in the low region affected the output digital state. These 13 features were studied as discussed in Table 1.
**FIGURE 7:** *Machine learning analysis. Design and implementation of the machine learning model for UTI disease state endotyping* TABLE_PLACEHOLDER:TABLE 1 The total size of the dataset was 120 rows and 13 columns. Data augmentation (a common technique used in machine learning data analysis to increase the amount of data by reorganizing already existing data for improved analysis without additional data collection) was done by subdividing the modulus of impedance used in sensor calibration into the real and imaginary and phase components to obtain a wide dataset for a more comprehensive analysis of the individual features. The RF model was tuned to obtain the maximum number of trees to achieve maximum accuracy for the given dataset. It was found that for the given dataset, 14 trees obtained the highest accuracy of $70.88\%$ using $80\%$ of the dataset for training and $20\%$ of the dataset for testing. The confusion matrix in Figure 7b shows the output from a single RF. Since most of the samples fell in the main diagonal, it can be concluded that the model was capable of correctly predicting and classifying the output disease states with high accuracy.
Next, the model was used to analyze the importance of the different features for UTI endotyping. Figure 7c shows the bar graphs of the features in descending order of importance and Figure 7d shows the values of the feature scores corresponding to each feature under analysis. It was found that the frequency was the least important feature while the phase angle of CRP was the most important feature for our dataset.
After the identification of the key features, a new RF model (Random Forest 2) was developed wherein only the 10 most important features were studied and the relatively less important features were dropped. This was done to reduce the computation time and costs of analysis, which is especially important when dealing with larger datasets with multiple levels of classification. As depicted in Figure 7a, for RF‐2, $80\%$ of the dataset was used for training while $20\%$ was used for testing.
For the reduced and optimized RF, that is, RF‐2, fewer trees, that is, a total of 10 trees were required to reach the same maximum accuracy of $70.88\%$, keeping the tree depth constant (Figure 7e). By comparing the confusion matrices in Figure 7b,e, it can be clearly seen that more elements fall on the main diagonal for RF‐2 than that for RF‐1. This means that by cutting out the clutter by way of removing the unimportant features, the model was able to classify better all four digital states at the output. It is important to mention that all the classes were balanced in the input dataset. To study the metrics corresponding to the ability of the model to classify the four digital output states, “precision,” “recall,” and F‐1 scores were studied. The formula for each of these metrics has been listed as follows: [1] Precision=True positivesTrue positives+False positives [2] Recall/Sensitivity=True positivesTrue positives+False negatives [3] F−1Score=2*Precision*RecallPrecision+Recall The values of the metrics have been listed in Figure 7f for all four classes. A high precision value of > = $80\%$ was found for States 1, 3, and 4, whereas it was $50\%$ for State 2. In terms of recall, State 2 was higher than States 1 and 3 and lower than State 4. This may mean that for the given dataset, for State 2, false negatives were low; however, false positives were high. State 2 corresponds to the UTI endotype of “infectious, pre‐symptomatic” and is associated with biomarker levels corresponding to low inflammation. In this case, for home‐based use, it is more significant to ensure low false negatives such that early‐stage UTIs do not go undetected. For State 4, corresponding to the state of “infectious, systemic,” the values of precision, recall, and F‐1 score was the highest. This is desirable as this is the state corresponding to peak illness and has the causative microorganism spread systemically as reflected by high levels of all target inflammatory biomarkers in urine. In this way, the implementation of target disease stratification and endotyping was demonstrated using a simple statistical machine learning algorithm. To our knowledge, this is the first demonstration of direct integration of raw impedance values from an electrochemical biosensor to train a machine learning model for multistate disease classification. This model is versatile and can be extended to more complicated disease states with more stratification levels by studying a broader panel of biomarkers.
## DISCUSSION AND CONCLUSION
In the current clinical workflow of management of UTIs that requires both the reporting of symptoms and detection of bacteria by the gold standard technique of urine culture, the patient, due to the lack of reliable self‐monitoring alternatives, must call to book an appointment with a clinician at the when feeling symptoms of a UTI. In the best‐case scenario (shown in Figure 7a), the patient visits the hospital or a doctor's office on the same day or the next day.
During this time, the UTI‐causing pathogen may ascend the urinary tract. At the hospital, the patient's urine is sent to a centralized lab for urine culture tests. The patient must wait for 2–4 days to get the results and is often prescribed a broad spectrum of antibiotics in the meantime. These prescriptions are often unnecessary, and the resulting overuse or misuse of antibiotics may result in antibiotic resistance and allergy. 47 This often makes subsequent treatment more complicated, incurs unnecessary medical expenses, and reduces the chances of treatment success. In many unfortunate cases, the ineffectiveness of prescribed antibiotics results in longer hospital stays especially not suitable for high‐risk groups (such as pregnant, pediatric, and geriatric populations who are also highly vulnerable to UTIs) in the current pandemic situation. Delays in diagnosis can result in severe sequelae as the causative pathogen can travel from the lower urinary tract to the upper urinary tract to cause pyelonephritis and may ultimately spread to the bloodstream causing sepsis and even death.
In their recent work, Zhang et al. propose a rapid method of detecting UTIs for prompt initiation of therapy and improved antibiotic stewardship. 48 However, their detection scheme relies on the visualization and analysis of E. coli in the urine sample. Since the method is phenotype‐driven, it lacks the ability to stratify UTI severity, which is critical for timely triage and treatment success. Also, this method only detects the presence of possible UTI‐causing bacteria in the urine but cannot detect if there is an immune response. The gold standard for UTI diagnosis requires the presence of a microbial pathogen in the urine and reported symptoms (i.e., dysuria, urgency, frequency), which are a result of the host immune response to infection. Bacteriuria in the absence of symptoms is called asymptomatic bacteriuria (ASB) and is not recommended to be treated with antibiotics. Thus, without accurate symptom reporting, which often occurs in elderly individuals, methods that only determine the presence of bacteria in the urine may misdiagnose ASB as UTI. Our work demonstrates a rapid, cost‐effective, disposable nonculture‐based electrochemical method to diagnose and manage UTI and recurrent UTIs by evaluating a panel of urine biomarkers (PGE2, IL‐6, and CRP) to map the severity of disease progression and disease endotyping (Figure 8).
**FIGURE 8:** *DigEST implementation, operation, and benefits. Schematic showing the (a) comparison between the current and the proposed clinical workflow for UTI diagnosis using DigEST and (b) Operation and key benefits of DigEST. Created using Biorender.com*
This work demonstrates the development of a novel nonculture‐based method to diagnose and manage UTIs and rUTI by evaluating a panel of urine biomarkers to map the severity of disease progression and disease endotyping. The biosensing concept is versatile and modular and can be applied to any disease model, which requires time‐critical stratification for efficacious treatment. To demonstrate proof of concept, we have chosen UTI as the disease model of interest. UTIs are common infections that affect individuals of all age groups and are associated with frequent episodes of recurrence and relapse, especially in post‐menopausal women. UTIs, though a common cause of mortality worldwide, is often undiagnosed (especially in resource‐challenged settings due to limited access to laboratory culture tests) and when left untreated can result in severe sequelae such as sepsis and even death, as the causative pathogen ascends from the lower urinary tract to the upper urinary tract and ultimately spread into the bloodstream. 12, 13, 43, 49, 50 Thus, tracking the progression of the disease, in a time‐critical fashion, is key for treatment success. To achieve this, we have developed a rapid UTI biosensor that measures inflammatory biomarkers which are expressed in urine in response to the underlying infection, regardless of the type of pathogen (commonly bacteria). Notwithstanding that there are several biomarkers associated with UTI, we have focused on three established inflammatory urinary biomarkers for our proof‐of‐concept study viz., PGE2, IL‐6, and CRP. 14, 25, 43 The novelty of our sensor platform is that it stratifies UTI progression and gives out a digital state output corresponding to the disease endotype. This is a first‐of‐a‐kind biosensor that endotypes and classifies disease states based on severity considering levels of host inflammatory biomarkers.
Being nonculture based, it is independent of the causative pathogen. Further, The DigEST platform is scalable and future versions may employ alternate or additional biomarkers like LPS to get an even more accurate assessment of the disease state and might help in predicting the risk of UTI relapses. The biosensing concept is versatile and modular and can be applied to any disease model, which requires time‐critical stratification for efficacious treatment.
Non‐Faradaic, label‐free EIS was used to study the subtle interfacial modulation due to the specific affinity capture of the target analytes PGE2, IL‐6, and CRP by their corresponding highly specific monoclonal antibody. Four disease states were studied and arbitrarily assigned outcomes viz. State 1 through 4 corresponding to “healthy,” “infectious, pre‐symptomatic,” “infectious, symptomatic” and “infectious, systemic.” These states were chosen arbitrarily, and the proposed model can be extended to more biomarkers and varied classification states, as the field of UTI biomarker discovery evolves. Further, since the sensor system is an essential “plug‐and‐probe”, different analytes can be detected using different capture probes such as aptamers, molecular imprinted polymers, and so on. 17 The sensor can be extended for endotyping other complex and heterogeneous diseases in other body fluid samples such as chronic obstructive pulmonary disease (COPD) in exhaled breath condensate patient samples. The sensitivity of the system can be enhanced for detecting trace molecules in femtomolar concentrations, if required, by replacing the generic sensor electrodes with decorated electrodes with surface modification to enhance surface reactivity and effective sensing surface area. 51 For scaling the sensor performance, larger and more diverse patient cohorts need to be studied. For truly personalized disease management, key disease‐relevant biomarkers need to be identified and corresponding biological pathways need to be studied closely, as the biomarker discovery field evolves. This sensor demonstrates the use of random forest as the algorithm for machine learning‐guided classification of the output disease states. However, given the nature of the signal extracted from the electrochemical sensor, and the complexity of the events at the double layer interface, the dataset can be further augmented, and other traditional and deep learning statistical models can be employed to best optimize the tradeoff between application and the computational costs post‐implementation as POC device.
## Reagents and materials
Monoclonal α‐PGE2 antibody was purchased from Arbor Assays (Ann Arbor, MI, USA). Monoclonal antibodies for IL‐6 and CRP and their corresponding antigens were obtained from Abcam (Cambridge, MA, USA). The antibody was aliquoted and stored at 4°C until further use. HPLC purified PGE2 antigen was obtained in synthetic powder form from Sigma Aldrich (St. Louis, MO, USA). The crosslinker DSP (dithiobis (succinimidyl propionate)) and Phosphate Buffer Saline (PBS) were obtained from Thermofisher Scientific Inc. (Waltham, MA, USA). Artificial urine was prepared using the recipe (MP‐AU or multipurpose‐artificial urine) by Sarigul et al. 52 and all the dilutions were prepared in deionized water. The urine buffer pH was adjusted using sodium hydroxide ($10\%$ NaOH) and Hydrochloric acid ($10\%$ HCl) obtained from Sigma Aldrich (St. Louis, MO, USA). Pooled human urine samples (pH ~6.5) were obtained from Lee Biosolutions (St. Louis). The sensor system was constructed using Metrohm 220 AT three‐electrode (gold working and counter electrode and silver reference electrode) screen‐printed sensor (Metrohm USA).
## Experimental design of validation of binding chemistry
Nicolet 6700 FTIR (Thermo Fisher Scientific) was used for the ATR‐FTIR studies. A liquid sample of 10 mM DSP crosslinker dissolved in DMSO was used as the baseline for the analysis. A 1:1 mixture of the monoclonal antibody (corresponding to the target analyte u.e., PGE2, IL‐6, or CRP) in PBS and DSP crosslinker dissolved in DMSO was incubated for 2 hours. For both the baseline DSP spectrum and all the DSP‐mAb conjugate spectra, 256 scans were collected, for a wavelength range of 4000–600 cm−1, at a resolution of 4 cm−1.
## Experimental design and optimization of EIS studies
The sensor consists of a standard three‐electrode system made up of gold working and counter electrodes and silver reference electrodes screen printed on a ceramic substrate. The 5 μl of 10 mM of DSP crosslinker (dissolved in DMSO) was incubated only on the working electrode for 2 h in dark for strong thiol bond formation. After DMSO wash and one 1X PBS wash, the monoclonal PGE2 antibody was immobilized on the gold working electrodes in 5 μl volume for overnight incubation for EDC‐NHS bond formation. The crosslinker concentration was selected based on the literature. ATR‐FTIR‐based spectroscopic validation (described in previous sections) was used to validate the suitability of the selected incubation time and antibody concentration for urine prostaglandin E2 biosensing. The volume of 5 μl for crosslinker and antibody was chosen to ensure that the entire working electrode (only WE) gets covered to ensure specificity of the response.
## Experimental design and testing of Boolean studies profiles: Simulated Boolean profiles
To simulate the samples for different endotypes, four different cocktails of the urine samples were prepared by 1:1:1 mixing samples with varying levels of the three biomarkers between two states, that is, high (h) or low (L). Mathematically, for three biomarkers and two states of levels/inflammation, 23 = 8 combinations are possible. However, only four feasible and physiologically relevant combinations of PGE2, IL‐6, and CRP have been demonstrated in this proof‐of‐concept study. These four states correspond to the output Boolean or digital logical states for disease endotype and severity analysis. The truth table corresponding to the biomarker combinations in the input sample, the associated Boolean state, and the example suggesting the perceived outcome have been depicted in Figure 6.
## Experimental design of human subject and ELISA studies
The human subject samples were collected as part of approved Institutional Review Board protocols (STU 082010‐016, MR 17‐120). Patient samples were collected during their visit to UTSW Medical Center and written consent was obtained before sample collection and participation in the study. As a proof of concept, three postmenopausal female subjects with active, symptomatic UTIs as diagnosed by clinical urine culture were studied. ELISA kit for PGE2 was purchased from Enzo Life Sciences Inc. (Farmingdale, NY, USA), IL‐6 was purchased from Abcam (Cambridge, MA, USA), and that for CRP was purchased from Sigma Aldrich (St. Louis, MO, USA). Clean‐catch urine collected from these patients was aliquoted and stored at −20 C and was retrieved later for analysis. Postanalysis was done by using the calibration dose–response curves for both methods described in the previous sections. Graphics were drawn using Biorender.com and Inkscape v1.
## Machine learning studies
Our models were built on Google's CoLab platform, which relies on a quad‐core Intel Xeon processor running at 2.00 GHz per core, 24 gigabytes of ram, and a Tesla V100‐SXM2‐16GB GPU. The model created was a “double random forest” model, which had two stages. The first stage was used to down‐select important features and using only these features the model was developed in the second stage. This was done to create an optimized base model with reduced computational costs that would feed the blended model. Sklearn library in Python was used, and bootstrapping was done randomly with replacement. The optimal number of trees for the simple random forest model, that is, RF1, and that for the optimized RF, that is, RF2 were chosen based on the number of trees that yield the maximum accuracy. The predictions of decision trees were bagged to get the final output class. The choice of random forest is dictated by the fact that this method is a power ensemble method (solves for the bias and high variance issues encountered in decision trees), works very well even with missing data, and is not affected drastically when new data are added. The capability to delineate the most important features based on random forest convergence was yet another important reason to choose it as the base learner model.
## Statistical analyses
The data in Figure 5a–c are represented as a Box and Whiskers plot (with min and max points as whiskers) with $$n = 9$$ ($$n = 3$$ intersensor and $$n = 3$$ intrasensor replicates). Similarly, Figure 5d–f has been represented as a Box and Whiskers plot (with min and max points as whiskers). The significance test was carried out using Student's t‐test and one‐way ANOVA with an α of 0.05. All the statistical analyses were performed using Graph Pad Prism version 9.1.2 (GraphPad Software Inc., La Jolla, CA, USA).
## AUTHOR CONTRIBUTIONS
Antra Ganguly: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); software (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Tahmineh Ebrahimzadeh: Data curation (supporting); methodology (supporting); validation (equal); writing – original draft (equal); writing – review and editing (equal). Jessica Komarovsky: Data curation (supporting). Philippe Zimmern: Conceptualization (equal); investigation (equal); project administration (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). Nicole De Nisco: Conceptualization (equal); formal analysis (equal); funding acquisition (lead); investigation (equal); project administration (equal); resources (equal); writing – review and editing (equal). Shalini Prasad: Conceptualization (equal); formal analysis (equal); investigation (equal); methodology (equal); project administration (equal); resources (equal); supervision (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal).
## CONFLICT OF INTEREST
Dr. Shalini Prasad has a significant interest in EnLiSense LLC, a company that may have a commercial interest in the results of this research and technology. The potential individual conflict of interest has been reviewed and managed by the University of Texas at Dallas, and it played no role in the study design; the collection, analysis, and interpretation of data; the writing of the article; or the decision to submit the article for publication. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10437.
## DATA AVAILABILITY STATEMENT
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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|
---
title: Subaqueous free‐standing 3D cell culture system for ultrafast cell compaction,
mechano‐inductive immune control, and improving therapeutic angiogenesis
authors:
- Gwang‐Bum Im
- Yu‐Jin Kim
- Tae Il Lee
- Suk Ho Bhang
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013761
doi: 10.1002/btm2.10438
license: CC BY 4.0
---
# Subaqueous free‐standing 3D cell culture system for ultrafast cell compaction, mechano‐inductive immune control, and improving therapeutic angiogenesis
## Abstract
Conventional 3D cell culture methods require a comprehensive complement in labor‐intensive and time‐consuming processes along with in vivo circumstantial mimicking. Here, we describe a subaqueous free‐standing 3D cell culture (FS) device that can induce the omnidirectional environment and generate ultrafast human adipose‐derived stem cells (hADSCs) that efficiently aggregate with compaction using acoustic pressure. The cell culture conditions were optimized using the FS device and identified the underlying molecular mechanisms. Unique phenomena in cell aggregation have led to extraordinary cellular behavior that can upregulate cell compaction, mechanosensitive immune control, and therapeutic angiogenesis. Therefore, we designated the resulting cell aggregates as “pressuroid.” Notably, external acoustic stimulation produced by the FS device affected the pressuroids. Furthermore, the pressuroids exhibited upregulation in mechanosensitive genes and proteins, PIEZO$\frac{1}{2.}$ CyclinD1 and PCNA, which are strongly associated with cell adhesion and proliferation, were elevated by PIEZO$\frac{1}{2.}$ In addition, we found that pressuroids significantly increase angiogenic paracrine factor secretion, promote cell adhesion molecule expression, and enhance M2 immune modulation of Thp1 cells. Altogether, we have concluded that our pressuroid would suggest a more effective therapy method for future cell therapy than the conventional one.
## INTRODUCTION
Different physical stresses, such as compression, tension, torsion, and shear stress, have been applied to cellular behavior regulation in 2D cell culture systems. 1, 2 Previous research has demonstrated that physical stress‐modulates 2D cell differentiation, proliferation, paracrine factor secretion, and cell survival rate. 3, 4, 5 Based on the beneficial changes in cellular behavior induced by the physical stresses applied to the 2D cell culture system, 3, 4, 5 the 3D cell culture system has adopted the physical stresses during cell culture. Unlike the 2D cell culture system, the 3D cell culture system applied physical stress primarily for producing cell aggregates 6 or inducing cell differentiation. 7 In fact, most studies on 3D cell aggregates have overlooked the critical cellular changes caused by physical stress generated from the 3D cell culture system.
Studies on stem cell spheroids, for instance, have revealed enhanced therapeutic effects and related molecular mechanisms found in cell aggregate. However, there is still a lack of analyses of single cellular events induced by physical stimulation applied from devices to form spheroid. 8, 9 In addition, it is difficult for representative 3D cell culture systems such as the hanging drop method (HD) and forced‐floating method (FFM) to replicate in vivo conditions such as the omnidirectional in vivo microenvironment. 6, 8, 9, 10, 11, 12 In particular, the HD method is labor‐intensive, time‐consuming, optimized for short‐period cell culture, and slow for inducing cell compaction, which remained significant issues (S. 13, 14).
Subsequently, using nonadhesive material and a curved bottom design, the FFM method can result in instant forcible cell aggregation through centrifugation, but it can also cause cellular damage during the procedure. 15 In addition, it is difficult to regulate the size of cell aggregates during cell culture, which is essential for enhancing the therapeutic effect based on oxygen and nutrient supply 6, 16, 17 without altering the initial cell culture conditions or cell culture plate design using conventional methods. In light of this, a comprehensive investigation of physical stimuli‐induced cellular behavior change is required for the development of a new efficient 3D cell culture system capable of overcoming the limitations of conventional 3D culture methods.
Among the various physical stimuli applied to 2D cell cultures, previous research has demonstrated that extracellular pressure can upregulate the expression of mechanosensitive genes and proteins. PIEZO1 and PIEZO2 are mechanosensitive genes and proteins that regulate cellular functions. 18, 19 Through mechanobiological responses, PIEZO1 and PIEZO2 promote cell differentiation or cytoskeletal reconstruction in 2D cultured stem cells. 20, 21, 22 However, mechanosensitive genes and proteins in 3D cultured stem cells have rarely been studied. Recent research has demonstrated that mesenchymal stem cell spheroids can be formed through self‐organization via the acoustic levitation technique. 23 As the study primarily focused on the spheroidal cell aggregate formation and spheroid differentiation, the biological effect on the 3D stem cell spheroid induced by generated physical stimulation, which may influence cellular behavior and therapeutic potential of stem cells, has not been considered. Previous studies employing ultrasound waves also considered the technique to be a type of 3D cell culture system comparable to conventional 3D cell culture systems due to the method's lack of exceptional advantages in cell differentiation. 23 In addition, detailed changes in the molecular mechanisms that affect gene and protein expressions or comparisons between this method and the conventional 3D cell culture method have not been identified.
This study devised an alternative method for cultivating 3D cell aggregates that circumvents the issues inherent in conventional 3D cell culture techniques. Using acoustic pressure, we developed a subaqueous free‐standing 3D cell culture (FS) device that can generate cell aggregates with ultrafast and efficient compaction (pressuroids). In addition, our FS device can maintain an omnidirectionally homogeneous cellular microenvironment that mimics in vivo conditions more effectively than conventional 3D cell culture techniques. In conventional 3D cell culture systems for generating cell aggregates, the FS device significantly reduced laborious and time‐consuming processes. Moreover, based on paracrine factor secretion, pressuroids demonstrated significantly enhanced M2 polarization and angiogenic effects compared to conventional 3D cell culture systems. Our experimental results revealed the effects of constant pressure‐induced mechanosensitive gene and protein (PIEZO$\frac{1}{2}$) expressions and subsequent molecular changes in 3D stem cell aggregates, which were rarely observed in previous studies. We examined the cellular mechanisms based on time‐dependent PIEZO$\frac{1}{2}$ expressions under constant pressure from the FS device, which increased cellular changes, including cell aggregation, adhesion, M2 polarization, therapeutic angiogenesis, and proliferation.
This study also includes simulation data regarding the cell culture system and environment of the FS device. The discovery of cellular mechanisms and the therapeutic effect of exogenous physical stimulation, such as hydrostatic pressure, using our new subaqueous free‐standing 3D cell culture device may provide opportunities for understanding and developing cell culture systems, that are more similar to the in vivo microenvironment than conventional 3D cell culture systems.
## Acoustophoretic force‐induced mechanism and FS device design
Subsequently, the FS device was designed to induce subaqueous free‐standing in the cells and cell aggregations using acoustic standing waves in the vertical direction of a cylindrical‐shaped cell culture vessel (Figure 1a). In order to calculate the acoustic standing wave distribution in the cell culture vessel, the pressure generated by the piezoelectric actuator to the water was calculated (Figure 1b). According to the symmetrical design of the actuator, the pressure was greatest at the center and decreased in the radial direction. Figure 1c depicts the calculation results of the acoustic standing wave distribution in the cell culture vessel as a function of the distance of approximately 67 mm between the reflector and the actuator when considering the volume of the medium to be used for cell culture. It was determined that the most stable and uniform standing wave was formed at this distance. Based on the calculation result, the height was set to 67.52 mm by turning the screw holder, and an acoustic standing wave was created in the FS device by applying a voltage to the actuator. Then, the cell suspension was poured into the activated FS device and observed time‐dependent cell aggregation (Figure 1d). The hADSC aggregates were generated within 30 min after activating the FS device.
**FIGURE 1:** *Characterization of FS device. (a) A schematic depicting the components of the FS device. (b) Acceleration magnitude and piezoelectric actuator relative deformation. (c) The acoustic pressure field as a function of height. The maximum pressure field was obtained at 67.52 mm. (d) Representative images demonstrating time‐dependent cell clustering after dispersing hADSCs to the FS device. (e) Acoustic pressure contour plot of a selected region (solid red rectangle) and single‐cell particle tracing results*
In order to elucidate the cause of the initial movement of cells by the acoustophoretic force induced by acoustic pressure difference under a stable acoustic standing wave, we constructed a model depicting the situation of cells after pouring a cell suspension. We assumed that a single cell is a spherical particle with a diameter of 30 μm and density of 1050 kg/m3. 24, 25, 26 We placed 4 × 105 particles/device between the actuator and the reflector. The previous calculation's spatial distribution of predefined acoustic standing waves was adopted. Moreover, we calculated the global trajectories of all particles; the trajectories of particles were displayed in a specific region of Figure 1e because the cells were too small compared to the size of the culture vessel. Randomly positioned cells were accelerated and moved to the node regions, leaving their initial positions. Within 0.03 s, the particles stably settled down in node regions. From 0 to 0.03 s, the trajectories of particles are displayed in Figure 1e and Movie S1.
## Optimizing cell culture conditions in FS device compared to conventional 3D cell culture methods
To determine the effect of acoustic pressure on hADSCs, we compared the gene modification of hADSCs cultured in an FS device to that of cells cultured using single‐cell, HD, and FFM methods (Figures 2 and S1). In fact, acoustic pressure influenced the expression of mechanosensitive ion channel genes, thereby regulating the expression of genes involved in cell proliferation, paracrine factor secretion, and cell death (Figure 2). PIEZO1 gene expression was significantly upregulated 3 h after hADSCs were cultured in the FS device (FS 3 h) relative to other groups (Figure S1A). The PIEZO2 gene was elevated in the HD and FS 6 h groups but decreased in the FFM group compared to the single‐cell group (Figure 2b). Compared to the single‐cell group, the FS 6 h and FS 12 h groups displayed the highest phosphoinositide 3‐kinase (PI3K) gene expression, which regulates angiogenesis. Figure 2c displays a decrease in PI3K gene expression in the HD and FFM groups relative to the FS 6 h and FS 12 h groups. 27 The expression of the representative angiogenic gene, VEGF, was elevated in all groups compared to the single‐cell group (Figure 2d). However, the FS 6 h and FS 12 h groups exhibited the highest VEGF expression level compared to the other groups (Figure 2d). Specifically, the FS 6 h group demonstrated significantly higher PIEZO2, PI3K, and VEGF gene expressions than the HD and FFM groups (Figure 2b–d). The expression of the cell cycle regulatory gene CCND1 was significantly higher in all FS groups (3, 6, and 12 h) compared to HD and FFM groups (Figure 2e). PCNA gene expression was downregulated in all groups except the FS 6 h group compared to the single‐cell group (Figure 2f). The CASPASE‐3 gene was reduced in all groups compared to the single‐cell group (Figure 2g). Based on gene expression results, the FS 6 h group exhibited enhanced proliferation, angiogenic properties, and cell viability compared to other time point results. The FFM group was excluded from the subsequent analysis due to its low gene expression levels for PIEZO, VEGF, PI3K, CCND1, and PCNA. In addition to the FS 6 h group, the HD group, which had the second‐highest VEGF gene expression and is well known for its therapeutic effect on various diseases compared to single cells, was selected for the subsequent analysis.
**FIGURE 2:** *Cellular mechanism analysis of human adipose‐derived stem cells (hADSCs) grown under diverse conditions (single‐cell; FFM, force floating method; FS, free‐standing device method; HD, hanging drop method). (a) Schematic of the cellular mechanism in the FS device that is induced by acoustic pressure. Gene expression of (b) PIEZO2, (c) PI3K, (d) VEGF, (e) CCND1, (f) PCNA, and (g) CASPASE‐3 in hADSC under different cell culture conditions (n = 6, *p < 0.05 and **p < 0.001 compared with the single‐cell group, #p < 0.05 and ##p < 0.001 compared to each other, N.S.: not statistically different with single‐cell group)*
## Comparison of cell aggregation character and re‐adhesion capacity between pressuroid and conventional HD‐derived cell aggregates
First, we studied the size and height of cell aggregates generated using the FS device (6 h, pressuroid). After pouring a suspension of hADSCs into the FS device, the cells moved rapidly toward the acoustic standing wave node and then aggregated. In an acoustic standing wave, the pressure difference is predominant in the vertical direction and approaches zero near the node of the wave. In contrast, due to the symmetry of the acoustic standing wave, the pressure difference along the radial direction is nearly insignificant. Therefore, the vertical growth of cell aggregate is geometrically constrained, whereas the radial growth of cell aggregate is unrestricted.
To estimate the effect of the acoustic standing wave on the growth of cell aggregates in a suspended state, we determined the movement of a large particle representing cell aggregates in the acoustic standing wave and the compressive stress applied to the particle surface by water molecules as a function of particle size. We gave the particles a spherical shape and a density of 1050 kg/m3. The diameter of the particles ranged between 200 and 1400 μm. All particles were initially positioned at a point (2.5 and 32.25 mm), and their 1‐s trajectories in the acoustic standing wave were computed. The calculation results (Figure 3a) confirmed that particles with a diameter of less than 800 m maintained a relatively stable position near a node, whereas particles with a diameter of more than 1000 μm remained in constant motion. This criterion for suspension stability in the FS device is because the particle size must be less than 937.5 μm for the ultrasound pressures to be nearly balanced (the wavelength of the acoustic wave in this work). Even within the size conditions for stable suspension, the smaller the particle size, the smaller the positional displacement as the magnitude of the acoustophoretic force applied to the particles decreases.
**FIGURE 3:** *Investigation on cell aggregation of FS 6 h. (a) Single‐particle tracking for investigating the size‐dependent stability of spheroids in an FS device; the red dot indicates the initial position of the particle. The particles size ranges between 200 and 1400 μm. (b) Stress applied to a single‐particle over time, as well as the average stress for 1 s. The scale bar represents 500 μm. (c) The circumference and height of FS 6 h (n = 25)*
In addition, the magnitude of the compressive stress exerted on each particle within the size range indicating stable suspension was calculated. The stress was calculated every 2 ms for 1 s, and the results are depicted in Figure 3b. Next, the stress exerted on the particles was calculated to fluctuate within a certain range by taking into account the frequency of 1.6 MHz of the acoustic wave and the movement of the particles. As a result, the average value of the stress for 1 s was determined, and it was discovered that the average stress increased as particle size increased. The average diameter of the pressuroids was 470 m ± 220 m, while the mean height was 200 μm ± 24 (Figure 3c).
To determine the effect of acoustic pressure on human adipocyte‐derived stem cells (hADSCs), we compared the cellular properties of hADSCs cultured in an FS device to those cultured with HD methods (Figure 4a). Compared to cell aggregates treated with HD for 6 h (HD 6 h group), pressuroid exhibited significantly increased cell‐to‐cell adhesion and gap junction‐related gene expressions (E‐CADHERIN, CX43, and ICAM, Figure 4b). 28, 29, 30 Furthermore, pressuroid showed significantly increased VEGF, COX‐2, and IL‐10 gene expression compared to HD 6 h group (Figure S2A). In contrast, we found that the expression of HIF‐1α in the cell aggregates was significantly lower in the pressuroid group than in the HD 24 h group (Figure 4c). As shown in Figures 4d and S2B, the signals for CX43 and E‐CADHERIN were significantly higher in the pressuroid than in the HD 6 h group, indicating ultrafast cell aggregation. Shear stress broke the cellular structures in HD 6 h and FS 3 h groups, indicating weak aggregation in both groups compared to pressuroids and HD 24 h groups with tight aggregation and intact structures (Figure S2C). Although pressuroids had similar levels of PIEZO1 and BCL‐2 protein expression compared to the cell cluster 24 h after HD (HD 24 h group) (Figure 4e), VEGF protein expression was significantly upregulated in pressuroids. These pressuroids demonstrated a higher population of early apoptosis than the HD 24 h group, whereas the population of necrosis decreased significantly (Figure 4f). As demonstrated in Figure 2, PIEZO2 expression was upregulated in pressuroids relative to the HD 24 h group (Figure 4g). Pressuroid sequentially increased PIEZO2 expression 3 h after increasing PIEZO1 expression (Figures 2 and 4g). After cell aggregation, we separated aggregates into single cells to examine the cell re‐adhesion property and postadhesion cell behavior in order to estimate the cell functions following in vivo cell transplantation at the wound site (Figure 4h). Moreover, 3 h after cell re‐adhesion, pressuroid cells exhibited significantly enhanced cell adhesion properties compared to the HD 24 h group (Figure 4i,j). As CCND1 is associated with cell adhesion properties, the upregulation of CCND1 expression in pressuroid increased cell adhesion (Figure 2e). Moreover, VEGF and HGF gene expressions were elevated in re‐adhesion cells from pressuroids, whereas CASPASE‐3 gene expression was comparable between the two groups (Figure 4h).
**FIGURE 4:** *Characterization of FS 6 h compared with HD 6 h and 24 h. (a) Schematic comparing the cellular properties of a pressuroid and a hanging drop. (b) Relative mRNA expression of E‐CADHERIN, CX43, and ICAM in HD 6 h and FS 6 h (n = 4, *p < 0.05 compared with HD 6 h group). (c) Relative mRNA expression of HIF‐1α in HD 24 h and FS 6 h (n = 4, *p < 0.05 compared with HD 24 h group). (d) Immunostaining for E‐cadherin+ (yellow), F‐actin (red), and DAPI (blue) in HD 6 h and FS 6 h. Scale bars = 250 μm. (e) Western blot analysis reveals the expression levels of PIEZO1, BCL‐2, and VEGF in HD 24 h and FS 6 h. The graph on the right depicts the quantitative analysis of protein expression as determined by Western blotting (n = 3, *p < 0.05 compared with the HD 24 h group). (f) FACS analysis of apoptosis in HD 24 h and FS 6 h cells stained with Annexin V/7‐AAD (n = 4, *p < 0.05 compared with the HD 24 h group). (g) Immunostaining for PIEZO+ (green), F‐actin (red), and DAPI (blue) in HD 24 h and FS 6 h. Scale bars = 250 μm. (h) Process flow diagram for the re‐adhesion assay of HD 24 h and FS 6 h. (i) Representative optical images of HD 24 h and FS 6 h at 3 h after re‐adhesion. Scale bars represent 250 μm. (j) Relative cell adhesion rate of HD 24 h and FS 6 h at 1 h and 3 h after re‐adhesion (n = 6, *p < 0.05 compared with the HD 24 h). (k) Relative mRNA expression of VEGF, HGF, and CASPASE‐3 in HD 24 h and FS 6 h after re‐adhesion (n = 4, *p < 0.05 compared with the HD 24 h group)*
## Effects of acoustic stimulation on the immunogenic regulation ability of hADSCs
Compared to other cell types, hADSCs do not induce a rapid inflammatory response following transplantation (low immunogenicity). 31 Additionally, when hADSCs are cultured in 3D aggregates, the ability to secrete anti‐inflammatory cytokines that promote tissue regeneration increases significantly. 32 Therefore, we examined whether or not the immunogenic responses of the pressuroid (FS 6 h) induced by acoustic stimulation were altered. The cell lysate was initially analyzed using a cytokine antibody array (Figure 5a–d). CXCL1, a proinflammatory factor, was expressed more in the HD group than in the pressuroid group. In contrast, IL‐1α and IL‐1β were significantly elevated in the pressuroid group compared to the HD group (Figure 5b). Both IL‐1α and IL‐1β play crucial roles in cell recruitment and proinflammatory cytokine regulation. 33 Moreover, when IL‐1 factors secreted by MSCs and hADSCs become autocrine, they stimulate the production of anti‐inflammatory cytokines by the cells themselves. 34, 35 In the pressuroid group, the expression of representative anti‐inflammatory factors (IL‐13, IL‐16, G‐CSF, and IL‐25) was elevated, whereas IL‐8 expression tended to decrease (Figure 5c). 36, 37, 38, 39 Specifically, the levels of IL‐13 and IL‐16 were significantly elevated. The expression of pro‐ and anti‐inflammatory cytokines IL‐32α and IL‐6 nearly doubled in the pressuroid group relative to the HD group (Figure 5d). 40, 41, 42 C‐X‐C motif chemokine ligand 12 (CXCL12), which promotes the migration and recruitment of immune cells in the lesion, was also upregulated (Figure 5d) 43. Subsequently, quantitative confirmation of changes in the expression of immunomodulatory factors in hADSCs was performed (Figure 5e). Reduced expression of the inflammation‐causing tumor necrosis factor‐α (TNF‐α) gene. 44 In contrast, the expression of VEGF and mannose receptor c‐type 1 (MRC‐1), which decreases inflammation, 45 was higher in the pressuroid groups than in the HD group. Similar to the results of the previous cytokine antibody array, we confirmed that the gene expression of IL‐1β, IL‐6, and CXCL12 was significantly higher in the pressuroid groups than in the HD group (Figure S3). In vitro experiments were used to assess the effects of inflammatory‐related cytokines secreted by pressuroids on immune cells (Figure 5f,g). We investigated the effect of cytokines secreted by pressuroids on the phenotype of macrophages, the innate immune cells at the forefront of the body's immune response. 46 THP‐1 cells differentiated into the M0 phenotype with PMA were exposed to a conditioned medium from a pressurized culture for 48 h in order to assess macrophage polarization. 47 THP‐1 cells (M0 phenotype) show a jagged cell morphology prior to differentiation into the M1 phenotype. When cells differentiate toward the M2 phenotype, they assume a round morphology. 48 In fact, rounder THP‐1 cells were observed in the pressurized group versus the HD group (Figure 5f). In the subsequent analysis of gene expression in THP‐1 cells, the expression of M1 markers IL‐6, TNF‐α, and CD80 decreased in the pressuroid group compared to the HD group, whereas the expression of M2 markers CD83 increased (Figure 5g).
**FIGURE 5:** *Enhanced immunogenic regulation capacity in FS hADSCs relative to HD 24 h hADSCs. (a–d) Analysis of immunomodulation‐related cytokine expression in FS hADSCs using a human cytokine array. (a) A representative image of the human cytokine array data and (b–d) a comparison of the quantitative differences. (e) Expression of immunomodulation‐related genes (TNF‐α, VEGF, and MRC‐1) in FS hADSCs analyzed with qRT‐PCR. The HD 24 h group served as the control group (n = 5, *p < 0.05, compared to HD 24 h group). (f) Morphology of THP‐1‐derived macrophages after treatment with HD or FS hADSCs' condition medium for 24 h. (g) qRT‐PCR analysis of the expression of macrophage marker genes (IL‐6, TNF‐α, CD80, and CD83) in THP‐1‐derived macrophages. The HD 24 h group served as the control group (n = 5, *p < 0.05, compared to HD 24 h group).*
## Accelerated wound healing by pressuroid generated from FS device
Subsequently, using a mouse wound healing model, the therapeutic efficacy of the hADSCs in the pressuroid group was evaluated. Following the induction of 2.0 × 2.0 cm2, full‐thickness skin defects, mice were treated with 3D hADSCs aggregate transplantation from HD or FS device. 49 We anticipated that the therapeutic effects of the pressuroid group would be superior to those of the HD group based on in vitro test results (Figure 6a). No treatment (NT), 3D hADSC aggregates collected from HD (0.75 × 106 cells/mouse, HD), and 3D hADSC pressuroids collected from FS device (0.75 × 106 cells/mouse, Pressuroid) were administered to the mice. Figure 6b depicts representative photographs of mice at 0, 3‐, 7‐, 10‐, and 14‐days post‐treatment. Skin wound areas were also measured at each time point. The closure of a wound is expressed as a percentage of the initial wound area. The pressuroid groups exhibited a significantly greater therapeutic effect than the other groups. The HD group demonstrated greater treatment efficacy than the NT group on Day 7, but the difference was insignificant by Day 10 and improved slightly by Day 14. In contrast, the pressuroid group demonstrated greater therapeutic efficacy than the NT group on Day 7, as well as the best results on Days 10 and 14 when compared to other groups. On Day 14, H&E and MT staining confirmed that fibrosis occurred less frequently in the pressuroid group than in other groups (Figure 6c). Blood vessel marker SM‐α and CD31 expressions were evaluated by IHC staining, with the highest SM‐α and CD31 expressions in the pressuroid group versus other groups (Figure 6d). 50, 51 In addition, the gene expression of SM‐α and CD31 was quantified, and the results showed that the pressuroid group had the highest expression levels relative to the other groups. Specifically, the gene expression of CD31 was dramatically elevated in the pressuroid group compared to the other groups (Figure 6 e). In addition, the expression of skin proteins was confirmed 14 days after treatment. Involucrin is an extracellular matrix (ECM) component that plays a crucial role in the barrier function of the skin. Laminin is an ECM component that makes up the skin's basement membrane. 52 IHC staining confirmed that the expression of involucrin and laminin in the skin tissue of the pressuroid group was greater than that of the other groups (Figure 6D). The expression of ECM‐related genes, including involucrin, Col 1, Col 4, Keratin 10, and Keratin 14, in the skin wound area of each group were analyzed. 53 Col 1 is one of the ECM proteins that make up the dermal interstitial and granulation tissue matrices. Col 4 is an ECM component of the basement membrane matrix. 54 Increased expression of Keratin 10 inhibits the proliferation of keratinocytes. 55 In contrast, basal keratinocyte‐expressed Keratin 14 is involved in re‐epithelization during wound healing. 55, 56 In particular, the involucrin gene expression was significantly elevated in the pressuroid group compared to other groups. Additionally, the Col 4 gene expression was significantly elevated in the pressuroid group compared to the NT group (Figure 6f).
**FIGURE 6:** *In vivo wound closure induced by FS hADSCs. (a) Schematic of the wound healing model for mouse skin using FS hADSCs. (b) Representative images of the wound on Days 0, 3, 7, 10, and 14 following the various treatments. Quantification of wound closure on Days 3, 7, 10, and 14 following treatment for all groups (n = 5, *p < 0.05, compared with that in the NT group, #p < 0.05, compared with each group). (c) Representative H&E‐ and MT‐stained images of skin wound model tissue sections 14 days after treatments. (d) Immunohistochemistry for SM‐a (green), CD31 (green), laminin (green), or involucrin (green) with DAPI (blue) in skin wounds 14 days after treatments. (e) Relative expression of SM‐a and CD31 in the wound region 14 days after treatments (n = 5, *p < 0.05, compared with that in the NT group; #p < 0.05, compared with each group). (f) Relative expression of ECM‐related genes (Involucrin, Keratin 14, Keratin 10, Col 4, and Col 1) in the wound region 14 days after treatments (n = 5, *p < 0.05, compared with that in the NT group; #p < 0.05, compared with each group)*
## DISCUSSION
Existing 3D cell culture systems struggle with labor‐intensive processes, prolonged cell culture time, and homogenous regulation of cellular function, despite ongoing efforts to develop efficient 3D cell culture systems. In addition, it is difficult to induce rapid cell compaction in a 3D cell aggregate, which is essential for cellular function and viability. This study created a subaqueous free‐standing 3D cell cluster system for ultrafast cell aggregation with compaction, mechano‐inductive M2 polarization, and the enhancement of therapeutic angiogenesis using acoustic pressure control. Previous acoustic levitation technique‐based cell culture methods have yielded intriguing results, such as globular cell aggregation and differentiation without specific gene and protein expression alterations when compared to conventional 2D cell culture methods. In contrast to previous research, we focused on optimizing gene and protein expression to enhance the therapeutic efficacy of 3D stem cell aggregates. In addition to the detailed simulation results of the FS device, several analyses, such as the variation in mechanosensitive molecular mechanisms, immunomodulation properties, and enhanced therapeutic effects in comparison to conventional methods, were investigated. This study demonstrated that pressuroids may be a novel and efficient 3D stem cell aggregate model with enhanced stem cell therapeutic efficacy compared to conventional 3D cell culture techniques.
The FS device was created by generating a single acoustic wave in the cell medium and then spatially constraining it to generate a standing wave to suspend the cells in the culture medium. These acoustic standing waveforms create a water pressure barrier in the medium, allowing cells to hover in the middle of the medium without a solid‐state supporter, with the expected result of rapidly forming cell aggregates by collecting individual cells at node points. The calculation results in Figure 1e explain the rate at which individual cells form a cluster in an acoustic standing wave. The contours represent locations with identical pressure values, and the direction of acoustic pressure difference is perpendicular to each contour line. In the white‐colored node regions of Figure 1e, the pressure difference becomes zero. The acoustophoretic force accelerates the particles in the direction of the pressure difference, and the force becomes zero in the node regions. Within a few microseconds, cells aggregated toward node points in the calculation depicted in Figure 1e, and as shown in Figure 1d, it was confirmed that cell clusters of a visible size were formed within minutes.
As expected, the acoustic pressure in the FS device significantly shortened the cell culture period and labor‐intensive process, which have been identified as issues with conventional 3D cell culture methods. 57, 58 In addition, we analyzed the effect of acoustic pressure in FS devices on stem cell behavior, including proliferation, compaction, cell cycle, cell death, and therapeutic potential after PIEZO$\frac{1}{2}$ upregulation. 59, 60, 61 The relative mRNA expression of PCNA, E‐CADHERIN, CX43, and ICAM confirmed that by optimizing 3D stem cell aggregate culture conditions with the FS device, the pressuroid rapidly generated enhanced cell proliferation and spheroid compaction. Previously, upregulation of PCNA, which functioned as a processivity factor for DNA polymerase δ, increased cell proliferation and enhanced wound healing. 62 E‐cadherin, Cx43, and ICAM, among other CAMs, enhance the self‐renewal and multipotency of stem cells. 63 E‐cadherin is a crucial mediator for intrinsic cell–cell contacts during cell aggregation among these CAMs. 64 CX43, composed of intercellular channels that enable direct cell‐to‐cell communication, plays a crucial role in stem cell adhesion and migration. 65 Furthermore, ICAM, also referred to as CD54, mediates interaction with proinflammatory macrophages and boosts the immunosuppressive function of stem cells. 66 In addition, prior studies demonstrated that ICAM‐deficient mice had delayed skin wound healing. 67 Intriguingly, in contrast to the inhibited cell proliferation observed in stem cell aggregates, our pressuroid exhibited significantly greater cell proliferation capacity than aggregates generated using conventional techniques. Due to the upregulation of PIEZO$\frac{1}{2}$ in the pressuroid, CCND1 was also upregulated, confirming an increase in PCNA (Figure 2). 68 According to previous research, cell aggregate compaction is crucial for enhancing both cell viability and therapeutic effects. Continuous acoustic pressure generated in the FS device caused single hADSCs in cell suspension to aggregate and became trapped in subaqueous acoustic nodes beginning with the inoculation phase. In contrast to cell aggregation following unidirectional gravity alone (HD group) or temporal pressure with added gravity (FFM group), our FS device promoted ultrafast 3D cell aggregation with intensified cell‐to‐cell contact due to forced omnidirectional intercellular contacts (Figure 2). Unlike aggregates in the HD and FFM groups, rapid cell aggregation in the FS device also induced an identical phase microenvironment around the overall surface of the pressuroid. Since almost half of a cell aggregate in the HD group and FFM group is facing a medium‐air interface or solid surface of cell culture plate closer than the pressuroid, 57, 58 the cellular microenvironment that can transfer supplements, nutrition, and wastes are not identical to subaqueous free‐standing pressuroids.
Furthermore, hADSC aggregates are formed in a free‐standing condition‐induced mechanosensitive gene and protein expression, which can upregulate cell proliferation and angiogenic paracrine factor secretion compared to the conventional method. As depicted in Figures 2 and 4, the expression of the representative mechanosensitive gene and protein PIEZO$\frac{1}{2}$ was upregulated in FS device‐cultured hADSC aggregates containing cells with extracellular force. 69, 70 Notably, PIEZO$\frac{1}{2}$ expression is associated with cell proliferation, angiogenic factor paracrine secretion, and cell cycle regulation. 71, 72, 73 Indeed, by increasing PIEZO$\frac{1}{2}$ expression with FS device culture, hADSC aggregates exhibited significantly enhanced PI3K, VEGF, CCND1, and PCNA expression. In contrast, CASPASE‐3 expression decreased (Figure 2). PI3K is involved in cellular processes, including growth, proliferation, differentiation, motility, and survival. 74 Specifically, the PI3K‐Akt signaling axis controls multiple essential processes in angiogenesis, including endothelial cell survival, migration, formation of capillary‐like structures, and VEGF formation. 27 Previously, HD and FFM promoted stem cell therapeutic effects by increasing VEGF expression. 75 Hypoxia‐inducible factor 1 alpha (HIF‐1α) upregulation activated by size‐dependent hypoxic core formation in the aggregate has been reported to be the cause of increased VEGF expression in HD or FFM cultured aggregates. 76 HIF‐1α induces VEGF upregulation to maintain cell viability under low oxygen and nutrient depletion conditions, which can result in cell death. Therefore, when the size of the aggregates becomes excessively large, the aggregated cells die rapidly. In contrast to HD or FFM culture, the FS device significantly increased VEGF gene expression in the absence of HIF‐1α expression, based on PIEZO$\frac{1}{2}$ and PI3K signaling (Figure 2d). The morphological difference between long‐term HD‐ or FFM cultured cell aggregates (sphere shape) and pressuroid (discoid shape) may have allowed the cells in the core of the pressuroid to transfer oxygen and nutrients via diffusion since the pressuroid is shorter than the cell aggregates previously reported. 77 In fact, after cell aggregation, the HD 24 h group demonstrated an increase in necrotic cell population due to restricted oxygen and nutrient delivery due to a larger omnidirectional diameter. Since the acoustic pressure at a height greater than 200 μm increased abruptly to 500 Pa, the thickness of hADSCs pressuroids was maintained. This result suggests that pressuroid can be formed easily and without difficulty to prevent excessive acoustic pressure from the FS device (Figure 3a–c). Therefore, the average height of the pressuroid was 200 m, and the average pressure was 12 Pa. (Figure 3a–c). CCND1, which encodes cyclin D1, plays an essential role in the progression of the cell cycle, 78 and PCNA, known as cyclin D1‐associated protein associated with cell proliferation activity, 79 were elevated in pressuroid compared to cell aggregates in the HD and FFM group. These findings may be attributable to the rapid cell compaction that promotes harmonious cell‐to‐cell communication. Previously, genetic modification, cellular membrane modification, and scaffold surface modification were used to improve cell‐to‐cell contact for regulating cell functions. However, physical stimulation of 3D cell culture devices has been studied infrequently to elicit cell‐to‐cell contact enhancement.
Pressuroids demonstrated further improvement in cell adhesion and inflammation regulation based on cell–cell communication. FS device induced rapid cell aggregation with elevated adhesion and gap junction proteins, including E‐cadherin, CX43, and ICAM, compared to HD cultured hADSC aggregates (Figure 4b,d). E‐cadherin, one of the most important cell adhesion molecules, increases in stem cell spheroids. 80 CX43, a ubiquitous gap junction protein, is essential for cell–cell communication [81]. ICAM is essential for stabilizing cell–cell interaction 82 and regulating immune responses. 83 Enhanced CCND1 expression in pressuroids increases cell adhesion as well. 84 Consequently, the reattachment of hADSCs detached from pressuroids was enhanced (Figure 4I,J). Altogether, these results indicate that enhanced cell–cell interaction in pressuroids affected cell adhesion capacity even after dissociation, which may be related to cell dissociation following in vivo injection of cell aggregates. The pressuroids generated by the FS device facilitated the secretion of cytokines that can recruit and activate immune cells and are related to inflammation. Simultaneously, the secretion of anti‐inflammatory cytokines that reduce inflammation and promote regeneration was higher in pressuroids than in HD cell aggregates (Figure 5). Immune modulation by hADSCs pressuroids is anticipated to maximize in vivo therapeutic efficacy because stem cells injected into a lesion can stimulate host immune cells. 85 Anti‐inflammatory cytokines stimulate the polarization of macrophages into the M2 type, which inhibits an excessive inflammatory response and promotes tissue regeneration. 86, 87 Compared to other groups, skin wounds in the pressuroid group healed rapidly with reduced fibrosis, increased vascular marker expression, and enhanced skin regeneration‐related ECM marker expressions (Figure 6). Specifically, the angiogenic capacity of transplanted pressuroids stimulated the formation of new blood vessels at wound sites. 88 Additionally, the ability of pressuroids to modulate the immune system may have been associated with functional skin regeneration rather than fibrosis via enhanced wound epithelization. 89 The cellular behavior of endothelial cells, fibroblasts, keratinocytes, and macrophages in the host is primarily responsible for skin wound healing. 90, 91, 92, 93 The intrinsic host cells could have been stimulated to speed up the therapeutic processes by significantly increasing paracrine factor secretions from the pressuroid in comparison to the conventional cell aggregate. Since the angiogenic paracrine factors observed in the pressuroids can cause angiogenesis and cell migration, the participation of microvascular endothelial cells in new blood vessel formation, 93 fibroblasts to granulation tissue induction, 91 and keratinocytes to re‐epithelization could have been upregulated compared to other groups [90]. Moreover, inflammatory‐related paracrine factors secreted by the pressuroids enhanced macrophage recruitment. 94 Under the influence of an anti‐inflammatory cytokine, the phenotype of flocked macrophages displaying enhanced polarization to anti‐inflammatory M2 macrophages 93 was enhanced. Subsequently, M2 macrophages may have promoted the migration and proliferation of endothelial cells, fibroblasts, and keratinocytes, thereby enhancing wound healing. 94 In summary, the transplanted pressuroids upregulated angiogenesis and epithelization in the wounds, thereby enhancing the skin wound healing effect.
## CONCLUSION
This study presents a subaqueous free‐standing cell culture device (FS device) for engineering pressuroid, a stem cell spheroid with a reinforced cytoskeleton. Acoustic pressure was applied via our FS device to efficiently elevate hADSCs and form pressuroid. The FS device induced ultrafast cell compaction elicited mechano‐inductive cellular immune responses and enhanced therapeutic angiogenesis in 3D hADSCs aggregates (pressuroids), distinguishing them from conventional 3D stem cell aggregates. Interestingly, the acoustic pressure generated from the FS device affected the hADSCs in the pressuroid and induced the expression of mechanosensitive genes and proteins. Compared to conventional 3D cell aggregates, enhanced proliferation, angiogenic paracrine factor secretion, cell‐to‐cell adhesion, and immunogenic regulation capacity were observed. Furthermore, the transplantation of pressuroids to skin lesions significantly accelerated and enhanced the therapeutic efficacy of acute wound closure and re‐epithelialization with angiogenesis. Thus, our FS device and pressuroid may suggest a new platform for future biomedical applications involving 3D cell aggregate‐related cell culture systems and cell therapy.
## Cell culture device
The piezoelectric actuator used in this study was made of lead zirconate titanate (PZT) and fabricated as a 20 mm in diameter, 1.3 mm thick, round disc with 1800 pF capacitance and 1.6 MHz resonance frequency. The details of the actuator are shown in Figure S4. The piezoelectric actuator was installed at the bottom of the cell culture vessel, and the reflector was positioned about the upper side. As the sidewall of the cell culture vessel, a borosilicate tube with an outer diameter of 50 mm and a wall thickness of 5 mm was used. Both the top and bottom sides of the vessel tube were plugged in with O‐ring sealed actuator and reflector components. In order to control the distance between the actuator and reflector to tune the resonance condition of acoustic waves, the holder of the reflector was designed to be movable. In order to maintain a temperature of approximately 36°C in the cell culture vessel, cooling water was circulated through the aluminum frame containing heat sources such as the actuator and the driving circuit.
## Acoustic pressure and particle tracing calculations
The Acoustics and Particle tracing module of COMSOL Multiphysics® 5.5 was used to calculate the spatial distribution of an acoustic standing wave generated by a piezoelectric actuator. Subsequently, the trajectories of small particles in the cell culture vessel containing an acoustic standing wave and the trajectories of a large particle in the acoustic standing wave formed in the cell culture vessel. In all calculations, PZT‐5A from the COMSOL library was considered an actuator material, and water was specified as the medium filling in the cell culture device. In addition, we assumed that all particles have a spherical shape and are composed of rigid matter, and we set all measurement parameters according to the actual cell culture apparatus. Figure S5 and Data [Link], [Link] summarize the modeling details for the above three calculations.
## Cell culture
hADSCs were purchased from Lonza (Walkersville, MD, USA) and cultured in cell culture dishes (Corning, Steuben, NY, USA) containing Dulbecco's modified Eagle's medium (Gibco BRL, Gaithersburg, MD, USA), supplemented with $10\%$ (v/v) fetal bovine serum (FBS; Gibco BRL) and $1\%$ (v/v) penicillin–streptomycin (PS; Gibco BRL), in a $5\%$ CO2 cell incubator at 37°C. The culture medium was changed every second day, and the hADSCs in passages 4–7 were used in subsequent experiments.
## Real‐time polymerase chain reaction
TRIzol (Ambion, Austin, TX, USA), chloroform (Sigma‐Aldrich), and $75\%$ (v/v) ethanol (Sigma‐Aldrich, in water) were used according to the manufacturer's instructions to isolate total RNA from cells. Complementary DNA was synthesized via reverse transcription using 1.5‐μg pure total RNA and the Primescript RT Master Mix (TaKaRa, Kusatsu, Japan). Subsequently, qRT‐PCR was performed using the SsoAdvanced Universal SYBR Green Supermix (Bio‐Rad, Hercules, CA, USA) and CFX Connect™ Real‐time PCR Detection System (Bio‐Rad). Analyzing the relative expression level using the 2−∆∆Ct method. Furthermore, glyceraldehyde 3‐phosphate dehydrogenase (GAPDH, in vitro) and beta‐actin (β‐actin, in vivo) served as the internal controls.
## Immunocytochemistry
The 3D hADSCs were fixed at room temperature with $4\%$ paraformaldehyde (Biosesang, Sungnam, Korea) for 10 min. Using a microscope (CKX53; Olympus, Tokyo, Japan), the morphology of the pressuroid was analyzed (FS 6 h). The fixed 3D hADSCs were embedded in a compound with optimal cutting temperature (O.C.T. compound, Scigen Scientific, Gardena, CA, USA). After freezing, samples were sectioned at −20°C into 10 μm sections. These sections containing spheroids were stained immunocytochemically to visualize E‐cadherin and PIEZO2 with anti‐E‐cadherin (Abcam), anti‐PIEZO2 (Abcam), and fluorescein (FITC)‐conjugated secondary antibodies (Jackson ImmunoResearch Laboratories, West Grove, PA, USA). In addition, these sections were stained with TRITC‐phalloidin, which contained a mounting medium (VECTASHIELD H‐1600; Vector, Burlingame, CA, USA) for F‐actin staining. Subsequently, they were counterstained with 4′,6‐diamidino‐2‐phenylindole (DAPI, Vector) and examined under a fluorescence microscope (DFC 3000G; Leica, Wetzlar, Germany).
## Western blot analysis
In order to prepare protein samples, cells or tissues were extracted in radioimmunoprecipitation assay buffer (Sigma‐Aldrich). The bicinchoninic acid assay was conducted to determine protein concentration (Thermo Scientific). Proteins were boiled for 5 min at 100°C in 4× Laemmli sample buffer (Thermo Scientific) containing β‐mercaptoethanol, and amounts of proteins were loaded onto a $10\%$ SDS‐PAGE gel. Next, the separated proteins were transferred to polyvinylidene fluoride membranes and blocked for 1 h at room temperature with 1× TBS‐T, containing $5\%$ skim milk was the next step. The membranes were incubated overnight with primary antibodies, washed with 1× TBS‐T, and incubated for 1 h at room temperature with secondary antibodies. After TBS‐T washes, protein bands were visualized with ECL reagent WESTSAVE UP (ABfrontier, Seoul, Korea), and membranes were exposed to x‐ray films. ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to analyze band expression. GAPDH was implemented as an internal control.
## Apoptosis assays
Following the manufacturer's instructions, an apoptosis assay was performed using a FITC annexin V apoptosis detection kit with 7‐AAD (BD Biosciences, San Diego, CA, USA). hADSCs in 3D were dissociated with trypsin–EDTA (Gibco BRL). The cells were incubated for 15 min at room temperature and in the dark with FITC Annexin V and 7‐AAD. Following staining, the cells were added to an annexin V binding buffer and analyzed using a flow cytometer (MACSQuant® VYB, Miltenyi Biotec, Bergisch Gladbach, Germany). Annexin V and 7‐AAD bind phosphatidylserine (PS) and cell nuclei, respectively, of nonviable cells. Annexin V−/7AAD− cells were thought to be alive, annexin V+/7AAD− cells were considered early apoptotic cells, and annexin V+/7AAD+ cells were considered late apoptotic or early necrosis cells.
## Re‐adhesion analysis
Cell aggregates were dissociated with trypsin–EDTA (Gibco BRL) for the re‐adhesion test and reseeded in cell culture plates. Following an incubation period of 1 or 3 h, the plates were washed with phosphate buffer saline (PBS) to remove unattached cells. Subsequently, using the Cell Counting Kit‐8, the relative cell adhesion rate of the cells following treatment was determined (CCK‐8; Dojindo Molecular Technologies, Inc., Kumamoto, Japan). The control group consisted of cells that were detached from HD 24 h and reattached for 1 h. Two hours after incubation with the CCK‐8 solution at 37°C, the optical density of each well was measured using a microplate reader (450‐nm; Tecan, Mannedorf, Switzerland).
## Human cytokine array
Proteome profiler human cytokine array kit (R&D Systems, Minneapolis, MN, USA) was used to analyze the expression of immunomodulatory cytokines in pressuroids according to the manufacturer's protocol. ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to quantify pixel density in each spot, and the average signal was calculated for the duplicate spots.
## THP‐1 cell culture and differentiation
American Type Culture Collection (Manassas, VA, USA) THP‐1 cells were purchased and cultured in cell culture flasks (Corning) with RPMI‐1640 medium (11875, Gibco BRL), supplemented with $10\%$ (v/v) FBS (Gibco BRL) and $1\%$ (v/v) PS (Gibco BRL), in a $5\%$ CO2 cell incubator at 37°C. Every second day, the nutrient medium was replaced. THP‐1 cells at passages 2–5 were used in the experiments. THP‐1 cells were differentiated with 100 nM ml of phorbol 12‐myristate 13‐acetate (PMA, Sigma‐Aldrich) for 2 days. Cells were washed with PBS and polarized toward the M1 or M2 phenotype by incubation for 2 days with a sample medium. Next, using a microscope, THP‐1 cells were observed after incubation (DFC 3000G).
## Wound healing and repair
Xylazine (10 mg/kg) and ketamine (100 mg/kg) were injected intraperitoneally into athymic mice (6 weeks old; body weight, 20 g; Orient Bio Inc., Sungnam, Korea) to induce anesthesia. The mid‐dorsal area of the skin of each mouse was sliced to make a full‐thickness skin wound (2.0 × 2.0 cm2). Next, the Institutional Animal Care and Use Committee of SKKU authorized all animal treatments and experimental procedures (SKKUIACUC2020‐06‐11‐1). Mice with skin wounds were randomly divided into three experimental groups ($$n = 6$$ per group): NT; HD‐treated group (HD; 0.75 × 106 cells suspended in 200‐μl per mouse); and pressuroid‐treated group (pressuroid; 0.75 × 106 cells suspended in 200‐μl per mouse). The NT group undergoing only wound modeling served as the negative control (NT). Tegaderm™ (3M Health Care, St. Paul, MN, USA), which is a common dressing material, was applied to all groups. 49 After initial treatments, the wound healing process was observed for up to 14 days. The percentage of the initial wound area was used to calculate wound healing: wound areaatatimeinitial wound area×$100\%$.
All samples were gathered in order to accurately compare wound healing between the various groups. In order to compare the wound healing process in each group, whole tissues from the dorsal wound area were also extracted for analysis. At 14 days, tissues in the wound region were collected, and then homogenized with homogenizer. TRIzol (Ambion), chloroform (Sigma‐Aldrich), and $75\%$ (v/v) ethanol (Sigma‐Aldrich, in water) were used to isolate total RNA from tissues according to the manufacturer's instructions.
## Histology and immunohistochemistry
Several histological analyses were performed on the wound tissues of athymic mice. Skin tissue samples were embedded in O.C.T. compound (Tissue‐Plus®; Scigen), frozen, and cut into 10 μm sections at −23°C. Under a light microscope, hematoxylin and eosin (H&E) and Masson's trichrome (MT) stains were used to evaluate overall tissue regeneration (CKX53; Olympus). To visualize protein expression and wound repair, sections were immunostained with anti‐SM‐α antibody (Abcam), anti‐CD31 antibody (Abcam), antilaminine antibody (Abcam), antiinvolucrin antibody (BioLegend, San Diego, CA, USA), and a fluorescein isothiocyanate‐conjugated secondary antibody (Jackson ImmunoResearch Laboratories). The sections were counterstained with DAPI (VECTASHIELD H‐1500, Vector) and examined under a fluorescence microscope (Leica).
## Statistical analysis
All statistical analyses utilized GraphPad Prism 7 software. In all experiments, triplicate data were analyzed using a one‐way analysis of variance with the Bonferroni test. Additionally, two independent samples were compared using a two‐tailed Student's t‐test. The statistical significance threshold was set at $p \leq 0.05.$ For all quantitative analyses, results were presented as the mean ± standard deviation.
## AUTHOR CONTRIBUTIONS
Gwang‐Bum Im: Conceptualization (equal); investigation (equal); methodology (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Yu‐Jin Kim: Conceptualization (equal); investigation (equal); methodology (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Suk Ho Bhang: Conceptualization (equal); methodology (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). Tae Il Lee: Conceptualization (equal); methodology (equal); supervision (equal); writing – review and editing (equal).
## CONFLICT OF INTEREST
The authors declare no conflict of interest.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10438.
## DATA AVAILABILITY STATEMENT
All data are available in the main text or the supplementary materials.
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|
---
title: SERS spectroscopy with machine learning to analyze human plasma derived sEVs
for coronary artery disease diagnosis and prognosis
authors:
- Xi Huang
- Bo Liu
- Shenghan Guo
- Weihong Guo
- Ke Liao
- Guoku Hu
- Wen Shi
- Mitchell Kuss
- Michael J. Duryee
- Daniel R. Anderson
- Yongfeng Lu
- Bin Duan
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013764
doi: 10.1002/btm2.10420
license: CC BY 4.0
---
# SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis
## Abstract
Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non‐ST‐elevation myocardial infarction, and ST‐elevation myocardial infarction. Surface‐enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K‐Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection ($86.4\%$) and overall prediction ($92.3\%$). SVM also possesses the highest sensitivity ($97.69\%$) and specificity ($95.7\%$). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.
## INTRODUCTION
Coronary artery disease (CAD) is represented by the accumulation of atheromatous plaques within the walls of the arteries that supply blood to the heart. 1 CAD is one of the major cardiovascular diseases and remains the leading causes of death worldwide. 2, 3, 4 CAD is responsible for about 7 million deaths worldwide. 5, 6 Based on the degree of stenosis and plaque characteristics, CAD patients can present clinically with cardiac symptoms and can be divided into different categories, that is, patients with stable nonobstructive plaques (SP), non‐ST‐elevation myocardial infarction (NSTEMI) and ST‐elevation myocardial infarction (STEMI). This reflects the continuum of CAD and with increased severity there are decreased lumen areas, greater plaque burden, more plaque rupture all of which are associated with a greater risk of mortality. 1, 7 Thus, there is still a huge need to develop new diagnostic and therapeutic approaches for CAD treatment, especially an early accurate CAD detection and timely intervention, which is expected to avert many late CAD events and deaths.
In the past few decades, various preventive and therapeutic strategies have substantially improved the prognosis of patients suffering from CAD. Many advanced techniques have been developed, reported, and clinically applied to the diagnostic and prognostic workup of CAD, such as electrocardiogram, 8, 9 echocardiography, 10 intravascular imaging, 11, 12 coronary angiography. 13, 14 Although these diagnostic methods have revolutionized the management of CAD patients, the prevalence of adverse cardiac events remains high. Imaging approaches, like coronary angiography, are invasive and should not be used to diagnose the early‐stage CAD. Some chemical agents used in cardiac stress testing, such as radiocontrast media may have potential side effects for CAD patients. As well, these stress tests are designed to detect significant CAD which represents a luminal loss of more than $50\%$. Novel biomarkers can predict and differentiate between CAD types in both the early and late stages, and may reduce unnecessary invasive coronary angiography and thus enhance predictive value. 15 However, the detection sensitivity of these biomarkers has been lower due to the use of large amounts of capturing and labeled detecting antibodies, which may increase the risk of false positivity due to nonspecific binding with nontarget analytes, especially for early CAD stage. 16, 17 In addition, patients may not have any symptoms in the early CAD stage reflective of nonobstructive CAD stage (SP stage). Therefore, the prompt, economical, and accurate low‐risk diagnosis and prognosis for CAD in asymptomatic patients are crucial to allow timely prevention and therapeutic treatments to improve patients' quality of life prior to the event. Enhancing the early diagnosis of CAD and utilization of therapeutic approaches to prevent progression is crucial for the management and prevention of CAD.
Small Extracellular vesicles (sEVs) are small lipid‐bilayer enveloped assemblies with sizes ranging from 20 nm to several micrometers. 18, 19 sEVs are secreted by all cells in both normal and diseased tissues, and can be further categorized based on their biogenesis, size, and biophysical properties, such as exosomes, apoptotic bodies, microvesicles, ectosomes, and other vesicles. 20, 21, 22, 23 sEVs are found in most biological fluids and contain a wide variety of cargo, such as proteins, lipids, nucleic acids and metabolites. 21, 24, 25 These cargoes are representative of their cellular origin and reflective of the pathological condition of the origin tissue and cells, which may serve as noninvasive diagnostic biomarkers in biological fluids. 26, 27, 28 Numerous studies have reported the value of exploiting sEV in diagnostic and therapeutic applications in the central nervous, 29, 30 cancer, 31, 32 and visceral organ diseases. 33, 34 The cargoes of these sEVs reflect the molecular content and pathology of their original cells. 23 Therefore, sEVs isolated from liquid and tissue biopsies can serve as potential biomarkers to follow disease progression. 35, 36, 37, 38, 39 Different diseases are expected to alter either the sEV contents or the sorting and packaging process 37, 39 and these alterations are expected to be detectable and be useful in diagnosis for evaluating disease activity and/or the response to therapy. 40 Plasma as an important component of blood, has the characteristics of representing systemic disease pathology. 41 In the cardiovascular system, sEVs are associated with endothelial cells, cardiac myocytes, vascular cells, progenitor and stem cells, and play an essential role in the development, injury and disease of the cardiovascular system. 28, 42 Thus, cardiovascular‐derived plasma sEVs have great potential as potential diagnostic biomarkers for CAD screening.
Surface‐enhanced Raman scattering (SERS) is a commonly used sensing technique in which inelastic light scattering by molecules is greatly enhanced when the molecules are absorbed onto corrugated metal surfaces (usually Au). 43, 44 The label‐free, nondestructive and noninvasive characteristics of SERS enable its biomedical application to the diagnosis of diseases, such as neurodegenerative disorders, 45 cancer, 46, 47 or diabetes. 48 This innovative technique has also been used to diagnose lung cancer by combing with exosomes by pattern analysis of SERS data. 49, 50 Thus, SERS has the potential to differentiate sEVs based on their different membrane lipid/protein contents along with other various functional groups. However, the Raman signatures of sEVs are expected to be highly complex due to the overlapping. The common solution is to analyze the entire *Raman spectra* as “fingerprint” input by leveraging the power of machine learning (ML). The sEVs collected from patients with different stages of CAD have “impact” on the entire *Raman spectra* (spectral shapes), although the changes are usually small and very difficult to be detected. By ML algorithms and large training data set, it is possible to detect common “patterns” from the hundreds of *Raman spectra* with training data and then the algorithm can perform prediction in blind tests. ML has been extensively applied in analyzing spectroscopic signals for complex bio‐samples and has achieved satisfying results. Thus, by using ML assisted analysis on a high‐dimensional SERS database, valuable information is expected to be extracted for accurate estimation and practical prediction of known CAD stages which when validated can be used clinically as a diagnostic tool.
In this study, we demonstrated a noninvasive, label‐free SERS technique to diagnose CAD by assessing and monitor progression of the disease. To the best of our knowledge, direct, label‐free in vitro characterization of sEVs for the early CAD stage diagnosis through SERS at a biomolecular level has not previously been demonstrated. A Raman spectral library of plasma‐derived sEVs from patients with various degrees of CAD, including SP, NSTEMI, and STEMI CAD stages, was developed. Plasma from patients without CAD was used as healthy control (HC) group. We hypothesized that SERS data combining with ML algorithms can accurately classify the sEVs from different CAD stages and be used to predict potential risks in CAD patients. Firstly, we isolated and characterized sEVs from human plasma samples with various degrees of CAD and collected their SERS signals (Figure 1a). Then, the SERS spectra of plasma‐derived sEVs were obtained by Raman microscopy and analyzed using five supervised ML models (Figure 1b), including Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), K‐Nearest Neighbor (KNN), Artificial Neural network (ANN), and XGBoost (XGB). Then, $90\%$ of SERS data set of sEVs were used to train the models. The supervised models classified the sEVs data into four clusters (one HC group and three stages of CAD). The remaining $10\%$ spectral data were used to predict their CAD stages through the models. The five methods used in this study are most common algorithms in ML for classification and prediction. Each method represents a typical type of algorithms in ML theory. All methods have predictive errors and statistical noises in the data, especially for large data set or data set with sampling limitations. Therefore, it is important to understand the performance difference among these methods. Thus, we compared the diagnosis performances of the five models and demonstrated robust classifications and high accurate diagnosis in plasma‐derived sEVs for early‐stage CAD detection and CAD progression monitoring through the introduction of ML algorithms.
**FIGURE 1:** *Schematic illustration of ML‐assisted sEV analysis for CAD diagnosis. (a) Isolation of sEVs from the human plasma of patients with different CAD stages, including HC, SP, NSTEMI and STEMI stages, and collection of spectroscopic data of plasma‐derived sEVs by SERS. (b) Overview of ML‐assisted plasma sEV classification and CAD diagnosis using sEVs SERS signal patterns. This illustration is created by BioRender.com with an authorized license*
## Plasma‐derived sEV isolation and characterizations
Plasma‐derived sEVs were isolated from patient plasma using a standard procedure of ultracentrifugation as shown in Figure 2a. Ten samples from each group were used to evaluate plasma‐derived sEV features. The AFM images showed that the vesicle morphology of the most vesicles was flattened sphere‐like with nanoscale sizes (Figure 2b), which was consistent with previous results. 51 The vesicles typically consisted of membrane vesicles of 50–200 nm in diameter according to the results of nanoparticle tracking analysis (NTA) (Figure 2c,d). The sEVs in the HC group had relatively smaller average size (Figure 2c). Additionally, the average size of vesicles in NSTEMI group was the largest among the four groups with significant difference (Figure 2c). The ranges of sEV concentration had wide distribution, and there was no significant difference among different groups (Figure 2d). The markers of sEVs, the tetraspanin (CD63) and endosomal pathway protein (Alix) were detected by Western blotting (Figure 2e). Both Alix and CD63 were expressed in sEVs and Calnexin (negative marker) was not detected. These results confirmed that the recovered vesicles were sEVs. Taken together, the size and content of typical sEV protein markers indicated that sEVs were successfully isolated from the human plasma samples from different stages of CAD patients.
**FIGURE 2:** *Isolation and characterizations of sEV from human plasma. (a) Scheme of isolation procedure of sEVs from human plasma. (b) AFM images of sEVs. (c, d) The size and concentration of sEVs by NTA test (n = 11, *p < 0.05). (e) Western blot analysis of biomarker proteins on sEVs*
## Averaged Raman spectra for four stages of CAD and QDA classification
To provide an overview of the *Raman spectra* of four CAD stages, all spectra after standard normal variate processing were simply averaged and shown in Figure 3a. In the fingerprint region, spectra showed Raman peaks appeared to originate from lipids and proteins which are the major contributors to sEV surface. For example, the vibrations contributed to symmetric ring breathing of tryptophan appeared as a peak at 755 cm−1. A peak at 830 cm−1 was observed corresponding to C—O—O vibration typical of phospholipids. Other peaks such as 856 cm−1 (glycogen), 879 cm−1 (C—C stretch proline ring), 1005 cm−1 (phenylalanine), 1126 cm−1 (C—C vibrations in lipid), 1244 cm−1 (amide III), 1450 cm−1 (CH2 bending vibration of proteins/lipids), and 1668 cm−1 (amide I) were also observed.
**FIGURE 3:** *Raman spectra and QDA analysis. (a) Averaged Raman spectra for four stages of CAD (HC, SP, NSTEMI, STEMI); (b) The PCA plot colored by QDA classification results of the total 775 Raman spectra from four stages of CAD. (c) Heat map of the prediction possibility (range 0–1) by QDA for four stages of CAD. (d) ROC curves for each representation stage and their AUC values. (e) Prediction accuracy of test sets performed in QDA, SVM, KNN, ANN, and XGB in 50 test rounds by randomly splitting 90% of data into a training set and 10% into a test set*
When we closely look at each individual spectrum as shown in Figure S1, the variations in spectra were significantly notable not only across the four CAD stages but even within the same CAD stage. The large variations are expected, because in spectroscopic measurement, the obtained data is semiquantitative, which means that analyzing only based on a single pathology‐related Raman spectroscopic peak is unlikely to be reliable and not suitable for diagnosis or classification of the disease status. The solution for better understanding of the data is to apply ML methods to extract the diagnosis or classification information based on the entire spectral pattern.
The first method we applied is discrimination analysis, which has been widely used to classify and predict *Raman spectra* of various biological and biomedical samples. 52 *In this* study, we chose QDA method since the classification (decision) boundaries of QDA can be learned quadratically and flexibly compared to another often used method, linear discrimination analysis (LDA). The PCA plot colored by QDA classification results is shown in Figure 3b, where each colored dot represents a spectrum. It clearly shows the capabilities to separate different sEV subpopulations based on the SERS spectra; however, overlaps are still observed. The overall classification accuracy performed by cross‐validation is $80.26\%$ with a sensitivity of $96.28\%$ and a specificity of $74.37\%$. The sensitivity and specificity were calculated by counting HC group as negative and other three CAD stages together as positive. The heat map (Figure 3c) summarizes the prediction possibility (range 0–1) by QDA for the HC group and three stages of CAD. Red color indicates the highest possibility [1], while blue color indicates the lowest possibility [0]. By supervised QDA, the heat map shows most predictions fall in the correct stages, but mispredictions and swings between two stages (yellow color, ~0.5) are still observed. To draw each receiver operating characteristic (ROC) curve, we counted the representation stage as a positive response, and the other three stages as the negative response for the calculation of Sensitivityacc and Specificityacc. As an example, in the ROC curve of HC, the Sensitivityacc and Specificityacc are calculated as results of HC response against other three stages (SP, NSTEMI, and STEMI) responses. Area under the curve (AUC) of ROC is the indicator of the goodness of fit for the model, and a value of 1 indicates a perfect fit and a value near 0.5 indicates that the model cannot discriminate among the stages. The AUC results by QDA for each stage range from 0.9366 to 0.9824, indicating the QDA method shows good fitting results (Figure 3d). Furthermore, due to possible overfitting by the cross‐validation method, we also verified the QDA model by randomly splitting data into training and testing sets. In each round, $90\%$ of data (total 697 spectra) was used as a training set, and the remaining $10\%$ of data (total 78 spectra) was used as a testing set. Only the training set was used to train the model, and the testing set was used for blind prediction. A total of 50 rounds were performed to evaluate the loss and accuracy change of the testing set. As shown in Figure 3e, we found the averaged overall accuracy of QDA is $79.9\%$ ± $3.9\%$, with a minimum of $71.8\%$ at round 10th and a maximum of $89.7\%$ at round 12th, which is consistent with the cross‐validation results.
## KNN, ANN, SVM, and XGB classifications and predictions
Besides QDA, we also implemented four other ML algorithms, including KNN, ANN, SVM, and XGB, to classify and predict CAD stages and compare the classification performances. Each method has its own advantages and weaknesses. 53 QDA method allows nonlinear fitting through data and the relationship between data and data interpretation could be established practically. KNN method depends on the nearby adjacent samples rather than the algorithm of discriminating the class domain for classification, which may be more suitable for dense overlapped data sets among multiple classes. ANN has more tolerance to noise and missing data, and is good at handling high‐dimensional data sets, but may have difficulty with data interpretation and algorithm structure understanding. SVM is also durable to noise and has advantage in handling a small set of data and overfitting issues. XGB is fast, but ideally requires nonoverlapped data set and an inappropriate training set may lead to distorting the decisions.
Same as QDA, the raw *Raman data* was preprocessed and performed by PCA initially. Fifteen PCs were chosen (Figure S2) as the new input variables to the models. Then to compare the classification accuracies from different methods, the model parameters also need to be estimated properly since those parameters will significantly affect the efficiency of the classification. Thus, obtaining the best possible set of parameters is critical, both from the computing cost and computing accuracy. Among these four classification methods, all of them have parameters that need to be determined. For ANN, the input is 15 PCs, and the output is the 4‐classification group. To avoid overfitting or underfitting, an ANN structure with two hidden layers was chosen. The first hidden layer was set to have 10 neurons, and the second hidden layer was set to have 6 neurons. For XGB, 5000 decision trees were chosen. For SVM, the radial basis function kernel was chosen. In KNN, the best accuracy was obtained when $K = 1$ after experimenting $K = 1$–30. For all methods, $90\%$ of the full data set was randomly split for training while the remaining $10\%$ was used for testing. The process was also repeated for 50 rounds. Figure 3e shows the prediction accuracies for all the five classification methods used in this study. Results demonstrated that the SVM provided the highest and most robust prediction accuracy of $92.3\%$ with a SD of $3.4\%$ among all five methods. XGB provides a low prediction accuracy of $82.6\%$, but still better than previously used QDA method ($79.9\%$). Among the 50 round tests of ANN, although the averaged prediction accuracy of ANN is $86.4\%$, there were 3 rounds of prediction accuracy below $70\%$, indicating ANN may lack stability due to a randomly chosen training set. The averaged confusion matrix from the 50 round tests is also shown in Table S1. For early CAD prediction (SP stage), QDA, SVM, and ANN had the prediction accuracy of $88.7\%$, $85.9\%$, and $86.4\%$, respectively, much higher than the other two methods, which indicates these ML algorithms may work better for early CAD detection. For the most severe stage (STEMI) prediction, all five algorithms have similar performance. QDA, SVM, KNN, ANN, and XGB had the prediction accuracy of $84.7\%$, $88.5\%$, $86.8\%$, $89.1\%$, and $84.2\%$, respectively. For HC group prediction, SVM and KNN have higher prediction accuracies of $95.7\%$ and $91.6\%$ over other three algorithms. Through Table S2, we found SVM has the overall best performance over other four algorithms for prediction of all stages. Table 1 shows the averaged confusion matrix of sensitivity and specificity from the 50 round tests. SVM method also had the highest sensitivity ($97.2\%$) and specificity ($95.8\%$) among all the methods used in this study, since major classification errors occurred within the disease groups (SP to be misclassified as NSTEMI and NSTEMI to be misclassified as SP). QDA shows the lowest specificity ($74.8\%$) among the five methods.
**TABLE 1**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Predicted class | Predicted class.1 | Unnamed: 5 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| Model | Sample comparison | | Positive | Negative | Sensitivity | Specificity |
| QDA | SP + NSTEMI + STEMI vs HC | Positive | 51.8 | 1.9 | 96.5% | 74.8% |
| QDA | SP + NSTEMI + STEMI vs HC | Negative | 6.0 | 17.8 | 96.5% | 74.8% |
| SVM | SP + NSTEMI + STEMI vs HC | Positive | 52.2 | 1.5 | 97.2% | 95.8% |
| SVM | SP + NSTEMI + STEMI vs HC | Negative | 1.0 | 22.8 | 97.2% | 95.8% |
| KNN | SP + NSTEMI + STEMI vs HC | Positive | 50.5 | 3.2 | 94.0% | 91.6% |
| KNN | SP + NSTEMI + STEMI vs HC | Negative | 2.0 | 21.8 | 94.0% | 91.6% |
| ANN | SP + NSTEMI + STEMI vs HC | Positive | 51.0 | 2.7 | 95.0% | 88.7% |
| ANN | SP + NSTEMI + STEMI vs HC | Negative | 2.7 | 21.1 | 95.0% | 88.7% |
| XGB | SP + NSTEMI + STEMI vs HC | Positive | 49.0 | 4.7 | 91.2% | 82.4% |
| XGB | SP + NSTEMI + STEMI vs HC | Negative | 4.2 | 19.6 | 91.2% | 82.4% |
## SVM as the best ML method in this study
The PCA plot colored by SVM classification results is shown in Figure 4a, where each colored dot represents a spectrum. The heat map (Figure 4b) summarizes the prediction possibility (range 0–1) by SVM for the HC group and three stages of CAD. Red color indicates the highest possibility [1] while the blue color indicates the lowest possibility [0]. The heat map shows almost all predictions fall in the correct stages with a few swings between two stages (yellow color, ~0.5). The AUC results by SVM for each stage range from 0.9888 to 0.9967, indicating the SVM method shows excellent fitting results in Figure 4c, Figure 4d,e shows the decision boundaries of SVM projected in the 2D PCA plot, PC1 vs PC2, and PC2 vs PC3, respectively.
**FIGURE 4:** *SVM analysis. (a) A PCA plot colored by SVM classification results of the total 775 Raman spectra from four stages of CAD. (b) Heat map of the prediction possibility (range 0–1) by SVM for four stages of CAD. (c) ROC curves for each representation stage and their AUC values. (d) Decision boundaries of SVM projected in 2D PCA plot (PC1 vs PC2). (e) Decision boundaries of SVM projected in the 2D PCA plot (PC2 vs PC3). (Black: HC; Red: SP; Green: STEMI; Blue: NSTEMI)*
## Challenges and future directions
sEVs serve as a mediator of intercellular communication between cells, and can be used as a noninvasive indicator of disease, 54, 55, 56 which is the strategy applied in this study to diagnose CAD status via detecting and analyzing SERS signals from sEVs. However, although as a promising clinical approach, the application of our study still faces several challenges. The first limitation is related to the major Raman signals that are mostly derived from sEV surface molecules (e.g., membrane proteins, lipids). 49, 57, 58 Thus, SERS signals may be influenced by various membrane molecules. 59 In order to identify protein signatures existing in both surface and interior of sEV, we will analyze sEV cargoes using other advanced technologies such as proteomics. 60 The second challenge is the patient number involved in this study. One solution is to expand the obtained Raman library; however, another issue may come out that the measurements between different batches of patient samples obtained from national wide may vary largely due to uncertain uniformity among different SERS methods and system errors coming from different Raman instruments. 44 Thus, it has to optimize the calibration condition before each test to improve the precision of detection and analysis. The third challenge is that the isolated plasma sEVs may contain a variety of sEVs derived from other organs and cells that are not related to the CAD. 34, 61 Therefore, our future study will focus on expanding the sample number and types of sEV samples to improve the robustness and reliability of our approach, such as detection of sEVs from heart tissues with different CAD stages.
## CONCLUSIONS
In summary, we successfully isolated sEVs from the human plasma samples from four stages of CAD patients, that is, HC, SP, NSTEMI, and STEMI. SERS measurements in conjunction with five ML algorithms were then applied for the classification and prediction of the sEV samples. The overall accuracy was $79.9\%$, $92.3\%$, $88.5\%$, $86.4\%$, and $82.6\%$ for QDA, SVM, KNN, ANN, and XGB, respectively. Among these five approaches, SVM shows the best prediction results on both early CAD detection ($86.4\%$) and overall prediction ($92.3\%$). SVM also possesses the highest sensitivity ($97.69\%$) and specificity ($95.7\%$). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for early CAD detection.
## sEV isolation and characterizations
Deidentified human plasma samples were donated by the Nebraska Cardiovascular Biobank and Registry (IRB approved protocol 133‐14‐EP). The human plasma was obtained from patients at the time of cardiac catheterization and immediately stored at −80°C for further use. Samples were obtained from patients who presented with chest pain and a positive stress test, and those who presented with a NSTEMI or a STEMI. Patients who had nonobstructive CAD (i.e., <$50\%$ lesions) at catheterization were defined as stable CAD patients (SP). The bank ID, Gender, Age, Race of each patient is recorded in Table S2. sEVs were isolated from patient plasma with three CAD stages, and the number of patients is 15 (SP stage), 15 (NSTEMI stage), and 17 (STEMI stage), respectively. The control samples (HC stage) were from 17 patients that had no signs of CAD. Among all sEV samples, 13 HC samples, 6 SP samples, 13 NSTEMI samples, and 8 STEMI samples were chosen for SERS test, and other samples not used in SERS test were applied for NTA. The human plasma was first centrifuged at 300 ×g for 5 min, 1000 × g for 20 min and then at 10,000 × g for 30 min sequentially. The supernatant was then filtered with a 0.22 μm filer, and ultracentrifuged (Sorval X + 80 Ultracentrifuge, Thermo Fisher) at 100,000 g for 70 min. Subsequently, the sEV pellet was washed with PBS and ultracentrifuged at 100,000 × g for 70 min. The collected sEVs were reconstituted in PBS buffer and then preserved at −80°C. For SERS analysis, parts of exosomes were resuspended in phosphate buffer (PB) (0.1 M, pH = 7.4). The size and concentration of the final sEVs were examined by NTA using a NanoSight (NS300) measurement. The exosome morphology was evaluated as previously described by using atomic force microscopy (AFM, Bruker). 62
## Western blot
The markers (CD63 and Alix) of the isolated sEVs were correspondingly detected by Western blotting as reported previously. 62, 63 The sEVs were lysed using the Mammalian Cell Lysis kit (Sigma–Aldrich) and quantified using Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific, 23227). The samples were preheated at 60°C for 15 min. Proteins were electrophoresed in an SDS‐polyacrylamide gel followed by transferring to PVDF membranes. The membranes were blocked with $5\%$ BSA in TBS‐Tween 20 and were then probed with antibodies specific for CD63 (1:1000, ab216130, Abcam), Alix antibody (1:1000, Proteintech), and calnexin (1:1000, Proteintech, negative biomarker for sEVs) overnight at 4°C. After three washes in TBS‐tween 20, membranes were incubated with the secondary antibody (Thermo Scientific) for 1 h and washed again. For visualization, blots were exposed to SuperSignal West Dura Extended Duration substrate and measured by the FluorChem R system (ProteinSimple).
## SERS measurements
The sEV samples were immediately measured within 12 hours after taking from −80°C. Gold‐coated glass slides (Ti/Au 40 nm/100 nm, Deposition Research Lab Inc.) were used for better suppression of fluorescence background and surface plasmonic enhancement for SERS. Five microliters of each sEV samples was dropped onto the gold slide and then measured immediately before drying at room temperature. Raman spectra were recorded using a commercial micro‐Raman microscope (Renishaw InVia Reflection) with 633‐nm diode laser excitation. The laser power was set to 10 mW. The laser beam was focused on the 5 μl droplet by using a 50× microscope objective with a numerical aperture of 0.75 (Leica n PLAN EPI 50×/0.75). The laser spot is estimated to be 1 μm in diameter. In the experiment, due to the sample availability, the sample size was experimentally boosted by automated measuring at different spots of the sEV droplet in 4 × 5 grid. Each *Raman spectrum* obtained for following analyzes was recorded with an exposure time of 1 s and accumulated by 10 times. The *Raman data* set is shown in Figure S3. Since the measurement was automated and conducted on the sEV droplets by mapping program, spectra with high background levels (high PBS peak and high background level) were obtained and then should be removed from the data set (Figure S4) to minizine the buffer influence on the following machine learning analysis. Thus, the total number of *Raman spectra* obtained is 775, including 238 from 13 HC samples, 120 from 6 SP samples, 257 from 13 NSTEMI samples, and 160 from 8 STEMI samples.
## Data preprocessing and classification methods
Data preprocessing of the raw *Raman spectra* included baseline correction and normalization. Baseline correction was performed by Vancouver Raman algorithm with five‐point boxcar smoothing and a five‐order polynomial fit. After baseline correction, the spectra were normalized using the standard normal variate technique, which can remove multiplicative error and preserve each preprocessed spectrum having same contribution to the following classification analysis.
## ML and classification methods
Classification analysis was adopted to identify the CAD stage based on SERS measurements. Raman spectra were processed to reduce dimensionality, and then input to classifiers for CAD stage prediction. Cross‐validation (CV) was adopted for robust model training and validation. The diagnostic capability of the classification models, characterized by sensitivity and specificity, was analyzed with Receiver Operating Characteristics (ROC). 64 Dimensionality reduction. Since the preprocessed spectra were of high dimensionality, classification analysis on these data directly can be computationally expensive. Principal component analysis (PCA), 65 as a widely used method for dimensionality reduction and feature extraction, was adopted to extract crucial information, that is, features, from the spectra data. For a n×p data matrix, [1] X=x1x2…xp,xi=x1ix2i…xniT,$i = 1$,…,p, a row vector in X belongs to Rp. PCA uses singular value decomposition (SVD) 66 to extract l principal components (PCs) from X, with l<p. Each PC consists of element [2] tkj=xj1xj2…xjp·wk,$j = 1$,2,…,n,$k = 1$,2,…,l, where wk is the weights extracted by SVD that map each row of with X to a PC. The extracted PCs are ranked based on the percentage of data variance they explain. For example, the 1st PC explains the highest percentage of data variance among all the PCs; the 2nd PC explains the 2nd largest percentage of data variance, so on and so forth. These PCs are then the features to be used in subsequent classification analysis. In this study, R software was used for PCA implementation. Feature selection. The PCA reduces the high dimensionality of the *Raman spectra* (1008 independent variables from 648 cm−1 to 1747 cm−1) to a few PCs. To determine the optimal number of PCs for following machine learning models, Quadratic Discriminant Analysis (QDA) was performed with leave‐one‐out cross‐validation method. 67 QDA is a classic type of binary classifier, which assumes normally distributed classes with unequal covariance. The two classes respectively follow F0=Nμ0∑0 and F1=Nμ1∑1. For a feature vector tj=tj1tj2…tjl, QDA predicts the likelihood ratio of the two classes, [3] Lj=2π∑1−1exp−12tj−μ1T∑1tj−μ12π∑0−1exp−12tj−μ0T∑1tj−μ0,$j = 1$,2,…,n *Given a* threshold of discrimination, T, if Lj<T, the tj is assigned class 0. Otherwise, it is assigned class 1. As shown in Figure S4, we compared the QDA classification accuracy for varying number of top PCs, and 15 PCs were chosen (accounts for almost $91.2\%$ of variations in the data) for a good balance between high accuracy and small number of features. Thus, these 15 PCs are used in all following ML models. Classification analysis. Four other supervised learning classification methods, SVM, KNN, ANN and XGB were also performed by R software on these 15 PCs which served as input variables. The $90\%$ SERS data set of sEVs were used to train the models. The remaining $10\%$ spectral data were used to predict the CAD stages through the models. Analysis of diagnostic capability. To gain insights into the diagnostic capability of the proposed method, we adopted receiver operating characteristic (ROC) curves to analyze the classifiers' sensitivity and specificity. The conventional ROC is a graphic representation of the diagnostic capability for a binary classification model. It visualizes the model's true positive rate (TPR) against the false positive rate (FPR) as the discriminant threshold varies. TPR: rate of correctly predicting class 1 (TPR = sensitivity);FPR: rate of falsely predicting class 1 (FPR = 1 − specificity); [4] TPR=TPTP+FN;FPR=FPFP+TN *For a* complete ROC curve, the area under curve (AUC) represents the capability of accurately predicting the positive cases, which is the larger the better. In our study, there are four classes. To enable the analysis with ROC curves, we counted the representation stage as positive response and other three stages as the negative response for calculation of Sensitivityacc and Specificityacc in ROC curves. However, in other conditions, Sensitivity and Specificity for the five ML models were calculated by counting HC group as negative and other three CAD stages as positive.
## Statistical analysis
The mean size of small extracellular vesicles is expressed as means ± SD. The statistical differences among multiple groups were analyzed by ANOVA. The p values are shown in the figures as *$p \leq 0.05$, which are considered to be statistically significant.
## AUTHOR CONTRIBUTIONS
Xi Huang: Data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); validation (equal); writing – original draft (equal). Bo Liu: Data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); validation (equal); writing – original draft (equal). Shenghan Guo: Data curation (supporting); formal analysis (equal); software (equal). Weihong Guo: Conceptualization (equal); data curation (supporting); formal analysis (equal); software (equal). Ke Liao: Data curation (supporting). Guoku Hu: Data curation (supporting). Wen Shi: Data curation (supporting); methodology (supporting). Mitchell Kuss: Data curation (supporting); methodology (supporting). Michael Duryee: Resources (equal); writing – original draft (supporting). Daniel Anderson: Conceptualization (equal); funding acquisition (equal); supervision (equal); writing – review and editing (equal). Yongfeng Lu: Conceptualization (equal); funding acquisition (equal); supervision (equal); writing – review and editing (equal). Bin Duan: Conceptualization (equal); funding acquisition (equal); project administration (lead); supervision (equal); writing – review and editing (equal).
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10420.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: Vascular stiffening in aging females with a hypertension‐induced HIF2A gain‐of‐function
mutation
authors:
- Eugenia Volkova
- Linda Procell
- Lingyang Kong
- Lakshmi Santhanam
- Sharon Gerecht
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013765
doi: 10.1002/btm2.10403
license: CC BY 4.0
---
# Vascular stiffening in aging females with a hypertension‐induced HIF2A gain‐of‐function mutation
## Abstract
Pulmonary arterial hypertension (PAH) is more prevalent in females than males; the causes of this sex difference have not been adequately explored. Gain‐of‐function (GOF) mutations in hypoxia‐inducible factor 2α (HIF2A) lead to PAH and thrombotic consequences in patients and mice. Additionally, multiple emerging studies suggest that elevated systemic arterial stiffening (SAS) occurs in PAH; this could have critical prognostic value. Here, we utilized a HIF2A GOF mouse model to determine how SAS can be used as a prognosticator in sex‐divergent PAH. We analyzed survival, vascular mechanics, and vascular phenotypes in young adult (8–16 weeks) and middle age (9–12 months) Hif2a GOF mice. We find that Hif2a heterozygous (HT) female mice, but not Hif2a HT male mice, exhibit poor survival, SAS upon aging, and decreased ability to withstand repeated physiological strain. Hif2a HT female mice also display thickening of the adventitial intima and increased collagen I and collagen III in all layers of the thoracic aorta. Our findings demonstrate differing PAH progression in female and male Hif2a GOF mice. Specifically, alterations in extracellular matrix (ECM) content led to vascular stiffening in aged females, resulting in poor survival. Moreover, we show that SAS emerges early in mice with PAH by coupling studies of vascular mechanics and analyzing vascular structure and composition. Importantly, we present a model for assessing sex differences in hereditary PAH progression and sex‐specific prognosis, proposing that aortic stiffening can be used to prognosticate future poor outcomes in PAH.
## INTRODUCTION
Pulmonary arterial hypertension (PAH) is a multifactorial disease characterized by loss of compliance and remodeling of PAs, resulting in right heart failure and death. PAH progression correlates with vascular stiffening and remodeling, vasoconstriction, endothelial dysfunction, inflammation, and thrombosis. 1, 2, 3, 4, 5 PAH‐associated arterial remodeling contributes to right heart failure via ventricular–vascular coupling. 6, 7, 8 PAH‐associated remodeling is typically characterized by the thickening of the three structural arterial layers. 9 This structural thickening is due to changes in the extracellular matrix (ECM) quantity and the composition of each layer.
The estimated incidence of PAH is 1.1–7.6 per million adults per year, with a prevalence of 6.6–26.0 per million adults per year. 10, 11, 12 Neither idiopathic nor heritable PAH is sex‐blind; both predominately affect females. 13, 14, 15 Interestingly, the balance of PAH incidence in the two sexes changes with age. The COMPERA European registry showed that the overall 1.8:1 female‐to‐male ratio fell to 1.2:1 when limited to patients older than 65. 16 Moreover, PAH is most commonly diagnosed in women between the ages of 30–60, while males are often diagnosed with PAH at older ages. 17 This asymmetric relationship concerning the time of initial diagnosis and incidence is inadequately understood. To address the gap in our understanding of the differences in prevalence and incidence of PAH in male and female patients, we aimed to examine PAH progression in the context of sex and age.
Accumulating evidence shows that PAH may also cause systematic arterial stiffness (SAS). 18, 19 Multiple prior clinical studies have correlated increases in SAS with PAH patient disease progression. 20, 21, 22 Specifically, increased vascular stiffness is positively associated with age and disease progression and negatively correlated with survival. 20, 21, 22 Nonetheless, the prognostic value of vascular stiffness remains incompletely understood but points to the possibility of PAH as a cause of accelerated arterial aging or to the potential for vascular interdependency. 23, 24 Thus, in the present study, we sought to determine if PAH pathogenesis accelerates the deterioration of systemic vascular mechanics with age and whether this links to mouse sex.
Hypoxia‐inducible factor (HIF) signaling plays a fundamental role in PAH pathogenesis. 25 HIF activity is regulated in an oxygen‐dependent manner; degraded in normal oxygen conditions, HIFs stabilize and accumulate in hypoxia. Multiple mutations in the HIF2A gene associated with erythrocytosis have been identified, with some patients developing pulmonary hypertension. 26, 27, 28, 29, 30 Specifically, missense mutations in H1F2A G537W impair HIF2a hydroxylation and have been shown to cause familial PAH. 30 Mice with missense Hif2a G536W mutations display mutation‐dependent erythrocytosis and pulmonary hypertension. 31, 32 Moreover, smooth muscle cells from mice and patients with missense H1F2A mutations exhibit increased stiffness and abnormal f‐actin fiber orientation. 32 In the present study, we sought to determine whether SAS occurs in familial hypertensive aging female mice compared with male counterparts, thereby providing SAS as a prognosticator of PAH. We study the differences in the temporal evolution of HIF2A‐driven PAH‐associated ECM changes. Specifically, we examine whether this can be attributed to sex, a variable infrequently assessed in the PAH field. We leverage the HIF2A GOF mouse model, previously established consistent with hereditary Hif2a G537R, and we couple survival, circumferential tensile testing (CTT), gene expression, and immunohistochemical (IHC) analyses to probe the contributions of collagens I, III, and elastin to physiological mechanical properties of the hypertensive thoracic aorta. 32
## Female Hif2a HT and HO mice display characteristics of PAH and erythrocytosis
Previous studies have shown that mutations in G537 of the HIF2A gene cause familial hypertension. 26, 31, 32 Thus, we focused on a Hif2a G536W knock‐in mutation in a C57BL/6 background mouse. 30 While prior studies have extensively characterized and assessed this mouse model as having pulmonary hypertension (RV pressure > 60 mm Hg in HO) and erythrocytosis, they have not confirmed the disease presentation of the female mice. 31, 32 We mated Hif2aG536W/+ (heterozygote; HT) mice and obtained both Hif2a HT and Hif2aG536W/G536W (homozygote; HO) mice. Hif2a+/+ (wild‐type; WT) littermates served as controls. However, all HIF2A gene mutations in humans are heterozygous 31; the homozygous mutation is lethal in humans.
First, we measured the mice's red blood cell (RBC) counts. The RBC count for aged female mice increased in a mutation‐dose‐dependent (Figure S1A). We then measured Fulton Index and normalized heart weight for combined middle‐aged and early aging female WT, Hif2a HT, and Hif2a HO mice. We observed increased normalized heart weight in aged female Hif2a WT mice (Figure S1B‐C). We next measured hematocrit and hemoglobin and marked increases in aged female Hif2a HT and Hif2a HO mice (Figure S1D,E). We found no statistically significant differences in the groups when measuring the white blood cell, platelet, and immune cell counts (Figure S1F,G). We performed the same analysis on aged male WT, Hif2a HT, and Hif2a HO mice and found similar trends except for increased statistical significance in the lymphocyte counts (Figure S2).
## Aging female Hif2a heterozygote and homozygote mice exhibit higher mortality than their male genetic counterparts
After confirming PAH in the mouse model, we performed aging studies to elucidate changes in the distribution of genotypes and their likelihood of survival to 12 months. Consistent with prior studies, Hif2a HO mice were born at a lower frequency than expected according to the *Mendelian* genetics (data not shown), suggesting the Hif2a HO mice have a survival disadvantage from birth. 31 Female mice exhibited mutation dose‐dependent differences in their mortality trends; a phenomenon that was not seen for the male mice (Figure 1). When directly compared, Hif2a HT female mice trended toward higher mortality than Hif2a HT male mice compared to their WT counterparts (Figure S3). In addition, our data suggest that female Hif2a HT and HO, mice have a disadvantage in survival with aging. Thus, we focused our study on female mice. Given the precipitous increase in mortality in the PAH group at ~13 months of age when compared with WT littermates, we defined the age groups of interest as being: young adult for 8–16 weeks, early middle age for 4–8 months, middle age for 9–12 months, and early aging for 13 months+. Our studies focused on a direct comparison of the young adult and middle age groups, as survival in the early aging group provided limited subjects for comparison.
**FIGURE 1:** *Sex differences in survival rates in WT and Hif2a mice with aging. Kaplan–Meier survival analysis for WT, Hif2a HT, and Hif2a HO (a) female (N = 65) and (b) male mice (N = 64). Significance level is set at *p < 0.05 for a log‐rank (Mantel‐Cox) test.*
## Middle age female Hif2a HT mice have stiffer thoracic aorta than their young adult counterparts
Emerging studies link SAS to PAH. Therefore, we next sought to examine whether female WT and Hif2a HT mice had any alterations in their response to functional tensile stress with aging; thus, we examined the response of the thoracic aorta using CTT. We focused on the young adult (8–16 weeks) and middle‐aged (9–12 months) age groups, which correlate approximately to young adolescence/young adulthood (13–20 years) and middle ages (30–40 years) in humans, respectively. 33 To assess the contribution of the ECM to the mechanical properties, we decellularized the thoracic aortas and measured the mechanical properties of cellular and acellular segments of the same thoracic aorta. CTT data were represented by the equation S = α exp (βλ), where α and β are constants determined by least squares curve fitting. We found that middle‐aged female Hif2a HT mice exhibit vascular stiffening, as evidenced by the left shift in the stress versus strain curve (Figures 2ai,bi and S4A,B). Aging female WT mice did not exhibit a similar trend, suggesting that the vascular stiffening phenotype is specific to the PAH mice. We examined the maximum tensile stress for female WT and Hif2a HT mice and found no statistically significant differences (Figure 2aii,bii). Finally, we examined strain at failure (Figure 2aiii,biii) and found a dramatic decrease between female Hif2a HT acellular young adult and middle age segments' strain at failure, suggesting that thoracic aortas of aged female Hif2a HT mice experience more significant changes in their ECM and that these changes dramatically impact their ability to handle physiological strain (Figure 2biii). The thoracic aorta of middle‐aged female Hif2a HT mice also exhibited statistically significant differences between the strain at failure of their cellular and acellular segments (Figure 2aiii,biii). The variability in the middle age female WT mouse cellular and acellular segments was greater than that of any of the other vessel subgroups, suggesting that aging also carries the consequences of increased variance in the composition and subsequent contribution of the ECM to vascular mechanical properties (Figure 2biii).
**FIGURE 2:** *Female Hif2a HT mice exhibit compromised vascular mechanics because of aging‐dependent disease progression. (a) Exponential fits of tensile curves of circumferential tensile testing (CTT) of young adult (YA, 8–16 weeks) and middle age (M, 9–12 months) female WT thoracic aorta segments (i), ultimate tensile stress (ii), and strain at failure (iii) (N = 6–11, n = 2–4). (b) Exponential fits of tensile curves of CTT of young adult (YA, 8–16 weeks) and middle age (M, 9–12 months) female Hif2a HT thoracic aorta segments (i), ultimate tensile stress (ii), and strain at failure (iii) (N = 5–10, n = 2–4). (c) Exponential fits of tensile curves of CTT of young adult (YA, 8–16 weeks) and middle age (M, 9–12 months) male WT thoracic aorta segments (i), ultimate tensile stress (ii), and strain at failure (iii). (N = 6–7, n = 2–4). (d) Exponential fits of tensile curves of circumferential tensile testing (CTT) of young adult (YA, 8–16 weeks) and middle age (M, 9–12 months) male Hif2a HT thoracic aorta segments (i), ultimate tensile stress (ii), and strain at failure (iii) (N = 7, n = 2–4). Solid lines indicate cellular segments, dashed lines indicate acellular segments, and dotted lines indicate bounds of SEM. Significance level is set at *p < 0.05 for a two‐way ANOVA (multiple comparisons performed using Tukey's test).*
To adequately assess sex differences in how PAH evolves with HIF2a GOF, we performed the preceding experiments with male mice. Here too, we decellularized the thoracic aortas and measured cellular and acellular segments of proximal aortic segments. We found that middle age male WT and Hif2a HT mice are less capable of withstanding repeated physiological stresses than their young adult counterparts, evident by the right shift in the stress versus strain curve (Figure 3ci,di). We also examined the maximum tensile stress of the thoracic aorta from male WT and Hif2a HT mice and found no statistically significant differences (Figure 3cii,dii). Finally, we examined strain at failure. Interestingly, we found statistically significant differences between young adult and middle age cellular segments of male WT, a difference we did not see in the female counterparts (Figure 3ciii).
**FIGURE 3:** *Hif2a HT middle age female mice exhibit a thicker adventitial layer than their wildtype counterparts. (a) Representative 40× histological images of Masson's Trichrome (MAS) and Verhoeff‐Van Gieson (VVG) stained of young adult (YA, 8–16 weeks) and middle age (M, 9–12 months) female WT and Hif2a HT mouse thoracic aorta. Scale bar is 75 μm. (b) Quantification of histological images of adventitial layer thickness (i), medial layer thickness (ii), and adventitial/media thickness ratio (iii) (N = 3–7). Significance level is set at *p < 0.05 for a two‐way ANOVA (multiple comparisons performed using Tukey's test).*
## Middle age female Hif2a HT mice exhibit thoracic aorta adventitial layer thickening compared to WT counterparts
Next, we examined the differences in the ECM by immunohistochemistry (IHC) analysis of thoracic aortic rings. As the middle age Hif2a HT female mice exhibited vascular stiffening and statistically meaningful differences in their strain at failure compared to young adult Hif2a HT female mice, we focused on these two age groups. Littermate female WT counterparts served as controls. We completed Masson's trichrome stain (MAS) and Verhoeff‐Van Giesson (VVG) staining and quantified the thickness of the adventitial layer, medial layer, and the adventitial/medial layer thickness ratio for young adult and middle age female WT and Hif2a HT mice (Figure 3a,b). Middle‐aged Hif2a HT females, but not young adult Hif2a females, exhibit a statistically significant difference in the adventitial layer thickness and the adventitial/medial layer thickness ratio compared to their age‐matched WT counterparts (Figure 3bi,iii). No differences in the medial layer thickness were observed (Figure 3bii). We further quantified the characteristics of the elastin in the medial layer, including the number of lamellae, lamellar thickness, and interlamellar distance (Figure S5). While young adult and middle‐aged mice had different lamellar thickness, no statistically significant differences were found between the WT and Hif2a HT mice. Thus, we further focused on the major ECM components of the adventitial layer, the collagens, and their differences across our groups of interest.
## Collagen I and collagen III accumulate in the aortas of middle age female HT mice compared to young adult female counterparts
First, we examined differences in the ECM components by performing qRT‐PCR on mouse abdominal aortas (Figure 4a). We did not find any statistically significant differences in levels of ELN, COL1A1, and COL3A1 transcripts. We next evaluated collagens I and III accumulation by performing IHC for collagen I and collagen III on young adult and middle age WT and HT female mouse thoracic aortas. We found that Hif2a HT female mice have more collagens I and III than WT female mice (Figure 4b; Figure S6).
**FIGURE 4:** *Middle age female Hif2a HT mice exhibit increased collagens I and III compared to wild‐type counterparts. (a) qRT‐PCR of relative expression of ELN, COL1A1, and COL3a1 transcripts in young adult (YA, 8–16 weeks) and middle age (M, 8–12 months) female WT and Hif2a HT mice (N = 3–4, n = 2). (b) Representative 40× IHC images of collagen I and collagen III in young adult (YA, 8–16 weeks) and middle age (M, 8–12 months) female WT and Hif2a HT mice (N = 3–7, n = 10–40). Bottom row are high‐magnification of the boxed areas for collagen III (green squares) (i) top scale bar is 50 μm, bottom scale bar is 10 μm. Yellow arrows mark collagen + regions of the medial layer. (c) Percentage of vessel area positive for collagens I and III + area (ii) (N = 3–7, n = 20–40). Significance level is set at *p < 0.05 for a t‐test (Kolmogorov–Smirnov comparison).*
## DISCUSSION
PAH is more prevalent in female patients than in male patients. While the causes of this sex difference have not been adequately explored, clinical database analyses have suggested several potential avenues. 34, 35, 36 Researchers have found that female PAH positively correlates with patient use of prescription weight‐loss drugs, recreational drugs, and oral contraceptive pills. 17 Researchers have also suggested that endogenous sex hormones, specifically 17β oestradiol and its metabolites, could cause increased female PAH incidence. 37 Finally, some studies hypothesize that the higher incidence of autoimmune diseases (including systemic sclerosis, systemic lupus erythematosus, rheumatoid arthritis, Sjogren's syndrome, thyroiditis) in females, when further associated with PAH, has the potential to increase inflammation and to drive disease progression. 38 However, these hypotheses do not adequately resolve why female PAH incidence is higher. 38 Recent studies point to elevated SAS in PAH patients. This suggests that arterial aging may be accelerated in patients suffering from PAH and contribute to a further increase in cardiovascular risk. An intriguing possibility is whether elevated stiffening can be used as a prognostic for disease severity or progression. Thus, our study focused on resolving how hereditary PAH pathogenesis modulates systemic vascular mechanics with age and whether this links to the mouse sex. We first observed that female Hif2a HT and Hif2a HO mice are less likely to survive at 12 months. This effect is mutation‐dose‐dependent; female Hif2a HO and HT mice had poorer survival than female Hif2a WT mice. We confirmed that female and male Hif2 HT and Hif2a HO mice each exhibit the hallmarks of PAH and erythrocytosis. When all heterozygous groups were compared, only female HT mice exhibited SAS due to aging. This was evidenced in the CTT data, where the thoracic aorta of aging female Hif2a HT mice display stiffening, evident by the left shift in the stress versus strain curve. Strain at failure of acellular aortic segments was markedly reduced with age in the female Hif2a HT mice, indicating increased brittleness and a compromised ability to withstand repeated physiological strain in the aged vessels. The male Hif2a HT mice did not exhibit a right shift in the stress–strain curve; aortic stiffening, evident in female Hif2a HT mice, was absent in males. Additionally, consistent across multiple age groups, menstrual status did not confer protective effects.
We then examined the adventitial and medial layers of the thoracic aorta. Middle age female Hif2a HT mice exhibited thickening in their adventitial layer that is not seen in young adult female mice of either genotype. Surprisingly, we did not see any RNA transcript level differences between the female, young adult, and middle age WT and Hif2a HT mice. When we further examined middle age and young adult female Hif2a HT and WT mice for expression of collagens I and III, we found that middle age female Hif2a HT mice, compared to WT counterparts, have significantly greater collagen I and III deposits throughout their vessel architecture. This trend was also present in the younger female HT mice but to a lesser effect. Moreover, both middle age and young adult Hif2a HT mice exhibited a high degree of heterogeneity in their collagen I content, which may cause the large variability observed in vessel mechanics. Overall, collagens I and III depositions were both age and genotype dependent. Consequently, we conclude that elevated ECM deposition is the mechanism responsible for Hif2a HT female mouse aortic stiffening. Taken together, these findings suggest that accelerated aging of the systemic circulatory system and aortic stiffening indicate a poorer prognosis for survival.
Prior clinical studies have reported accelerated SAS associated with PAH; however, the biological foundations for this phenomenon remain undetermined and include systemic inflammation, main PA distension leading to irregular systemic hemodynamics, and pulmonary‐vascular oxygen and mechanical interdependency. 18, 19, 23, 24 Prior research has also shown that in the systemic circulation, SMC stiffness can compensate for a more compliant ECM. 39, 40, 41 We propose that this mouse model be utilized as a cell source for future in vitro studies to assess the mechanical contributions of sex‐differentiated SMCs to vascular mechanics; we predict that ECM and cell‐based changes are combinatorial in their functional results. These in vitro studies could also continue addressing unexplored sex‐specific PH differences. The Hif2a mutation compromises ECM strength with altered deposition of collagens leading to the lowered strain of failures. Further studies focused on matrix remodeling enzymes, including enzymes implicated in both crosslinking and degradation of the ECM, would further resolve the pathways driving the vascular functional decline.
We acknowledge that our study has limitations. Due to the structure and the already long timescale of the study, our Kaplan–Meier curves do not exceed 12 months. We do not address the cause of death in female PAH mice. We did not establish the causality between the Hif2a mutation and collagen deposition in the systemic vasculature. We also did not delve into sex‐specific differences in PAH cells. Examining the differences between male and female Hif2a HT and HO ECs and SMCs in vitro could elucidate additional causes for sex‐divergent disease progression. While we show that ECM compositional changes underlie the stiffening phenotype, the specific mechanism remains to be elucidated. In this context, further examination of the contributions of crosslinking enzymes, such as lysyl oxidase and lysyl oxidase homologs 1 and 2, and matrix metalloproteases (MMPs), such as MMP‐1, ‐2, and ‐14, might clarify matrix‐remodeling differences between male and female Hif2a HT mice and set the direction for available therapeutic solutions. Finally, delving into measurements of endogenous sex hormones in the blood and serum of male and female Hif2a HTs over time could further clarify disease progression and its mediators.
In summary, using the Hif2a GOF mice, we demonstrate different physiological consequences and PAH progression in female and male mice. This phenomenon recapitulates the higher prevalence and incidence of hereditary PAH in female patients. Moreover, we show that SAS emerges early in mice with PAH by coupling studies of vascular mechanics, using circumferential tensile testing, and analyzing vascular structure and composition. This is exacerbated in females when compared with age‐matched males. Aortic stiffening in PAH females is caused by alterations in ECM composition, leading to accelerated aging and increased mortality, suggesting that aortic stiffening could prognosticate poorer outcomes. Further, these findings will provide a model for assessing sex differences in hereditary PAH progression and therapeutic efficacy.
## Animal model
All animals used in these studies were maintained under protocols approved by the Animal Care and Use Committee at Johns Hopkins University School of Medicine. Hif2a HT mice were initially generated by the laboratory of Professor Frank Lee. 31 Mating age‐matched nonlittermate Hif2a HT mice generated subsequent WT, Hif2a HT, and Hif2a HO mice. See Table 1 for details of the major resources used in these studies. Polymerase chain reactions (PCR) on young adult tail DNA were used to identify genotype, using primers for G536W. Mice were aged until they fit one of the predetermined age ranges, being a young adult (8–16 weeks), early middle age (16 weeks‐9 months), middle age (9–12 months), and early aging (13 months+). Animals were housed on a 12‐h light/dark cycle and were given access to food and water ad libitum. Animals were massed, sacrificed, and tissues were immediately collected for further experiments.
**TABLE 1**
| Reagent/resource | Source | Identifier |
| --- | --- | --- |
| Animals | Animals | Animals |
| Mus musculus | Frank Lee 31 | C57/BL6, Male and Female |
| Antibodies | Antibodies | Antibodies |
| Collagen I | Novus | Cat#: NB600‐408 |
| Collagen III | Abcam | Cat#: ab7778 |
| Chemicals | Chemicals | Chemicals |
| Trizol reagent | LifeTech | Cat#:15596018 |
| Ammonium hydroxide (NH4OH) | Sigma‐Aldrich | Cat#: 221228 |
| Sodium dodecyl sulfate (SDS) | Bio‐Rad | Cat#: 1610301 |
| Formaldehyde, 37 wt% | Fischer Scientific | Cat#: F79‐500 |
| Critical commercial assays | Critical commercial assays | Critical commercial assays |
| TaqMan Gene Expression Master Mix | Thermo Fischer Scientific | Cat#: 4369016 |
| Dual Endogenous Enzyme Block | Agilent Technologies | Cat#200389‐2 |
| ImmPRESS HRP anti‐rabbit IgG polymer detection kit | Vector Laboratories | Cat# MP‐7401 |
| ImmPACT DAB HRP substrate | Vector Laboratories | Cat# SK‐4105 |
| Oligonucleotides | Oligonucleotides | Oligonucleotides |
| ACTB (Mm02619580_g1) | Thermo Fischer Scientific | Cat# 4453320 |
| ELN (Mm00514670_m1) | Thermo Fischer Scientific | Cat# 4453320 |
| COL1A1 (Mm00801666_g1) | Thermo Fischer Scientific | Cat# 4453320 |
| COL3A1 (Mm00802300_m1) | Thermo Fischer Scientific | Cat# 4453320 |
| Software and algorithms | Software and algorithms | Software and algorithms |
| ImageJ | Kevin Eliceiri 42 | ImageJ |
| Excel | Microsoft | Excel |
| MATLAB | Mathworks | MATLAB |
| Prism Version 9.1.2 | GraphPad Software Inc. | PRISM |
Sex‐matched Hif2a HT and Hif2a HO mice were compared to their respective controls, the WT mice. Each individual mouse was treated as an experimental unit and is labeled as N# in the figure legends; all cases for n# refer to technical replicates. The number of experimental units allocated to each group at each portion of the study is detailed in the figure captions. For the CTT testing, the number of experimental units used was determined by performing a power analysis and setting an $80\%$ confidence interval. While a $90\%$ confidence interval would have been preferred, it required prohibitively large quantities of experimental units for some of the categories. For the Kaplan–Meier curves, the number of experimental units was determined based on colony size and available statistics after 2.5 years. All healthy experimental units that did not have other underlying medical problems were included in the experiment. Unhealthy experimental units that significantly deviated from the standard were excluded, some of the reasons why experimental units were excluded are obesity and anal prolapse. Randomization was not used during any part of the testing. For the cofounding variable of cage position on a rack, all mice tested during the same week, irrespective of the analysis, were stored on the same shelf of a rank and with the same proximity to the light for a minimum of 2 weeks. All researchers were aware of group allocation at all stages of the experiment and no blinding was conducted.
## Kaplan–Meier analysis
We used a total of $$n = 126$$ mice for Kaplan–Meier curves. Of these, female WT $$n = 13$$, female Hif2a HT $$n = 39$$, female Hif2a HO $$n = 13$$, male WT = 13, male Hif2a HT = 36, male Hif2a HO = 15. Mice were tracked from approximate birth until their natural biological death or were censored prior to natural biological death if used for other terminal experiments. Log‐rank (Mantel‐Cox) tests were performed in GraphPad Prism 9 to evaluate the statistical significance.
## Heart mass and complete blood cell count
Prior to vessel extraction and cleaning, blood was collected via cardiac puncture. Blood was collected in EDTA tubes, stored at 4°C for a maximum of 30 min, and a complete blood cell count (CBC) was performed by the Johns Hopkins University Phenocore, Phenotyping, and Pathology Core. After vessel extractions were complete, the heart was dissected on a surgical microscope, and components were massed. Fulton's index was defined as the weight of the right ventricle divided by the combined weight of the left ventricle and the septum.
## Tensile testing
Circumferential tensile testing was performed following protocols described previously 43, 44 with minor modifications. 45, 46 The thoracic aortas were harvested, cleaned, and cut into 1.25–1.5 mm length segments. The length of each segment was imaged using a microscope set to a magnification of ×4. The length was measured using Image J software (NIH). The rings were then mounted onto pins on an electromechanical puller (DMT560). After appropriate system calibration and pin alignment, the pins were separated using an electromotor at a 50 μm/s to apply radial force onto the specimen until breakage. Displacement and force were continuously recorded. The thickness of the intimal and medial layers (t) and stress‐free lumen diameter (D i) of representative samples were measured from concentric rings cut adjacent to the pulled segments, imaged at ×4 magnification, and quantified using Image J software. Engineering Stress (S) was calculated by normalizing force (F) to the initial stress‐free area of the specimens (S=F/(2t*L)). Engineering strain (λ) was calculated as the displacement ratio to the initial stress‐free diameter. The raw data was processed in Excel (Microsoft) and MATLAB (MathWorks), where an exponential fit, using least‐squares fitting, was applied to the tensile testing data. All vessel segments that did not comply with the exponential fit with a minimum coefficient of determination, R 2 of 0.95 were excluded from the exponential fit curves and from the raw data set. All calculations were done using Excel (Microsoft) and MATLAB (MathWorks).
## Thoracic aorta ring decellularization
The thoracic aorta rings were decellularized as published previously. 47 Briefly, after cleaning, half of the rings were decellularized by washing in 50 mM NH4OH and $0.2\%$ sodium lauryl sulfate (SDS) on an orbital shaker for 3 h. Three 30‐min washes in PBS followed decellularization. Testing of all thoracic aorta rings was performed in Ca2+‐free Krebs solution.
## qRT‐PCR
Total RNA was isolated from the abdominal aorta using TRIzol (Invitrogen) according to the manufacturer's instructions. 48 Extracted total RNA was quantified using an ultraviolet spectrophotometer and validated for having no DNA contamination. RNA was transcribed using reverse transcriptase M‐MLV and oligo(dT) primers (Promega, Madison, WI). The TaqMan PCR step was performed using TaqMan Universal PCR MasterMix and Gene Expression Assay (Applied Biosystems, Foster City, CA) for genes outlined in the text. The PCR step was performed with a StepOne Real‐Time PCR system (Applied Biosystems) according to the manufacturer's instructions. The relative expressions of these genes were normalized to the ActB amount in the same sample by using the manufacturer's ΔΔCT method. The comparative method was used to calculate the amplification differences between different samples for each primer set.
## Histology, IHC staining, and quantification
For histological sectioning and staining, harvested thoracic aortas were fixed in $4\%$ paraformaldehyde in buffered PBS for 24 h. Masson's Trichrome (MAS) and Verhoeff van Giesson (VVG) staining were performed by the Johns Hopkins University Reference Histology Core. IHC staining was performed as previously described using collagen I (Novus NB600‐408, 1:100) and collagen III (Abcam ab7778, 1:500). Stained tissues were imaged with an upright microscope (Nikon Accuscope 3000, DS‐F12). 49 Image analyses were performed using MATLAB (MathWorks) and ImageJ (NIH).
The medial and adventitial layer thickness was measured on MAS‐stained slides by measuring the length of each layer perpendicular to approximate vessel curvature. Ten measurements were taken per 40× image, and a minimum of 10 40× images were averaged per animal ($$n = 10$$+). Elastin lamellar thickness, interlamellar distance, and the number of lamellae were measured on VVG‐stained slides by measuring the length of each object perpendicular to approximate vessel curvature. Ten measurements were taken per 40× images, and a minimum of 10 40× images were averaged per animal ($$n = 10$$+), except for the number of lamellar layers, which was measured in one location per 40× image.
Collagen I and collagen III staining analysis was performed using a custom MATLAB code based on the MATLAB internal “Color Thresholder” app and normalized to the total area of the vessel in each 40× image. Cohorts were protein and age matched to select ideal +positive thresholder values.
## Statistics and reproducibility
For all experiments, “n” denotes technical replicates while “N” represents biological replicates. The number of technical replicates and biological replicates varies depending on the experiment and is noted specifically in each figure legend. Two‐tailed t‐tests or ANOVA were performed to determine significance. All graphs were drawn using GraphPad Prism 9. Significance levels were set at *$p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001.$
## AUTHOR CONTRIBUTIONS
Eugenia Volkova: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing (equal). Linda Procell: Data curation (supporting); formal analysis (supporting). Lingyang Kong: Data curation (supporting); formal analysis (supporting. Lakshmi Santhanam: Conceptualization (supporting); methodology (equal); supervision (supporting); writing – review and editing (equal). Sharon Gerecht: Conceptualization (equal); funding acquisition (lead); methodology (equal); resources (lead); supervision (lead); writing – original draft (equal); writing – review and editing (equal).
## CONFLICT OF INTEREST
The authors declare no competing interests.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10403.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: The efficacy of anti‐proteolytic peptide R7I in intestinal inflammation, function,
microbiota, and metabolites by multi‐omics analysis in murine bacterial enteritis
authors:
- Taotao Sun
- Xuesheng Liu
- Yunzhe Su
- Zihang Wang
- Baojing Cheng
- Na Dong
- Jiajun Wang
- Anshan Shan
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013768
doi: 10.1002/btm2.10446
license: CC BY 4.0
---
# The efficacy of anti‐proteolytic peptide R7I in intestinal inflammation, function, microbiota, and metabolites by multi‐omics analysis in murine bacterial enteritis
## Abstract
Increased antibiotic resistance poses a major limitation in tackling inflammatory bowel disease and presents a large challenge for global health care. Antimicrobial peptides (AMPs) are a potential class of antimicrobial agents. Here, we have designed the potential oral route for antimicrobial peptide R7I with anti‐proteolytic properties to deal with bacterial enteritis in mice. The results revealed that R7I protected the liver and gut from damage caused by inflammation. RNA‐*Seq analysis* indicated that R7I promoted digestion and absorption in the small intestine by upregulating transmembrane transporter activity, lipid and small molecule metabolic processes and other pathways, in addition to upregulating hepatic steroid biosynthesis and fatty acid degradation. For the gut microbiota, Clostridia were significantly reduced in the R7I‐treated group, and Odoribacteraceae, an efficient isoalloLCA‐synthesizing strain, was the main dominant strain, protecting the gut from potential pathogens. In addition, we further discovered that R7I reduced the accumulation of negative organic acid metabolites. Overall, R7I exerted better therapeutic and immunomodulatory potential in the bacterial enteritis model, greatly reduced the risk of disease onset, and provided a reference for the in vivo application of antimicrobial peptides.
## INTRODUCTION
Inflammatory bowel disease (IBD), a global health care problem, is a chronic recurrent gastrointestinal inflammatory disease of complex etiology, subdivided into two phenotypes: Crohn's disease (CD) and ulcerative colitis (UC). 1, 2 Recently, IBD incidence and prevalence continue to increase worldwide, with an estimated approximately 1.5 million IBD people in North America. 3 Numerous studies have determined that IBD mainly affects the distal ileum and colon and involves complex interactions between intestinal flora, host genetic and immune factors, and environmental stimuli, among which enteric bacteria are critical in the pathogenesis of inflammatory bowel disease, especially Escherichia coli. 4 *Escherichia coli* can adhere to and invade intestinal epithelial cells (IECs), survive and replicate inside macrophages without inducing cell death, and induce a high production of pro‐inflammatory cytokines and chemokines, thereby impairing the intestinal mucosal barrier and promoting IBD development. 5, 6, 7 Ana Nemec et al. found that nitric oxide (NO) production was investigated in the lungs, thoracic aorta, heart, liver, spleen, kidneys, and brain of mice inoculated orally with *Escherichia coli* ATCC 25922. 8 *The* generalized increase in NO production in the short and long terms indicates a host response to E. coli administered by the oral route of infection. Given this, using antibiotics (i.e., norfloxacin, ciprofloxacin, ofloxacin, and fleroxacin) to control the proliferation of *Escherichia coli* in intestinal mucosa may be a potential therapeutic strategy for IBD. 9, 10 However, numerous studies have recently proved that the extended use or misuse of antibiotics can cause a persistent expansion of antibiotic resistance genes in the human gut microbiota and disrupt the normal microbiota ecological structure and epithelial barrier function, causing serious secondary infection. 11, 12 As highlighted in the last WHO global report, antimicrobial resistance has reached an extremely alarming level in various infection sources, including E. coli. Therefore, existing antibiotics have failed to satisfy clinical treatment needs, and there is an urgent need to develop novel antimicrobial agents against bacterial infection enteritis.
Antimicrobial peptides (AMPs), as an important component of the innate immune system, are expressed on the epithelial surface and in neutrophils in mammals, and have broad‐spectrum activity against bacteria, fungi, viruses, and so on. Consequently, AMPs have garnered immense attention as a potential class of peptide‐based antimicrobial agents for combating bacterial infections. 13, 14 Indeed, peptides‐based drugs constitute the fastest growing market in the pharmaceutical industry. According to the report, the global peptide therapeutics market was valued at US$ 25.0 Bn in 2018 and is anticipated to expand at a CAGR of $7.9\%$ from 2019 to 2027 (https://www.transparencymarketresearch.com/peptide-therapeutics-market.html). However, most peptide drugs, including AMPs, are difficult to administer orally due to an enzymatic barrier that limits systemic bioavailability, yet oral administration is the recommended technique for treating gastrointestinal inflammatory illnesses. 15, 16, 17 Accordingly, improving AMPs' protease resistance was crucial to substituting antibiotics in IBD treatment.
In earlier research, we successfully developed an anti‐proteolytic peptide R7I (IRPI IRPI IRPI IRPI IRPI IRPI IRPI‐NH2), which had great bactericidal activity against all 16 tested Gram‐negative bacteria (GMMBC = 4.97 μM) with low cytotoxicity. Additionally, R7I showed good salt tolerance and serum stability in vitro and still maintained excellent activity in a mouse peritonitis model. Importantly, compared with other anti‐proteolytic peptides containing unnatural amino acids, R7I was completely composed of natural amino acids and had dramatic resistance to a high concentration of protease hydrolysis (trypsin, chymotrypsin, and pepsin), suggesting that R7I could be produced cheaply through biological expression systems and had a broader application prospect. 18 Therefore, we further investigated the efficacy of R7I in bacterial infection enteritis through oral administration. As displayed in Figure 1, we introduced an E. coli‐infected mouse model of enteritis to investigate the efficacy of R7I. Herein, we stated that R7I has excellent stability. In the inflammation model, R7I protected intestinal barrier and function, reducing intestinal inflammation and balancing intestinal flora disorders. In addition, R7I reduced liver damage and promoted liver‐related metabolic functions.
**FIGURE 1:** *(a) Establishment of bacterial enteritis model in mice. The damage caused by Escherichia coli invading the mice's body through oral administration. (b) Therapeutic mechanism of anti‐proteolytic peptide R7I in mice.*
## R7I had better stability and metabolic characteristics in vivo
Previous studies have demonstrated that R7I exhibits excellent resistance to pepsin, trypsin, chymotrypsin, and proteinase K in vitro. 18 To get closer to the real in vivo effect, we first extracted the serum, intestinal fluid, and gastric fluid from mice to determine the function of R7I in these fluids. As depicted in Figure 2c, we demonstrated that melittin as a control was completely inactivated in both gastric and small intestinal juices after incubation for 1 h. In contrast, the antimicrobial activity of R7I was unaffected after 8 h incubation in the gastric juice. In the small intestine fluid, MIC values against E. coli of R7I increased slightly from 2 to 4 μM after 4 h incubation, indicating that R7I activity was slightly affected in the small intestinal environment. Additionally, the serum environment had no negative effect on R7I activity, but slightly compromised the activity of melittin.
**FIGURE 2:** *(a) Schematic diagram of the experimental scheme. Red and green arrows indicate the number of oral administrations and corresponding time points, respectively. (b) The experimental group design. (c) R7I MIC changes after incubation with serum, gastric juice, and small intestine fluid against Escherichia coli 25922. Minimum inhibitory concentrations (MICs, μM) were determined as the lowest peptide concentrations that killed greater than 95% of the bacterial cells. A value >64 indicates no detectable antibacterial activity at 64 μM. GAJ, gastric juice; SIJ, small intestinal juice; PBS2.0 and PBS7.0, phosphate‐buffered saline (pH = 2.0 or pH = 7.0); E. coli, Escherichia coli 25922*
Subsequently, we observed the metabolic process of (FITC)‐labeled R7I in vivo. Figure 3 displays that R7I was present in large quantities in the small intestine after 1 h, and a high fluorescent signal remained in the intestine after 4 h. After 8 h, the fluorescent signal in the intestine and organs began to diminish.
**FIGURE 3:** *Fluorescence retention of anti‐enzymatic peptide R7I in mice. The C57BL/6 male mice were fasted for 12 h before treatment, after which the blank group was given saline and the experimental group was given 20 mg/kg R7I to start the clock. Samples were taken at 1 h, 4 h, and 8 h (organs and each mid‐gut), respectively. The blank group served as a control, and all samples were photographed using a fluorescent microscope (unified light source setting).*
Overall, the above data indicate that R7I had excellent resistance to enzymatic hydrolysis and had the potential for oral administration.
## R7I could protect intestinal mechanical and immune barriers
The intestine is the first line of defense against foreign pathogens, and intestinal permeability can indirectly reflect the status of the intestinal barrier. As demonstrated in Figure 4a, d‐lactic acid (DLA) and diamine oxidase (DAO) contents in the control group treated with the bacterial solution were significantly increased ($p \leq 0.05$) compared with the blank group. After oral administration of various R7I doses and 20 mg/kg colistin sulfate, DAO, and DLA levels in most treatment groups were significantly decreased ($p \leq 0.05$), with the best results in the 20 mg/kg R7I group. In addition, we reported that partial dose concentrations of R7I and colistin could raise secreted immunoglobulin A (SlgA) levels compared to the control group, without statistical significance, and the highest content in R7I‐20 group was 2.59 mg/L. In addition, R7I significantly increased total antioxidant levels and decreased MDA levels in the small intestine ($p \leq 0.05$, Figure S1).
**FIGURE 4:** *(a) Serum concentrations of d‐lactic acid, diamine oxidase, and secreted immunoglobulin A (SlgA) (n = 6). DLA, d‐lactic acid; DAO, diamine oxidase. (b) Inflammatory factor and tight junction proteins levels of relative mRNA expression in ileal and colonic tissues (n = 6). (c) Inflammatory factor proteins were examined by Western blot in the mouse colon and ileum (n = 3). A 40 μg sample of protein was taken. Beta‐actin was employed as an internal control. β‐Actin, beta‐actin, 42 kDa; IL‐4, interleukin 4, 30 kDa; IL‐6, interleukin 6, 24 kDa; IL‐10, interleukin 10, 19 kDa; TNF‐α, tumor necrosis factor alpha, 26 kDa. (d) H&E‐stained histological sections of intestinal tissues. Scale bars, 400 μm. In the ileum, red arrows stand for inflammatory cell infiltration; blue arrows stand for the partial separation of the mucosal layer from the lamina propria; yellow arrows stand for epithelial cell detachment. In the colon, yellow arrows stand for adhesion to intestinal villus structures and detachment of epithelial cells; red arrows stand for localized areas of cell proliferation in the serosal layer. The data were presented as mean ± SEM. One‐way ANOVA with a Tukey post‐test was used to determine statistical significance. In a bar chart, different lowercase letters indicate significance (p < 0.05).*
Given the excellent performance of 20 mg/kg R7I, we further probed its effects on intestinal inflammation and tight junction proteins at mRNA and protein expression levels (Figure 4b,c). Compared with the blank group, the mRNA relative expression levels of pro‐inflammatory factors IL‐1β, IL‐6, and TNF‐α were significantly elevated in the control group ($p \leq 0.05$), while anti‐inflammatory factors IL‐4 and IL‐10 were significantly reduced ($p \leq 0.05$) (IL‐10 in the ileum was an exception). After being treated with 20 mg/kg R7I, the mRNA and protein relative expression levels of pro‐inflammatory factor IL‐6 were significantly lower in the ileum, and those of TNF‐α were significantly lower in the colon and ileum. Meanwhile, the mRNA and protein relative expression levels of anti‐inflammatory factors IL‐4 and IL‐10 significantly increased in the colon. These interesting results suggested that R7I might play different regulatory roles in the inflammatory factor pathway of the colon and ileum. Additionally, for tight junction proteins, the mRNA relative expression levels of Occludin and Claudin 1 in the colon of the control group were significantly reduced compared to the blank group ($p \leq 0.05$). However, the 20 mg/kg R7I increased the mRNA relative expression levels of Occludin and Claudin 1 in the colon without statistical significance ($p \leq 0.05$).
Subsequently, we visually observed the protective effects of R7I on intestine injury by determining intestine histological changes. In Figure 4d, for the ileum, the control group had a large infiltration of inflammatory cells in the field of view (red arrows), and localized areas of the mucosal layer were separated from the lamina propria (blue arrows). In the 20 mg/kg R7I group, the tissue structure was basically normalized, only with a small number of detached epithelial cells (yellow arrow). In the blank group, the morphology of the intestinal villi was intact, and the thickness of the intestinal muscularis was uniform. In the case of the colonic tissue, the intestinal villi of the control group were structurally adherent, and epithelial cells were shed (yellow arrows). The serosal layer cells were proliferated in the local area in R7I‐20 group (red arrows). The tissue structure of the blank group was basically normal. In summary, R7I‐20 group restored the normal morphology of the intestine.
## R7I had a good protective effect on the liver injury caused by E. coli infection
The weakened intestinal mucosal barrier functions propagated and translocated numerous intestinal pathogenic bacteria, and releasing several toxic substances, such as endotoxin, would cause a systemic inflammatory response and organ injuries. Consequently, the blood biochemical indices were performed to determine whether oral R7I administration could alleviate other organ injuries in bacteria‐infected mice. As displayed in Figure 5a, liver‐associated indexes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), total bilirubin (TBIL), albumin (ALB) and globulin (GLB)) and kidney associated indexes (creatinine (CREA), urea nitrogen (BUN)) in the blank group were significantly different from those in the control group treated with bacterial solution ($p \leq 0.05$). After oral administration of various R7I doses and 20 mg/kg colistin sulfate, we found that 20 mg/kg R7I could significantly decrease AST, ALT, and TBIL levels in the serum of bacterially infected mice compared with the control group ($p \leq 0.05$), and relieve the elevation of LDH, CREA and CK levels, without statistical significance ($p \leq 0.05$). In contrast, other doses of R7I and colistin sulfate could also decrease the levels of AST, TBIL ($p \leq 0.05$), and ALT ($p \leq 0.05$), but had little effect (or even an increasing trend) on LDH and CREA levels. Given this, we preliminarily determined that R7I was more effective in protecting the liver function of bacteria‐infected mice.
**FIGURE 5:** *(a) Serum biochemical indices such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total bilirubin (TBIL), cholinesterase (CHE), creatinine (CREA), creatine kinase (CK), urea nitrogen (BUN), lactate dehydrogenase (LDH), total protein (TP), albumin (ALB), and globulin (GLB) were measured. The data was presented as mean ± SEM (n = 6–10). One‐way ANOVA with the Tukey post‐test was used to determine statistical significance. A p value of <0.05 was regarded as significant. (b) Antioxidant levels in the liver of mice (n = 6). (c) H&E‐stained histological sections of liver tissues. Scale bars, 400 μm. In the liver, black arrows stand for dilated central vein; red arrows stand for inflammatory cell infiltration; green arrows stand for vacuolated change.*
The inflammation releases multiple inflammatory cytokines causing oxidative stress, further aggravating the liver injury and even promoting hepatocytes apoptosis. Consequently, attenuated oxidative stress is one of the most important measures to treat liver injury. As described in Figure 5b, the levels of malondialdehyde (MDA), a marker of oxidative stress, CAT, and SOD in the control group were significantly higher than that in the blank group, and T‐AOC levels were significantly lower than those that in the blank group, implying that bacterial infections did disrupt the disruption of antioxidant/prooxidant equilibrium in the liver. Notably, after oral administration of R7I and colistin sulfate, MDA levels in the liver significantly dropped compared with the control group. Among these, 20 mg/kg R7I could also significantly downregulate CAT levels ($p \leq 0.05$), increase SOD and GSH contents ($p \leq 0.05$), and restore T‐AOC levels ($p \leq 0.05$) in the liver in bacteria‐infected mice. In comparison, the antioxidant effects of other doses of R7I and colistin sulfate were slightly less.
Based on the above results, 20 mg/kg R7I showed more effective protection effects against liver injury than other treatment groups. Similarly, histological observations of livers strongly supported the protective effect of 20 mg/kg R7I. As displayed in Figure 5c, the hepatocytes in the blank group were tightly arranged and of uniform size, without evident pathological changes. However, after treatment with bacteria, the liver tissue was abnormally structured with loosely arranged tissue and dilated central veins (black arrows), inflammatory cell infiltration was observed in many parts of the tissue (red arrows), and numerous vacuolar‐like changes were visible in the field of view (green arrows). Excitingly, in the R7I‐20 group, although there remained a few inflammatory cells distributions (red arrows), the liver tissue was tightly arranged and returned to normal.
Overall, we considered that 20 mg/kg R7I had great protective effects on the liver injury caused by E. coli infection.
## Effects of R7I on organ functions of the small intestine and liver
Each organ function is critical in the overall health system. Digestion and absorption in the small intestine provide energy and nutrients for various life activities, while the liver is primarily responsible for material metabolism. In addition to immunity and injury examinations, the intestinal tract and liver must be examined for the bacterial attack. Below, we employed transcriptome analysis to examine functional changes in the small intestine and liver in R7I‐20 group. The sequencing data were further filtered to remove some spliced and low‐quality reads, obtaining an average number of 40,309,056 high‐quality sequence reads. The average percentage of high‐quality sequence reads in sequencing reads was $90.71\%$. Principal components analysis (PCA) was conducted on each sample based on the amount of expression. PCA analysis can cluster similar samples together with closer proximity, indicating higher similarity. In the case of the small intestine (Figure 6a), R7I‐20 group was similar to and overlapped with the blank group and separated from the control group (PC1 = $98.9\%$). For the liver, R7I‐20 group was similar to and overlapped with the control group, and they were separated from the blank group (PC1 = $97\%$). The Venn diagram (Figure 6d) displayed the presence of 290 unique differential genes in the small intestine in Control vs. R7I‐20 compared to the other two combinations. There were 14 differential genes common to all three combinations. Furthermore, it can be observed that the number of differential genes in Blank vs. Control is much greater than in Blank vs. R7I‐20, 5.86 times ($\frac{680}{116}$), indicating that R7I‐20 effectively reduced the number of differential genes caused by bacterial treatment. In the liver, there were 37 unique differential genes in Control vs. R7I‐20 compared to the other two combinations. There were 34 differential genes common to all three combinations. However, there was no significant difference in the number of differential genes between Blank vs. R7I‐20 and Blank vs. Control ($\frac{1033}{662}$). The volcano plot depicts A vs. B with 709 upregulated genes and 300 downregulated genes in the small intestine and 66 upregulated genes and 47 downregulated genes in the liver (Figure 6c). Figure 6e depicts the protein interaction network analysis for Control vs. R7I‐20, with blue representing downregulated genes and orange representing upregulated genes, and the combined score is shown by the size of circles, with connecting lines reflecting the interactions. Overall, R7I‐20 group had a greater effect on the small intestine than the liver.
**FIGURE 6:** *Small intestine and liver transcriptome analysis. (a) Principal component analysis (PCA); different colors indicate different groupings. (b) Statistics of differential expression results, horizontal coordinates indicate the comparison group for differential analysis, vertical coordinates demonstrate the number of differential genes, and colors indicate up or downregulation. (c) Volcano map of differential expressed genes (DEGs) in the small intestine and the liver (Control vs. R7I‐20). The horizontal coordinate is log2FoldChange, and the vertical coordinate is the significance level against 10, taking the negative logarithm of the value. The two vertical dashed lines in the graph are the thresholds for expression difference multiples; the horizontal dashed line is the significance level threshold. Colors indicate whether the gene is upregulated, downregulated, or nonsignificantly differentially expressed. (d) Venn diagram of DEGs. (e) Protein network interaction analysis (Control vs. R7I‐20). Blue indicates downregulated genes, orange indicates upregulated genes, and the combined score is indicated by the size of the circle, with the linkage representing the interaction. Disconnected nodes are not displayed in the network. (f) Top 20 terms for GO enrichment analysis of DEGs (Control vs. R7I‐20). p value calculated by hypergeometric distribution method (significant enrichment is defined as p < 0.05, n = 4).*
We next performed functional classification of differential genes in each combination by Gene ontology (GO) and *Kyoto encyclopedia* of genes and genomes (KEGG) enrichment analyses to better understand the specific roles played by R7I, focusing on control and R7I‐20 groups. As revealed in Figure 6f, in the cellular components of the small intestine, R7I‐20 (Control vs. R7I‐20) upregulated membrane and membrane‐associated components (GO:0016020, GO:0031224, GO:0016021, and GO:0016020), cell membrane network (GO:0005783), collection of membrane structures involved in intracellular transport (GO:0012505), continuous membrane network of outer nuclear and endoplasmic reticulum membranes, and microvilli (GO:0005903). Among the biological processes involved, R7I‐20 upregulated lipid and small molecule metabolic processes (GO:0006629 and GO:0044281) and transmembrane transport (GO:0055085 and GO:0006811). In molecular function, R7I‐20 upregulated transmembrane transporter activity (GO:0022857) and the direct movement of substances (e.g., macromolecules, small molecules, and ions) within cells, outside cells or between cells (GO:0005215). The KEGG enrichment analysis of the small intestine was presented in detail in Table S2. Figure 6f displayed that in the cellular components of the liver R7I‐20 upregulated the collection of membrane‐like structures involved in intracellular transport (GO:0005615, GO:0005789, GO:0042175, and GO:0005783). In biological processes, R7I‐20 primarily facilitated the synthesis and metabolic processes of lipid‐like substances, including cholesterol, steroids (GO:0006629, GO:0019216, GO:0008610, GO:0046890, and GO:0006694), small molecule biosynthesis, and metabolic processes (GO:0044283 and GO:0044281). The liver KEGG enrichment analysis was presented in detail in Table S3. These results indicated that R7I‐20 group restored the normal physiological functions of the liver and intestine to a certain extent, ensuring the normal functioning of the organism. Pathway analysis of the small intestine and liver of the control and R7I‐20 groups as compared to the blank group were displayed in Tables S4–S7.
## R7I promotes homeostasis of intestinal flora
Intestinal flora can influence intestinal health, especially in inflammatory bowel disease, irritable bowel syndrome, and other intestinal disorders. Following that, the cecal contents 16 S rRNA gene was sequenced to explore intestinal flora changes. Twelve samples from three groups (Control, R7I‐20, and Blank) were sequenced, and 1,058,307 sequences matched the forward and reverse primers in the original data, with an average of 88,192 per sample. A total of 592,473 high‐quality sequences were generated by Dada2 method after denoising and clustering. Subsequently, species taxonomic annotation was conducted. The average sequence length of the entire sample was 415 bp. The taxonomic annotation unit disclosed that the control group (E. coli infection) reduced the proportion of ASVs at the family level while increasing them at the order level (Figure 7a). As displayed in Figure 7e, ASVs in three groups (Control, R7I‐20, and Blank) were 8270, 9608, and 10,125, respectively. There were at least 2550 ASVs shared by the two groups, of which 718 were shared by the three groups. Each of the three groups had 6406 (Control), 7515 (R7I‐20), and 8264 (Blank) unique ASVs. In Figure 7b, the proportion of Control and R7I‐20 of Bacteroidetes decreased to $9.95\%$ and $13.04\%$, respectively, while that of Blank was $31.45\%$. Proteobacteria was increased in R7I‐20 ($13.43\%$), compared with Control ($4.76\%$) and Blank ($0.59\%$) groups. Firmicutes in Control increased to $83.15\%$, and the ratio of Firmicutes to Bacteroidetes increased.
**FIGURE 7:** *Gut microbiota analysis by 16S rRNA sequencing. (a) Statistics on the number of taxonomic units of species composition. (b) Analysis of taxonomic composition at the phylum level. (c) The rarefaction curve, with the horizontal coordinate being the leveling depth and the vertical coordinate being the median value of the alpha diversity index calculated 10 times versus the box plot. Comparing the number of ASV/OTU in different samples at the same sequencing depth (Blank > R7I > Control). (d) Alpha diversity index. In this process, Chao1 and observed species index were used to represent richness, and Shannon and Simpson's indexes were used to represent diversity. Significance was tested using Kruskal–Wallis rank‐sum test and Dunn's test as a post hoc test. (e) ASV/OTU Venn diagram. (f) Orthogonal projections to latent structures discriminant analysis (OPLS‐DA). Each dot represents a sample, with different colored dots indicating different groupings. (g) Analysis of differences between groups (analysis of similarities, p < 0.05). (h) Genus level random forest analysis. From top to bottom, species were of decreasing importance to the model. (i) LDA effect size analysis. The significance passed the Kruskal–Wallis test. (j) Differential analysis of metabolic pathways was predicted using PICRUSt2. Using the metagenomeSeq method, the horizontal coordinate indicates log2 (fold change). Only statistically significant differences were found between the control and blank groups, and the control group was upregulated. Each set of data contained four biological replicates.*
Figure 7c illustrated the rarefaction curve in which the alpha diversity index of the sample tended to be flat with the increased sequencing depth. The alpha diversity index was arranged from large to small as Blank, R7I‐20, and Control. Chao1 and observed species index had no significant difference in characterization richness between the three groups, and R7I‐20 was between Control and Blank groups (Figure 7d). Similarly, Shannon and Simpson indexes showed no significant difference in uniformity between the three groups. In Figure 7f, orthogonal partial least squares discriminant analysis (OPLS‐DA) was employed to analyze the difference in species abundance composition at the species level. The results revealed that the top two principal components accounted for $52.4\%$ and $21.4\%$ of the variation in the total data, respectively. The distance of sample centers between Control and Blank was 6.14 times greater than that between R7I‐20 and Blank, primarily due to the projection on PC1. Analysis of similarities (Anosim) was utilized to analyze the differences in species composition between groups. As described in Figure 7g, the difference between Control and Blank groups was significant ($$p \leq 0.02$$).
The random forest analysis indicated the top 20 genera in relative abundance at the genus level for the three groups (Figure 7h). After that, all classification levels were analyzed simultaneously by LDA effect size (LEfSe). In Figure 7i, when the LAD score was >4, the difference advantage of Control groups was Clostridia and Clostridiales, the difference advantage of R7I‐20 group was Proteobacteria, and the difference advantage of the Blank group was Lactobacillaceae, Lactobacillales, Lactobacillus, and bacillus. Only PWY0‐1338 ($p \leq 0.05$) and ALL‐CHORISMATE‐PWY ($p \leq 0.01$) pathways were upregulated in the Control group compared with the Blank group in the differential analysis of predicted metabolic pathways (Figure 7j). The species composition of metabolic pathways indicated that both pathways were caused by the increase of unclassified Enterobacteriaceae in Control group. However, R7I‐20 indicated no difference from the other two groups.
## R7I reduces the accumulation of harmful metabolites in the gut
Intestinal flora changes inevitably affected the composition of metabolites, which in turn were closely linked to the health status of the organism. Therefore, it was necessary to understand metabolite changes in the small intestine. Raw data were converted to the common (mz.data) format by Agilent Masshunter Qualitative Analysis B.08.00 software (Agilent Technologies, USA). In R software platform, XCMS program was used in peak identification, retention time correction, and automatic integration pretreatment. Following that, the data were subjected to internal standard normalization. Visualization matrices containing sample name, m/z‐RT pair, and peak area were obtained. A total of 2425 and 6580 features were acquired in positive and negative modes, respectively. After qualitative analysis, data matrices were imported into R, followed by multivariate analysis, focusing more on Control vs. R7I‐20.
We used PCA modeling methods to examine the aggregation degree of QC samples (Figure 8a). In this project, differential metabolites were screened out by variable importance in the projection (VIP) value of orthogonal projections to latent structures discriminant analysis (OPLS‐DA) model (VIP > 1) and independent sample t test ($p \leq 0.05$) in Figure 8b. The detailed data in Control vs. R7I‐20 was presented in the differential metabolite heat map in Figure 8d. Metabolites analysis with significant differences is summarized in Tables S8 and S9 (Control vs. R7I‐20). Among them, propionic acid, methylmalonic acid, and 2‐methyl‐3‐ketovaleric acid were significantly increased in the control group. In R7I‐20 group, PS (18:$\frac{1}{20}$:4), PE‐NME2 (18:$\frac{3}{20}$:5), PE (18:$\frac{3}{22}$:6), PE (14:$\frac{0}{22}$:5), PI (16:$\frac{0}{16}$:0), and PE‐NME (16:$\frac{0}{24}$:0) were significantly increased ($p \leq 0.05$). Therefore, R7I‐20 group reduced the accumulation of harmful organic acid metabolites and increased lipid and glycerophospholipid metabolites. The differential metabolites were mapped to KEGG ID by the online software MetaboAnalyst. The differential metabolite pathway enrichment analysis is presented in Figure 8c. Glycerophospholipid metabolism, sphingolipid metabolism, pentose and glucuronate interconversions, porphyrin, and chlorophyll metabolism were significantly enriched in Control vs. R7I‐20. The significantly different metabolites in the small intestine of the control and R7I‐20 groups compared with the blank group are displayed in Tables S10–S13.
**FIGURE 8:** *Small intestine metabolite assay. (a) The overall PCA scores are plotted with quality control (QC), positive, and negative metabolites. (b) The multivariate statistical method PCA and orthogonal partial least squares discriminant analysis (OPLS‐DA) was used to analyze the control and R7I‐20 groups. (c) Analysis of differential metabolite pathway enrichment (Control vs. R7I‐20). (d) Differential metabolite heatmaps (Control vs. R7I‐20). Pos, positive; neg, negative. Each group contained four biological replicates.*
## Ethics approval and consent to participate
The protocols used in this experiment were approved by the Northeast Agricultural University Institutional Animal Care and Use Committee. All the Animal care and treatment were complied with the standards described in the “Laboratory Animal Management Regulations” (revised 2016) of Heilongjiang Province, China.
## Synthesis and characterization of peptides
R7I (IRPI IRPI IRPI IRPI IRPI IRPI IRPI‐NH2) and fluorescein isothiocyanate (FITC)‐labeled R7I were synthesized by GL Biochem Corporation (Shanghai, China), and it was determined by matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS; Linear Scientific Inc.). The peptide purity (>$95\%$) and retention time were tested by reversed‐phase high‐performance liquid chromatography (HPLC).
## Stable metabolism of R7I in vivo
The C57BL/6 male mice aged 6 weeks were provided by Liaoning Changsheng Biotechnology Co., Ltd., China and euthanized at 10:00 am. The serum was collected and mixed with R7I (v/$v = 4$:1) to give a final peptide concentration of 1.28 mM. The stomach (cut in half) and the contents of the entire small intestine of the mice were collected and placed in a sterile eppendorf tube. Sterile PBS was added to gastric tissue (w/$w = 1$:1, pH = 2.0) and small intestine contents (w/$w = 1$:9, pH = 7.0), and mixed thoroughly by shaking. The mixture was centrifuged to obtain the supernatant as gastric juice and small intestine juice. R7I was dissolved with the corresponding pH PBS. The prepared gastric and small intestine fluids were then mixed separately with R7I (v/$v = 1$:1) to give a final peptide concentration of 1.28 mM. Melittin was used here as a control. The peptide mixture was incubated in a 37°C incubator for 1, 4 and 8 h. Finally, the minimum inhibitory concentrations (MICs) test was performed to verify peptide activity with *Escherichia coli* 25922. 18 Subsequently, the C57BL/6 male mice (6 weeks old) were fasted for 12 h before treatment, and the blank group was instilled with saline (200 μl), while the experimental group was instilled with 20 mg/kg of body weight FITC‐R7I (200 μl) to start the clock. Specimens were collected at 1 h, 4 h, and 8 h (each organ and each mid‐gut), respectively. The blank group served as a control, and all samples were photographed under a fluorescent microscope set up with a uniform light source.
## The enteritis model and experimental design
C57BL/6 male mice that were 6 weeks old were provided by Liaoning Changsheng Biotechnology Co., Ltd., China. All mice (20.00 ± 2.00 g, 6 weeks old) were randomly divided into 6 groups ($$n = 12$$), and they were fed adaptively for 3 days. During the whole experiment, mice can get clean water and a laboratory standard diet ad libitum, under controlled environmental conditions ($40\%$–$60\%$ relative humidity, 25 ± 2°C temperature; lighting cycle, 12 h/d). The standard lab diet contained $18\%$ crude protein, $4\%$ crude fat, $5\%$ crude fiber, $8\%$ ash, and $10\%$ moisture.
In pre‐experiments, we had observed that lower peptide concentrations did not make a significant difference. The mice were divided into six groups randomly: [1] the control group (E. coli + normal saline); the administration group (E. coli + R7I) is set to three concentration gradients: [2] 20 mg/kg, [3] 30 mg/kg, and [4] 40 mg/kg of body weight; [5] the antibiotic group (E. coli + colistin sulfate), 20 mg/kg of body weight; and [6] the blank group with only normal saline (Figure 2b).
Based on the results of the pre‐experiment, we determined the appropriate concentration of the bacteria. The strain E. coli ATCC 25922 frozen at −20°C was incubated overnight in LB medium (37°C, 220r). The bacterial broth was transferred to a new LB medium at a ratio of 1:100 for 3–5 h to reach the logarithmic growth period. The bacterial precipitate was centrifuged and washed three times with saline to adjust the colony count to 2.0 × 109 CFU/ml.
The detailed processing process was shown in Figure 2a. The blank group was gavaged with 200 μl of saline, and all other groups were gavaged twice with E. coli ATCC 25922 (200 μl) once a day (d1‐2). R7I at different concentrations and colistin sulfate were given twice (12 h intervals) on the d3 and once on d4‐5 by oral administration. Correspondingly, an equal amount of saline was given to the blank and control groups in the same way. On day 6, all mice were euthanized by cervical dislocation under full anesthesia with isoflurane.
## Collection of blood, tissues, and organs samples
The collected blood was centrifuged for 10 min at 3000 rpm at 4°C. The supernatant was taken and stored at −80°C for subsequent testing. The liver and part of the middle ileum were cut and placed in precooled $4\%$ paraformaldehyde for hematoxylin eosin (H&E) staining. The Liver, small intestine, colon, and cecum contents were stored separately in cryogenic vials at −80°C.
## Blood biochemical analysis
Serum biochemical indices of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total bilirubin (TBIL), cholinesterase (CHE), creatinine (CREA), creatine kinase (CK), urea nitrogen (BUN), lactate dehydrogenase (LDH), total protein (TP), albumin (ALB), and globulin (GLB) were measured by the automatic biochemical analyzer (Roche, Cobus‐Mira‐Plus, Roche Diagnostic System Inc., Basel, Switzerland).
## Histological analysis
Histological analysis was performed by hematoxylin–eosin staining (H&E). In brief, liver, ileum, and colon of mice were fixed overnight with $4\%$ paraformaldehyde‐PBS, dehydrated, and embedded in paraffin blocks. Then the samples were cut into 5 μm sections that were deparaffinized and hydrated and then stained with H&E for histological analysis.
## Antioxidant function of the liver and small intestine
Total antioxidant capacity (T‐AOC), superoxide dismutase (SOD), glutathione peroxidase (GSH‐Px), malondialdehyde (MDA), and catalase (CAT) enzyme activities in the liver were determined using commercially available kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) according to the manufacturer's instructions.
## The intestinal barrier
To detect the content of diamine oxidase (DAO), d‐lactic acid, and secretory immunoglobulin A (SlgA) in serum, using mouse ELISA kit instruction (Alphabio, Tianjin, China).
## RNA extraction and real‐time quantitative PCR
Briefly, total RNA was extracted with Trizol, and then RNA was reversely transcribed into cDNA after genomic DNA was removed by a two‐step method according to the instructions of the kit. SYBR Green method was used to analyze the mRNA expression of specific primers for different genes. The reagents and kits were purchased from Nanjing Vazyme Biotech Co., Ltd., China. Subsequently, real‐time PCR was performed using CFX Connect (Bio‐Rad, Hercules, CA, USA). Primer 5.0 software was used to design primers for specific genes (Table S1). The internal control gene was β‐actin. The relative abundance of target genes was calculated using the 2−ΔΔCt approach. The expression of target gene mRNA in the control group was taken as the baseline relative to the treatment group (i.e., fold‐change).
## Assessment of protein expression by western blot analysis
Total protein from ileum and colon tissues was separated using commercial protein extraction reagents (Beyotime, Shanghai, China). Reducing SDS‐PAGE electrophoresis was conducted to separate 40 μg protein, which was then transferred to PVDF membranes (Millipore, Billerica, MA, USA). Afterward, membranes were blocked with $5\%$ skimmed milk for 2 h in Tris‐Tween saline buffer, followed by incubation using primary antibodies. The HRP‐conjugated secondary antibodies were subsequently incubated for 2 h at room temperature. The Alpha Imager 2200 software (Alpha Innotech Corporation, San Leandro, CA, USA) was employed to develop protein blots. Lastly, protein signals were quantified digitally and normalized to the relative expression of β‐actin. ImageJ software (National Institutes of Health, MD, USA) was used to determine the band density. See Supporting Information for antibody information.
## RNA‐Seq analysis
The sequencing service was provided by Shanghai Personal Biotechnology Co., Ltd., China. Briefly, total RNA was extracted from the liver and ileum. The cDNA libraries were constructed and then sequenced using the Illumina NovaSeq sequence platform. A total of 24 samples with 4 biological replicates per treatment were available for RNA‐seq analysis. The filtered Reads were matched to the mouse reference genome (Mus_musculus.GRCm39.dna.primary_assembly.fa) using HISAT2 (http://ccb.jhu.edu/software/hisat2/index.shtml) software. Differential analysis of gene expression was performed using DESeq, and the conditions for screening differentially expressed genes were: expression difference ploidy |log2FoldChange| > 1 and significance p value <0.05. Based on the results of differential gene expression analysis, PPI pairs containing the differential genes with a score > 0.95 were screened using the STRING database (https://string-db.org/cgi/input.pl), and then protein interaction networks were mapped. GO enrichment analysis and KEGG enrichment analysis were performed using topGO and cluster profiler, respectively. In the analysis, the gene list and gene number of each term were calculated using the differential genes annotated by GO term, and the gene list and gene number of each pathway were calculated using the differential genes annotated by KEGG pathway, so as to determine the main biological functions of differential genes.
## Metabolites analysis
The metabolites in small intestine (ileum portion) of control group, R7I‐20 group, and blank group were detected. About 50 mg of each sample was weighted out and 400 μl methanol (containing 5 μg/ml 2‐chloro‐l‐phenylalanine as internal standard) was added to it. The mixture was mixed by a vortex mixer for 1 min and homogenized for 3 min at 60 Hz twice. Then the mixture was centrifuged at 13,000 rpm, 4°C for 10 min. The supernatant was transferred to sampler vials for detection. An in‐house quality control (QC) was prepared by mixing an equal amount of each sample. Detailed information on the working model can be found in Supporting Information.
## 16S rRNA amplicon sequencing
Then, we determined the flora of cecum contents. The V3‐V4 hypervariable regions of 16 S rDNA were amplified (forward: ACTCCTACGGGAGGCAGCA, reverse: GGACTACCAGGGTATCTAATCCTGTT), and the amplified products were then sequenced by the Illumina platform. DADA2 method mainly included primer removal, quality filtering, denoising, splicing, and clustering. Greengenes database (Release 13.8, http://greengenes.secondgenome.com/) was selected for species annotation. Sequencing service was provided by Shanghai Personal Biotechnology Co., Ltd., China. The data were analyzed by using the free online platform Personalbio GenesCloud (https://www.genescloud.cn/).
## Statistical analysis
Results are reported as means ± standard errors of the mean (SEM) and evaluated with one‐way analysis of variance (ANOVA). The Tukey test was used to detect differences among treatments. The SPSS V25 (SPSS Inc., Chicago, IL, USA) software was used for all statistical analyses. A p value of <0.05 was considered statistically significant. All data was visualized using Graphpad Prism 8.0 (Graphpad Inc., CA, USA).
## DISCUSSION
With over one million people worldwide suffering from inflammatory bowel diseases such as Crohn's disease and ulcerative colitis, the need for novel antibiotics and treatments is pressing. 19 For any new drug, developing new antibiotics requires a balance between optimizing efficacy in killing bacteria and minimizing toxicity to human or animal hosts, as well as establishing suitable physicochemical properties to allow appropriate delivery and pharmacokinetics. 20, 21 Therefore, we have thoroughly described in detail the actual in vivo efficacy of the anti‐enzymatic peptide R7I.
Before this, for serum and low pH gastric juice, R7I function was hardly affected. In the small intestinal fluid, R7I activity was only slightly disturbed and was far superior to that of the melittin. Unused data revealed that melittin was inactivated even after incubation with the small intestinal fluid for 1 min. This strongly suggested that R7I was resistant to enzymatic hydrolysis and could be administered orally.
Following that, we examined the function of the intestinal barrier. Intestinal permeability embodies digestive and absorptive functions, as well as the barrier function. Serum d‐lactate reflects the degree of intestinal mucosal damage and permeability, and DAO reflects the integrity and degree of damage to the physical barrier of the gut. 22 R7I and Colistin significantly reduced d‐lactic acid and DAO, mitigating damage to the intestine and mucosa, and lower R7I‐20 concentrations presented better results. SIgA is critical in protecting mucosal surfaces against pathogens and maintaining homeostasis with commensal flora. 23, 24, 25 Additionally, SIgA contributes to reducing pathogen‐mediated pro‐inflammatory responses in epithelial cells. 26 R7I‐20 and Colistin increased SIgA content without statistical significance, and the positive effect on SIgA was diminished with increasing R7I concentrations. These findings suggested that high R7I concentrations were the inappropriate choice.
Subsequently, the gut was assayed for inflammatory factors and tight junction proteins. In the inflammatory response, IL‐10 suppresses inflammatory cell activation, migration, and adhesion by downregulating the expression of major histocompatibility complex class II (MHC II) on the surface of monocytes, reducing their antigen‐presenting effects and downregulating T‐lymphocyte activity. 27 Meanwhile, IL‐10 inhibits the synthesis and release of inflammatory factors. 28 IL‐4 exhibits immunomodulatory effects on B‐lymphocytes, T‐lymphocytes, mast cells, and macrophages. 28, 29 As an inflammatory cytokine produced by macrophages or monocytes during acute inflammation, tumor necrosis factor α (TNF‐α), is responsible for various intracellular signaling events leading to necrosis or apoptosis, and IL‐6 is a member of the pro‐inflammatory cytokine family that induces the expression of various proteins associated with acute inflammation. 30, 31, 32, 33 We found that R7I‐20 decreased pro‐inflammatory factors IL‐6 and TNF‐α in the ileum, while in the colon, R7I‐20 increased anti‐inflammatory factors IL‐4 and IL‐10 and decreased TNF‐α. These findings suggested a regulatory role for R7I‐20 in the intestinal inflammatory pathway. Moreover, tight junction is an important form of intercellular junction and the most critical structure forming the mechanical barrier of mucosa. R7I‐20 slightly increased Claudin 1 and Occludin expressions in the ileum and colon, without statistical significance.
Regarding the relevant indicators of serum, it can be found that after the mice were administrated twice with the bacterial liquid, the violent increase of ALT and AST indicated that the liver was seriously damaged, and unexpected elevation of lactate dehydrogenase represents liver disease acute hepatitis or certain malignancies. 34 The liver has an essential role in TBIL metabolism, including the uptake, binding, and excretion of unbound bilirubin in the blood by hepatocytes. 35, 36 R7I and colistin provided some relief from ALT, AST, and TBIL and mitigated liver damage from E. coli infection. In addition, R7I reduced LDH and CREA levels at low concentrations, but R7I and Colistin did not clearly improve BUN, while CREA and BUN concentrations indirectly reflected glomerular filtration function. 37 CK is required to pass through the liver and be excreted by the bile, and any abnormality in the whole process can cause elevated alkaline phosphatase levels. 38 R7I‐20, 40 was found to slow down the rise of CK.
Regarding liver antioxidant function, CAT levels in the control group were significantly higher than in the blank group. In contrast, CAT levels in R7I‐20 group were similar to the blank group status, indicating abnormal activation of the hepatic peroxisome pathway leading to abnormal CAT levels. No significant improvement was found for GSH and SOD. Bacterial treatment resulted in a decrease in T‐AOC and a slight increase in R7I‐20. In addition, oxygen radicals act on lipids to cause peroxidation, the end product of which is MDA, which causes cross‐linking and polymerization of proteins, nucleic acids, and other living macromolecules, damaging cell structure and function. 39 All three concentrations of R7I and colistin significantly reduced MDA levels. Overall, R7I mitigates the oxidative process in the liver and plays a protective role. In paraffin sections, R7I‐20 treatment reduced the central venous dilatation in the liver, and the tissue was more compact, but a small amount of sexual cell infiltration remained present. R7I‐20 reduced the inflammatory cell infiltration in the ileum, and a small amount of cell proliferation remained in the colon. This indicates that R7I was effective in relieving damage to the liver and intestinal tract.
According to transcriptome analysis, R7I‐20 treatment greatly reduced the number of small intestinal differential genes by 5.86‐fold ($\frac{680}{116}$) compared to the control group, but there was no difference in the liver, indicating that E. coli damage in the gut can be treated quickly and effectively. PCA revealed similarities between the R7I‐20 group and the blank group in the intestine, which was a positive finding but not in the liver (Figure 6a). This may be closely related to the route of oral administration and metabolism. In the small intestine enrichment analyses, R7I‐20 enhanced membrane‐associated and microvilli‐associated processes and facilitated transmembrane transport activities such as signaling and substance exchange between cell membranes. R7I‐20 group greatly improved protein and fat digestion and absorption, as well as bile secretion pathways. The specific genes are displayed in Table S2. The family of solute carriers (SLC), which plays a crucial part in the physiological process of moving from cellular uptake of nutrients to uptake of drugs and other xenobiotic compounds, is one of the most upregulated membrane transporter proteins. SLC transporters were responsible for transporting numerous molecules, including nutrients, metabolites, exogenous substances (e.g., phytochemicals), small molecule drugs, and metal ions, implying that SLCs were involved not only in key physiological processes, such as intestinal nutrient absorption but also in specific cellular tasks. 40, 41 In addition, diacylglycerol acyltransferase (DGAT) is involved in forming of triacylglycerols and is linked related to intestinal fat absorption, regulating plasma triglyceride concentrations, fat storage in adipocytes, and energy metabolism in muscle. 42 Furthermore, ABCG5 and ABCG8 form an obligate heterodimer that is critical in the selective transport of dietary cholesterol into and out of intestinal cells, as well as the selective excretion of sterols into bile by the liver. 43 However, cholesterol is the only sterol synthesized by acetyl coenzyme A and utilized by mammals, and since few cells can metabolize cholesterol, its removal by bile and intestinal secretions is essential to maintaining homeostasis. 44, 45 In summary, R7I ameliorated intestinal physiological dysfunction caused by bacterial invasion. In the liver, we stated that R7I mainly upregulated steroid biosynthesis, fatty acid degradation, and glutathione metabolism (Table S3). It has been revealed that NAT8 overexpression or downregulation prevented or exacerbated H2O2‐induced apoptosis, respectively. 46 SC5D catalyzes the synthesis of 7‐dehydrocholesterol from lathosterol and impaired activity also leads to cholesterol deficiency, resulting in lathosterol accumulation. 47 NAT8 and SC5D upregulation in the R7I‐20 group maintained normal liver function.
Following that, R7I significantly reduced the increase of negative metabolites in the control group while upregulating some positive metabolites in the metabolite analysis of the small intestine. Research has indicated that mouse models were transfected with Crohn's disease‐associated adherent invasive *Escherichia coli* (AIEC), in which elevated intestinal propionic acid levels led to AIEC recovery and remarkably increased toxicity. 48 Therefore, exposure to propionic acid leads to AIEC resistance and increased virulence. 48 Enzyme inhibition by methylmalonic acid may lead to various metabolic disorders and inhibit mitochondrial energy generation in mammals deficient in vitamin B‐12. 49 In addition, methylmalonic acid accumulation was a major feature of methylmalonic aciduria. 50 2‐Methyl‐3‐ketovalic acid was a known pathological metabolite associated with propionic acidosis, especially during ketoacidosis. 51 These negative metabolites were significantly elevated in the control group compared to R7I‐20 group. Phosphatidylserine (PS (18:$\frac{1}{20}$:4)) was a multifunctional bioactive lipid and an essential anionic phospholipid for cell membranes playing important roles in physiological processes such as apoptosis, inflammation, and coagulation. 52, 53 *Recent data* have revealed the potential of phosphatidylserine to protect against atherosclerosis, reflecting its ability to suppress inflammation, regulate blood coagulation, and enhance high‐density lipoprotein (HDL) function. 54 PI (16:$\frac{0}{16}$:0), a phosphatidylinositol, was an important lipid, a key membrane component and a participant in important metabolic processes, as well as a major source of arachidonic acid in animal tissues. 55, 56 R7I‐20 group greatly increased the levels of these two lipids in the small intestine and reduced the amount of harmful organic acids compared to the control group, protecting intestinal health. Moreover, R7I‐20 group increased glycerophospholipid metabolites, including dimethylphosphatidylethanolamine (PE‐NMe2 (18:$\frac{3}{20}$:5)) and phosphatidylethanolamine (PE (18:$\frac{3}{22}$:6), PE (14:$\frac{0}{22}$:5)), jointly promoting glycerophospholipid metabolism and sphingolipid metabolism. Compared to the blank group, metabolites such as p‐Cresol glucuronide, leukotriene C4, various forms of acylcarnitine, lithocholic acid, and cortisol were significantly increased in the small intestine of the control group (Tables S10 and S11). In contrast, metabolites such as cortisol, cholestane‐3,7,12,25‐tetrol‐3‐glucuronide, palmitoyl glucuronide, trans‐2‐dodecenoylcarnitine, and rock bile acid were significantly upregulated in R7I‐20 group (Tables S12 and S13). Numerous diseases have been described that cause disruptions in energy production and intermediate metabolism in organisms, characterized by the production and excretion of unusual acylcholines. 57 P‐cresol is a neurotoxic and nephrotoxic carcinogenic aromatic substance produced by gut microbe fermentation, and it is the precursor of the prototypical protein‐bound uremic toxin p‐cresol sulfate (p‐CS). 58, 59 *Glucuronidation is* used to assist in the excretion of toxic substances, drugs, or other substances that cannot be used as an energy source. 60, 61 The glucuronic acid is attached to the substance through a glycosidic bond, and the resulting glucuronide has a much more water soluble than the original substance and is eventually excreted by the kidneys. 60 Numerous clinical investigations have demonstrated that inflammation alters gut microbes and their metabolites, and that the affected gut and gut microbes, in turn, stimulate immune responses and metabolic activity, leading to chronic inflammation. 62, 63, 64, 65 From the results, E. coli infestation resulted in a noteworthy reduction in species composition differences (Control vs. Blank), with Control being much further away from Blank group than R7I‐20 in OPLS‐DA. In contrast, R7I‐20 did not cause a decrease in the composition and abundance of the intestinal flora due to its attributed antimicrobial activity, possibly linked to the lower concentration. The protective effect of R7I‐20 on the stability of intestinal flora was reflected in the taxonomic annotation unit of the species, the rarefaction curve, Chao1, and the total number of ASVs. Some studies have disclosed that colonization by clostridia is particularly deleterious. Clostridia were involved in developing of necrotizing enterocolitis (NEC), the dominant bacterium in the control group. 66 In R7I‐20 group, there remained the threat of Desulfovibrio in the Proteobacteria phylum, but another dominant strain, Odoribacteraceae, has been revealed to efficiently synthesize isoalloLCA. 67 The isoalloLCA was a special bile acid that fights many infections such as *Clostridium difficile* and Enterococcus faecalis. 67 It was a counter to the disruption of the homeostatic balance of the intestine. In contrast, Lactobacillus was mainly present in the blank group, promoting digestion and inhibiting spoilage multiplication and pathogenic bacteria in the intestine.
In the analysis of predicted metabolic pathway differences, we also identified pathway alterations caused by unclassified Enterobacteriaceae. PWY0‐1338 (MetaCyc Pathway: polymyxin resistance) was upregulated in the control group. Polymyxins are produced by Gram‐positive bacterium *Paenibacillus polymyxa* and can be selectively toxic to Gram‐negative bacteria by interacting with phospholipids, particularly lipid A, to disrupt the structure of bacterial cell membranes. 68 Some Gram‐negative bacteria, specifically *Salmonella typhimurium* and Escherichia coli, can become resistant to polymyxin by modifying their lipid A structure by the attaching of 4‐amino‐4‐deoxy‐l‐arabinopyranose (l‐Ara4N) groups to one or more phosphate groups. 69 In addition, the control group significantly upregulated ALL‐CHORISMATE‐PWY (MetaCyc Pathway: superpathway of chorismate metabolism). Chorismate is the principal common precursor of the aromatic amino acids l‐tryptophan, l‐tyrosine, and l‐phenylalanine, as well as the essential compounds 5, 6, 7, 8‐tetrahydrofolate, ubiquinone‐8, menaquinol‐8 and enterobactin (enterochelin). Enterobactin is a catecholate siderophore produced almost exclusively by Enterobacteria, although it has been reported in some Streptomyces species. Additionally, research has proven that Enterobactin‐mediated high‐affinity iron acquisition is critically important for Gram‐negative bacterial pathogens to survive and infect the host. 70 Evidently, R7I effectively reduced these risks, as they were undetectable in R7I‐20 group.
## CONCLUSION
For E. coli causing adverse effects on the organism, this study revealed that R7I greatly slowed down this process. This included reducing inflammatory factors, maintaining intestinal barrier function, facilitating small intestinal digestion and absorption and hepatic fatty acid metabolism, reducing negative organic acid metabolites while increasing lipid and glycerophospholipid metabolites, stabilizing microbial community composition and abundance, and effectively reducing the abnormal metabolic pathway by E. coli (polymyxin resistance, superpathway of chorismate metabolism). *In* general, R7I exhibited good anti‐enzymatic stability and presented potential application prospects in treating bacterial infection‐associated inflammatory bowel disease. These efforts and results provided a thorough basis and reference for advancing orally administered therapeutic peptides and proteins and contributing to the strategy of alternative antibiotics.
## AUTHOR CONTRIBUTIONS
Taotao Sun: Conceptualization (equal); data curation (lead); formal analysis (lead); methodology (lead); validation (equal); visualization (lead); writing – original draft (lead); writing – review and editing (equal). Xuesheng Liu: Data curation (equal); methodology (equal); project administration (equal); validation (equal). Yunzhe Su: Methodology (equal); project administration (equal); validation (equal). Zihang Wang: Data curation (equal); methodology (equal); project administration (equal); validation (equal). Baojing Cheng: *Formal analysis* (equal); methodology (equal); project administration (equal); validation (equal). Na Dong: *Formal analysis* (equal); methodology (equal); project administration (equal); validation (equal). Jiajun Wang: Conceptualization (equal); methodology (equal); project administration (equal); writing – review and editing (equal). Anshan Shan: Conceptualization (lead); formal analysis (equal); funding acquisition (lead); project administration (equal); writing – review and editing (lead).
## CONFLICT OF INTEREST
The authors declare no competing interests.
## DATA AVAILABILITY STATEMENT
The raw sequencing data of the transcriptome and 16S rRNA generated in this study has been deposited in the NCBI SRA database (Bioproject ID: PRJNA858747 and PRJNA858286). The raw metabolome data after processing is shown in Supporting Information (Metabolite raw data.xlsx).
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|
---
title: Characterization of human islet function in a convection‐driven intravascular
bioartificial pancreas
authors:
- Ana G. Santandreu
- Parsa Taheri‐Tehrani
- Benjamin Feinberg
- Alonso Torres
- Charles Blaha
- Rebecca Shaheen
- Jarrett Moyer
- Nathan Wright
- Gregory L. Szot
- William H. Fissell
- Shant Vartanian
- Andrew Posselt
- Shuvo Roy
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013798
doi: 10.1002/btm2.10444
license: CC BY 4.0
---
# Characterization of human islet function in a convection‐driven intravascular bioartificial pancreas
## Abstract
Clinical islet transplantation for treatment of type 1 diabetes (T1D) is limited by the shortage of pancreas donors and need for lifelong immunosuppressive therapy. A convection‐driven intravascular bioartificial pancreas (iBAP) based on highly permeable, yet immunologically protective, silicon nanopore membranes (SNM) holds promise to sustain islet function without the need for immunosuppressants. Here, we investigate short‐term functionality of encapsulated human islets in an iBAP prototype. Using the finite element method (FEM), we calculated predicted oxygen profiles within islet scaffolds at normalized perifusion rates of 14–200 nl/min/IEQ. The modeling showed the need for minimum in vitro and in vivo islet perifusion rates of 28 and 100 nl/min/IEQ, respectively to support metabolic insulin production requirements in the iBAP. In vitro glucose‐stimulated insulin secretion (GSIS) profiles revealed a first‐phase response time of <15 min and comparable insulin production rates to standard perifusion systems (~10 pg/min/IEQ) for perifusion rates of 100–200 nl/min/IEQ. An intravenous glucose tolerance test (IVGTT), performed at a perifusion rate of 100–170 nl/min/IEQ in a non‐diabetic pig, demonstrated a clinically relevant C‐peptide production rate (1.0–2.8 pg/min/IEQ) with a response time of <5 min.
## INTRODUCTION
Type 1 diabetes (T1D) is an autoimmune disease that affects around 34 million people worldwide. 1 Clinical islet transplantation by infusion into the portal vein is an attractive treatment for T1D due to its minimally invasive nature. Though islet transplantation has successfully treated patients with unstable T1D, 2 its wider applicability is hindered by tissue donor shortage and the need for chronic immunosuppressive therapy, 3 which has been shown to negatively affect the islets and their recipients. 4 In most cases, achieving insulin independence requires more than one islet infusion, and less than $50\%$ of patients are insulin independent 5 years after intraportal transplantation. 5 The shortage in donor islets is exacerbated by poor engraftment due to inadequate oxygenation during organ procurement and islet preparation for portal venous infusion. After intraportal infusion and before revascularization, oxygen delivery occurs by diffusion from the surrounding blood and liver tissue, resulting in critically low oxygen tensions, below 40–50 mmHg. 6 Moreover, a large percentage of islets are destroyed by the instant blood‐mediated inflammatory reaction (IBMIR). 7 Despite the engraftment challenges and complications from immunosuppressive therapy, 8, 9 islet transplantation remains a promising experimental treatment for T1D because of its ability to reproduce physiologic insulin secretion kinetics and eliminate hypoglycemic episodes.
Encapsulation is a promising approach to transplant islets without systemic immunosuppression. Our group is investigating the development of an intravascular bioartificial pancreas (iBAP) using silicon nanopore membranes (SNM) fabricated using microelectromechanical systems (MEMS) technology. 10, 11, 12, 13 The SNM feature submicron pores and exhibit high hydraulic permeability at physiologic blood pressures to support increased rates of convective mass transport, and potentially, sustain clinically relevant islet densities by overcoming the limitations of diffusive transport characteristic of extravascular bioartificial pancreas devices. 8 Previous work has demonstrated that the molecular selectivity and hydraulic permeability of SNM are significantly greater than polymeric membranes. 14, 15, 16 Furthermore, studies with murine islets have shown that the SNM serve as an immune barrier under convective mass transport and support insulin production and islet viability both in vitro and in vivo. 10, 11, 12 While murine islets are useful for demonstrating preliminary device feasibility, adult human islets are a more clinically appropriate tissue for the iBAP. Hence, we transitioned our testing to adult human islets, and here, we report on the potential of the iBAP to support human islet function. While our previous islet encapsulation studies utilized both silicon nanopore membranes (SNM) with <50 nm‐wide pores 10 and silicon micropore membranes (SμM) with 1 μm‐wide pores, 12 we focused this investigation around larger SNM with 450 nm‐wide pores to investigate the effects of higher hydraulic permeability on islet function. First, we modeled in silico the oxygen consumption profiles 17, 18 of islets seeded at three different densities within scaffolds at in vitro (pO2 = 160 mmHg, atmospheric) and in vivo oxygen levels (pO2 = 95 mmHg, arterial). Then, we evaluated their glucose‐insulin kinetic profiles through in vitro glucose‐stimulated insulin secretion (GSIS) assays at various density levels and ultrafiltration rates (normalized to islet quantity, also referred to as “perifusion” rates) of 14–200 nl/min/IEQ. The optimal perifusion rate was determined based on the outcomes of computational modeling and in vitro testing. Finally, a proof‐of‐concept demonstration experiment to evaluate insulin production was conducted in vivo via implantation of an iBAP prototype in a non‐diabetic pig, followed by intravenous glucose tolerance test (IVGTT).
## Governing equations
The glucose‐dependent oxygen consumption model presented here is an adaptation of the work described by Buchwald. 18 This model couples convective flow across the microchannels and diffusive transport with consumption rates across the islet tissue. The Navier‐Stokes and mass continuity equations for incompressible Newtonian fluids describe the velocity field (u) due to convection (Equations 1 and 2) while the diffusion model is defined by the standard diffusion equation for incompressible fluids (Equation 3): [1] ρ∂udt−η∇2u+ρu∙∇u+∇p=F [2] ∇∙$u = 0$ [3] ∂c∂t+∇∙−D∇c=R−u∙∇c where ρ denotes density (kg/m3), η is viscosity (Pa s = kg/m s), p corresponds to pressure (Pa = kg/(m s2)), and F is the volume force (N/m3 = kg/(m2 s2)). In Equation [3], c refers to the concentration of the species of interest (mol/m3), D is the diffusion coefficient (m2/s), the del operator ∇=i∂∂x+j∂∂y+k∂∂z and R represents the consumption term or the reaction rate (mol/m3 s). Both the glucose (Equation 4) and oxygen (Equation 5) consumption rates are assumed to follow Michaelis–Menten‐type kinetics. The metabolic demands of insulin production due to changes in glucose levels affect the local oxygen consumption, which is represented by a modulating function (φo,g) dependent on glucose concentration (Equation 6). This function is defined by a base‐rate φbase and a component that changes with metabolic demand along with the insulin secretion rate as a function of glucose concentration. As a first estimate, the base‐rate was assumed to represent $50\%$ of the total rate possible and the scaling factor φsc was also equal to 1.8. Furthermore, a step‐down function (δ) was also included to account for cell necrosis and suppress the oxygen uptake when its local concentration dropped below the critical value (Ccr,oxy). [ 4] Rgluc=Rmax,gluc∙cgluccgluc+CHf,gluc [5] Roxy=Rmax,oxy∙coxycoxy+CHf,oxy∙φo,gcgluc∙δcoxy>Ccr,oxy [6] φo,gcgluc=φscφbase+φmetab∙cglucnins2,gluccglucnins2,gluc+CHf,ins2,glucnins2,gluc The model was developed using a finite element method (FEM)‐based approach implemented with COMSOL Multiphysics (Burlington, MA). Here, δ was COMSOL's smoothed Heaviside function with a continuous first derivative and without overshoot, δcoxy>Ccr,oxy=flc1hscoxy−1.0×10−4,0.5×10−4. The parameters selected for the generalized Michaelis–Menten expression remained the same for each species (see Reference 15 and Table 1). All islets were assumed to be the size of an islet equivalent (1 IEQ = 150 μm in diameter) and a stepwise increase to 28 mM glucose concentration 19 was added to correlate the in vitro insulin production data to the spatial oxygen distribution at any given time in the GSIS assay.
**TABLE 1**
| IEQ (No.) | Islet density (v/v%) | Ultrafiltration rate (μl/min) | Perifusion rate (nl/min/IEQ) |
| --- | --- | --- | --- |
| 3600.0 | 20.0 | 50.0 | 14.0 |
| 1800.0 | 10.0 | 50.0 | 28.0 |
| 1800.0 | 10.0 | 100.0 | 56.0 |
| 500.0 | 2.5 | 50.0 | 100.0 |
| 500.0 | 2.5 | 100.0 | 200.0 |
## Model geometry and boundary conditions
Figure 1a shows a schematic of the hexagonal arrangement in our microchannel islet scaffold design displaying a 1100 μm distance from center to center between microchannels. Single microchannel models of the $2.5\%$, $10.0\%$, and $20.0\%$ (v/v) islet densities shown in Figure 1b were created in COMSOL and their outer walls were defined by the symmetry boundary condition. Comparisons between iterations of extra‐fine and normal element size mesh showed no major differences in the results. Therefore, all simulations presented here were ran using COMSOL's default normal element size mesh. For all liquid–solid interfaces, the no‐slip ($u = 0$ m/s) boundary condition was used. Obeying Henry's Law, the inflow or initial concentrations of oxygen were set to 0.1284 mol/m3 (95 mmHg) and 0.2156 mol/m3 (160 mmHg) for in vivo or in vitro concentrations, respectively. The basal glucose concentration (L1) was set at 5 mM. In addition, convective flow was employed to solve for outflow of species n (−D i ∇c $i = 0$), and outlet pressure at the outlet was assumed to be 0 Pa with “no viscous stress.” The continuity equation was used to solve for the diluted species across the islets. Table 1 summarizes the perifusion rates tested for the various islet density levels and ultrafiltration rates.
**FIGURE 1:** *Oxygen model setup of islet scaffold (a). Top view of the islet scaffold design with hexagonal arrangement of 150‐μm diameter microchannels (red arrows) and a 550 μm radius tissue unit (dashed outline). (b) 3‐D geometries of the tissue unit (3 mm‐height, 150 μm diameter) showing islet distributions at 2.5, 10.0, and 20.0 v/v% loading densities.*
## Human islet receipt and culture
Freshly isolated human islets were extracted from deceased donor pancreata by the UCSF Islet Production Core (San Francisco, CA). The islets were cultured overnight after isolation at $5\%$ CO2 and 37°C in Connaught Medical Research Laboratories (CMRL) 1066 medium (Nucleus Biologics, CA) NIH CIT supplemented with the addition of $0.5\%$ Human Serum Albumin (Grifols, Spain), 10 U/mL of Heparin (Fresenius Kabi, Germany), 2 μg/mL DNAse (Genentech, CA) and 20 μg/mL Ciprofloxacin (Hospira, IL). Human islets were also sourced from Prodo Laboratories, Inc (Aliso Viejo, CA) and cultured with their proprietary medium (PIM(S) supplemented with Human AB Serum).
## Glucose‐stimulated insulin secretion
The islet scaffold presented here builds on the work reported by Song et al. 10 The chambers were fabricated from biocompatible 316L stainless‐steel grade metal that was CNC machined by Hayes Manufacturing Services, Inc. (Sunnyvale, CA). Approximately $2.5\%$, $10.0\%$, and $20.0\%$ (v/v) of islet equivalents (1 IEQ = 150 μm diameter islet) per 36 μl chamber were immobilized in $3\%$ (w/v) ultra‐low gelling agarose (Sigma: 9012‐36‐6) scaffolds. Islets were mixed with the agarose solution at 37°C and pipetted into the void region above the hexagonal arrangement (Figure 2a). Next, an array of wires was aligned with the islet chamber and pushed through to create ~150 μm‐diameter microchannels while curing at 4°C for 10 min (Figure 2b). After curing the islet‐agarose mixture in the islet chamber and removing the wire‐array, islet scaffolds exhibited microchannels with ~800 μm center‐to‐center separation (Figure 2c). This inter‐microchannel distance, which resulted from constraints of the available fabrication methods, is lower than the tissue unit in the oxygen model, which can therefore be considered a “worst‐case” scenario. Figure 2d and e shows representative images of the islet scaffold in the chamber at $2.5\%$ (v/v) density, which corresponds to the scaffolds tested at either 100 or 200 nl/min/IEQ (see Table 1).
**FIGURE 2:** *Islet scaffold fabrication and chamber design (a). Islet scaffold construction on the 316 L stainless‐steel chamber consisting of a hexagonal configuration of 56 holes (~150 μm‐diameter). (b) Wire‐array alignment with islet chamber for islet scaffold construction and (c) isometric view of islet scaffold showing hexagonally arranged microchannels and islets. Encapsulated human islets in (d) the islet chamber and (e) islet scaffold with microchannels removed from the chamber (post‐testing in the iBAP). The red sectioned arrow encloses the 150 μm microchannels with radial diffusion distances ≤400 μm and the black arrow shows the IEQs.*
The mock circuit loop was set up as previously described 10, 11, 12 and all connections were made with platinum‐cured silicone LS‐25 tubing (Cole Parmer: 96410‐14). d‐Glucose (Sigma‐Aldrich: SLBX5177) was added to the basal 5 mM media supplemented with $10\%$ fetal bovine serum (Gibco: 16000‐04) to create a high glucose concentration level of 28 mM. A stabilization period was implemented with 5 mM glucose for 2 h and then ultrafiltrate samples were collected for 16 min (L1). The concentration was subsequently increased to 28 mM (H) for 30 min and then reduced to 5 mM for 32 min (L2). The insulin content in the ultrafiltrate samples was quantified using an enzyme‐linked immunosorbent assay (ELISA) kit (Mercodia: 10‐1113‐01; Uppsala, Sweden). Absorbance values were acquired at a 450 nm wavelength using a SpectraMax M5 microplate reader (Molecular Devices: MV06103; Washington, DC). To calculate the insulin production relative to the perifusion rate, the insulin concentration was multiplied by the ultrafiltration rate and then divided by the number of IEQs within the islet scaffold. The stimulation index was calculated as the ratio of the insulin production at the first peak during high glucose exposure to the baseline average insulin production at low glucose.
## iBAP device assembly and preparation
The SNM‐based iBAP (Figure 3a) was comprised of a polycarbonate blood flow‐path sandwiched by an SNM‐islet chamber stack on each side and sealed with polycarbonate backsides containing ultrafiltrate (UF) outlets. SNM were microfabricated as previously described. 20 The patterned silicon wafer was diced into 1 × 1 cm squares resulting in single chip SNM with an active membrane area of 36 mm2 and 3.12 × 106 pores per chip. Scanning electron microscopy of the SNM showed uniform pores with 450 nm width, 4 μm length and 1 μm in height (Figure 3b and c). To prevent protein fouling, the SNM surface was coated with diethylene glycol dimethyl ether, which is also known as diglyme, (Sigma:111‐96‐6) at Plasmatreat USA, Inc. (Hayward, CA). This step was achieved by performing a two‐stage treatment in the Plasmatreat Aurora™ plasma reactor with dual side‐wall electrodes. The plasma treatment step consisted of an oxygen plasma cleaning at a flowrate of 250 cm3/min, for 2 min followed by polyethylene oxide plasma polymerization for 20 min at 300 W with argon at 6 cm3/min. The chamber was evacuated to 25 mTorr at the beginning and end of each stage. Ellipsometry measurements after vapor diglyme deposition revealed an average coating thickness of 15 nm. All polycarbonate components and SNM were disinfected with $70\%$ ethanol for 45 min and washed with sterile water inside a laminar flow hood three times. The stainless‐steel islet chambers were steam autoclaved at 121°C along with the fastening screws and the assembly tools, while the agarose solution was sterile filtered and kept at 37°C during the islet scaffold construction. The device was assembled in a sterile field within the laminar flow hood to house 500 IEQ ($2.5\%$, v/v) in the islet chamber and primed with CMRL 1066 medium for transportation to the animal surgical suite.
**FIGURE 3:** *Assembly of the SNM‐based iBAP (a). Exploded view of the iBAP components with the islet chamber design exhibiting a 6 × 6 × 1 mm cavity (36 μl volume). (b) Scanning electron microscopy (SEM) image of the SNM (1.8k magnification). (c) Close‐up SEM image of the 450 nm‐wide pores (16k magnification).*
## Intravenous glucose tolerance test
The IVGTT study with the iBAP in a single 38‐kg non‐diabetic Yucatan pig was approved by the Institutional Animal Care and Use Committee (IACUC) review committee at Covance (San Carlos, CA). The animal was treated with 325 mg aspirin daily for 3 days and fasted overnight prior to surgery. The animal was placed under inhaled general anesthesia, and monitored continuously visually and using telemetry, pulse oximetry, and an esophageal temperature probe during all procedures. Following surgical dissection of the right anterolateral neck, thin walled 6 mm diameter polytetrafluoroethylene (ePTFE) vascular grafts (Gore‐Tex SRRT06030040L) were anastomosed to the carotid artery and external jugular vein and then connected to the flow inlet and outlet of the iBAP device (Figure 4a). Prior to vessel clamping and anastomosis creation, the animal was anticoagulated with intravenous heparin 100 u/kg, which was readministered to maintain activated clotting time between 250 and 350 s. The graft‐device interface was reinforced with an injection molded Dragon Skin 10a silicone coating (Smooth‐on, Inc., Pennsylvania) applied circumferentially onto the exterior of ePTFE graft. A 7 Fr silicone Hickman catheter (Becton Dickinson 0600310) (Figure 4b) was connected to stainless steel barbs for ultrafiltrate collection (Figure 4c). While perifused, and with the pig under general anesthesia, the encapsulated islets were stabilized for 75 min at fasting blood glucose levels of 80–100 mg/dl. The pig was then subjected to an intravenous glucose tolerance test (IVGTT) wherein 0.5 g of d‐glucose/kg body weight was administered as a bolus via a central line. Ultrafiltrate samples were collected at time zero and then every 4–15‐min intervals for 90 min. Following completion of sample collection, the animal was euthanized according to AVMA Guidelines for Euthanization of Animals. 21
**FIGURE 4:** *In vivo testing of the SNM‐based iBAP (a). Anatomic location for device placement in pigs (inset) showing vascular anastomoses of the iBAP to the internal carotid artery (ICA) and the external jugular vein (EJV). (b) iBAP with blood flow where the red and blue arrows denote the inlet arterial and venous flows, respectively. (c) Clear ultrafiltrate (UF) at tip of catheter connected to the exit of the iBAP islet chamber.*
The IVGTT protocol employed here is an adaptation of the work by Hara et al. 22 Blood glucose was measured at every timepoint with an Accu‐Chek Compact Plus glucometer. Due to the high cross reactivity between human and porcine insulin, a human C‐peptide ELISA (Mercodia: 10‐1141‐01) was used to test the ultrafiltrate samples to determine contribution of the encapsulated islets.
## Statistical analysis
Results were expressed as the mean ± standard deviation of the mean (SD). Multiple sample comparisons were done with two‐way analysis of variance (ANOVA) followed by post hoc Tukey test while sample pairs were evaluated using Student's t‐test with the Holm‐Sidak correction method. All statistical analyses were performed with GraphPad Prism 6 (San Diego, CA) and p values <0.05 were considered statistically significant.
## Microchannel oxygen modeling
The perifusion rates were modeled at incoming atmospheric (160 mmHg) and arterial (95 mmHg) oxygen tensions to recreate in vitro and in vivo conditions (Figure 5a). The resulting oxygen profiles were extracted 13 min after the introduction of high glucose to capture the maximal first‐phase insulin release. The oxygen concentration steadily decreased with the radial distance of the tissue unit as oxygen diffused from the microchannel into the agarose‐islet region where it was consumed by the islets. As expected, the islets farthest away from the microchannel in the radial direction showed the lowest oxygen concentration and represented the worst‐case scenario within the islet scaffold. Also, the oxygen concentration within the tissue unit (both axial and radial directions) increased as the incoming perifusion rate was increased. The post‐processing cut‐line feature in COMSOL was used to visualize the oxygen concentration in the worst‐case scenario for each condition. The results obtained from the simulation at atmospheric oxygen tension indicate that 28 nl/min/IEQ ($10.0\%$ density, 50 μl/min) is the lowest tested rate supporting islet function; since its worst‐case scenario drops slightly below 25 mmHg (0.034 mol/m3), which corresponds to the threshold for uninhibited maximal insulin production 23, 24 (Figure 5b). Simulations at arterial pO2 levels revealed that the maximal insulin production was supported by perifusion rates ≥100 nl/min/IEQ ($2.5\%$ density, 50 μl/min) (Figure 5c).
**FIGURE 5:** *Simulated in vitro and in vivo oxygen concentrations in the tissue unit as a function of islet perifusion rate 10 min after introducing glucose (28 mM) in the bulk fluid (a). Surface plots of oxygen concentration gradient across the tissue unit with radial and longitudinal cross‐sections halfway through the unit. The average oxygen concentration at the islet cores farthest from the microchannel (worst‐case scenario) plotted at (b) 160 mmHg and (c) 95 mmHg inlet pO2. Simulations at arterial pO2 levels suggest that at least 100 nl/min/IEQ (***) is required to be within the oxygen threshold of uninhibited maximal insulin production (0.034 mol/m3). See Table 1 for correlation between perifusion rate and islet density loading levels. Statistical significance is expressed as *p < .05 and **p < .001*
## Effect of perifusion rate in vitro, pO2
= 160 mmHg
The five perifusion rates that were modeled were also evaluated for GSIS. In vitro results show that 28 nl/min/IEQ ($10.0\%$ density, 50 μl/min) is the lowest perifusion rate that can sustain islet function in the microchannel islet scaffold as a marked response to changes in glucose levels can still be observed (Figure 6a). Conversely, the 14 nl/min/IEQ rate ($20.0\%$ density, 50 μl/min) demonstrated poor response to glucose stimulation. A 2‐way ANOVA analysis with post hoc Tukey test corroborates this claim as there was no statistical significance upon comparing the L1, H and L2 phases for the lowest perifusion rate tested, whereas insulin secretion at high‐glucose (H) was consistently higher than at low glucose (L1) for all others. *In* general, higher insulin production is achieved at higher perifusion rates. The 100 nl/min/IEQ rate ($2.5\%$ density, 50 μl/min) exhibited greater insulin production at the 28 mM glucose level compared to lower perifusion rates and showed no statistical significance compared to the insulin production rate corresponding to 200 nl/min/IEQ ($2.5\%$ density, 100 μl/min) at the same glucose level. Interestingly, the 100 nl/min/IEQ rate resulted in a higher stimulation index compared to that corresponding to the 200 nl/min/IEQ rate; these indices were 13.86 ± 7.29 and 5.30 ± 2.48, respectively. Additionally, the insulin production decreased significantly when transitioning from the stimulatory to post‐stimulatory phases (H → L2) for the two highest perifusion rates studied (Figure 6b).
**FIGURE 6:** *Glucose‐stimulated insulin secretion (GSIS) at 160 mmHg pO2 (a). Average pre‐ and post‐stimulatory phases (L1 and L2) and phase 1 of the physiologic biphasic response. (b) GSIS curves for 14, 28, 56, 100 and 200 nl/min/IEQ over time. A biphasic response was observed for a minimum perifusion rate of 28 nl/min/IEQ. Higher perifusion rates resulted in higher stimulation and insulin production (*) as well as greater shutdowns in insulin production (**, H → L2). See Table 1 for correlation between perifusion rate and islet density loading levels. Statistical significance is expressed as *p < .05 and **p < .001*
## In vivo intravenous glucose tolerance test
In vivo results were assessed based on the human C‐peptide concentration in the ultrafiltrate using a pig model with an implanted device as described in the Methods. With an islet density of $2.5\%$ (v/v), the implanted iBAP exhibited ultrafiltration of 50–85 μl/min through the microchannels, which resulted in perifusion rates of 100–170 nl/min/IEQ. It should be noted that the ultrafiltrate generated via the SNM and exposed to the islets is free of hemoglobin. These perifusion rates led to a stable (but slightly downward trending) C‐peptide production during exposure to fasting blood glucose (BG) levels in the animal (Figure 7). Since the observed perifusion rates in vivo remained between 100 and 200 nl/min/IEQ, the insulin/C‐peptide secretion can be correlated to the predicted oxygen profiles at arterial pO2 levels. The pre‐stimulatory phase, which served as a stabilization period, of the IVGTT established C‐peptide baseline of 1.06 ± 0.32 pg/min/IEQ. After administration of the glucose bolus, elevated C‐peptide production was observed immediately thereafter (2.77 ± 0.05 pg/min/IEQ), and at 30, 50 and 60 min of the IVGTT (2.62 ± 0.21, 2.31 ± 0.08, and 3.76 ± 0.01 pg/min/IEQ, respectively) (Figure 7). The C‐peptide production oscillated over time with a post‐glucose bolus average of ~2 pg/min/IEQ. As the BG levels decreased, the C‐peptide production approached its pre‐stimulatory, or stabilization, value of ~1 pg/min/IEQ.
**FIGURE 7:** *In vivo intravenous glucose tolerance test (IVGTT) in a non‐diabetic pig. The SNM‐based iBAP with 500 IEQ and a perifusion rate of 100–170 nl/min/IEQ showed stable C‐peptide production in the fasting period. Time 0 marks the administration of the glucose bolus, and elevated C‐peptide production is observed as the glucose concentration increases. The C‐peptide production approached its basal level as the blood glucose returned to fasting levels.*
## DISCUSSION
Advances in islet encapsulation have led to the creation of both extravascular and intravascular bioartificial pancreas (BAP) devices, 25 with significantly more effort focused on extravascular devices. Extravascular devices rely on passive diffusion of solutes, with diffusion distances typically greater than 500 μm. 26 The oxygen tension at the outer surface of extravascular devices is at most 45 mmHg, 27 which, coupled with the large diffusion distance, may result in poor islet oxygenation, necrosis, and limited and delayed glucose‐stimulated insulin secretion (GSIS). 28 However, oxygenation in extravascular BAP devices can be enhanced by implementing oxygen‐generating materials in the islet microenvironment, 29, 30 attaching an exogenous oxygen supply, 31, 32, 33 or incorporating an in situ oxygen generating device. 34 Recently, Yang et al. reported a convection‐enhanced macroencapsulation device (ceMED), which can be categorized as an extravascular BAP that delivers insulin through diffusion into subcutaneous tissue. 35 Another promising approach is the use of pre‐vascularized devices to facilitate revascularization in the islets. 36, 37 Intravascular BAP devices may improve GSIS compared to extravascular devices as they can deliver arterial pO2 levels (80–100 mmHg) to the encapsulated islets, compared to the low pO2 levels (10–50 mmHg) at the surface of extravascular devices. 38 Previous groups have achieved long‐term xenogeneic islet function in intravascular diffusion‐based devices without immunosuppression 39, 40, 41; however, the need for exogenous insulin was not fully eliminated and their translation to the clinic was obstructed by device patency issues and failure at the artery/device connection. 42 Furthermore, GSIS delays were observed in both the diffusion‐based 43 and, to a lesser extent, in the convection‐based intravascular devices 44 previously investigated. The low hydraulic permeability of polymer membranes in these devices limited the mass transport of oxygen and likely led to deficient GSIS outcomes. 45 Safety concerns around complications with prior intravascular BAP devices have limited enthusiasm for clinical adoption, and consequently, recent research efforts are largely focused on extravascular devices. However, the advances in biomaterials and minimally invasive surgical techniques over the last two decades offer some hope for the successful development and clinical translation of safe and effective intravascular BAP devices. These innovations along with the experience in the surgical implantation, hemodynamic changes, and complication management of over 75,000 prosthetic grafts in hemodialysis patients 46 have generated a knowledge database that can be used for the clinical implementation of next‐generation intravascular devices for islet therapy. Indeed, intravascular BAP devices are still under investigation as demonstrated by the recent report by Han et al., where acellular arteriovenous grafts are embedded islets on the outer surface. 47 In the current work, we used a computational model of oxygen consumption to analyze a scaffold unit with 1100 μm center‐to‐center spacing between microchannels, while the fabricated scaffolds exhibited a center‐to‐center separation of 800 μm. The shorter diffusion distance in the fabricated scaffolds should result in better oxygenation for most islets than those shown for computational model (Figure 5b and c), which is equivalent to a “worst‐case” scenario. The in vitro results demonstrated that the scaffold geometry in the iBAP produced comparable outcomes to traditional islet perfusion systems 48; the GSIS curves above 14 nl/min/IEQ exhibit all the features associated with healthy, non‐encapsulated human islets as reported by Henquin and co‐workers. 49 These features include a biphasic secretion pattern whose first phase is indicated by a peak in insulin production within the first 15 min of high glucose exposure followed by a sustained second phase, and a transient increase after switching back to low (5 mM) glucose exposure. This transient increase in insulin production is followed by a drop to baseline insulin production at 5 mM. While the dampening in GSIS response can be attributed to the insufficient oxygen supply at <28 nl/min/IEQ, high perifusion rates can sometimes adversely affect the glucose‐insulin kinetics profile, possibly due to shear‐induced damage at the periphery of the islets. 50 This effect might indeed be the case for the 200 nl/min/IEQ perifusion rate, where a slight delay in insulin production and a lower stimulation index were observed, the latter which is due to higher baseline insulin production at low glucose. Despite the slight delay in the GSIS profile, the 200 nl/min/IEQ perifusion rate still exhibits the desirable features of the biphasic response.
After connection to the vasculature of the non‐diabetic swine, the SNM‐encapsulated human islets, which are exposed to hemoglobin‐free ultrafiltrate, exhibited promising C‐peptide production. The C‐peptide production curve, which peaked at ~4.0 pg/min/IEQ, generated from the in vivo IVGTT with the non‐diabetic pig corresponds to in vitro perifusion rates between 100 and 200 nl/min/IEQ. During the stabilization period at fasting glucose levels, the C‐peptide production was stable but trending slightly downward and comparable to in vitro studies at low glucose levels. This data suggests that human islets within the iBAP can sense plasma blood glucose and secrete insulin in a physiologically normal pattern during fasting glucose levels. 48 Furthermore, after initiating the IVGTT, the adult human islets rapidly released insulin, as determined by human C‐peptide measurements, with a time delay of <5 min. This rapid release of insulin is a key requirement for a functional bioartificial pancreas to achieve normoglycemia. It has been estimated the GSIS response must be <15 min for normal physiologic BAP funciton. 51 Interestingly, the glucose‐insulin kinetics exhibited an oscillating profile as opposed to the biphasic pattern observed during in vitro GSIS studies with the SNM‐based iBAP and islet perfusion studies with adult human islets. 49, 52, 53 Instead, the islets displayed a pulsatile secretion pattern (Figure 7), with decreasing periodicity or faster oscillations as the BG approached a basal value. This oscillation phenomenon has been reported from plasma insulin concentrations in post‐absorptive periods. 54 *It is* possible that the C‐peptide measurements and insulin response from the human islets may have been affected by the function of the native porcine pancreas. The final spike in C‐peptide production at the end of the IVGTT could be due to the slight increase in BG, which is known to increase the amplitude of oscillations in insulin secretion, or simply within measurement error of the glucometer.
Our investigation is associated with several limitations that must be satisfactorily addressed for the successful development of a scaled‐up iBAP suitable for future clinical translation. The in vitro studies were short‐term and conducted with an iBAP prototype that held no more than 3600 islets. While the in vivo pig study with the implanted iBAP showed some promising preliminary data, it was performed with lowest islet density ($2.5\%$) for just 90 min and with a single pig. Future work will need to examine effects of increased islet loading density levels and C‐peptide trends during the low glucose phases of the IVGTT. Studies will need to be conducted with a statistically significant number of pigs and for longer periods (few hours to multiple days to many months). To compare our iBAP results more readily with published literature on encapsulated islets, experiments could be performed at low and high glucose levels of 2.8 and 16.7 mM, respectively, and use a physiologic salt solution as the medium for GSIS experiments to avoid the confounding influence of insulinotropic factors in culture media. For translational relevance to the clinical setting, the iBAP design will need to be scaled up to house an increased SNM area, and therefore generate higher ultrafiltrate volumes, to support a greater number of islets.
## CONCLUSIONS
The prototype SNM‐based iBAP supported adult human islet function in vitro as well as in a healthy Yucatan pig. The oxygen profile models showed that a minimum perifusion rate of 28 nl/min/IEQ and 100 nl/min/IEQ is needed to sustain islets for glucose production in vitro and in vivo, respectively. The animal test demonstrated the potential feasibility of a future scaled‐up device to provide clinically relevant C‐peptide production with 100–200 nl/min/IEQ perifusion rates. Based on simulated oxygen profiles and insulin production in both basal and stimulatory phases, the results of this investigation will inform future islet dosing and device scalability studies required to systemically deliver insulin to treat pigs with chemically induced T1D, and ultimately, T1D patients.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: Temporal evaluation of efficacy and quality of tissue repair upon laser‐activated
sealing
authors:
- Deepanjan Ghosh
- Christopher M. Salinas
- Shubham Pallod
- Jordan Roberts
- Inder Raj S. Makin
- Jordan R. Yaron
- Russell S. Witte
- Kaushal Rege
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013809
doi: 10.1002/btm2.10412
license: CC BY 4.0
---
# Temporal evaluation of efficacy and quality of tissue repair upon laser‐activated sealing
## Abstract
Injuries caused by surgical incisions or traumatic lacerations compromise the structural and functional integrity of skin. Immediate approximation and robust repair of skin are critical to minimize occurrences of dehiscence and infection that can lead to impaired healing and further complication. Light‐activated skin sealing has emerged as an alternative to sutures, staples, and superficial adhesives, which do not integrate with tissues and are prone to scarring and infection. Here, we evaluate both shorter‐ and longer‐term efficacy of tissue repair response following laser‐activated sealing of full‐thickness skin incisions in immunocompetent mice and compare them to the efficacy seen with sutures. Laser‐activated sealants (LASEs) in which, indocyanine green was embedded within silk fibroin films, were used to form viscous pastes and applied over wound edges. A hand‐held, near‐infrared laser was applied over the incision, and conversion of the light energy to heat by the LASE facilitated rapid photothermal sealing of the wound in approximately 1 min. Tissue repair with LASEs was evaluated using functional recovery (transepidermal water loss), biomechanical recovery (tensile strength), tissue visualization (ultrasound [US] and photoacoustic imaging [PAI]), and histology, and compared with that seen in sutures. Our studies indicate that LASEs promoted earlier recovery of barrier and mechanical function of healed skin compared to suture‐closed incisions. Visualization of sealed skin using US and PAI indicated integration of the LASE with the tissue. Histological analyses of LASE‐sealed skin sections showed reduced neutrophil and increased proresolution macrophages on Days 2 and 7 postclosure of incisions, without an increase in scarring or fibrosis. Together, our studies show that simple fabrication and application methods combined with rapid sealing of wound edges with improved histological outcomes make LASE a promising alternative for management of incisional wounds and lacerations.
## INTRODUCTION
Soft tissue trauma, including lacerations and surgical incisions, require effective and rapid closure in order to minimize blood loss, prevent infection and promote healing. Surgical sutures and staples are the most commonly used devices for approximating soft tissue trauma including in the skin. 1, 2 Although effective in superficial layers of skin, sutures do not integrate with the tissue, do not lead to immediate closure, and generally do not demonstrate optimal performance in deeper layers of the tissue, including in the hypodermis. In addition, tissue strength is suboptimal at early times after tissue approximation, which, by itself and in case of infections, can compromise effective healing.
Localized conversion of laser light energy to heat energy using endogenous or exogenous chromophores 3 results in rapid photothermal sealing of soft tissues. 3, 4, 5, 6, 7, 8, 9, 10, 11 Laser‐activated sealants (LASEs) in which exogenous chromophores are incorporated within a biomaterial sealant matrix offer promising alternatives to sutures and staples. In this approach, laser irradiation of the LASE‐tissue interface and the concomitant photothermal response can facilitate interdigitation of LASE biomolecules and tissue proteins, which results in rapid sealing and effective repair of soft tissues. We have previously reported the fabrication and characterization of LASEs as an approach for the rapid sealing and repair of ruptured tissues. 11, 12, 13, 14, 15 The LASE system comprises of three components: (i) a matrix consisting of biomaterials, such as elastin‐like polypeptides, collagen, or silk fibroin, which integrate with the tissue upon sealing and act as a scaffold for aiding repair, (ii) chromophores including gold nanorods (GNRs) or the FDA‐approved dye, indocyanine green (ICG), which convert laser light energy to heat energy (photothermal energy conversion), thus resulting in a local increase in temperature, and (iii) a hand‐held near‐infrared (NIR) laser tuned to 808 nm that is used to carry out the tissue sealing procedure using LASEs. The rapid bonding of wound edges mediated by interdigitation of tissue proteins, leading to rapid sealing, has been demonstrated for temperatures ranging from 50–60°C. 16, 17 A recent report which investigated the effect of temperature on tissue sealing observed highest welding strengths of tissue at 55°C. Use of elevated temperatures greater than or equal to 65°C led to denaturation of tissue proteins and negatively impacted tissue tensile strength. 18 *Our previous* results have shown that LASE‐mediated tissue sealing results in improved recovery of tissue biomechanical properties in live mice, compared to Vetbond, a cyanoacrylate‐based skin glue. 11, 14 In addition to facilitating sealing and repair, LASEs can be loaded with antibacterial drugs in order to combat methicillin‐resistant *Staphylococcus aureus* infection in at surgical site, thus protecting the tissue. 15 ICG is an FDA‐approved dye that absorbs and emits light in the NIR region of the wavelength spectrum. Upon irradiation with NIR lasers, approximately $85\%$ of the energy absorbed by the dye is converted into heat, which makes ICG a good photoconverter for various applications including photothermal sealing and photodynamic therapy. 19, 20 In addition, ICG dye has a relatively short clearance period of 60–80 min from the body and is excreted unchanged via bile. 21 The biodistribution and toxicity profiles of ICG dye are better understood compared to that for nanoparticles that are used as chromophores. It may also be possible to minimize batch‐to‐batch variation in LASE properties and performance by using the well‐established ICG dye.
In this study, we carried out a detailed investigation into the efficacy of laser tissue sealing using functional, biomechanical, visual (imaging), and histological evaluation at different time points during the course of healing following surgery, and compared these outcomes to those seen with sutures. ICG dye‐loaded silk fibroin (“silk”) films were used for sealing 1 cm, full‐thickness incisional wounds in BALB/c immunocompetent mice and transepidermal water loss (TEWL), and ultimate tensile strength (UTS) of skin were determined in order to investigate functional and biomechanical recovery, respectively, following tissue approximation. A combination of ultrasound (US) and photoacoustic imaging (PAI) along with histological evaluation was carried out in order to further visualize and gain insights into LASE‐mediated tissue repair. 22, 23, 24, 25, 26, 27 These findings indicate that LASE‐mediated tissue sealing is significantly more effective at restoring function and biomechanical properties of skin compared to sutures at early time points following surgery.
## Materials
Silkworm (Bombyx mori) cocoons were purchased from Mulberry Farms as a source of silk fibroin protein (henceforth referred to as silk). Sodium carbonate (Na2CO3), and lithium bromide (LiBr) were purchased from Millipore Sigma for silk fibroin extraction from silkworm cocoons. Dialysis bags, 3.5 kDa molecular weight cut‐off (Spectra/Por), were purchased from Fisher Scientific to facilitate purification of silk fibroin. ICG dye was purchased from MP Biomedicals (#ICN15502050) and stored at 4°C. All solutions were freshly prepared in nanopure water (NPW; resistivity ~18.2 MΩ cm; Millipore Filtration System). BALB/c mice were purchased at ~10 weeks from Charles River Laboratories. Commercially available 4‐0 Monosof™ Monofilament Nylon Sutures (Medtronic) were purchased from esutures.com.
## LASE fabrication
Silk fibroin was extracted from B. mori silkworm cocoons using previously described protocols. 14, 28 Briefly, silkworm cocoons were degummed in a boiling 0.02 M Na2CO3 (Sigma‐Aldrich) solution for 30 min, washed in NPW three times, and dried at room temperature (RT). Degummed silk fibroin was dissolved in 9.3 M LiBr solution at 60°C for 4 h, centrifuged to separate insoluble contents, and dialyzed for 72 h at 4°C against a 3.5 kDa membrane in order to remove LiBr and impurities. Dissolved silk fibroin solution was centrifuged at 14,000 rpm for 20 min to remove remaining impurities. Stock ICG solution (5 mM dissolved in NPW) was added to aqueous silk fibroin solution (6 wt%) and homogenously mixed to obtain a final ICG concentration of 0.1 mM. This solution (500 ml) was poured over 2 cm × 2 cm square plastic coverslips and dried overnight at RT to obtain silk‐ICG LASE films or simply LASE films. The LASE films generated using this method had approximately 0.31 mg per film and all films were stored at RT prior to further use.
## LASE characterization
Absorbance spectra of ICG solution (0.1 mM), as‐prepared LASE, LASE dissolved in saline to form a viscous paste, and LASE after laser irradiation were recorded from 400 to 999 nm using UV–Vis absorption spectroscopy (Synergy 2 Multi‐Mode Reader; BioTek Instruments). Absorbance spectra of NPW and silk fibroin film (LASE without ICG) were also recorded as controls for ICG solution and LASE, respectively. A hand‐held, continuous wave NIR laser (LRD‐0808; Laserglow Technologies), coupled with armored optical fiber with FC/PC connector (#AFF2001X, 1 m in length, and 200 μm in core diameter), and tuned to 808 nm was used for laser irradiation. The fixed laser spot size was 2 mm. A FieldMate laser power meter was used to measure the power of the laser beam, and power density of the laser beam was calculated by dividing the power of the laser beam by the area of the beam. An A325sc infrared (IR) camera (FLIR), equipped with a 10 mm 45° lens, was used to determine the surface temperature of LASE during laser exposure.
## Sealing of full‐thickness incisional wounds in mice
BALB/c mice (10–12 weeks, weighing ~22–25 g; Charles River Laboratories) were used in this study and were housed in groups of five until surgery. All animal care and procedures were performed in strict compliance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) at Arizona State University. Before surgery, mice were anesthetized with 120 mg/kg ketamine and 6 mg/kg xylazine (100 μl cocktail) by intraperitoneal injection. Dorsal hair was clipped, and the skin was prepped using three alternating swabs of chlorhexidine gluconate and $70\%$ isopropyl alcohol. Two 1‐cm full‐thickness incisions were made side‐by‐side on the back of each mouse spaced roughly 2 cm apart using sterile scalpel blades (#15; Integra Miltex). 29 In case of suture‐closed incisions, four evenly spaced simple interrupted knots were used to close a 1‐cm incision using 4‐0 nylon suture (#SN5699G; Medtronic; Monosof Black 18″ P‐13 cutting). For LASE‐sealed incisions, 10 μl of phosphate‐buffered saline (PBS; pH 7.4) were topically applied and a 1.2 cm × 0.5 cm LASE film was placed over the incision; contact of LASE with PBS resulted in quick dissolution of the film to form a viscous paste between the incision edges. The incision edges were approximated using a forceps, and the incised edges were aligned prior to laser sealing. The LASE‐tissue interface (incision line) was irradiated at a rate of 0.5 mm/s with the NIR laser tuned to 808 nm (CW) for 1 min while keeping the incision line approximated using forceps. The laser was applied at an angle between 60° and 80° to the skin at a power density of ~5.1 W/cm2 (~160 mW power output, ~2 mm laser beam diameter), corresponding to temperatures in the range of 50–60°C at the skin−LASE surface. 15 Closure of left and right incisions with sutures or LASE were randomized. The mice were allowed to recover on heating pads until mobile and were housed individually. Incisions were assessed every day for up to 7 days postsurgery for any signs of infection, suture removal, or wound dehiscence and mice with any of these conditions were removed from the study.
## Measurement of TEWL of healing wounds
TEWL is a measurement of change of water vapor density across the stratum corneum layer and is used to assess the barrier function of skin. Disruption to skin due to trauma, injury, wounds results in elevated TEWL levels and is indicative of weaker barrier function. 30, 31 *In this* study, TEWL was measured on Days 2, 4, 7 postsurgery using a portable, closed chamber VapoMeter device (#SWL5580; Delfin Technologies). The VapoMeter was fitted with a small adapter (4.5 mm diameter, ~16 mm2 surface area) and a closed chamber was created on skin contact during the duration of measurement (~9–15 s). Ambient relative humidity and temperature (°C) were recorded during every measurement using a room sensor (#RHD1367) supplied along with the VapoMeter. For every TEWL reading, three consecutive readings were acquired along nonoverlapping regions over an incision area and the chamber was passively ventilated between every measurement. 32, 33 TEWL readings of unwounded skin of sham mice were acquired on the same days. In all cases, TEWL values were recorded using the DMC software (Delfin Technologies) and values are displayed as mean ± standard error of mean from six independent mice ($$n = 6$$).
## Biomechanical recovery of skin strength
Following euthanasia, rectangular section of the healed skin (~2 cm × 1 cm) were excised around the incision area on Days 2 and 7 postclosure to investigate the biomechanical recovery of skin following suturing or laser sealing. In case of suture‐closed incisions, sutures were removed prior to tensile strength measurements in order to obtain strength of the healed skin alone. Excised skin samples were secured in clamps and stretched until failure stretched at a rate of 2 mm/s under constant tension using a TA.XT Plus texture analyzer (Texture Technologies Corp.). UTS was determined from the maximum force of the tissue prior to failure, where the maximum force (F) and cross‐sectional area of the tissue sample (A, length of skin sample 1 cm and tissue thickness 500 μm) determined the UTS (σ, Pa) of healed skin (σ = F/A). The tensile strength of unwounded skin (~2 cm length × 1 cm) was also tested for comparison. All tensile strengths are displayed as mean ± standard error of mean from six independent experiments ($$n = 6$$). UTSof healed skininPa=Maximum forceatskin ruptreinNCross−sectional area of skininm2length×thickness. Percentage intact skin strength for healed skin were calculated as a difference between UTS for each group either on Day 2 or Day 7 postclosure from unwounded skin strength, with the difference then converted to a percentage. % intact skin strength=UTSof healed skinonDay2orDay7UTSof unwounded skinnoincision controlonDay2orDay7×100.
## US and PAI of LASE‐tissue interface
Similar to studies on biomechanical recovery of skin, rectangular sections of the healed skin (~2 cm × 1 cm) were excised around the incision area on Days 2 and 7 postclosure, collected in biopsy cassettes, and stored in ice‐cold 1X PBS (10 mM sodium phosphate, 1.8 mM potassium phosphate, 2.7 mM potassium chloride, 137 mM sodium chloride, pH 7.4) prior to US and PAI. All skin specimens were imaged within ~4 h of necropsy and skin collection. Skin specimens were removed from 1X PBS, blotted to remove excess buffer and embedded in a $1.5\%$ agarose gel (Millipore Sigma; #A9539; low EEO) within an in‐house 3D printed tray (Figure 1a). The outer section of the tray was filled with deionized water in order to submerge the agarose layer (Figure 1a). High‐resolution US and PAI were carried out with the MX550D (50 MHz) linear array transducer, fiber bundle and motor setup of the Vevo 3100 + LAZR‐X (VisualSonics) at the University of Arizona, Tucson, AZ. A transducer jacket (VisualSonics; Figure 1bii) was used to combine the transducer (Figure 1bi) and fiber bundle (Figure 1biii), allowing the laser light to be directed to a region 7 mm away from the transducer head. The transducer with jacket was lowered into the water bath to achieve opto‐acoustic coupling for PAI with an optimal standoff required for the LAZR‐X system of approximately 7 mm. The spatial resolution of the imaging system is ~30 microns transverse normal, ~50 μm azimuth, and ~ 300 μm slice thickness. For 3D scanning of skin, the transducer with jacket was moved incrementally at a step size of 150‐μm in the elevational direction across the length of each skin sample (Figure 1biv). For each skin specimen, PA data were obtained at eight wavelengths (40‐nm increments from 680 to 960 nm) for each depth/width cross‐section of the 3D scan, along with a standard pulse echo US image. A center slice for each sample is chosen for full PA spectrum characterization (5‐nm increments from 680 to 960 nm). Furthermore, length/width sample cross‐sections are compiled from the 3D data set in image postprocessing.
**FIGURE 1:** *Set up for ultrasound (US) and photoacoustic imaging (PAI) for skin incisions closed with sutures of sealed with LASEs. (a) Prior to imaging, excised skin samples were removed from ice‐cold 1X PBS and placed over a layer of 1.5% agarose low electroendoosmotic (EEO) cooled to room temperature in a 3D printed sample tray. Following this, another layer of agarose solution (~35–45°C) was poured over the skin samples to completely embed the tissues within the agarose layers. The sample tray was then filled with deionized water to form a layer over the agarose layer. (b) (i) Scanning of skin samples were carried out using the Vevo 3100 motor. (ii) The MX550D (50 MHz) linear array transducer and jacket consisting of the fiber bundles (iii) is lowered into the sample tray submerged in water to facilitate opto‐acoustic coupling with a 7 mm standoff from the skin samples (iv). (c) Normalized photoacoustic signal of LASE, suture, and skin sections in the range of 680–960 nm.*
Spectral unmixing was performed on the obtained PA signal to discern the tissue constituents. The VevoLAB software (VisualSonics) is utilized for such spectral unmixing, where three wavelength components (680, 800, 960 nm) are used to discern LASE signal from that of the weakly absorbing normal skin. Multi‐wavelength unmixing for ICG content carried out using the Vevo system has been shown previously to produce accurate results for deep tissue imaging in tissue phantoms and murine subjects. 34, 35 Control skin has a relatively weak and flat PA spectrum across the wavelength band, implying that the strong ICG absorption at 800 nm can be used to identify LASE within the samples (Figure 1c), considering that ICG is mixed with silk to form the LASE.
## Tissue collection and processing for histology analyses
Following euthanasia, healed tissues were carefully excised, flattened between two foam biopsy sponges in a tissue cassette, and fixed by submersion in $10\%$ neutral‐buffered formalin (#HT501128; Sigma‐Aldrich) for a minimum of 72 h at RT. Tissues were dehydrated through a graded alcohol series and paraffin embedded with Paraplast Plus (#19217; EMS Diasum) by manual processing (Table S1). Individual 5‐μm thick sections were cut with an Accu‐Cut SRM 200 Rotary Microtome (Sakura Finetek USA) and collected on charged glass slides (Hareta, Springside Scientific) in a floating water bath (XH‐1003; IHC World). Slides were dried overnight at 37°C and stored at RT until use.
## Hematoxylin and eosin staining
Dried sections on charged glass were deparaffinized and rehydrated thorough xylene and graded alcohols into tap water. Rehydrated sections were submerged in a solution of Hematoxylin (Gill No. 2; #GHS232; Sigma‐Aldrich) for 3 min, differentiated by 6–12 quick dips in acid alcohol ($0.3\%$ hydrochloric acid in $70\%$ ethanol), and blued in a solution of ammonium water ($0.2\%$ ammonium hydroxide in distilled water) for 30 s. Slides were further submerged in a solution of $0.5\%$ Eosin Y (#318906; Sigma‐Aldrich) in distilled water (acidified with $0.2\%$ glacial acetic acid vol/vol) for 4 min followed by dehydration through $90\%$ and absolute ethanol, further dehydrated in two changes of $100\%$ xylene, dried and mounted in CytoSeal XYL (Richard‐Allan/Thermo Fisher Scientific). Samples were imaged on an Olympus BX43 upright microscope equipped with an Olympus DP74 CMOS camera operated by cellSens Standard software (Olympus Corporation).
## Picrosirius red staining
Dried sections on charged glass were deparaffinized and rehydrated thorough xylene and graded alcohols into tap water. Rehydrated sections were submerged in a $0.1\%$ solution of Picrosirius Red composed of Direct Red 80 (#365548; Sigma‐Aldrich) in a saturated aqueous solution of picric acid (#P6744; Sigma‐Aldrich) for 1 h at RT to achieve stain saturation. Slides were washed twice in acidified water ($0.5\%$ glacial acetic acid in distilled water) for 2 min each. Slides were dehydrated through an abbreviated $90\%$ and absolute ethanol series, further dehydrated in xylene and mounted in CytoSeal XYL (Richard‐Allan/Thermo Fisher Scientific). Brightfield images were collected on an Olympus BX43 upright microscope equipped with an Olympus DP74 CMOS camera operated by cellSens Standard software (Olympus Corporation).
## Immunohistochemistry
Captured sections were rehydrated and overnight epitope retrieval was performed in sodium citrate buffer at 60°C. Tissue sections were blocked with $5\%$ BSA in TBS containing $0.2\%$ Tween‐20 (TBST) at RT for 1 h and incubated overnight with primary antibodies for iNOS (Abcam; ab15323; rabbit polyclonal; 1:50), Arginase‐1 (Cell Signaling Technologies; #93668; rabbit monoclonal; 1:200), or Ly6G (Invitrogen; #14‐5931‐82; rat monoclonal; 1:100). Secondary antibodies were probed for 2 h at RT using HRP‐(rabbit) or AP‐(rat) conjugates (Jackson Immunolabs). Tissues were developed with ImmPACT DAB (HRP) or Vector Red (AP) substrate (Vector Labs). Arginase‐1 and Ly6G sections were counterstained with hematoxylin while iNOS sections were left without counterstain. Tissues were dehydrated through alcohol and xylene and mounted with Cytoseal XYL. Images were collected on an Olympus BX43 upright microscope equipped with an Olympus DP74 CMOS camera operated by cellSens Standard software. Images were quantified in ImageJ/FIJI.
## Image analyses
Morphometric features of healing were assessed in ImageJ/FIJI. Images were calibrated according to magnification. Epidermal gap was measured as the linear distance between the two epidermal faces of the wound edges and identified by canonical appearance of the epidermis via hematoxylin and eosin (H&E) staining on Days 2 and 7 postinjury. Histological scar area was measured as the area weakly stained by Picrosirius Red and bounded by the basement of the epidermis and above the hypodermis, and the edges of the mature collagen in periwound tissue strongly stained by Picrosirius red on Day 7 postinjury. 36 Dermal gap was measured as the linear distance between intact areas of dermis, indicated by bundled collagen fibers.
## In vivo live mouse US of sealed wounds
Mice (2‐day post‐incision) were anesthetized using ketamine/xylazine cocktail. Once under surgical anesthesia (confirmed by toe pinch), mice were placed on a heated mat and imaged by US with a GE Logiq e Nextgen ultrasound system equipped with a 10–22 MHz transducer (Figure S1). Mice were euthanized postimaging and tissues were fixed and processed as described below. US data were transferred into ImageJ/FIJI and calibrated according to the length of the transducer's dimensions (19.3 mm field width). To enhance better feature visualization, the US images were normalized by using an ImageJ native Bandpass Filter function, and epidermal/dermal gap was quantified by linear measurement. H&E‐stained images for matched mice were evaluated and epidermal/dermal gap was quantified by linear measurement. Correlation between B‐mode US images acquired in vivo and H&E histology for Day 2 incisions was evaluated by simple linear regression with $95\%$ confidence interval in GraphPad Prism.
## Statistical analyses
Data from absorbance, TEWL, and skin UTS are presented as mean ± standard error of the mean. Differences between groups were assessed using two‐way analysis of variance followed by Fisher's LSD test using GraphPad Prism version 9.2.0 (GraphPad). A $p \leq 0.05$ was considered statistically significant.
## RESULTS AND DISCUSSION
Laser sealing is an attractive approach for the sutureless approximation of tissues, including skin, and possesses several potential advantages including fast operation times, low scarring, and faster recovery of tissue function. However, the temporal dependence of the efficacy and quality this approach has not been investigated thoroughly. We, therefore, carried out detailed studies to investigate the efficacy of laser sealing in live mice in a temporal manner and compared findings with those seen with sutures. In addition to biomechanical recovery with UTS, functional recovery of barrier function of skin (using TEWL measurements), US and photoacoustic visualization and histology studies were used to develop a more comprehensive investigation into the quality and efficacy of tissue repair following laser sealing.
## Generation and characterization of LASE films
Silk fibroin (“silk”)‐ICG LASE films (Figure 2a) were prepared using solvent evaporation methods as described in our previous reports. 15 Briefly, aqueous solutions of silk fibroin (6 wt% or 60 mg/ml) with 0.1 mM ICG were cast and dried overnight at RT resulting in the generation of LASE films following solvent evaporation. Light absorption analyses indicated that the LASE films displayed a characteristic absorbance similar to that of ICG dye (Figure 2b). Upon addition of saline solution, LASE films dissolve to form an adhesive viscous paste, which is suitable for sealing incisional wounds. The viscous paste maintains absorbance properties in the NIR window as seen in case of dry LASE films. This viscous adhesive paste was irradiated using a hand‐held 808 nm continuous wave laser under conditions (power output ~100 mW, power density ~3.2 W/cm2, 1 min, LASE surface temperature ~50–60°C) similar to those used for in vivo skin sealing. No significant shifts in absorbance profiles of these irradiated viscous LASE pastes were seen (Figure 2b). The retention of absorbance properties by laser‐irradiated LASEs warrants the use of optical visualization methods for probing LASE following tissue sealing in subsequent applications.
**FIGURE 2:** *Silk‐ICG laser‐activated sealants (LASEs). (a) Representative image of a 2 cm × 2 cm LASE film fabricated from silkworm silk fibroin and indocyanine green (ICG) dye; the green color of the LASE is because of the ICG dye. (b) Absorbance spectra of ICG dye alone (dashed and dotted purple line), LASE film—as it is fabricated (dashed blue line), LASE in a viscous paste form after addition of saline, which was used to mimic a moist environment in wound beds (dashed red line), and post‐laser irradiation in the paste form (dotted black line). Data shown are mean ± standard error of the mean of n = 4 independent LASE films. (c) Photothermal response of LASE on ex vivo porcine skin irradiated using a continuous wave NIR laser tuned to 808 nm at varying laser power density from 1.6 to 2.4 W/cm2 in a 15 s “on” and 15 s “off” cycle (3 cycles total). Photothermal responses of silk films (with no ICG dye added, red dashed line) and porcine skin (blue dashed line) following irradiation with the laser at 2.4 W/cm2 are also shown. The region shaded in light blue color (temperature range from ~50°C to ~60°C) indicates the optimal temperature window for laser tissue sealing. Each photothermal response curve is a mean of n = 3 independent experiments.*
We also investigated the photothermal response of LASE films irradiated with a NIR (808 nm), continuous wave hand‐held laser turned on for 15 s (“on” cycle) and off for 15 s (“off” cycle) for a total of 3 cycles. For photothermal response studies, a LASE section was applied to an ex vivo porcine skin where the LASE section turned into a viscous paste upon contact with the skin and the surface temperature was recorded using an IR camera. Upon irradiation with a laser, a rapid increase in surface temperature of the LASE‐tissue interface was observed due to efficient photothermal conversion of the embedded ICG dye in the LASE matrix. This photothermal response was reproducible over 3 cycles and varied using different laser power densities (1.6–2.4 W/cm2) (Figure 2c). Irradiation of porcine skin alone and LASE without ICG even at the highest laser power density tested (2.4 W/cm2) did not result in any increase in temperature (red and blue dotted line, respectively, Figure 2c). Surface temperatures in the range of 50–60°C (shown using blue shaded region) optimal for tissue sealing were achieved by modulating the laser power density (Figure 2c).
## In vivo sealing of skin incisions: Barrier function recovery and healed skin strength
Full‐thickness incisional dorsal skin wounds in BALB/c mice, 1‐cm in length, were sealed using LASE or approximated with 4‐0 Nylon sutures. In the case of laser‐sealed incisions, the LASE‐tissue interface (incision line) was irradiated for 1 min at a power density of ~5.1 W/cm2 (~160 mW power output, ~2 mm laser beam diameter), corresponding to temperatures in the range of 50–60°C at the skin−LASE surface (Figure 3a). Mice were allowed to recover and representative images of incisions on Days 0 (immediately after surgery), 2, 4, and 7 are shown in Figure 3b. Mice without incisions were surgically prepped, recovered in a similar manner, and were used as controls in subsequent studies. Following wounding and closure of skin incisions, barrier function recovery of the healing skin was determined in a noninvasive manner using measurements for TEWL. TEWL is a marker of skin permeability which measures water loss thorough the stratum corneum layer and is one of the standard methods to evaluate barrier function of skin. Any damage or trauma to the skin barrier leads to an increase in TEWL levels compared to intact or unwounded skin. 32 TEWL levels of the 1‐cm long incisions closed with LASE or sutures were determined on Days 2, 4, and 7 postsurgery from three nonoverlapping regions (shown by white arrows) over the incision line (Figure 3c). The skin in the incisional region closed with LASE or sutures demonstrated gradual decrease in TEWL values. At Day 2 postwounding, the average TEWL value for incisions closed with LASE (29.5 ± 1.9 g/m2 h) was significantly lower than sutured incisions (41.6 ± 4.4 g/m2 h; $$p \leq 0.0001$$). On Days 4 and 7 postwounding, TEWL values of LASE‐sealed incisions were lower compared to those with sutures (Day 4: 20.6 ± 0.6 vs. 25.5 ± 0.7 g/m2 h; $$p \leq 0.0893$$ and Day 7: 17.3 ± 1.1 vs. 21.2 ± 1.7 g/m2 h; $$p \leq 0.1717$$), but the differences were not statistically significant (i.e., p values were not <0.05). Average TEWL values of unwounded skin on Day 2, 4, and 7 postwounding were considered as baseline on those corresponding days (Figure 3d).
**FIGURE 3:** *Functional and biomechanical recovery of skin following suture closure and LASE sealing in Balb/c mice. (a) Photothermal response of LASE‐skin interface during in vivo sealing irradiated using a continuous wave NIR laser tuned to 808 nm at a laser power density of ~5.1 W/cm2. The region shaded in light blue color (temperature range from ~50°C to ~60°C) indicates the optimal temperature window for laser tissue sealing. The photothermal response curve shows data that are a mean of n = 3 independent experiments. (b) Representative images of 1‐cm long skin incisions closed with four, simple interrupted 4‐0 nylon sutures or LASE on Days 0 (immediately after closure), 2, 4, and 7 postwounding; control is unwounded skin surgically prepared similarly to incised skin. (c) Representative image showing three approximate locations at which TEWL measurements were carried out (white arrows) for each type of closure method. (d) Transepidermal water loss (TEWL) of healed skin and unwounded control skin on Days 2, 4, and 7 postwounding. TEWL value (in g/m2 h) for each incision type is the average TEWL measurement from three nonoverlapping spots over the incision line shown in b. Data shown are mean ± standard error of the mean of n = 6 mice. (e) Ultimate tensile strength (UTS) and recovery, that is, %UTS of intact skin strength (secondary axis shown in red) of healed skin on Days 2 and 7 postwounding for suture‐closed and LASE‐sealed incisions. Data shown are mean ± standard error of the mean of n = 6 mice. Statistical significance was determined using two‐way ANOVA followed by Fisher's LSD test and individual p values are shown; p < 0.05 are considered statistically significant.*
In the above TEWL studies, Days 2, 4, and 7 postinjury were chosen in order to investigate early, mid, and later stages of wound repair following closure by primary intention as in case of incisional wounds. The time point of Day 2 postinjury is a good temporal representative of the early inflammatory phase, which helps with clearance of tissue debris from injury and kickstarts processes that prepare the wound for subsequent stages of repair, that is, proliferation and remodeling. Day 7 postinjury was chosen as a likely representative of the later remodeling phases, and Day 4 likely captures proliferation and/or potentially the transition from the proliferation stage to the later remodeling stage. During proliferation and remodeling stages, deposition of collagen matrix, angiogenesis, and maturation of granulation tissue are key events, and newly deposited matrix leads to an increased tensile strength of healing wounds. This phase can vary in length based on the wound site, tissue type, and type of injury. Mouse skin heals by contraction, which is different from skin healing by re‐epithelialization in humans. Contraction‐facilitated healing in mice shows faster kinetics of closure (e.g., over a 4–7‐day period) compared to wound healing dynamics seen in humans. To that end, interrogation at an earlier time point, that is, Day 2, can lead to meaningful insights into the efficacy of different wound approximation devices including sutures and LASEs.
Mice were euthanized on Days 2 or 7 postwounding to evaluate biomechanical recovery of the healing skin both at an early and late time point. At the earlier healing timepoint (Day 2 postwounding), LASE‐sealed incisions had higher UTS (0.87 ± 0.13 MPa) compared to sutured incisions (0.47 ± 0.08 MPa; $$p \leq 0.0464$$). The UTS of unwounded (no incision control) skin of BALB/c mice of the same age range was 2.67 ± 0.17 MPa and was used to compare the efficacy of healing using sutures and LASE. Incisions closed with sutures and LASE resulted in a UTS recovery of approximately 20.4 ± $2.6\%$ and 35.4 ± $3.6\%$, respectively, relative to that of intact skin on Day 2 postclosure (secondary axis in Figure 3e). Our results indicate improved efficacy in recovering the skin tensile strength at an earlier timepoint with LASE, which is consistent with our previous observations with silk‐GNR gold nanorod sealants for incisional skin repair. 14 At the latest healing time point (Day 7 postwounding), UTS of LASE‐ and suture‐closed skin increased to 1.15 ± 0.23 and 1.11 ± 0.16 MPa, respectively (not significant; $$p \leq 0.8720$$). At this time point, suture and LASE closures resulted in a recovery of approximately 41.6 ± $4.9\%$ and 43.1 ± $7.1\%$ in UTS, respectively, relative to that of intact skin (Figure 3e; secondary axis). This is likely because of the contractile forces in mice skin that aid healing at later durations postsurgery.
Effective functional and biomechanical recovery of skin at early times following injury or surgery is critical particularly considering that different pathologies influence the rate of wound healing. For example, diabetic humans and mice demonstrate delayed wound healing. To that end, our approach of following the efficacy of incisional wound healing using TEWL measurements with time is well‐suited to address temporal progress of healing including in different pathologies that influence tissue repair. For slower healing wounds, faster closure and effective tissue repair are imperative in order to prevent infections and further complications. To that end, the LASE approach, which shows better barrier function (TEWL) and biomechanical (UTS) recovery at earlier time points (Day 2), has the potential to also engender better outcomes in hosts with slower healing wounds.
## US and PAI
For PAI, a full spectrum scan between wavelengths of 680–980 nm, with a 5‐nm increment, was carried out for a representative center slice of every skin sample. LASE PA signal shows a maximum at 800 nm, which is expected given the absorption of ICG. Control skin displays weak PA signal across all wavelengths, while the black‐colored suture produces strong broadband signal. For PAI and spectral unmixing, signal data must be acquired at minimum of three wavelengths to distinguish three separate constituents (i.e., skin, suture, LASE). Observing the PA signal spectrum of each constituent, wavelengths of 680, 800, and 960 nm are chosen for the spectral unmixing. A 3D scan is then carried out for each sample with data being acquired at the three unmixing wavelengths (Figure 4a–c). It was qualitatively observed that there was a considerable drop in normalized PA signal at 800 nm on Day 7 postclosure compared to Day 2 postclosure (Figure 4b,c). This is further observed as reduced PA signal from LASE identified by the spectral unmixing technique on Days 2 and 7 postclosure. The maximum depth of penetration of LASE signal in the wound bed from four independent LASE‐sealed skin incisions was calculated from the normalized PA signal overlaid on individual US images at both the timepoints (Figure 5a). The average depth of the LASE signal in the wound bed was 1.4 ± 0.2 and 0.6 ± 0.45 mm on Day 2 and Day 7 postclosure samples, respectively (Figure 5b). The PA signal depth can be indicative of persistence of LASE in the wound bed as the healing of the incisional wound progresses over time.
**FIGURE 4:** *Normalized sample PA signal. Computed transverse slices of B‐mode scans co‐registered with normalized PA signal at 680, 800, and 960 nm for (a) control skin without any incision surgically prepped similarly to skin samples with incisions (b) skin incisions sealed using LASE at Day 2 postclosure and sealing (c) skin incisions sealed using LASE at Day 7 postclosure and sealing. Co‐registered B‐Mode and PA images are obtained by selecting a slice from the 3D scan data set that corresponds with approximately 500 μm subsurface depth (scale bar in yellow = 5 mm).* **FIGURE 5:** *Depth profile of LASE in wound bed. (a) Cross‐section of B‐mode scans superimposed with the normalized PA signal from LASE at 800 nm shown in green for skin samples at Day 2 postclosure and Day 7 postclosure. (b) The depth profiles of photoacoustic signal from LASE in the wound are represented as mean ± standard error of mean of n = 4 LASE‐sealed skin samples at each timepoint (scale bar in yellow = 5 mm).*
## Histological evaluation of LASE‐sealed and suture‐closed skin sections
During wound healing, re‐epithelialization is a crucial step for restoring barrier function and preventing exposure to pathogens that cause surgical site infections. 37 We visualized the cellular and tissue processes that lead to skin healing using a histological analysis (Figure 6a,b). At Day 2 postclosure, suture‐closed skin incisions had a lower epidermal gap compared to LASE‐sealed incisions (Figure 6a,c). The increased epidermal gap seen in LASE‐sealed incisions may be attributed to heat ablation of keratinocytes in the immediate periphery of the LASE. 13 However, we observed a continuity of closure in the LASE‐sealed wounds, despite the difference in epidermal gap, due to the occupancy of the gap by the LASE material itself, analogous to an eschar. By Day 7 postclosure, no significant difference in epidermal gap was observed between the two groups. Dermal gap (the distance between the collagen fronts of the dermis at the wound edge) was not different between suture of LASE‐sealed wounds at Day 2 ($$p \leq 0.2870$$) or Day 7 ($$p \leq 0.5216$$), although as expected there was a reduction of dermal gap within the treatment groups between Days 2 and 7 for suture ($$p \leq 0.0867$$) and LASE ($$p \leq 0.0331$$) (Figure 6a,d). We also evaluated the histological scar area, observed through picrosirius staining, to determine if there is a difference in initial scarring during the healing period in incisions closed with suture or LASE (Figure 6b,e). 38 Histological scar areas of 0.07 ± 0.01 and 0.08 ± 0.01 mm2 ($p \leq 0.05$) were seen in case of suture‐closed and LASE‐sealed incisions, respectively, at Day 7 postclosure, indicating that both resulted in similar levels of scarring based on these analyses (Figure 6e). Nascent collagen deposition was not appreciable on Day 2 postclosure; thus, no scar area was yet present (data not shown). This is expected considering the longer timeline necessary for development of scar‐like formation. Taken together, these results indicate that LASE‐sealed incisions exhibit rapid and robust sealing with minimal effect on scarring or tissue integrity, while also providing an improvement in early barrier function and tissue strength. While both sutures and LASE‐sealed incisions exhibited some degree of epidermal and dermal gap, sutures are an interrupted sealing method (i.e., sutures have empty space between individual placements) and tissue puckers and is open between each knot. LASE, on the other hand, provide a continuous seal across the length and width of the incision, bridging the tissue space and providing a more complete protection from the environment, in a manner similar to a natural eschar, but on‐demand and with high strength. This, in part, contributes to the higher biomechanical and functional recovery seen with LASE sealing.
**FIGURE 6:** *Histological evaluation of skin sections during the course of healing following closure with sutures or sealing with LASE. (a) Representative hematoxylin and eosin (H&E) stained micrographs of the wound sections (×4 magnification) showing the epidermal gap (black arrows) and dermal gap (red line) on Days 2 and 7 postclosure (scale bar = 200 μm). (b) Representative picrosirius red stained micrographs of the wound sections (×10 magnification) showing the scar area in the granulation tissue (black dotted line area) at Day 7 post closure. (c and d) Quantification of epidermal gap and dermal gap in skin sections (in μm) closed with suture or LASE on Days 2 and 7 postclosure. (e) Quantification of histological scar area (in mm2) in skin sections closed with suture and LASE on Day 7 postclosure. Data shown are mean ± standard error of mean of n = 6 mice per group. Statistical significance was determined using two‐way ANOVA (for epidermal gap quantification) and one‐way ANOVA (for scar area quantification) with Fisher's LSD post hoc analysis. *p < 0.05 is considered significant.*
Immunohistochemical analysis of tissue sections indicated that wounds treated with LASE had a significant reduction in Ly6G‐positive infiltrating neutrophils at 2‐day postclosure ($$p \leq 0.0192$$) (Figure 7a). While there was a nonsignificant trend toward increased iNOS‐positive proinflammatory macrophages at 2‐day postclosure ($$p \leq 0.0940$$) and Arginase‐1‐positive proresolution macrophages at 7‐day postclosure ($$p \leq 0.1672$$) (Figure 7b,c), we observed an enhancement of Arginase‐1 response (proresolution macrophages) at 7‐day postclosure versus 2 days with LASE ($$p \leq 0.0207$$) which did not reach significance for sutures ($$p \leq 0.2693$$). These data indicate that sealing wounds with LASE induces an augmentation of immune cell behavior at early and late stages of wounding, with an enhancing effect on the arginase‐1‐positive prohealing macrophage response and a distinct effect on the dynamics of infiltrative neutrophils. The role of neutrophils in healing wounds is evolving, with recent evidence suggesting both positive and negative roles in regulating the healing process. 39 While neutrophils play an early role in protecting against infection, they are also drivers of early signals to stimulate repair. Neutropenia is associated with slower healing and deficiency of several signals involved in neutrophil function can result in impaired healing. 40, 41, 42, 43 Conversely, persistence of neutrophils within a wound can delay healing and an overabundance of neutrophil‐derived PAMPs, such as neutrophil extracellular traps (NETs), can lead to chronic wounds and have become therapeutic targets to improve wound healing. 44, 45, 46, 47 Here, we show that neutrophils are present in both sutured and LASE‐sealed wounds at Day 2, but in much lower abundance in LASE‐sealed wounds, with levels equalizing by Day 7. Most studies investigating the role of neutrophils in wound healing utilize excisional wounding models, which proceeds by secondary intention healing (granulation). Incisional wounding, performed here, proceeds by primary intention healing and may utilize different biological mechanisms. Our finding that reduced neutrophils in LASE‐sealed incisional wounds compared to sutured incisional wounds is in agreement with a recent study by Heuer et al. ,50 which showed that mouse laparotomy wounds (primary intention healing) treated with DNase I (to deplete NETs) or with PAD4 knockout (to genetically inhibit NET formation) exhibited significant improvements in healing quality. Thus, a controlled or tuned down neutrophil response—as likely induced by LASE sealing—may positively affect incisional (primary intention) wound healing.
**FIGURE 7:** *Immunohistochemical evaluation of incised skin during the course of healing. (a) Representative micrographs and quantification of wound sections (×4 magnification) stained for Ly6G (pink chromogen) with hematoxylin counterstain at Days 2 and 7 postclosure (scale bar = 200 μm). (b) Representative micrographs and quantification of wound sections (×4 magnification) stained for Arginase‐1 (brown chromogen) with hematoxylin counterstain at Days 2 and 7 postclosure (scale bar = 200 μm). (c) Representative micrographs and quantification of wound sections (×4 magnification) stained for iNOS (brown chromogen) without nuclear counterstain at Days 2 and 7 post closure (scale bar = 200 μm). Data shown are mean ± standard error of mean of n = 5–7 mice per group. Statistical significance was determined using two‐way ANOVA (for epidermal gap quantification) and one‐way ANOVA (for scar area quantification) with Fisher's LSD post hoc analysis. Significance indicated as *p < 0.05; **p < 0.01.*
## US evaluation of in vivo sealed incisions in live mice
We sought to evaluate the fidelity of US to interrogate LASE‐ and suture‐sealed incisions in live mice using a portable, clinical US system; the portable nature and clinical application of this system was considered useful for potential translational applications. Live, anesthetized mice were evaluated with a linear probe transducer in B‐mode operating at 22 MHz. Manually collected US images were compared to matched H&E‐stained sections from the same mice and the linear dimension of the wound width was compared (Figure 8). We found a high degree of correlation between US and histopathology measurements (linear regression $y = 1.140$x − 182.5; R 2 = 0.985, $$n = 4$$ each group). Thus, in vivo US, using a clinically relevant system, provides an accurate representation of wound properties in mice with incisions sealed by both sutures and LASE.
**FIGURE 8:** *Live animal ultrasound evaluation of suture‐ and LASE‐sealed wounds. (a) Representative photographs, ultrasound imaging data, and H&E images for suture‐ and LASE‐sealed linear incisions imaged by ultrasound in the live animal at Day 2 postclosure. (b) Linear correlation with 95% confidence intervals for the epidermal/dermal gap of wounds sealed by sutures (red) or ICG LASE materials (blue) measured by ultrasound (x‐axis) or histology (y‐axis). N = 4 per group.*
## CONCLUSION
Tissue adhesives are an alternative and effective method of skin closure following surgical incisions or traumatic lacerations. Here, we comprehensively evaluated the efficacy and quality of silk fibroin‐ICG based LASE for rapid sealing of skin incisional wounds in mice compared to conventional suturing using a temporal study of functional, biomechanical, and histological evaluation in addition to US and PAI. We evaluated healing outcomes at different timepoints in the repair process and our results show LASE‐sealed incisions had earlier recovery of skin barrier function compared to suture‐closed incisions as indicated by lower TEWL rate. At the same timepoint, significant increase in biomechanical recovery of skin was observed in case of LASE‐sealed incisions compared to suture‐closed incisions. Higher biomechanical and functional recovery of skin can prevent dehiscence of wounds early in the healing period and also protect against surgical site infections. US and PAI of skin incisions closed with sutures and sealed with LASE demonstrated that these structures can be identified by their unique optical absorption properties and help quantify and track their presence within a sample volume at least several days postsurgery. The noninvasive dual modality platform can potentially be applied in vivo to track these changes at the skin interface over time. Histological analyses of skin at the end of the healing period in our study (Day 7 postclosure) showed no difference in epidermal gap and scar area compared to suture‐closed incisions which can be indicative of no excessive scarring or fibrosis in using LASE as a skin closure method. However, it is important to note that mouse skin heals by contraction, which poses significant limitations in using mouse models as indicators of scarring. Further studies in relevant animal models (e.g., porcine models) will be key to further compare scarring caused by sutures and LASEs. Evaluation of translational potential of LASEs for application in humans will also require detailed studies in porcine models, studies in animal models of specific pathologies including slow‐healing wounds (e.g., in diabetes) and wounds that are susceptible to infection. To that end, future work will involve a comprehensive investigation into bioactives that can accelerate tissue repair following laser sealing and into delivery of effective antimicrobial drugs for combating infections. In all these studies, a comprehensive picture of functional, biomechanical, and histological performance of LASEs will be obtained in order to investigate the potential for translating this technology for clinical use.
## AUTHOR CONTRIBUTIONS
Deepanjan Ghosh: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing (lead). Christopher M. Salinas: Data curation (equal); formal analysis (equal); methodology (equal); writing – original draft (equal); writing – review and editing (supporting). Shubham Pallod: Data curation (supporting); writing – review and editing (supporting). Jordan Roberts: Data curation (supporting); formal analysis (supporting). Inder Raj S. Makin: Data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); resources (supporting); validation (equal); writing – review and editing (equal). Jordan R. Yaron: Data curation (lead); formal analysis (lead); methodology (lead); writing – original draft (equal); writing – review and editing (supporting). Russell S. Witte: *Formal analysis* (equal); investigation (equal); methodology (equal); resources (equal); validation (supporting); writing – review and editing (equal).
## CONFLICT OF INTEREST
Kaushal *Rege is* affiliated with a start‐up company, Synergyan, LLC. Other authors declare no conflict of interest.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10412.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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---
title: Synergy of antioxidant and M2 polarization in polyphenol‐modified konjac glucomannan
dressing for remodeling wound healing microenvironment
authors:
- Huiyang Li
- Xiaoyu Liang
- Youlu Chen
- Kaijing Liu
- Xue Fu
- Chuangnian Zhang
- Xiaoli Wang
- Jing Yang
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013815
doi: 10.1002/btm2.10398
license: CC BY 4.0
---
# Synergy of antioxidant and M2 polarization in polyphenol‐modified konjac glucomannan dressing for remodeling wound healing microenvironment
## Abstract
Effective skin wound healing and tissue regeneration remain a challenge. Excessive/chronic inflammation inhibits wound healing, leading to scar formation. Herein, we report a wound dressing composed of KGM‐GA based on the natural substances konjac glucomannan (KGM) and gallic acid (GA) that accelerates wound healing without any additional drugs. An in vitro study showed that KGM‐GA could not only stimulate macrophage polarization to the anti‐inflammatory M2 phenotype but also decrease reactive oxygen species (ROS) levels, indicating excellent anti‐inflammatory properties. Moreover, in vivo studies of skin wounds demonstrated that the KGM‐GA dressing significantly improved wound healing by accelerating wound closure, collagen deposition, and angiogenesis. In addition, it was observed that KGM‐GA regulated M2 polarization, reducing the production of intracellular ROS in the wound microenvironment, which was consistent with the in vitro experiments. Therefore, this study designed a multifunctional biomaterial with biological activity, providing a novel dressing for wound healing.
## INTRODUCTION
Skin tissue is the natural barrier that protects the body from environmental damage and microbial infestation. 1 When the skin is damaged, pathogens are more likely to invade the body and cause inflammation and infection, affecting the process of wound healing. 2 Currently, several treatments, including growth factors 3, 4, 5 and stem cells, 6, 7 can drive wound healing, but these approaches are limited by their high cost and side effects. 8 However, there have been few studies on regulating the wound microenvironment to utilize the inherent regenerative capacity of the host. Most treatment methods mainly focus on the process of structural repair, and nonfibrotic healing of damaged tissues is still difficult to achieve. 9 Effective restoration of wound tissue integrity and function remains a global health issue.
Macrophages, which play an important role in tissue repair, are highly plastic cells and can be polarized into classic M1 type (inflammatory phenotype in the early stage) and M2 type (anti‐inflammatory phenotype in the mid‐stage), and these cells can shift between phenotypes under certain conditions. 10, 11 M2 macrophages have been shown to secrete a range of anti‐inflammatory factors, such as interleukin‐10 (IL‐10) and transforming growth factor‐β (TGF‐β), and secrete healing factors that reduce the inflammatory response, promote angiogenesis, and create a regenerative microenvironment. 12 Appropriate regulation of the M1‐to‐M2 shift is critical in promoting tissue repair and coordinating skin healing. Studies have confirmed that cytokines, such as interleukin‐4 (IL‐4) and interleukin‐13 (IL‐13), 13, 14 can be used to polarize M2 macrophages. However, these recombinant proteins are unstable in the body and difficult to effectively deliver, and excessive use may cause serious side effects, thus limiting their application. Konjac glucomannan (KGM) is a natural polysaccharide that consists of d‐glucose and d‐mannose linked by a β‐1,4 glycosidic chain. 15 The abundant carbohydrate receptors expressed on the surface of macrophages interact with these sugar units to activate murine monocytes/macrophages, such as the mannose receptor (MR), which responds to mannan. 16 In addition, KGM has also been reported to be nontoxic, biodegradable, and biocompatible. 17 Moreover, KGM exhibits outstanding liquid adsorption capacity owing to its structure, which contains plentiful hydroxyl and carboxyl groups that can attract water molecules through hydrogen bonding and van der Waals forces. 18 These excellent properties of KGM are conducive to its application in wound healing.
In addition to regulating macrophage phenotype to resist inflammation, controlling the level of reactive oxygen species (ROS) in impaired wounds has become another research direction. Many studies have shown that ROS play an important role in the wound microenvironment. 19, 20, 21 After skin injury, the wound surface produces abundant ROS, which is one of the defense mechanisms against bacterial infections. 22 However, high levels of ROS, such as hydrogen peroxide (H2O2), can cause oxidative stress in the impaired wound and trigger a series of harmful effects, such as cell aging, 23 fibrotic scarring, 19 and inflammation. 24 In addition, ROS can significantly limit angiogenesis, leading to endothelial dysfunction. 20 ROS can also inhibit the function of endogenous stem cells and macrophages, hinder the regeneration of wound tissue, and ultimately impede the process of wound healing. 22, 25 Therefore, studying biomaterials that have the ability to eliminate ROS and control oxidative damage in the microenvironment of impaired wounds is a potential treatment strategy to promote wound healing. Gallic acid (GA) belongs to a class of natural polyphenol compounds that are widely found in the plant kingdom. 26 GA has potential anti‐inflammatory and antioxidant effects and can directly upregulate the expression of antioxidant genes. Previously, GA was used in traditional medicines for the treatment of various chronic skin diseases, such as psoriasis and vitiligo. 27, 28 Recent studies have shown that GA promotes wound healing and accelerates the migration of keratinocytes and fibroblasts. 29 In addition, due to its excellent antioxidant properties and lack of toxicity, GA can be used to modify functional materials to improve their physical, chemical, and biological properties. 30 This study prepared a polymer dressing designed to improve the wound microenvironment, which could regulate the conversion of macrophages to the M2 phenotype while controlling the level of ROS in the impaired wound (Scheme 1). In this study, KGM was conjugated with GA by esterification to prepare a material for skin repair. Our research results showed that in the absence of any exogenous cytokines or drugs, KGM‐GA could significantly upregulate M2 macrophage polarization and eliminate excess ROS, which may provide insight into the development of efficient dressings for improving the wound healing process.
**SCHEME 1:** *Schematic illustration showing the synthesis of konjac glucomannan–gallic acid (KGM‐GA) and KGM‐GA dressing for accelerating wound healing through M2 macrophage polarization and scavenging reactive oxygen species (ROS).*
## KGM‐GA synthesis and structural characterization
The reaction of KGM and GA occurred between the hydroxyl group of KGM and the carboxylic group of GA to form the ester linkage using the EDC/DMAP‐mediated coupling reaction. The procedure to synthesize KGM‐GA is illustrated in Scheme 1. The structure of KGM‐GA was first confirmed by Fourier transform infrared (FTIR). Figure 1a shows the FTIR spectra of KGM and GA‐KGM. In the FTIR spectrum of KGM, the absorption band of the carbonyl of the acetyl groups was observed at 1727 cm−1. The intense peak at 1632 cm−1 was attributed to the in‐plane deformation of the water molecule. In contrast to the spectrum of KGM, the band at approximately 1727 cm−1 was also ascribed to the carbonyl, which changed from a small shoulder in pure KGM to a distinct peak in KGM‐GA. The spectrum of KGM‐GA clearly demonstrated successful chemical conjugation. The UV–vis spectra of GA, KGM, and KGM‐GA are shown in Figure 1b. We observed no absorbance peak of the KGM solution within the detection range. In contrast, the KGM‐GA solution showed evident absorbance peaks at 215 and 263 nm, which also appeared in the UV–vis spectra of GA. These results indicate that KGM‐GA was successfully synthesized. The conjugation percentage was calculated by determining the amount of GA conjugated in KGM‐GA according to the standard curve at 263 nm, and the content of GA was determined to be $3\%$ KGM‐GA. Scanning electron microscopy (SEM) images showed that KGM‐GA has a loose, high‐porosity structure (Figure 1c), which was conducive to air permeability and promoted the transfer of nutrients.
**FIGURE 1:** *Structural characterization and biocompatibility assessments. (a) Fourier transform infrared (FTIR) spectrum of konjac glucomannan (KGM) and KGM‐GA. (b) UV–vis spectra of gallic acid (GA), KGM, and KGM‐GA. (c) Scanning electron microscopy (SEM) image of KGM‐GA. (d) Viability of Raw 264.7 cells treated with KGM, GA, and KGM‐GA, respectively, for 24 h at different concentrations. (e–g) Blood parameters in normal mice (control group), and mice treated with KGM‐GA after 14 days. (h) Evaluation of in vivo toxicity of KGM‐GA to major organs (heart, liver, spleen, lung, and kidney) at 14 days after treatment compared with normal mice (control group). The data were conducted by two‐way ANOVA with Sidak's post hoc test. The data are represented by means ± SD (n = 3, ns
p > 0.05).*
## Biocompatibility of KGM‐GA
The cytotoxicity of KGM, GA, and KGM‐GA toward Raw 264.7 and L929 cells was evaluated using CCK‐8 and live/dead cell staining assays. As shown in Figure 1d and Figure S1, within a certain concentration range, none of the groups showed obvious cytotoxicity, and the viability of Raw 264.7 cells exceeded $80\%$ and was $95\%$ for L929 cells. Moreover, cell viability was examined using live and dead cell staining in the KGM‐GA group (Figure S2). The majority of cells were green, and at the test concentrations, there was no noticeable cytotoxicity. Next, the effect of KGM‐GA on the hemolysis of red blood cells was studied. As shown in Figure S3, 1 mg/ml KGM‐GA affected the integrity of erythrocytes in the same way as the control PBS buffer (negative group), indicating good biocompatibility.
The in vivo biological safety of a material is a key factor in its application. Next, we evaluated the effects of KGM‐GA on mouse blood chemistry and major organ histopathology to determine in vivo biocompatibility. As shown in Figure 1e–g, complete blood panel analysis showed no obvious differences in hematology at 14 days after treatment with KGM‐GA in wounded mice compared to that of the control ($p \leq 0.05$). Moreover, no necrosis, congestion, or hemorrhage was observed in the heart, liver, spleen, lung, or kidney (Figure 1h). These results demonstrated the excellent biocompatibility of KGM‐GA in vivo.
## Regulation of macrophage polarization in vitro
Macrophages are essential regulators of wound healing and are known to have many functions, including participating in inflammation and promoting tissue repair and regeneration. 31 According to their activation states and functions, they can be divided into M1 and M2 phenotypes, which play different roles in wound healing. Despite M1 cells playing a critical role in defending against external pathogens in the early stage, 32 chronic M1 activation and continuous secretion of proinflammatory cytokines delay wound healing. 33 In contrast, M2 macrophages can secrete anti‐inflammatory cytokines and extracellular matrix components, which are necessary for the late stage of tissue repair, 34 and studies have shown that the infiltration of M2 macrophages reduces scar formation. 35 First, to evaluate the effects of different concentrations of KGM‐GA on the polarization of macrophages, we performed flow cytometric analysis of the M1 marker CD86 and M2 marker CD206 in Raw 264.7 cells. As shown in Figure S4, the expression of CD206 increased in response to KGM‐GA in a concentration‐dependent manner. However, at a concentration of 100 μg/ml, the expression of CD86 also increased significantly. Then, we calculated the ratio of CD206 to CD86 and found that the maximum occurred at a concentration of 50 μg/ml KGM‐GA, which was used in subsequent experiments (Figure S5).
Morphological changes in macrophages are related to their functional polarization. Previous studies used different signals to stimulate morphological changes in macrophages from a round shape to an elongated shape, which tended to be M2 macrophages. 36, 37 Based on the close relationships between the morphology and phenotype of macrophages, we first observed changes in Raw 264.7 macrophage morphology. Briefly, Raw 264.7 cells were incubated with KGM, GA, or KGM‐GA (50 μg/ml) solutions for 48 h, and cell morphology was captured with a microscope. Figure 2a shows that RAW 264.7 cells in the control group appeared round and contractive in morphology, and treatment with GA did not significantly change their morphology. In contrast, macrophages activated by KGM or KGM‐GA displayed elongated shapes and improved spreading morphology. A schematic diagram of cell morphological changes is shown in Figure 2b, and the formula for calculating elongation was described in previous research. 38 As shown in Figure 2c, the control group exhibited an elongation of 1.15 ± 0.18, while the KGM and KGM‐GA groups exhibited elongations of 2.15 ± 0.7 and 2.77 ± 0.82, respectively, which were significantly different from those in the control group. As demonstrated by many studies, enhanced macrophage elongation is believed to be closely related to the M2 phenotype.
**FIGURE 2:** *The morphological change of macrophages in vitro. (a) The morphology of Raw 264.7 cells was observed by optical microscope after 48 h exposure konjac glucomannan (KGM), gallic acid (GA), and KGM‐GA, respectively. Red arrowheads indicated round and elongated macrophages. (b) The Schematic of elongation calculation method. (c) Statistical data of the cell elongation change of Raw 264.7 macrophages. Tests were conducted by one‐way analysis of variance (ANOVA) with Tukey post hoc analysis. The data are presented as the mean ± SD (n = 10). ***p < 0.001, *p < 0.05 compared to control group*
To further evaluate the effects of KGM‐GA on M2 polarization, we examined the expression of M1 and M2 macrophage markers. Immunofluorescence staining revealed that after 48 h of incubation with KGM‐GA, the expression of the M2 surface marker CD206 was significantly increased, and the intensity was greater than that induced by KGM (Figure 3a,b). In addition, these results suggested that KGM‐GA was a potent driver of M2 polarization without significantly stimulating the transformation of cells to the M1 phenotype. Quantitative analysis was followed by flow cytometry. As shown in Figure 3c,d, after KGM‐GA treatment, the percentage of cells expressing CD206 among total cells was $20.98\%$ ± $4.1\%$, which was nearly 5.4 times higher than those in the control group, more than 3.8 times higher than those in the GA group, and approximately 2.2 times than those in the KGM group. In addition, we found that all groups expressed low levels of CD86, and there was no significant difference among the groups. Moreover, some cells coexpressed CD86 and CD206, indicating a transitional state between the M1 and M2 phenotypes in these macrophages. We examined the expression of M1 and M2 macrophage markers.
**FIGURE 3:** *Konjac glucomannan–gallic acid (KGM‐GA) regulates polarization of macrophages in vitro. (a) Representative images of CD206 immunofluorescence staining of Raw 264.7 after 48 h cultured with KGM, GA, and KGM‐GA, respectively. (b) Quantitative measurement of CD206 signals. (c, d) Flow cytometry results of Raw 264.7 after cells were stained with M1 marker CD86 and M2 marker CD206. The ratios of M2 phenotypes were also presented. (e–i) The secretion of IL‐10, TGF‐β, tumor necrosis factor‐α (TNF‐α), interleukin‐1β (IL‐1β), and interleukin‐6 (IL‐6) in culture supernatants of Raw 264.7 cells stimulated by lipopolysaccharide (LPS). Tests were conducted by one‐way analysis of variance (ANOVA) with Tukey post hoc analysis. The data are presented as the mean ± SD (n = 3). ***p < 0.001, **p < 0.01, and *p < 0.05 compared to control group; ###p < 0.001 compared to KGM group*
## KGM‐GA inhibited the inflammatory response
To investigate whether KGM‐GA was involved in mediating the inflammatory effect of macrophages, Raw 264.7 cells were cultured with LPS to stimulate inflammation. After being treated with equivalent amounts of PBS, KGM, GA, and KGM‐GA, we measured the secretion of anti‐inflammatory IL‐10 and TGF‐β and proinflammatory tumor necrosis factor‐α (TNF‐α), interleukin‐1β (IL‐1β), and interleukin‐6 (IL‐6), by ELISA. As shown in Figure 3e, the secretion of IL‐10 in the KGM‐GA, KGM, and GA groups was significantly increased in comparison with that in the PBS group, and IL‐10 in the KGM‐GA group was also significantly enhanced compared with that in the KGM and GA groups. In addition, KGM and KGM‐GA significantly increased the secretion of TGF‐β by macrophages, which can be considered the result of the paracrine signaling in M2 macrophages (Figure 3f). TGF‐β promotes the proliferation and migration of fibroblasts and the production of ECM, which is beneficial in tissue repair. In contrast, KGM‐GA reduced the secretion of the proinflammatory cytokines TNF‐α, IL‐1β, and IL‐6 in comparison with that in the PBS group (Figure 3g–i). Although KGM and GA also inhibited the production of proinflammatory cytokines, the relative reduction in TNF‐α, IL‐1β, and IL‐6 in the KGM‐GA group was also significantly higher than that in the other groups. These results demonstrated that KGM‐GA inhibited the secretion of proinflammatory cytokines, enhanced anti‐inflammatory cytokine secretion in an LPS environment and exhibited potential in inflammatory‐related diseases.
## ROS scavenging activities of KGM‐GA in vitro
The potential antioxidant activity of KGM‐GA was evaluated in this study (Figure 4a). Free radicals and H2O2 were selected as representative ROS to examine the ROS scavenging activities of KGM‐GA by DPPH and Amplex Red assays, respectively. As shown in Figure 4b, after being incubated with KGM for 1 h, almost no change was observed in the level of the DPPH radical. However, KGM‐GA showed dose‐dependent activity, the color of DPPH gradually changed from modena to yellow, and nearly $75\%$ of DPPH was eliminated at the maximum concentration (1 mg/ml) (Figure 4c). Furthermore, the H2O2‐scavenging activity of KGM‐GA was studied. The concentration of H2O2 could be maintained at a low level, and more than $60\%$ H2O2 was scavenged when the KGM‐GA concentration was 500 μg/ml compared with that in the control group (Figures 4d and 5e). Collectively, these results demonstrated that KGM failed to scavenge free radicals or H2O2 under the evaluated conditions. However, KGM‐GA showed amazing ROS scavenging activities, similar to that of GA due to the exposure of the polyphenol structure of GA.
**FIGURE 4:** *Reactive oxygen species (ROS) scavenging activities of konjac glucomannan–gallic acid (KGM‐GA) in vitro. (a) Schematic illustration of the ROS scavenging process with KGM‐GA. (b, c) Free‐radical scavenging ability of KGM, GA, and KGM‐GA. (d, e) H2O2 scavenging ability of KGM, GA and KGM‐GA. ***p < 0.001, **p < 0.01 compared to control group. (f) Confocal images of ROS levels in L929 cells using DCFH‐DA probe after incubation with 200 μM H2O2 and different treatments. (g) Quantitative analysis of fluorescence intensity of DCFH‐DA. (h, i) Statistical analysis of ROS levels in L929 cells under different treatment conditions by flow cytometer. Tests were conducted by one‐way ANOVA with Tukey post hoc analysis. The data are presented as the mean ± SD (n = 3). ***p < 0.001 compared to positive group* **FIGURE 5:** *Effects of KGM‐GA on cell migration and vessel formation in vitro. (a) Cell migration images of L929 cells cultured with KGM, GA and KGM‐GA for 12 h after incubation with 100 μM H2O2. (b) Representative images of HUVECs tube formation after incubation with 100 μM H2O2 and different treatments. (c) Quantitative analysis of cell migration assay for L929 cells. (d) The level of VEGF in the culture supernatants of L929 cells treated with KGM, GA, and KGM‐GA for 48 h. (e) Quantitative analysis of tube formation for HUVECs. Tests were conducted by one‐way ANOVA with Tukey post hoc analysis. The data are presented as the mean ± SD (n = 3). ***p < 0.001, **p < 0.01, and *p < 0.05 compared to control group*
Furthermore, we evaluated the ability of KGM‐GA to control intracellular ROS levels. In the positive control group, L929 cells were incubated with 200 μM H2O2 to induce oxidative stress damage and imitate the ROS environment, while cells cultured with medium alone served as the negative control. After being stimulated for 2 h, cells were treated with different interventions for an additional 2 h, and the intracellular ROS level was measured by a standard DCFH‐DA assay. As shown in Figure 4f,g, the fluorescent signals of DCFH‐DA were significantly increased in response to H2O2, and KGM slightly reduced ROS levels. In comparison, when the cells were treated with GA or KGM‐GA, the intracellular ROS level obviously decreased, indicating that KGM‐GA could alleviate oxidative stress induced by H2O2. Moreover, the intracellular ROS levels were also quantitatively analyzed by flow cytometry (Figure 4h,i). The relative percentage ROS in L929 cells was reduced from nearly $35\%$ to less than $20\%$ after treatment with KGM‐GA. KGM slightly eliminated intracellular ROS levels in L929 cells, which may be related to KGM promoting the proliferation of fibroblasts by stimulating metabolism in cells, as reported. 17 KGM was modified with GA, and KGM‐GA exerts an excellent effect against oxidative stress, which is highly suitable for wound healing and regeneration.
## Cell migration and proangiogenic responses in vitro
Fibroblasts play a vital role in tissue repair. To evaluate the effect of KGM‐GA on fibroblasts, the migration of L929 cells was examined. As shown in Figure 5a, incubation with a certain concentration of H2O2 affected the migration of cells compared with those in the normal group. However, cells exposed to KGM, GA, and KGM‐GA showed a significant increase in migration compared with those in the control groups. After 12 h, cells exposed to KGM‐GA showed the highest migration ($43.6\%$ ± $2.4\%$), followed by those exposed to PBS ($5.1\%$ ± $7.3\%$), KGM ($31.4\%$ ± $2.0\%$), and GA ($31.6\%$ ± $7.1\%$) (Figure 5c). In addition, after being incubated with different materials for 48 h, the level of VEGF in the cell supernatant was measured. As shown in Figure 5d, KGM‐GA obviously improved the secretion of VEGF, suggesting that KGM‐GA promotes angiogenesis.
Since angiogenesis is a key process in skin tissue repair, we next evaluated the ability of KGM‐GA to drive angiogenesis in HUVECs. In a hydrogen peroxide environment, the vessel‐forming capability of HUVECs was examined by the tube formation assay. As shown in Figure 5b,e, in the normal group, the cells connected to form networks after 2 h of culture in Matrigel. However, under H2O2 conditions, the tube‐forming ability of HUVECs was seriously affected. Although sporadic tube formation was observed, the length was short, and the tubes did not form a dense network structure, indicating that the formation of blood vessels was blocked in the ROS environment. Furthermore, incubation with KGM, GA, and KGM‐GA enhanced tube formation in HUVECs in the ROS microenvironment. In particular, the effect of KGM‐GA was the most significant, and the cells formed junctions and compact tubes, as evidenced by increased branch lengths, which was not inferior to that of the normal group. Overall, KGM‐GA can significantly resist ROS produced by H2O2 to promote cell migration and vessel formation, indicating that KGM‐GA is capable of accelerating the wound healing process.
## KGM‐GA improves wound healing in mice
To evaluate the effect of KGM‐GA on wound healing, we created wounds on the backs of mice as in vivo models (Figure 6a). The wounds were treated with PBS, KGM, GA, and KGM‐GA, respectively, and images were captured at 0, 3, 7, and 14 days (Figure 6b,c). The images illustrated the progression of wound closure after treatment. Wounds in each group gradually decreased over time within 14 days, but the speed of wound closure in the KGM‐GA group was significantly faster than that in the other groups. At 3 days after the procedure, the wound area of KGM‐GA group ($31.1\%$ ± $0.85\%$) was significantly smaller than that of KGM ($51.2\%$ ± $8.22\%$), GA ($62.1\%$ ± $1.19\%$) and control group ($64.8\%$ ± $5.86\%$). After 7 or 14 days of treatment, the images further showed the best wound healing in the KGM‐GA group. To further assess the regeneration of skin tissues, we used H&E‐stained sections of healing areas for histological analysis of the wound (Figure 6d). First, on Day 7, the wound in the control group did not heal, with poor epithelialization and reduced granulation tissue formation. In contrast, the wound epidermis in the KGM‐GA group had formed, which exhibited a good therapeutic effect on granulation tissue. On Day 14, the control group showed a severe inflammatory response with the infiltration of inflammatory cells, while the epidermis, the dermis, sweat glands, and hair follicles appeared in the KGM‐GA group, indicating that the wound achieved complete skin regeneration (Figure S6). Furthermore, the average length of the wound edge was calculated and is shown in Figure 6e. The length in KGM‐GA group was significantly smaller than that in the control group (Figure 6d). In addition, the results of picrosirius red staining showed that collagen deposition in the KGM‐GA group was higher (red staining) and collagen fibers were arranged more regularly than in the control group (Figure 6f). CD31 is a vascular endothelial growth factor that can promote angiogenesis. An anti‐CD31 antibody was used to stain newly formed blood vessels in the wound. On Day 7, the count of capillary densities showed that a significantly higher density of capillaries was produced in KGM‐GA (Figure S7).
**FIGURE 6:** *In vivo wound healing for mice under KGM‐GA treatment. (a) Scheme showing the process of the wounds treated by cultured with KGM, GA, and KGM‐GA. (b) Representative images of the wounds after treatment with various dressings at Days 0, 3, 7, and 14 post wounding. (c) Quantification of the process of wound area for all groups. (d, e) H&E staining of wound sections in all groups at Day 7. The note of the black dotted lines indicated the wound. Then, quantification of the process of wound edge for all groups. (F) Picrosirius Red staining of wound sections in all groups at Day 7. (g, h) Representative immunofluorescence data and the statistic results of CD31 stained sections at Day 14. Tests were conducted by one‐way ANOVA with Tukey post hoc analysis. The data are presented as the mean ± SD (n = 6). ***p < 0.001 and **p < 0.01 compared to control group*
Moreover, after 14 days of treatment (Figure 6g,h), KGM‐GA promoted the expression of CD 31 and induced a higher density of capillaries (90.1 ± 5.3 capillaries per mm2) than in the control group (18.3 ± 3.0 capillaries per mm2), KGM group (56.1 ± 5.6 capillaries per mm2), or GA group (50.9 ± 9.2 capillaries per mm2). These results indicated that KGM‐GA effectively accelerated the wound healing process.
## Regulation of the ROS microenvironment and macrophage phenotype in vivo
Based on the ability of KGM‐GA to eliminate ROS in vitro, we further investigated whether KGM‐GA could decrease oxidative stress in the skin wound healing model. After 7 days, the injured skin tissue was stained with DHE. From Figure 7c,d, abundant ROS were produced in the wound skin, which affected the healing of skin tissue, while GA and KGM‐GA treatment significantly reduced tissue ROS levels compared with KGM and PBS treatment. Next, we evaluated whether KGM‐GA could regulate macrophage polarization in vivo. On Day 14, the injured skin was collected for immunofluorescence staining to observe the expression of CD68 (a marker of all macrophage subsets) and CD206 (a marker of M2 macrophages) in the wound tissue. As shown in Figure 7a,b, in the subcutaneous layer of the wounds, the distribution of CD206‐positive cells in the KGM‐GA group was more widespread than that in the other groups and was approximately 4.2‐fold greater than that in the PBS group, indicating that KGM‐GA could stimulate macrophage polarization to the M2 phenotype in vivo.
**FIGURE 7:** *Konjac glucomannan–gallic acid (KGM‐GA) induced M2 macrophage polarization and alleviated reactive oxygen species (ROS) level. (a) Representative pictures of CD206/CD68 immunofluorescence staining of KGM, GA and KGM‐GA on Day 14 post wounding. (b) Statistical data of the percentage of M2 macrophages. (c) Representative pictures of DHE staining at Day 7 post wounding. (d) Statistical data of ROS levels in wound. Tests were conducted by one‐way ANOVA with Tukey post hoc analysis. The data are presented as the mean ± SD (n = 6). ***p < 0.001 compared to control group; ##p < 0.01 compared to GA group, ###p < 0.001 compared to KGM group*
## DISCUSSION
The repair and regeneration of skin injuries caused by traumatic injury, surgery, or disease remain major clinical challenges and contribute to increased health care costs. 39 Wound healing is a dynamic, intertwined, and complex process that includes multiple stages, and the prolongation of the inflammatory period adversely affects subsequent tissue regeneration. 40 On the one hand, excessive inflammation at the damaged tissue site prolongs the inflammation period and delays the wound healing process, which may lead to the development of pathological fibrosis or scarring, thereby destroying normal tissue structure and function. 41 On the other hand, delayed wound healing will greatly increase the risk of infection, which further aggravates the healing process and leads to a vicious cycle. 40 Therefore, effective therapies for healing wounds targeting the inflammatory microenvironment of the injured site should be considered. It has been widely acknowledged that macrophages are important regulators of the wound healing process and are involved in advancing inflammation and promoting tissue repair and regeneration. 32 Many studies have shown that the conversion of proinflammatory macrophages (M1 type) to an anti‐inflammatory phenotype (M2 type) is critical for normal wound repair and fibrosis reduction. 42 In addition, inflammation is intimately associated with oxidative stress. 43 An excessive inflammatory response at the wound site results in the production of a large amount of ROS, 44, 45 which can aggravate local tissue damage, reduce the migration and proliferation of fibroblasts, keratinocytes, and endothelial cells, and delay wound healing. 46 Recent studies have focused on unilateral regulation of macrophage polarization or the control of oxidative stress. A variety of biologically active substances have been used to modulate the phenotype of macrophages, including related cytokines, 47, 48 receptors, 49 small molecules, 50 and mesenchymal stem cells. 10, 40 However, their application is hindered because of uncontrollable biological activity. The use of biological materials to regulate cell fate and activity may lead to the development of new therapeutic strategies. Here, the natural polysaccharide KGM has attracted our attention due to its ability to regulate macrophage polarization and exposed chemical structures that are easily modified. 16, 51 Recently, hydrolysates of KGM were shown to inhibit the production of proinflammatory cytokines and skew macrophage differentiation from M1 to M2 in the colon in mice with colitis. 51 Furthermore, Jingjing Gan et al. prepared a type of KGM‐modified silica nanoparticle that could effectively induce MR aggregation on macrophages, thereby stimulating the cells to polarize to the M2 phenotype. 52 Moreover, the polyphenol GA has been widely used due to its beneficial properties, including antioxidant and anti‐inflammatory properties. Previous studies have shown that polysaccharides are easily bound to other functional molecules, thus obtaining some properties, including antioxidant activity. 53, 54 *In this* study, KGM was modified by GA through a simple and efficient technique to synthesize the wound dressing KGM‐GA, which exhibits antioxidant activity due to the reduction potential of hydroxyl groups in the aromatic structure of the phenolic ring. We also conducted in vitro and in vivo experiments to study the effect of KGM‐GA on the polarization of macrophages.
A wound dressing in the clinic is required to have excellent biocompatibility. The main components of KGM‐GA are two known natural products that exhibit good biocompatibility in vivo and in vitro, which is a prerequisite for acceptance and application, avoiding side effects caused by uncontrollable doses of biologically active molecules. 8 More surprisingly, the combination of KGM‐GA did not weaken the biological activity, and the effect was better than that of KGM or GA alone. A possible explanation for this effect might be that the reaction was initiated by the hydroxyl group in KGM and the carboxyl group in GA. Thus, the groups that exerted the corresponding biological activity were not destroyed. Enhanced antioxidant properties and the ability of KGM‐GA to regulate the M2 phenotype were confirmed in subsequent experiments. In addition, fibroblast migration to and proliferation within the wound site are prerequisites for wound granulation during the proliferation stage, and angiogenesis is an indispensable step in the remodeling phase during wound healing. 55 Excessive ROS accumulation inhibits fibroblast migration and angiogenesis, which was also confirmed in our experiments. Further statistical analysis revealed that KGM‐GA downregulated inflammation and improved oxidative stress to create an environment for the migration of L929 cells and tube formation by HUVECs.
Prior studies have shown that inherent contraction of skin wounds can only induce closure of the epidermis but cannot promote regeneration of the intact epidermal layer and mature dermis. 19 This study confirmed that the application of KGM‐GA not only promoted wound healing but also improved skin tissue regeneration. Notably, KGM‐GA shortened the time required for wound closure. This effect may be related to the anti‐inflammatory effect of KGM‐GA on macrophages by promoting M2 polarization and inhibiting M1 polarization in the early stage of wound healing, shortening the transition time from the inflammation stage to the tissue formation stage. In addition, the low toxicity and antioxidant environment provided by KGM‐GA may help improve wound healing rates. In addition, the low toxicity and antioxidant environment provided by the GA component of KGM‐GA may help improve the wound healing rate. A relatively low level of ROS in the wound is considered to be the key to stimulating the release of cytokines, improving cell viability, and promoting blood vessel formation. 56 Collectively, these results demonstrated that KGM‐GA could create a beneficial microenvironment for promoting tissue recovery and regeneration.
In most situations, wound repair will face unexpected challenges due to exposure to pathological conditions, such as aging, obesity, and diabetes. In diabetic wounds, macrophages exhibit a reduced capability to induce the phenotypic switch from M1 to M2 due to hyperglycemia and the presence of excessive glycosylation residues, resulting in accumulation and enrichment of M1 macrophages, causing excessive inflammation. 57, 58 Therefore, wound healing in diabetes requires modulation of the polarization state of macrophages to reduce local inflammation. Currently, some studies have used polysaccharides to regulate macrophage phenotype to treat diabetic wound healing and have achieved certain therapeutic effects. 59, 60, 61 In addition, excessive ROS accumulated in diabetic wounds hinders the regeneration of wound tissue. 19, 20 Furthermore, in recent decades, inflammatory signals have not only been thought to influence wound healing but are also driving factors for diseases such as atherosclerosis 62, 63 and cancer. 64 The polymer we synthesized can regulate macrophages and combat oxidative stress without the aid of cytokine or gene delivery. The insights gained from this study may be of assistance in providing new strategies for the treatment of pathological wound injury and other inflammatory diseases.
## Materials
KGM was purchased from Karmachem (Shanghai, China). GA and 4‐dimethylaminopyridine (DMAP) were purchased from Heowns (Tianjin, China), 1‐ethyl‐3‐(3‐dimethylaminopropyl) carbodiimide hydrochloride (EDC. HCl) was obtained from Shanghai Medpep Co., Ltd. (Shanghai, China), and 2,7‐dichlorodihydrofluorescein diacetate (DCFH‐DA) was obtained from Sigma‐Aldrich (St. Louis, MO, USA). 1,1‐Diphenyl‐2‐picrylhydrazyl free radical (DPPH) was purchased from TCI (Shanghai, China). The CCK‐8 cell proliferation and cytotoxicity assay kit, live/dead cell staining kit and hematoxylin–eosin (HE) staining kit were purchased from Solarbio (Beijing, China). Dihydroethidium (DHE) was purchased from Beyotime (Shanghai, China). A Picrosirius Red staining kit was obtained from G‐CLONE (Beijing, China). Anti‐mouse ELISA kits for TNF‐α, IL‐6, IL‐1β, IL‐10, and TGF‐β and anti‐human ELISA kits for VEGF were obtained from Thermo Fisher Scientific (Beijing, China). Primary antibodies against CD68, CD206, and CD31 were purchased from Abcam (Cambridge, UK).
## Cells and animals
The mouse L929 cell line, Raw264.7 cell line, and human umbilical vein endothelial cells (HUVECs) were obtained from the Cell Bank of Chinese Academy of Sciences. Female BALB/C mice were purchased from SPF (Beijing) Biotechnology Co. Ltd. (Beijing, China) (Approval No.: SCXK(Jing): 2019‐0010). All animal management procedures were reviewed and ethically approved by the Center of Tianjin Animal Experiment ethics committee and authority for animal protection (Tianjin, China). ( License for use of experimental animals: approval No.: SYXK (Jin):2019‐0002).
## Synthesis and characterization of KGM‐GA
KGM‐GA was synthesized via an esterification reaction according to a previously reported method. First, KGM (1 g) was dissolved in 200 ml of distilled water with magnetic stirring at 50°C until completely dissolved. Then, a mixture of GA (0.21 g, 1.11 mmol), EDC·HCl (0.355 g, 1.85 mmol), and DMAP (0.226 g, 1.85 mmol) was dissolved in distilled water (50 ml), and the solutions were stirred thoroughly for 0.5 h prior to the condensation reaction. Finally, the two solutions were completely mixed and reacted with stirring at 50°C for 72 h. After being cooled to room temperature, the reaction mixture was precipitated in absolute ethanol, repeatedly dissolved and precipitated three times. The final product (GA‐KGM) was obtained after drying at 60°C in a vacuum.
The chemical structure of GA‐KGM was confirmed by FTIR spectrometry (Thermo Fisher Scientific, Waltham, MA, USA). The content of GA in KGM‐GA was determined by measuring the absorbances of the GA and KGM‐GA solutions (0.5 mg/ml) at 263 nm by using a UV–vis spectrophotometer (PerkinElmer, USA). The morphology and structure of KGM‐GA were observed by scanning electron microscopy (SEM, ZEISS, Germany).
## Cytotoxicity and blood compatibility assay
L929 cells and Raw 264.7 cells were used for cytology experiments. RPMI‐1640 medium containing $10\%$ fetal bovine serum (FBS) was used for L929 cell maintenance, and Dulbecco's Modified Eagle Medium containing $10\%$ FBS was used for Raw264.7 cell maintenance. The cells were seeded in 96‐well plates (5000 cells/well) and incubated for 24 h at 37°C in a $5\%$ CO2 atmosphere. Then, the cells were incubated with a series of concentrations of KGM, GA, and KGM‐GA for 24 h. Cell viability was evaluated by CCK‐8 cell proliferation and cytotoxicity assay kits (Solarbio, CA1210), and the absorbance at 450 nm was measured using a microplate reader (Thermo). Moreover, a live/dead cell staining kit (Solarbio, CA1630) was used to stain the cells. Finally, the images of the cells were observed by a fluorescence microscope from the Nikon Corporation (Tokyo, Japan).
The effect of KGM‐GA on erythrocyte hemolysis was examined according to a previously reported method. 65 In brief, blood was collected from the orbital sinus and centrifuged at 3000 rpm for 15 min. Then, the erythrocytes were washed with PBS and treated with distilled water as a positive control, PBS as a negative control, or various concentrations of KGM‐GA for 2 h at 37°C. The samples were centrifuged at 3000 rpm for 15 min, and the absorbance of the supernatant at 540 nm was measured by a microplate reader.
## KGM‐GA regulation of Raw 264.7 macrophage morphology
For morphological analysis, Raw 264.7 cells (1 × 105 cells per well) were seeded in 24‐well plates and incubated overnight at 37°C, followed by treatment with KGM, GA, or KGM‐GA (50 μg/ml) solutions. After 48 h, the cells were washed with PBS three times to remove excess materials. The morphology of macrophages was captured by a microscope.
## Immunofluorescence analysis of Raw 264.7 macrophages
For the in vitro macrophage polarization assay, Raw 264.7 cells (1 × 105 cells per well) were seeded in confocal dishes and treated with or without KGM, GA, or KGM‐GA for 48 h at 37°C. Then, the cells were washed three times in PBS and fixed with $4\%$ paraformaldehyde for 20 min. Subsequently, the cells were incubated with $5\%$ bovine serum albumin (BSA) for 40 min at room temperature. After that, the cells were treated with 300 μl of a rabbit polyclonal antibody against CD206 (1:1000, Abcam, ab64693) diluted in PBS containing $1\%$ BSA overnight in a wet box at 4°C. Then, the cells were washed with PBS and incubated with a goat polyclonal IgG FITC‐conjugated secondary antibody (1:100, HA1004, Huabio) diluted in PBS containing $1\%$ BSA at room temperature. After 1 h, the cells were stained with DAPI and observed by a fluorescence microscope.
## Flow cytometric analysis Raw 264.7 macrophages
Raw 264.7 cells were seeded in a 24‐well plate (1 × 105 cells per well) in culture medium containing KGM, GA, or KGM‐GA for 48 h. After that, the cells were collected by centrifugation (1000 rpm, 5 min) and washed with PBS. Then, the cells were stained with PE‐conjugated anti‐mouse CD206 and FITC‐conjugated anti‐mouse CD86 antibodies for 20 min at 4°C and examined using a BD Accuri™ C6 flow cytometer.
## ELISA analysis of proinflammatory and anti‐inflammatory factors
RAW264.7 cells were incubated in 24‐well plates (1 × 105 cells per well) for 24 h and then stimulated with lipopolysaccharide (LPS, 2 μg/ml). After 24 h, the medium was removed, and fresh medium containing KGM, GA, or KGM‐GA was added and incubated for 48 h. Then, we collected the cell supernatants and measured the levels of IL‐1β, IL‐6, TNF‐α, interleukin‐10 (IL‐10), and transforming growth factor‐β (TGF‐β) by ELISA.
## ROS‐scavenging ability evaluation
The H2O2 scavenging capacity of KGM‐GA was tested by the Amplex Red assay (Invitrogen, USA). Various concentrations of KGM, GA, or KGM‐GA (125–1000 μg/ml) were incubated with 40 μΜ H2O2, and the solution was left in a shaker (150 r/min) at 37 °C for 1 h. After the reaction, the concentration of the remaining H2O2 was determined according to the Amplex Red assay, and the H2O2‐scavenging ability was calculated.
In addition, to evaluate the free‐radical scavenging ability of KGM‐GA, a DPPH ethanol solution (200 μg/ml) was prepared. Subsequently, different concentrations of KGM, GA, or KGM‐GA (125–1000 μg/ml) were added to the same volume of DPPH solution and incubated for 1 h at 37°C. The absorption of the reacted solution at 515 nm was recorded by a microplate reader and used to calculate the free‐radical scavenging ability of KGM‐GA.
Furthermore, to study the intracellular ROS scavenging ability of KGM‐GA, L929 cells were seeded at a density of 1 × 105 cells/well in confocal dishes for 24 h. Then, the cells were cultured with 200 μM H2O2 for 1 h. After being incubated, the culture media was removed and replaced with fresh media as a positive control or fresh media containing KGM, GA, or KGM‐GA (50 μg/ml). After 2 h, the cells were stained with 10 μM/L dichlorofluorescein diacetate (DCFH‐DA) for 30 min at 37°C. The fluorescence of the L929 cells was observed by a confocal fluorescence microscope from the Nikon Corporation (Tokyo, Japan). Meanwhile, L929 cells were seeded in 24‐well plates and incubated with H2O2 (200 μM) for 1 h and different treatments for 2 h. The cells were washed with PBS three times before the DCFH‐DA probe was added. The intracellular ROS level was evaluated by a BD Accuri™ C6 flow cytometer.
## L929 cell migration and VEGF expression assay
The migration of L929 cells was evaluated. The cells were cultured in 24‐well plates (1 × 105 cells/well) with FBS‐free medium for 24 h. Then, the cell monolayer was scratched in a straight line using a 200 μl pipette tip and washed with PBS to remove cell debris. After that, the cells were incubated with PBS, KGM, GA, or KGM‐GA containing 100 μM H2O2. Images of the scratched L929 cells were taken at 0 and 12 h and analyzed using ImageJ software. The cell migration rate was calculated as follows: Cell migration (%) = [(A0‐At)/A0] × $100\%$. A0 is the scratch area at 0 h, and *At is* the scratch area without cell migration at 12 h.
In addition, VEGF expression in L929 cells was measured by ELISA. In brief, the cells were seeded in 24‐well plates (1 × 105 cells/well) and treated with KGM, GA, or KGM‐GA for 48 h. Finally, the supernatants were collected to measure VEGF levels.
## HUVEC tube formation assay
For the tube formation assay, 100 μl of Matrigel (BD, USA) and 100 μl of medium were mixed, added to a precooled 48‐well plate and incubated at 37°C. After 30 min, HUVECs (3 × 104 cells/well) were gently added to the culture plate and incubated with PBS, KGM, GA, or KGM‐GA containing 100 μM H2O2 for 4 h. Tube formation was captured by a microscope, and the total tube length was quantified using ImageJ software.
## Wound healing in vivo
The therapeutic effect of KGM‐GA on wound healing was evaluated using a wound model. Female BALB/C mice (18–21 g, 6–7 weeks) were anesthetized by $4\%$ chloral hydrate, and then the hair on the dorsal skin was shaved. Full‐thickness circular wounds with diameters of 6 mm were created by surgical procedures on the backs of the mice. The mice were randomly divided into four groups ($$n = 6$$) and treated with 50 μl of a saline solution dispersion of KGM, GA, or KGM‐GA (20 mg/ml) at the wound site. The other mice were treated with saline solution as controls. Images of the wounds were captured at Days 0, 3, 7, and 14 post‐wounding using a digital camera.
## Histological analysis
After 7 and 14 days, the wound tissues were collected from the mice, fixed with $4\%$ formaldehyde, and then embedded in paraffin. Then, the paraffin‐embedded tissues were sectioned into 6 μm thick slices for hematoxylin and eosin (H&E) and picrosirius red staining.
## Immunofluorescence analysis
The skin sections were deparaffinized and rehydrated and then blocked with goat serum at room temperature for 30 min. The skin sections were treated at 4°C overnight with the following primary antibodies: CD68 mouse polyclonal antibody (1:200, Abcam, ab955), CD206 rabbit polyclonal antibody (1:200, Abcam, ab64693) or CD31 rabbit polyclonal antibody (1:50, Abcam, ab28364). Then, the sections were washed with PBS and incubated with Alexa Fluor 594 goat anti‐mouse IgG (1:500, Invitrogen, A11032) and FITC‐labeled goat anti‐rabbit IgG (1:200, Huabio, HA1004) or Cy3‐labeled goat anti‐rabbit IgG (1:200, Beyotime, A0516) and secondary antibodies at room temperature for 2 h. Finally, the sections were counterstained with DAPI and mounted with antifade mounting medium. Fluorescence images were captured by a fluorescence microscope.
## ROS measurement in wound sites
The wound specimens at Day 7 post‐wounding were washed with PBS three times. After that, the skin sections were incubated with 2.5 μM dihydroethidium (DHE) at 37°C for 30 min and imaged with a fluorescence microscope.
## In vivo biocompatibility of KGM‐GA
At 14 days of treatment, blood samples were collected for complete blood panel analysis. In addition, major organs, including the heart, liver, spleen, lung, and kidney, were removed, and paraffin sections were prepared. HE staining was performed to assess tissue structure. All data are compared with normal mice.
## Statistical analysis
The data are presented as the mean ± standard deviation. Where appropriate, a one‐way or two‐way analysis of variance (ANOVA) was performed to assess statistical significance. Statistical evaluations were performed using GraphPad Prism 8.0, and p values <0.05 were considered statistically significant.
## CONCLUSION
In this study, we present a simple and efficient technique to synthesize a polymer with good biocompatibility. KGM‐GA exhibited obvious antioxidant properties and the ability to regulate M2 macrophages, thereby inhibiting the secretion of inflammatory factors in vitro. In a mouse model of full‐thickness skin injury, KGM‐GA alleviated oxidative stress and the inflammatory response at the wound site, accelerated wound closure and collagen deposition and enhanced angiogenesis, leading to rapid skin regeneration. Our results suggest that KGM‐GA is an exceptionally meaningful and promising agent for wound healing.
## AUTHOR CONTRIBUTIONS
Huiyang Li: Data curation (lead); formal analysis (lead); investigation (equal); methodology (equal); validation (equal); writing – original draft (equal). Xiaoyu Liang: Investigation (equal); writing – review and editing (supporting). Youlu Chen: Writing – review and editing (supporting). Kaijing Liu: Writing – review and editing (supporting). Xue Fu: Writing – review and editing (supporting). Xiaoli Wang: Methodology (equal). Jing Yang: Conceptualization (equal); funding acquisition (equal); methodology (lead); project administration (lead); supervision (equal); writing – review and editing (lead). Chuangnian Zhang: Conceptualization (equal); funding acquisition (equal); methodology (lead); supervision (equal).
## CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## DATA AVAILABILITY STATEMENT
Data available on request from the authors.
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|
---
title: Liposome–trimethyl chitosan nanoparticles codeliver insulin and siVEGF to treat
corneal alkali burns by inhibiting ferroptosis
authors:
- Xiaojing Xiong
- Huiting Jiang
- Yukun Liao
- Yangrui Du
- Yu Zhang
- Zhigang Wang
- Minming Zheng
- Zhiyu Du
journal: Bioengineering & Translational Medicine
year: 2023
pmcid: PMC10013822
doi: 10.1002/btm2.10499
license: CC BY 4.0
---
# Liposome–trimethyl chitosan nanoparticles codeliver insulin and siVEGF to treat corneal alkali burns by inhibiting ferroptosis
## Abstract
Alkali burns are potentially blinding corneal injuries. Due to the lack of available effective therapies, the prognosis is poor. Thus, effective treatment methods for corneal alkali burns are urgently needed. Codelivery nanoparticles (NPs) with characteristics such as high bioavailability and few side effects have been considered effective therapeutic agents for ocular diseases. In this study, we designed a new combination therapy using liposomes and trimethyl chitosan (TMC) for the codelivery of insulin (INS) and vascular endothelial growth factor small interfering RNA (siVEGF) to treat alkali‐burned corneas. We describe the preparation and characterization of siVEGF‐TMC‐INS‐liposome (siVEGF‐TIL), drug release characteristics, intraocular tracing, pharmacodynamics, and biosafety. We found that siVEGF‐TIL could inhibit oxidative stress, inflammation, and the expression of VEGF in vitro and effectively maintained corneal transparency, accelerated epithelialization, and inhibited corneal neovascularization (CNV) in vivo. Morever, we found that the therapeutic mechanism of siVEGF‐TIL is possibly relevant to the inhibition of the ferroptosis signaling pathway by metabolomic analysis. *In* general, siVEGF‐TIL NPs could be a safe and effective therapy for corneal alkali burn.
## INTRODUCTION
Corneal alkali burns are one of the most common emergencies in ophthalmology, accounting for $11.5\%$–$22.1\%$ of all ocular traumas. 1 *As a* result of corneal alkali injury, the ocular surface and anterior eye segment are extensively damaged, causing permanent vision impairment or even complete blindness. 2 It has been reported that corneal oxidative stress occurs immediately after alkali damage, which precedes the corneal inflammatory response. 3 During alkali burn‐induced injury, excessive oxidative stress in the cornea, oxidative changes occur in cellular macromolecules, and lipid peroxidation occurs in the membrane, 4 leading to an antioxidant/pro‐oxidant imbalance in corneal tissues. On the other hand, the activity of antioxidant enzymes is decreased, while the expression and activity of catalytic enzymes running at physiological levels or even increases, leading to an increase in reactive oxygen species (ROS) production and a decrease in ROS decomposition. 5 These factors can cause a high level of oxidative stress, eventually resulting in excessive intracorneal inflammation, scarring, and corneal neovascularization (CNV). 6 Similar to oxidative stress, CNV plays a critical role in the pathophysiology of corneal alkali burns. CNV increases vascular permeability, which exacerbates inflammation, chronic edema, lipid exudation, and corneal scarring, potentially resulting in permanent vision loss. 7 Currently, topical corticosteroids and nonsteroidal anti‐inflammatory drugs (NSAIDs) remain the top priorities. However, these treatments can delay wound healing, and long‐term use of corticosteroids can lead to increased intraocular pressure (IOP), cataracts, and an increased risk of infection. 8 Even though various other treatment options have been available in the clinic, such as amniotic membrane transplantation, their effectiveness has not been optimal in the past two decades. 9 Consequently, it is urgent to explore a more efficient and safe treatment for severe corneal alkali burns.
Insulin (INS), a hypoglycemic hormone, is mainly used in the clinical treatment of diabetes. However, a growing body of research has identified INS as a potential antioxidant. Ramalingam et al. 10 demonstrated that INS pretreatment of cells could inhibit the cytotoxicity induced by H2O2, inhibiting apoptosis and increasing the PI3K/Akt survival pathway. Research by Rajasekar et al. 11 proved INS could alleviate memory impairment in rats by reducing oxidative nitrogen stress. In fact, over the years, many studies have shown that INS can treat corneal injuries. Yang et al. 12 identified that INS promoted corneal nerve repair and wound healing in mice with type 1 diabetes. Cruz‐Cazarim et al. 13 showed that INS could treat dry eye syndrome and corneal injuries. Morever, a recent clinical study suggested that topical INS was an effective way to safely promote the healing of persistent epithelial defects in patients who were unresponsive to standard treatment. 14 However, the use of INS in the treatment of corneal alkali burns has been rarely reported. We hypothesize that the local application of INS may be a promising strategy for the treatment of corneal alkali burns due to its antioxidant capacity.
In addition to inhibiting oxidative stress, the treatment of CNV is also essential for corneal alkali burns. CNV can be effectively treated by inhibiting vascular endothelial growth factor (VEGF) and its receptors, which modulate angiogenesis. 15 Anti‐VEGF antibodies are therefore used to treat CNV, either through topical or subconjunctival applications. 16 Unfortunately, anti‐VEGF antibodies are generally limited due to their poor efficacy, side effects, and drug resistance. 7 RNA interference is a powerful approach to knocking down target genes. 17 VEGF small interfering RNA (siVEGF) reduces VEGF expression and CNV. 18, 19, 20, 21 Accordingly, it is reasonable to speculate that siVEGF could enhance the therapeutic effects of INS on corneal alkali injury, and codelivery of INS and siVEGF may provide a new combination therapy for corneal alkali injury.
It is well‐established that corneal physiological and physical barriers impair drug and siRNA penetration. To improve bioavailability, nanopharmaceuticals have been extensively developed to deliver siRNA or ocular drugs to treat ocular diseases. 22, 23, 24 A wealth of studies have suggested that INS‐loaded liposomes could increase the bioavailability of INS. 25, 26 However, the poor stability of liposomes leads to the rapid release of the encapsulated drugs, which impairs the therapeutic effects of drugs. Chitosan (CS) is a deacetylated pyran polysaccharide isolated from chitin that is biocompatible, nontoxic, and biodegradable and has been widely used to prepare nanocarriers such as micelles and nanoparticles (NPs). CS can also be used as a coating for liposomes to improve their stability in vitro and in vivo. 27 Furthermore, CS can form NPs and be loaded with negatively charged nucleic acids and have been considered promising carriers for gene delivery. 28 Morever, this encapsulation protects nucleic acids from host nucleases. 29 *As a* quaternary CS derivative, trimethyl chitosan (TMC) also possesses these properties, and it is preferred due to its high water solubility, ionic stability, and cationic density. 30 *In this* study, we developed a novel eye drop formulation based on liposomes and TMC to encapsulate and deliver INS and siVEGF. We expect these NPs to enhance the treatment efficacy of corneal alkali burns through the cooperative effects of INS and siVEGF, as well as their enduring effects and high bioavailability. We also explored the potential mechanism of the siVEGF‐TMC‐INS‐liposome (siVEGF‐TIL) NPs in treating corneal alkali burn.
## Materials and reagents
Human recombinant INS, perfluorooctyl bromide (PFOB), and 1,1′‐dioctadecyl‐3,3,3′,3′‐tetramethylindo‐tricarbocyanine iodide (DiR) were bought from Sigma‐Aldrich. 1,2‐Stearoyl‐sn‐glycerol‐3‐phosphoethanolamine‐N‐[methoxy (polyethylene glycol)‐2000] (DSPE‐mPEG2000), soybean phosphatidylcholine (SPC) and cholesterol were purchased from Avanti Polar Lipids Inc. TMC (viscosity 10–50 mPa s, degree of deacetylation $85\%$, degree of trimethyl substitution $50\%$) was bought from Golden‐Shell. 1,1′‐Dioctadecyl‐3,3,3′,3′‐tetramethylindocarbocyanine perchlorate (DiI), 2‐(4‐amidinophenyl)‐6‐indolecarbamidinedihydrochloride (DAPI), Lipofectamine 2000, total superoxide dismutase (SOD) assay kit with WST‐8, lipid peroxidation malondialdehyde (MDA) assay kit, glutathione (GSH) assay kit were bought from Beyotime. Cell Counting Kit‐8 (CCK‐8) was obtained from Dojindo. Both human and rat siVEGF and FAM‐labeled siVEGF (siVEGFFAM) were obtained from Gene Pharma Co., Ltd. The sense strand of human siVEGF was 5′‐GCAGAUUAUGCGGAUCAAATT‐3′ and the antisense strand was 5′‐UUUGAUCCGCAUAAUCUGCTT‐3′. The sense strand of rat siVEGF was 5′‐CCCAUGAAGUGGUGAAGUUTT‐3′ and the antisense strand was 5′‐AACUUCACCACUUCAUGGGTT‐3′. The primary antibodies of cystine/glutamate exchanger (xCT), glutathione peroxidase (GPX) 4, VEGF, β‐actin, and second antibodies were purchased from Abcam. The ELISA kit was used for analyzing the number of cytokines purchased from Multi Sciences Inc. Hematoxylin–eosin (H&E) staining kit and immunohistochemistry (IHC) kit were obtained from Servicebio Inc. CHCl3 was obtained from Chuandong Chemical Co. Ltd. A Millipore water purification system provided deionized water. All other reagents were of analytic grade.
Human corneal epithelial cells (HCECs) were provided by BeNa Culture Collection (BNCC337876) and cultured with Dulbecco's Modified Eagle Medium (Gibco) medium containing $10\%$ FBS (EVERY GREEN) and $1\%$ penicillin–streptomycin and incubated under a $5\%$ CO2 atmosphere at 37°C.
## Synthesis of NPs
First, INS‐loaded liposomes were prepared using a reversed‐phase evaporation method. Briefly, an appropriate mass ratio of hybrid lipid (12 mg SPC, 4 mg DSPE‐mPEG2000, and 4 mg cholesterol) was dissolved into 5 mL trichloromethane (CHCl3): absolute ether (1:1), 1 mL INS solution (4 mg/mL) in citric–Na2HPO4 buffer (pH 3.0) was added. The mixture was ultrasonicated in a water bath to form w/o emulsion and then transferred into a 100 mL round‐bottomed flask, which was subsequently evaporated under reduced pressure with a rotating speed of 50 rpm at 30°C for 3 h to remove the organic solvent. Afterward, 4 mL citric acid–Na2HPO4 buffer (pH 5.6) was added to hydrate the films until a homogeneous dispersion and this mixture was transferred to a 10 mL EP tube. Then, 0.5 mL PFOB was added to the mixture, which was sonicated (55 W, four 3 min) with a sonicator (Sonics & Materials Inc.) in an ice bath, and then centrifugation was performed at 6000 rpm/min for 5 min. Supernatants were removed and sediments were collected and resuspended by phosphate‐buffered saline (PBS; pH 7.4), then stored at 4°C for further use. Subsequently, an aliquot of INS‐lip was mixed with the same volume of TMC (0.5 mg/mL) solution in PBS and then shaken and incubated at 4°C for 1 h to prepare TMC‐INS‐lip (TIL). Finally, siVEGF was loaded by electrostatic adsorption with an optimal ratio to obtain siVEGF‐TMC‐INS‐lip (siVEGF‐TIL). siVEGF‐TMC‐lip (siVEGF‐TL) was made using the same protocol but without INS. Similarly, Empty‐TMC‐lip (TL) was also prepared using the same protocol but with the omission of INS and siVEGF.
## Characterization of NPs
The morphology NPs was observed by light microscope transmission electron microscope (TEM) (Hitachi H‐7600). The particle size and zeta potential were measured using a laser particle size analyzer system (Nano, ZS90; Malvern Instrument Ltd). UV–vis–NIR absorption spectrum of INS solution at different concentrations, INS‐lip, TIL, and siVEGF‐TIL were recorded using a UV–vis–NIR spectrophotometer (UV‐3600; Shimadzu) at room temperature. To determine the encapsulation efficiency (EE) and drug loading capacity (DLC), INS‐lip, TIL, and siVEGF‐TIL were centrifuged with an ultracentrifuge at 20,000 × g and 4°C for 30 min. The supernatant was measured for free INS using ultra‐high‐performance liquid chromatography (UHPLC; Shimadzu20) equipped with an ultraviolet (UV) variable wavelength detector and Ultimate XB‐C18 column. For UHPLC measurement, the mobile phase was a mixture of water, acetonitrile, and trifluoroacetic acid with a ratio of 68.5:31.5:0.1. The flow rate was 1 mL/min and the detection wavelength was set at 220 nm. The entrapment efficiency and content were calculated by Equations [1] and [2]: [1] EE%=mass of encapsulated drug/total mass of drug×$100\%$, [2] DL%=mass of encapsulated drug/mass of total liposomes×$100\%$.
Loading siRNA capacity and siRNA protection of NPs: Different amounts of TIL NPs prepared in the light of the above method were added to the siVEGF to make the mixed solution (TIL NPs:siVEGF = 0–9:1) and then incubated for 1 h to fully combine the siVEGF with TIL NPs. All samples were evaluated by electrophoresis on a $2\%$ (wt/vol) agarose gel at 80 mV for 40 min. For nuclease protection, the siVEGF and siVEGF‐TIL NPs were incubated with or without RNase at 37°C for 30 min, followed by halting the nuclease activity at 80°C for 5 min. Next, the siVEGF‐TIL NPs incubated with or without RNase were shaken at 4°C for 2 h. Then, the samples were examined with $2\%$ (wt/vol) agarose gel and the electrophoresis was performed at 80 V for 40 min.
## In vitro drug release from NPs
For the determination of INS release from NPs, the INS‐lip, TIL, and siVEGF‐TIL were placed in dialysis bags (MWCO 3.5 kDa) and immersed in 50 mL PBS (0.2 mol/L, pH 7.4), then incubated at 37°C and 100 rpm. At each predetermined time, 200 μL of the sample was drawn and replaced with an equal volume of fresh medium. The content of INS was measured by UHPLC. As for the siVEGF release from siVEGF‐TIL, siVEGFFAM‐TIL was placed in dialysis bags (MWCO 50 kDa) and immersed in 10 mL of PBS at 37°C with 100 rpm. At each scheduled time, a 200 μL release medium was withdrawn and supplemented with a 200 μL fresh medium. the amount of siVEGFFAM (λ excitation/λ emission = 480 nm/520 nm) was detected by a microplate reader (BioTek Instruments Inc.).
## Cellular uptake of NPs
HCECs cells were seeded in confocal dishes at a density of 1 × 104 cells per well and divided into two groups randomly, including the INS‐lip group and TIL group. After culturing for 24 h, the culture medium was replaced with the medium containing INS‐lip or TIL (all NPs were stained with DiI [λ excitation/λ emission = 549 nm/565 nm]), respectively. After different intervals of incubation, cells were washed to remove noninternalized particles, fixed with $4\%$ formaldehyde, and nuclei were stained with DAPI (λ excitation/λ emission = 364 nm/454 nm). The process of cellular uptake was observed with a confocal laser scanning microscope (CLSM) (Nikon). Furthermore, the quantitative intracellular uptake of INS‐lip and TIL at different intervals was analyzed with flow cytometry (FCM).
## Dispersion and retention of NPs on ocular anterior segments
The ocular surface of Sprague Dawley (SD) rats was live monitored using an FLI system (Cri Inc.) to access the retention of fluorescent NPs. SD rats were divided into two groups ($$n = 3$$ per group, INS‐lip, and siVEGF‐TIL), pentobarbital sodium ($3\%$, 1 mL/kg) was used to sedate rats, then 5 μL NPs containing DiR (λ excitation/λ emission = 748 nm/780 nm) were dropped onto the ocular surface, respectively. Subsequently, the relative fluorescence intensity of NPs staying in the corneas for each preset period was observed and recorded with an in vivo imaging system Spectrum (LB983; Bold).
A similar test for topical delivery of NPs to rat's corneas was performed as follows: rats from the three groups ($$n = 3$$ per group, INS‐lip, siVEGF‐TIL, and siVEGFFAM‐TIL) also were treated with the corresponding NPs, respectively (all NPs were stained with DiI [λ excitation/λ emission = 549 nm/565 nm]) and placed in a dark environment for 4 h. Sequentially, the corneas of each rat were enucleated, followed by fixation with a $10\%$ neutral buffered formalin solution. The tissues were embedded in frozen section media for cryosection and cut into 8 μm sections with a cryostat microtome (CM1860; LEICA). The cryosections were washed with PBS twice to remove the frozen media, followed by staining with DAPI for 30 min. The distribution of NPs in the cornea was observed by fluorescence microscopy (Eclipse Ti‐S; Nikon).
## Colocalization and cell transfection of siVEGF‐TIL
HCECs cells were seeded in confocal dishes at 1 × 104 cells/well and cultured for 24 h. After that, the culture medium was replaced with the medium containing siVEGFFAM‐TIL NPs (TIL were stained with DiI) and incubated for 3 h. Finally, the coverslips were observed with a CLSM (Nikon). Approximately 1 × 104 HCECs cells/well were seeded in six‐well plates and cultured for 24 h. The siVEGFFAM‐TIL was added to each well, separately, followed by incubation for 24 h, with Lipofectamine 2000 as a control. Transfection efficiencies were measured by FCM.
## RNA extraction and real‐time polymerase chain reaction
*The* gene expressions of VEGF were analyzed by real‐time polymerase chain reaction (qRT‐PCR). The designed primers are listed in Table S4. The total RNA was extracted using a Steady Pure Universal RNA Extraction Kit (Accurate Biotechnology, Hunan Co., Ltd.). Approximately 1000 ng of total RNA was extracted. Reverse transcription was performed by Evo M‐MLVRT Kitt (Accurate Biotechnology, Hunan Co., Ltd.). After that, qRT‐PCR was performed using CFX96 Real‐Time System (Bio‐Rad) with SYBR Green Supermix (Accurate Biotechnology, Hunan Co., Ltd.). The relative gene expression levels were calculated by the 2−∆∆ C t method using GAPDH as a control. *Each* gene was analyzed in triplicate to reduce randomization error.
## CCK‐8 assays
HCECs were plated into a 96‐well plate at the density of 5 × 103 cells per well and cultured in incubators for 24 h. The growth medium was replaced by the serum‐free medium containing different concentrations of INS and various NPs (TL, siVEGF‐TL, INS‐lip, TIL, siVEGF‐TIL). After being cultured for 24 h, the cells were washed thrice with PBS, and then freshly prepared CCK‐8 solutions were added to each well. The CCK‐8 was used to detect cell viability in vitro according to the manufacturer's instructions for CCK‐8. Absorbance at 450 nm was measured by a microplate reader (BioTek Instruments Inc.).
## In vitro inhibition of oxidative stress, inflammation, and neovascularization by NPs
To investigate the antioxidant stress, anti‐neovascularization, and anti‐inflammatory capacity in vitro, HCECs cells were exposed to H2O2 (1 mmol/L) for 4 h, followed by the addition of TL, siVEGF‐TL, INS, INS‐lip, TIL, siVEGF‐TIL for another 24 h incubation. The levels of SOD, MDA, GSH, VEGF, CD31, TNF‐α, IL‐6, and MMP‐9 were detected by commercial kits following the specifications provided by the manufacturer precisely. The experiments were carried out in triplicate.
First, the levels of SOD, GSH, and MDA were examined to verify the ability of NPs to inhibit oxidative stress in vitro. Despite decreased SOD and GSH levels in all H2O2‐induced groups, the INS, INS‐lip, TIL and siVEGF‐TIL groups showed higher levels of SOD and GSH than the PBS group, and this increase was most significant in the TIL and siVEGF‐TIL groups (Figure 5a,b). In contrast, MDA levels were markedly increased in all H2O2‐induced groups. However, all reagents containing INS alleviated this change in MDA levels, and TIL and siVEGF‐TIL showed the strongest inhibitory effect (Figure 5c).
**FIGURE 5:** *In vitro inhibition of oxidative stress, inflammation, and neovascularization by NPs. SOD activity (a), GSH concentration (b), MDA content (c), TNF‐α (d), IL‐6 (e), MMP‐9 (f), VEGF (g), and CD31 (h) concentrations in H2O2‐activated human corneal epithelial cells that received different treatments (n = 3 per group). Results were presented as the mean ± SD. *p < 0.05; **p < 0.01; ***p < 0.001. Comparison between each group and the normal group (#
p < 0.05; ##
p < 0.01; ###
p < 0.001). GSH, glutathione; INS, insulin; INS‐lip, insulin liposome; MDA, malondialdehyde; siVEGF‐TL, siVEGF‐trimethyl chitosan‐coated liposome; siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome; SOD, superoxide dismutase; TIL, trimethyl chitosan‐coated insulin liposome; TL, trimethyl chitosan‐coated liposome.*
TNF‐α, IL‐6, and MMP‐9 concentrations in cells were further analyzed to determine the anti‐inflammatory effect of NPs. As shown in Figure 5d–f, the concentrations of TNF‐α, IL‐6, and MMP‐9 in all H2O2‐induced groups were markedly increased. INS, INS‐lip, TIL, and siVEGF‐TIL treatment reduced the levels of secreted TNF‐α, IL‐6, and MMP‐9. Among them, TIL and siVEGF‐TIL had the strongest anti‐inflammatory effects. These experiments demonstrated that INS and reagents containing INS could suppress oxidative stress and the inflammatory reaction induced by H2O2, and TIL and siVEGF‐TIL were the most potent.
Next, ELISA was used to examine VEGF and CD31 levels to evaluate the ability of NPs to inhibit neovascularization. The levels of VEGF and CD31 were markedly increased in H2O2‐induced groups, except the siVEGF‐TIL group. As shown in Figure 5g,h, among the experimental groups, the best therapeutic effect was observed in cells treated with siVEGF‐TIL. VEGF and CD31 levels decreased to 6.70 ± 0.60 and 4.03 ± 0.55 pg/mg protein, respectively. In contrast, weaker suppression of VEGF and CD31 was observed in the TIL and siVEGF‐TL groups than in the siVEGF‐TIL group.
These results showed that although TIL can inhibit oxidative stress and inflammation, it can not effectively resist neovascularization. siVEGF‐TL has a certain inhibitory effect on neovascularization, but its ability to inhibit oxidative stress and inflammation is poor. However, siVEGF‐TIL combines INS and siVEGF, which not only can facilitate HCECs resistance to oxidative stress and the inflammatory response but also inhibit VEGF and CD31 expression in HCECs under oxidative stress.
## The model of alkali burn of the male SD rat
The animal care and procedures were complied with the Principles of Laboratory Animal Care. It conformed to the standards of the ARVO Statement for the use of animals in Ophthalmic and Vision Research. The Science and Technology Ethics Committee of The Second Affiliated Hospital of the Chongqing Medical University approved this research protocol. Two hundred SD rats (male, 7–8 weeks of age) were obtained from the Animal Experiment Center of Chongqing Medical University (Chongqing, China). All animals weighed between 150 and 200 g were housed at constant temperature (20 ± 1°C) and humidity (50 ± $5\%$). Their diet consisted of standard rat chow and water ad libitum. The right eyes of SD rats were used and the left eyes served as the normal group. SD rats were anesthetized by an intraperitoneal injection of pentobarbital sodium ($3\%$, 1 mL/kg), and local anesthesia was performed by topical application of $0.5\%$ tetracaine (Bausch & Lomb). A 3 mm diameter filter paper soaked with 1 N sodium hydroxide (NaOH) was placed on the central corneal surface for 40 s followed by thorough rinsing with a large amount of sterile isotonic saline ($0.9\%$ sodium chloride [NaCl]) for 1 min, immediately. 31, 32, 33 The depth of corneal injury was involving corneal epithelium and superficial stroma which was confirmed by H&E (Figure S1).
## Clinical evaluations
After alkali burn, the SD rats were randomized into six groups (PBS, siVEGF‐TL, INS, INS‐lip, TIL, siVEGF‐TIL). Five microliters different reagents were dropped into the right eye twice a day respectively. No treatment for left eyes. To observe the degree of corneal opacity, corneal epithelial repair, and CNV, alkali‐burned corneas were examined by portable slit lamps before and after fluorescein sodium staining every day and photographed on Days 1, 3, 7, and 14. The IOP was measured using a handheld tonometer (iLab tonometer; iCare). Corneal opacity was scored using a scale of 0–4 (Grade 0 = completely clear; Grade 1 = slightly hazy, iris and pupils easily visible; Grade 2 = slightly opaque, iris and pupils still detectable; Grade 3 = opaque, pupils hardly detectable; and Grade 4 = completely opaque with no view of the pupils). The corneal epithelial healing rate was calculated according to the following formula (k represents the corneal epithelial healing rate, S 0 represents the 0‐day staining area, and S t represents the observed staining area): [3] k=S0−St×$100\%$.
For CNV, the total corneal area and vessel area were manually selected with ImageJ. The CNV area was presented as the percentage with the following formula: [4] vesselareachosen/totalcornealarea×$100\%$.
## UHPLC–MS metabolomics analysis
On Day 14 after the alkali burn, corneal tissues of the PBS group and INS group were collected ($$n = 8$$ per group) and 25 mg of each sample was weighed into an EP tube, and 500 μL extract solution (methanol:acetonitrile:water = 2:2:1, with the isotopically labeled internal standard mixture) was added. Then the samples were homogenized at 35 Hz for 4 min and sonicated for 5 min in an ice water bath. The homogenization and sonication cycle was repeated three times. Then the samples were incubated for 1 h at −40°C and centrifuged at 12,000 rpm for 15 min at 4°C. The resulting supernatant was transferred to a fresh glass vial for analysis. The quality control sample was prepared by mixing an equal aliquot of the supernatants from all of the samples. LC–MS/MS analyses were performed using a UHPLC system (Vanquish; Thermo Fisher Scientific) with a UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm) coupled to Orbitrap Exploris 120 mass spectrometer (Orbitrap MS; Thermo Fisher Scientific). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 ammonia hydroxide in water(pH 9.75) and acetonitrile. The autosampler temperature was 4°C, and the injection volume was 2 μL. The Orbitrap Exploris 120 mass spectrometer was used for its ability to acquire MS/MS spectra on information‐dependent acquisition (IDA) mode in the control of the acquisition software (Xcalibur; Thermo Fisher Scientific). In this mode, the acquisition software continuously evaluates the full scan MS spectrum. The ESI source conditions were set as follows: sheath gas flow rate as 50 Arb, Aux gas flow rate as 15 Arb, capillary temperature 320°C, full MS resolution as 60,000, MS/MS resolution as 15,000 collision energy as $\frac{10}{30}$/60 in NCE mode, spray Voltage as 3.8 kV (positive) or − 3.4 kV (negative), respectively.
The mechanism by which siVEGF inhibits VEGF expression and CNV is currently well understood. Briefly, siVEGF binds with RISC, causing the decomposition of the target mRNA to prevent it from being translated into a functional protein. However, the mechanism by which INS affects corneal alkali burn is unclear and was investigated in this study. 42 INS is an anabolic agent; therefore, we hypothesized that INS could treat alkali‐burned corneas, through metabolic regulation. Metabonomics is the accurate metabolomic analysis of dynamic metabolic changes in cells, tissues, and whole organisms. 43 At 14 days after alkali burns, the corneal tissues of the PBS group and INS group were subjected to UHPLC–MS metabolomics analysis ($$n = 8$$ per group). Principal component analysis showed a trend in metabolites that were partially separated between the PBS group and INS group, indicating differences among them (Figure S4A,B). To further determine the differences in metabolic profiles between the two groups, orthogonal projection to latent structure‐discriminant analysis (OPLS‐DA) score plots were constructed. As shown in Figure 7a,b, separations in the INS group and PBS group were recognized in both cationic and anionic modes. Then, permutation analysis of the OPLS‐DA model was performed, and the results indicated that the OPLS‐DA model fitting was valid and stable in both ion modes (Figure S4C,D). The altered metabolites were investigated by volcano plot analysis based on the criteria of fold change (FC) > 1 and $p \leq 0.05.$ As shown in Figure 7c,d, red represents upregulated metabolites, and blue represents downregulated metabolites in the PBS group compared with the INS group. Significantly altered metabolites were selected according to VIP > 1 and $p \leq 0.05$ and were identified by searching the database. Forty‐nine altered metabolites were shown to be significantly different under anionic mode (Figure 7e; Table S2). Based on KEGG analyses, 27 essential signaling pathways associated with these altered metabolites were identified, with 17 associated with significantly higher levels of glutamate in the PBS group than the other group (Table S3).
**FIGURE 7:** *UHPLC–MS metabolomics analysis. OPLS‐DA score plot of the PBS group and INS group under the cationic (a) and anionic mode (b). Volcano plot of the untargeted metabolomics according to the criteria (FC > 1; p < 0.05) in cationic (c) and anionic mode (d). compared with the INS group, red stands for upregulated metabolomics, and blue stands for downregulated in the PBS group. (e) Heatmap of the differential metabolites under the anionic mode of untargeted metabolomics. The blue color represents the low relative level of each metabolite, and the red color represents the high relative level of each metabolite. (f) Differential abundance score of KEGG pathway. Different colors indicate that the pathway belongs to different metabolic classifications. The regular value of the line segment indicates that the pathway is upregulated as a whole. On the contrary, it indicates that the pathway is downregulated as a whole. The size of the endpoint of the line segment indicates the amount of material annotated in the path. (g) KEGG pathways plotted of ferroptosis. Red stands for upregulated metabolomics in this pathway in the PBS group, when compared with the INS group. Pathway maps are displayed with copyright permission from KEGG. INS, insulin; OPLS‐DA, orthogonal projection to latent structure‐discriminant analysis.*
Glutamate is a nonessential amino acid that naturally occurs in the L‐form and plays an important role in protein and carbohydrate metabolism, boosting resistance to hypoxemia, stimulating oxidation processes, preventing potential redox decreases, affecting glycolysis in tissues, and exerting hepatoprotective effects. 44 In addition, glutamate is a pivotal regulator of ferroptosis. 45 *In this* metabolomic analysis, the ferroptosis pathway was significantly enhanced in the PBS group compared with the INS group, and the differential abundance score was 1 (Figure 7f,g). Ferroptosis is closely associated with oxidative stress. Therefore, we hypothesized that INS and all the INS‐loaded NPs in this study could treat alkali‐burned corneas by decreasing glutamate levels and inhibiting the ferroptosis pathway.
## Western blot
The total proteins were extracted from five whole corneas per group at 14 days. The Bicinchoninic Acid Protein Assay Kit (Beyotime) was used to determine the concentration of protein. The protein samples were separated at a constant voltage in sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred to the Immobilon‐P membrane (IPVH00010; Millipore). Membranes were incubated with QuickBlock Western (Beyotime) for 30 min at 37°C and then immersed in primary antibodies including anti‐β‐actin (1:1000), anti‐VEGF (1:1000), anti‐xCT (1:1000), anti‐GPx4 (1:1000) under 4°C overnight. Subsequently, after being washed with TBS/Tween 20 twice, the membranes were probed with secondary antibodies for 1 h at room temperature, followed by washes with TBS/Tween 20. Finally, these membranes were visualized by the ECL detection system (Bio‐Rad). Gray values were measured to analyze the expression level of target proteins.
## Antioxidant stress and anti‐inflammatory activity in vivo
Corneas of each group at 14 days were harvested and the levels of SOD, GSH, and MDA were quantified by commercial kits according to the instructions. Also, the corneal tissues were collected and homogenized with RIPA lysate, followed by centrifugation at 15,000 rpm for 15 min. Then, the levels of Glu, TNF‐α, IL‐6, and MMP‐9 in the supernatant were detected by commercial ELISA kits according to the procedure provided by the manufacturer.
## Histological and immunohistochemical analysis
At 14 days after treatment with different reagents, the normal and alkali‐burned corneas were enucleated for histological and immunohistochemical analysis, fixed in $10\%$ buffered formalin, and successively dehydrated in a series of concentrations of ethanol and dimethylbenzene. Afterward, the treated tissues were fixed in paraffin, and tissue slices (thickness 8 mm) were stained with H&E. In addition, the level of CD31 in the corneal tissues was identified using IHC.
## Statistical analysis
Statistical analysis was performed by the GraphPad Prism 7 program. Quantitative data were reported as mean ± standard deviation. Two‐group comparisons were conducted using a two‐tailed Student's t‐test. One‐way analysis of variance followed by Tukey's multiple comparisons test was used for multigroup comparisons. $p \leq 0.05$ was considered statistically significant.
## Preparation and characterization of NPs
To overcome the physiological and physical barriers of the cornea and improve the bioavailability of drugs, INS was loaded into liposomes (INS‐lip) and coated with TMC to prepare TMC‐coated INS liposomes (TIL). Then, siVEGF was added to optimize the effect of the NPs (siVEGF‐TIL) on the treatment of corneal alkali burns (Figure 1a). To examine the formation of siVEGF‐TIL NPs, zeta potential analysis, particle size analysis, and TEM was performed. INS‐lip could be coated with TMC to prepare TIL via the electrostatic interaction between negative phospholipids and positive TMC, resulting in charge reversal and the enlargement of particle size. Compared with negatively charged INS‐lip (−25.05 ± 3.04 mV), TIL showed a stronger positive charge (+15.52 ± 4.78 mV), and TIL (183.23 ± 12.61 nm) exhibited larger diameters than INS‐lip (124.22 ± 6.04 nm), which indicated that TMC was successfully coated on INS‐lip. As positive NPs, TIL can adsorb negative siRNA; thus, siVEGF‐TIL had a smaller zeta potential (12.40 ± 0.40) and larger diameter (216.63 ± 4.51) than TIL, providing evidence of the loading of siVEGF in TIL (Figure 1b,c; Table S1). TEM images indicated that INS‐lip, TIL, and siVEGF‐TIL had the uniform and spherical appearances; notably, TIL and siVEGF‐TIL were both coated with a transparent TMC shell (Figure 1d).
**FIGURE 1:** *Preparation and characterization of NPs. (a) Schematic illustration of the preparation approach to synthesize siVEGF‐TIL. (b) Zeta potential and (c) size of INS‐lip, TIL, and siVEGF‐TIL (n = 3 per group). (d) Transmission electron microscopy (TEM) images of INS‐lip, TIL, and siVEGF‐TIL (scale bar = 100 nm). (e) UV–vis–NIR spectrum of INS, TL, INS‐lip, TIL, and siVEGF‐TIL. (f) Assessment of loading capacity. Agarose gel electrophoresis reflects the amount of unbound siVEGF. With increases in TIL/siVEGF mass ratio, the amount of unbound siVEGF gradually decreased. (g) RNase protection assay in agarose gel electrophoresis. siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome; INS‐lip, insulin liposome; TIL, trimethyl chitosan‐coated insulin liposome; 1, siVEGF; 2, siVEGF‐TIL; 3, siVEGF‐TIL 2 h (siVEGF‐TIL NPs shook at 4°C for 2 h).*
To demonstrate the feasibility of entrapping INS, UV–vis–NIR spectra were recorded. As Figure 1e shows, INS‐lip, TIL, and siVEGF‐TIL featured the characteristic absorption of INS at 274 nm, suggesting the successful encapsulation of INS into NPs, while TMC‐coated liposomes (TL) had no characteristic INS absorption peaks. EE and DL directly influence treatment efficacy and the administration of NPs. Adequate doses of medication are a prerequisite for effective treatment. The INS EE% values of INS‐lip, TIL, and siVEGF‐TIL were 64.58 ± $2.75\%$, 65.98 ± $3.96\%$, and 64.75 ± $6.22\%$, respectively. The INS DL% of INS‐lip, TIL, and siVEGF‐TIL were 25.83 ± $1.10\%$, 26.39 ± $1.58\%$, and 25.90 ± $2.49\%$, respectively. INS EE% and DL% showed no significant differences among INS‐lip, TIL, and siVEGF‐TIL ($p \leq 0.05$), which indicated that neither TMC nor siVEGF affected the EE or DL of INS liposomes (Table S1). To confirm the siRNA binding capabilities of TIL, agarose gel electrophoresis was performed after mixing the TIL with siVEGF at different TIL/siRNA ratios. As shown in Figure 1f, the migration of siVEGF in the gel gradually slowed as TIL ratios increased. Almost no free siVEGF could be detected at mass ratios above 5, demonstrating the complete binding of siVEGF by TIL conjugates. The capability of TIL to protect siRNA from nuclease degradation was verified by incubating siVEGF‐TIL with RNase A for 30 min. As shown by agarose gel electrophoresis assays, the naked siVEGF RNase (−) group had a free RNA band, while the naked siVEGF RNase (+) group had no visible bands, indicating that siVEGF had been degraded in the presence of RNase. Regarding siVEGF‐TIL, both the RNase (+) and RNase (−) groups showed no apparent bands, but siVEGF‐TIL NPs shook at 4°C for 2 h with or without RNase both could observe the bands, indicating that siVEGF could be released from the NPs and siVEGF‐TIL could protect siVEGF from RNase degradation (Figure 1g).
## Sustained release of INS and siRNA in vitro
As shown in Figure 2a, the cumulative release of INS from INS‐lip, TIL, and siVEGF‐TIL was 86.38 ± $3.22\%$, 85.06 ± $1.93\%$, and 83.54 ± $1.56\%$, respectively. In INS‐lip, the cumulative release of INS increased suddenly after 8 h, which might be attributed to the rupture of INS‐lip, contributing to the quick release of drugs. Due to the electronic interactions between TMC and phospholipids, which could promote the stability of the liposomes, the release of INS was slow and sustained in TIL. 34 At the same time, siVEGF‐TIL and TIL had similar INS release curves and cumulative release, verifying that siVEGF did not affect the release of INS. Furthermore, the siRNA release kinetics of siVEGF‐TIL revealed sustained‐release properties (Figure 2b).
**FIGURE 2:** *Sustained release of INS and siRNA in vitro. INS release kinetics (a) in INS‐lip, TIL, and siVEGF‐TIL, and siVEGF release kinetics (b) in siVEGFAM‐TIL. INS‐lip, insulin liposome; TIL, trimethyl chitosan‐coated insulin liposome; siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome.*
Overall, siVEGF‐TIL and TIL showed ideal sustained release, which was conducive to maintaining concentrations of drugs and genes in the cornea and thus provided potent and prolonged therapeutic efficacy. 35 In addition, the sustained‐release system can decrease the side effects of drugs on the cornea and significantly improve medication safety. 36 Furthermore, the sustained release of drugs can reduce dosing frequency, which is one way to enhance patient adherence. 37
## Efficient delivery of NPs in vitro and in vivo
Efficient intracellular uptake of NPs is required to improve the therapeutic efficacy of drugs. 38 Therefore, a CLSM was performed to examine the intracellular uptake of NPs in this study. As shown in Figure 3a,b, in HCECs, DiI‐TIL could be phagocytized faster and more effectively than DiI‐INS‐lip. The FCM results (Figures 3c,d) showed more red fluorescence in the cells that were incubated with DiI‐TIL than in cells treated with DiI‐INS‐lip, especially at 1 and 2 h ($p \leq 0.05$). As shown in Figure 3e,f, fluorescent images were used to evaluate the residence time of NPs on the ocular surface. The attenuation of the fluorescence intensity in the DiR‐INS‐lip group was significantly faster than that in the DiR‐TIL group($p \leq 0.05$). Four 4 h later, the fluorescence signal decreased 10‐fold in DiR‐INS‐lip‐treated eyes, whereas it only decreased 2‐fold in DiR‐TIL‐treated eyes, indicating an improved residence time of TIL compared with INS‐lip. Morever, to verify NPs permeation into the tissue, the distribution of the NPs throughout cornea layers in posttreatment corneal cryosections was investigated by fluorescence microscopy. Red fluorescence was detected throughout deep stromal layers in eyes collected 4 h after DiI‐TIL drop application, whereas red fluorescence was observed only in the corneal epithelium in the DiI‐INS‐lip group (Figure 3g). Compared with negatively charged INS‐lip, positively charged TIL could enhance corneal permeability and significantly increase drug transcorneal penetration through strong interactions with the negatively charged corneal surface and the substantial tissue adhesive property of TMC. 39
**FIGURE 3:** *Efficient delivery of NPs in vitro and in vivo. Efficient delivery of NPs in vitro and in vivo. Intracellular uptake of INS‐lip (a) and (b) TIL as observed by CLSM after various intervals of incubation with human corneal epithelial cells. The scale bars are 25 μm. (c) Flow cytometry analysis of intracellular uptake of INS‐lip or TIL labeled with DiI. (d) Quantitative results following flow cytometry analysis of cellular uptake in INS‐lip or TIL groups (n = 3 per group). Results were presented as the mean ± SD.*p < 0.05; **p < 0.01; ***p < 0.001. Representative fluorescence images of rat eyes (e) and quantification (n = 3 per group) of the fluorescence signal (f) at different time points after topical administration of INS‐lip or TIL. Results were presented as the mean ± SD. (g) DiI‐stained INS‐lip and TIL suspensions were dropped into rat eyes and the permeation capacities of the nanoparticles into corneal tissues were evaluated after 4 h. Scale bar = 50 μm. n = 3 per group. CLSM, confocal laser scanning microscope; INS‐lip, insulin liposome; TIL, trimethyl chitosan‐coated insulin liposome.*
## Colocalization of siRNA and TIL and the efficacy of siVEGF‐TIL mediated VEGF downregulation
As shown in the merged confocal microscopic images (Figure 4a), after 3 h of incubation, the majority of siVEGFFAM overlapped with DiI‐TIL, demonstrating the strong colocalization of siVEGF and TIL in HCECs. CLSM showed that siVEGFFAM was colocalized with DiI‐TIL in corneal cryosections and penetrated the deep stromal layer at 4 h after the administration of eye drops containing siVEGFFAM‐DiI‐TIL (Figure 4b).
**FIGURE 4:** *Colocalization of siRNA and TIL and the efficacy of siVEGF‐TIL mediated VEGF downregulation. (a) CLSM images showing the colocalization of TIL labeled with DiI (red) and siVEGF labeled FAM (green) when siVEGF‐TIL was incubated with human corneal epithelial cells for 3 h. The nuclei were stained with DAPI (blue). The scale bars are 25 μm. n = 3 per group. (b) siVEGF‐TIL suspensions were dropped into rat eyes and the colocalization of TIL labeled with DiI (red) and siVEGF labeled FAM (green) into corneal tissues was evaluated after 4 h. Scale bars = 50 μm. n = 3 per group. (c) Flow cytometry analysis of transfection rates of siVEGF‐TIL and siVEGF‐Lipo2000, the siVEGF was labeled with FAM. Results were presented as the mean ± SD. n = 3 per group. (d) The expression of the VEGF gene in cells after treating H2O2‐HCECs with different content of siVEGF in siVEGF‐TIL for 24 h (n = 3 per group). Results were presented as the mean ± SD. *p < 0.05; **p < 0.01; ***p < 0.001. CLSM, confocal laser scanning microscope; siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome; TIL, trimethyl chitosan‐coated insulin liposome.*
The transfection efficiency of siVEGF‐TIL was examined by FCM, and siVEGFFAM‐Lipofectamine 2000 (siVEGFFMA‐Lipo 2000) served as the control. The concentration of siVEGF in the various formulations used was 100 nM. The transfection rate was approximately 63.92 ± $5.36\%$ for siVEGFFAM‐TIL and 68.20 ± $5.90\%$ for siVEGFFMA‐Lipo2000 (Figures 4c and S2). Compared with siVEGFFMA‐Lipo2000, siVEGF‐TIL exhibited almost the same transfection efficiency. These results showed that siVEGF‐TIL had adequate transfection efficiency in vitro.
To identify the efficiency of siVEGF‐TIL in downregulating VEGF expression, qRT‐PCR was performed. As shown in Figure 4d, qRT‐PCR demonstrated that compared with that in the normal group, VEGF expression was increased in all H2O2‐induced HCECs groups. After treating H2O2‐HCECs with different levels of siVEGF in siVEGF‐TIL for 24 h, VEGF expression was significantly downregulated in the siVEGF (30 nM)‐TIL, siVEGF (50 nM)‐TIL, and siVEGF (100 nM)‐TIL groups, and higher levels of siVEGF had better effects. There was a 2.06‐fold stronger effect on VEGF downregulation in the siVEGF (100 nM)‐TIL group than the siVEGF (50 nM)‐TIL group, suggesting that siVEGF (100 nM)‐TIL was the best choice to alleviate oxidative stress‐induced increases in VEGF. Therefore, siVEGF (100 nM)‐TIL was selected for subsequent experiments.
## NPs improve the viability of H2O2
‐stimulated HCECs
As shown in Figure S3A–E, all groups showed good HCECs viability after 24 h of coculture with different concentrations of INS (50–500 μg/mL) or different types of NPs (100–2000 μg/mL), demonstrating good biocompatibility and ensuring the validity of subsequent experiments. The cells in the INS (300 μg/mL), INS‐lip (1000 μg/mL), TIL (1000 μg/mL), and siVEGF‐TIL (1000 μg/mL) groups showed the highest viability (133.35 ± $11.74\%$, 140.60 ± $8.61\%$, 143.51 ± $9.12\%$, and 141.29 ± $15.42\%$, respectively), therefore these conditions were selected for the further experiments. For consistency with other NPs, TL (1000 μg/mL) and siVEGF‐TL (1000 μg/mL) were selected, and cell viability was 98.15 ± $9.03\%$ and 102.31 ± $11.78\%$, respectively. The viability of H2O2‐induced HCECs in the PBS, TL, siVEGF‐TL, INS, INS‐lip, TIL, and siVEGF‐TIL groups was also verified by CCK‐8 assays (Figure S3F). Although H2O2 significantly decreased cell viability in all groups, it is noteworthy that reagents containing INS could increase cell viability. Among them, the TIL and siVEGF‐TIL groups exhibited the highest cell viability (74.38 ± $8.42\%$ and 73.98 ± $3.47\%$) compared to the other groups, which showed that TIL and siVEGF‐TIL were the most effective reagents to resist the oxidative stress induced by H2O2.
## Clinical evaluation of healing in the corneal alkali burn rat model
Alkali burns were applied to SD rats to produce experimental corneal injury and induce CNV. To assess the therapeutic efficacy of siVEGF‐TIL in vivo on corneal alkali burns, we compared the extent of the burn response and neovascularization in cauterized corneas treated with PBS, siVEGF‐TL, INS, INS‐lip, TIL, and siVEGF‐TL. Each eye drop was topically administered twice per day for 14 consecutive days to SD rats. Representative images showing corneal opacity, epithelial defects, and CNV were obtained using a mobile phone and a portable slit lamp (Kowa) on the 1st, 3rd, 7th, and 14th days after treatment (Figure 6a–c). On the first day, the corneas had similar opacity scores in all groups ($p \leq 0.05$). On third day, the opacity of the corneas in the INS, INS‐lip, TIL, and siVEGF‐TIL groups began to recover, whereas opacity in the PBS and siVEGF‐TL groups remained the same, and anterior chamber bleeding was observed (Figure 6a). On the seventh day, the opacity of the cornea was further alleviated in all groups, including the PBS and siVEGF‐TL groups. These effects were most pronounced in the TIL and siVEGF‐TIL groups (corneal opacity score 0.8 ± 0.45). Remarkably, corneal opacity scores were significantly increased again in the PBS and siVEGF‐TL groups due to worsened scar tissue and CNV on the 14th day. Furthermore, corneal opacity was not significantly attenuated in the INS group (corneal opacity score 1.8 ± 0.45) compared with that on the seventh day (corneal opacity score 1.8 ± 0.84). In contrast, corneal opacity was reduced in the INS‐lip, TIL, and siVEGF‐TIL groups, and the corneas were almost completely transparent after 14 days of treatment in the TIL and siVEGF‐TIL groups (corneal opacity score 0.4 ± 0.55 and 0.5 ± 0.45, respectively) (Figure 6a,f).
**FIGURE 6:** *Clinical evaluation of healing in the corneal alkali burn rat model. (a) The anterior views of alkali‐burned eyes were photographed on 1, 3, 7, and 14 days after corneal alkali burn. (b) After the cornea was stained with fluorescein sodium, the corneal epithelium was observed on 1, 3, 7, and 14 days by the portable slit lamps. The green area represents the corneal epithelial defect stained with fluorescein. (c) The side views of alkali‐burned eyes were photographed on 1, 3, 7, and 14 days to observe corneal neovascular outgrowths. (d) Scores of the corneal opacity in each group on Days 1, 3, 7, and 14 (n = 5 per group). Results were presented as the mean ± SD. (e) Curves indicated rates of corneal epithelial healing during 1, 3, 7, and 14 days of the treatment (n = 5 per group). Data are presented as mean ± SD. (f) The proportion of CNV area in each group on 3, 7, and 14 days (n = 5 per group). Data are presented as mean ± SD. (g) Intraocular pressure on Days 0, 1, 3, 7, and 14 in each group. Data are presented as mean ± SD. INS, insulin; INS‐lip, insulin liposome; siVEGF‐TL, siVEGF‐trimethyl chitosan‐coated liposome; siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome; TIL, trimethyl chitosan‐coated insulin liposome.*
The degree of corneal wound healing was evaluated by fluorescein sodium dripping and the corneal epithelia were photographed on the 1st, 3rd, 7th, and 14th days after the alkali burn. On Day 1, the TIL (16.25 ± $2.01\%$) and siVEGF‐TIL (15.53 ± $3.08\%$) groups exhibited minimal corneal area staining with fluorescein. On Day 3, corneal epithelial defects decreased to only punctate defects in the INS‐lip (14.85 ± $2.18\%$), TIL (8.78 ± $1.02\%$), and siVEGF‐TIL (8.86 ± $1.25\%$) groups. However, the defect area was present in nearly $50\%$ of the cornea in the PBS (44.51 ± $3.48\%$) and siVEGF‐TL groups (47.18 ± $2.88\%$). Corneas healed faster and recovered completely by Day 7 in the INS, INS‐lip, TIL, and siVEGF‐TIL groups, whereas corneal healing was significantly delayed in the PBS and siVEGF‐TL groups (Figure 6b,e).
CNV did not develop during the first 3 days. On the seventh day, CNV was observed in all groups the except siVEGF‐TIL group. On the 14th day, CNV completely reached the burn area, serious scarring appeared on the cornea in the PBS group, and a small amount of CNV appeared in the INS, INS‐lip, and TIL groups. Conversely, CNV was undetectable in the siVEGF‐TIL group (Figure 6c,f). These results showed that siVEGF‐TIL maintained corneal transparency, accelerated epithelialization, and effectively inhibited CNV.
Alkalis saponify the fatty acids in cell membranes, which results in membrane disruption and dissolution; alkali quickly penetrates through the cornea into the deeper parts of the eye, and hyphema is present in the anterior chamber, followed by increased IOP. 40 On the other hand, early direct chemical injury can cause tissue shrinkage and disruption of the trabecular meshwork and outflow channels. Subsequent chronic inflammation may lead to synechiae and angle closure, which contribute to secondary increased IOP. 41 Figure 6g shows that the baseline IOP of the rats did not significantly differ among the groups. Statistically significant differences in IOP were first noted on the third day, from then, the median IOP was significantly increased in the PBS group and the siVEGF‐TL group. On Day 14, the median IOP in the PBS group and the siVEGF‐TL group was 38.99 ± 4.72 and 37.90 ± 6.74 mmHg, respectively, while there was no significant difference in the mean and baseline IOP in the other groups. This result proved that INS, INS‐lip, TIL, and siVEGF‐TIL could effectively alleviate alkali burn‐induced damage and prevent an increase in IOP.
## NPs may treat corneal alkali burn by inhibiting the ferroptosis pathway
Ferroptosis is a form of regulated cell death that is driven by peroxidative damage to polyunsaturated fatty acid‐containing phospholipids in cellular membranes. Specifically, ferroptosis is induced by suppressing xCT and GPX4 activity and promoting the accumulation of ROS and a reduction in GSH. 46 Excessive levels of extracellular glutamate can impair or inhibit cysteine uptake via xCT, resulting in GSH depletion. 45 GSH depletion decreases GPX4 activity, and lipid peroxides can not be suppressed and metabolized, ultimately accelerating ferroptosis. 47 ELISA was performed to compare glutamate levels between each group. The ELISA results showed that glutamate was dramatically increased in all alkali‐burned corneas, but TIL and siVEGF‐TIL treatment could inhibit this outcome (Figure 8a). Next, the levels of GSH in all groups were examined. As shown in Figure 8b, GSH levels in the PBS and siVEGF‐TL groups were significantly decreased. Conversely, when alkali‐burned corneas were treated with INS, INS‐lip, TIL, and siVEGF‐TIL, GSH levels increased significantly, particularly in response to TIL and siVEGF‐TIL treatment. Furthermore, Western blot (WB) analysis was used to measure the protein expressions of xCT and GPX4. The results showed that xCT and GPX4 expression was significantly suppressed in the PBS and siVEGF‐TL groups and was increased in the INS group and various INS‐loaded NPs groups, especially in the TIL group and siVEGF‐TIL group (Figure 8c–e).
**FIGURE 8:** *NPs may treat corneal alkali burn by inhibiting the ferroptosis pathway. Glu content (a), and GSH concentration (b) in normal corneas and alkali‐burned corneas that received different treatments (n = 3 per group). Results were presented as the mean ± SD. **p < 0.01; ***p < 0.001. Comparison between each group and the normal group (#
p < 0.05; ##
p < 0.01; ###
p < 0.001). (c) Representative Western blots showing xCT and GPX4 in normal corneas and alkali‐burned corneas that received different treatments. The quantification of the Western blot assay for relative expression of xCT (d) and GPX4 (e). Results were presented as the mean ± SD. *p < 0.05; **p < 0.01; ***p < 0.001. Comparison between each group and the normal group (#
p < 0.05; ##
p < 0.01; ###
p < 0.001). n = 3per group. SOD activity (f) and MDA content (g) in normal corneas and alkali‐burned corneas that received different treatments (n = 3 per group). Results were presented as the mean ± SD. **p < 0.01; ***p < 0.001. Comparison between each group and the normal group (#
p < 0.05; ##
p < 0.01; ###
p < 0.001). Glu, glutamate; GPX4, glutathione peroxidase; GSH, glutathione; INS, insulin; INS‐lip, insulin liposome; MDA, malondialdehyde; siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome; siVEGF‐TL, siVEGF‐trimethyl chitosan‐coated liposome; SOD, superoxide dismutase; TIL, trimethyl chitosan‐coated insulin liposome; xCT, cystine/glutamate antiporter.*
We preliminarily confirmed that after alkali damage to the cornea, glutamate in the cornea increased significantly, which activated ferroptosis. INS could inhibit ferroptosis by reducing glutamate levels and activating xCT and GPX4. Liposomal entrapment of INS enhanced this effect. In addition, TIL and siVEGF‐TIL have better adhesion and permeability than other formulations, further improving bioavailability and strengthening the power of the drugs to inhibit glutamate and ferroptosis.
Ferroptosis produces large amounts of ROS, which leads to a severe oxidative stress response. Accordingly, we examined the oxidative stress level in the alkali‐burned corneas of SD rats treated with different reagents. As shown in Figure 8f,g, alkali burn caused a sharp reduction in the level of SOD and an obvious increase in the level of MDA in corneas. After treatment with the various preparations, the MDA concentration was significantly reduced, and the SOD activity had been partially restored in all alkali‐burned corneas compared with those in the PBS group, except for the siVEGF‐TL group. Among the preparations, TIL and siVEGF‐TIL treatment had the most evident effect, demonstrating their superior abilities to inhibit oxidative stress.
## In vivo inhibition of inflammation and neovascularization by NPs
Corneal alkali burn can lead to oxidative stress and severe inflammatory reactions, which can promote each other. As shown in Figure 9a–c, alkali‐burned corneal tissues showed a sharp increase in the inflammatory cytokines TNF‐α, IL‐6, and MMP‐9. These cytokines were significantly reduced when the corneas were treated with INS, INS‐lip, TIL, and siVEGF‐TIL compared with PBS. Similar to the in vitro results, corneas treated with TIL and siVEGF‐TIL exhibited the largest decrease in cytokine levels.
**FIGURE 9:** *In vivo inhibition of inflammation and neovascularization by NPs. TNF‐α (a), IL‐6 (b), and MMP‐9 (c) concentrations in normal corneas and alkali‐burned corneas that received different treatments (n = 3 per group). Results were presented as the mean ± SD. **p < 0.01; ***p < 0.001. Comparison between each group and the normal group (#
p < 0.05; ##
p < 0.01; ###
p < 0.001). (d) VEGF protein expression in normal corneas and alkali‐burned corneas that received different treatments was determined by Western blot. (e) Quantitative analysis of VEGF protein expression in Western blot (n = 3 per group). Results were presented as the mean ± SD. **p < 0.01; ***p < 0.001. Comparison between each group and the normal group (#
p < 0.05; ##
p < 0.01; ###
p < 0.001). (f) Immunohistochemical staining of CD31 expression in corneal tissues of each group. Scale bar = 100 μm. (g) Quantitation of CD31 positively stained corneal sections in each group (n = 3 per group). Results were presented as the mean ± SD. **p < 0.01; ***p < 0.001. Comparison between each group and the normal group (#
p < 0.05; ##
p < 0.01; ###
p < 0.001). INS, insulin; INS‐lip, insulin liposome; siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome; siVEGF‐TL, siVEGF‐trimethyl chitosan‐coated liposome; TIL, trimethyl chitosan‐coated insulin liposome.*
CNV is a severe complication of corneal injury that not only affects the transparency and avascular nature of the cornea but also makes the cornea more vulnerable to inflammatory reactions. 48 To verify the ability of NPs to inhibit neovascularization, VEGF protein expression was measured by WB analysis. Compared with that in the PBS group, VEGF expression in corneas in the INS, INS‐lip, TIL, and siVEGF‐TIL groups was reduced, and the strongest anti‐VEGF effect was observed in the siVEGF‐TIL group (Figure 9d,e). In addition, the expression of the endothelial cell‐specific marker CD31 is one of the parameters that could be used to confirm neovascularization. Positive staining for CD31 by IHC was significant in the PBS and siVEGF‐TL groups, and the areas decreased in the INS, INS‐lip, TIL, and siVEGF‐TIL groups; CD31 was hardly expressed in the siVEGF‐TIL group (Figure 9f,g). Interestingly, unlike in the cell experiments, siVEGF‐TL did not inhibit neovascularization in alkali‐burned corneas. This may be because oxidative stress and inflammatory reactions first occur in alkali‐burned corneas, and then they stimulate angiogenic factors and promote neovascularization. 6 Therefore, inhibiting VEGF without controlling oxidative stress and inflammation does not inhibit CNV. However, siVEGF‐TIL treatment combines the ability of INS to inhibit oxidative stress and inflammation with the ability of siVEGF to inhibit neovascularization; morever, this treatment exhibits superior penetration and adsorption to enhance the bioavailability of drugs and genes, contributing to good therapeutic effects on corneal alkali burns.
## Biocompatibility of siVEGF‐TIL in vivo
In vivo biocompatibility was assessed by corneal stimulation assessment in normal SD rat eyes treated with the different formulations, followed by corneal examination using a slit‐lamp microscope (Figure 10a). After 30 days of the various treatments, no evidence of corneal opacity, CNV, inflammation, or congestion was found in any corneas. The integrity of the corneal epithelium was evaluated by fluorescein staining. The results showed that the corneal epithelium was intact. In addition, corneal anatomy was examined by H&E staining, and the results showed that the corneas in each group had a regular appearance, were closely and orderly arranged and lacked inflammatory cells or CNV (Figure 10b). Morever, H&E staining of the major visceral organs (heart, liver, spleen, lung, and kidney) revealed that various reagents in this study treatment did not cause significant histological changes. As a result, siVEGF‐TIL NPs have no obvious toxic effects and have excellent biocompatibility, paving the way for clinical applications (Figure 10c).
**FIGURE 10:** *Biocompatibility of siVEGF‐TIL in vivo. After 30 days of dropping different reagents three times a day respectively, in vivo biocompatibility of no administration (healthy) and different formulations treated groups were evaluated by slit‐lamp examination, fluorescein sodium staining (a), and H&E staining (b) on Day 30. Scale bar = 50 μm; n = 3. The biocompatibility of different NPs in organs of rats including lung, kidney, liver, heart, and spleen was examined by H&E staining. Scale bar = 100 μm; n = 3. INS, insulin; INS‐lip, insulin liposome; siVEGF‐TIL, siVEGF‐trimethyl chitosan‐coated insulin liposome; siVEGF‐TL, siVEGF‐trimethyl chitosan‐coated liposome; TIL, trimethyl chitosan‐coated insulin liposome.*
## CONCLUSION
To the best of our knowledge, this is the first study using a liposome‐TMC nanosystem for the delivery of siVEGF/INS as a combination therapy to treat corneal alkali burns. siVEGF‐TIL treatment showed significant effects in alleviating oxidative stress‐induced HCEC damage (in vitro) and alkali injury in corneas (in vivo). Morever, siVEGF‐TIL treatment had the ideal properties of NPs, including good biosafety profiles, lack of toxicity, facile preparation, adherence, and sustained release, suggesting that this strategy holds potential as a novel delivery platform for the cornea. Furthermore, the molecular mechanism of siVEGF‐TIL treatment was revealed in this study. We found that corneal alkali burn was linked to the regulation of ferroptosis, which could be suppressed by INS. This is also the first report showing the effects of INS on ferroptosis. Notably, siVEGF‐TIL could substantially inhibit both ferroptosis and CNV, eventually preventing alkali damage in corneas. siVEGF‐TIL treatment is an up‐and‐coming therapeutic agent for future clinical applications in corneal damage. There were still many shortcomings in this study. Only male rats were used in this study because males were more susceptible than females to corneal alkali burn. This research did not compare siVEGF TIL NPs with existing treatments for corneal alkali burns (such as topical corticosteroids and NSAIDs). The absolute concentration of INS or siVEFG in the NPs was not detected, which was a limitation of this study regarding the further clinical translation of siVEGF‐TIL treatment.
## AUTHOR CONTRIBUTIONS
Xiaojing Xiong: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); software (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Huiting Jiang: Investigation (equal); methodology (equal); software (equal); validation (equal); visualization (equal). Yukun Liao: Investigation (equal); methodology (equal); software (equal); validation (equal). Yangrui Du: Project administration (equal); validation (equal); visualization (equal). Yu Zhang: Conceptualization (equal); investigation (equal); project administration (equal); validation (equal). Zhigang Wang: Methodology (equal); resources (equal); validation (equal); writing – review and editing (equal). Minming Zheng: Investigation (equal); methodology (equal); writing – review and editing (equal). Zhiyu Du: Conceptualization (equal); formal analysis (equal); funding acquisition (equal); investigation (equal); project administration (equal); writing – review and editing (equal).
## CONFLICT OF INTEREST STATEMENT
The author declare no conflict of interest.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10499.
## DATA AVAILABILITY STATEMENT
Data available on request from the authors.
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|
---
title: Hyaluronic acid‐g‐lipoic acid granular gel for promoting diabetic wound healing
authors:
- Shixi Zhang
- Yuqing Pan
- Zhiyuan Mao
- Jiahui Zhang
- Kunxi Zhang
- Jingbo Yin
- Chen Wang
journal: Bioengineering & Translational Medicine
year: 2022
pmcid: PMC10013829
doi: 10.1002/btm2.10402
license: CC BY 4.0
---
# Hyaluronic acid‐g‐lipoic acid granular gel for promoting diabetic wound healing
## Abstract
Diabetic patients are prone to developing chronic inflammation after trauma and have persistent nonhealing wounds. Reactive oxygen species (ROS) and recurrent bacterial infections at the site of long‐term wounds also further delay skin wound healing and tissue regeneration. In this study, a granular gel (which exhibits ROS scavenging and antibacterial properties) is fabricated based on hyaluronic acid‐g‐lipoic acid (HA‐LA). Briefly, HA‐LA is synthesized to fabricate HA‐LA microgels, which are further assembled by Ag+ via its coordination effect with disulfide in dithiolane to form a granular gel. The extrudable bulk granular gel possesses a shear‐thinning feature and is immediately restored to a solid state after extrusion, and this can be easily applied to the whole wound area. Therefore, the grafted LA not only allows for the construction of the granular gel but also removes excess ROS from the microenvironment. Additionally, the presence of Ag+ realizes the assembly of microgels and has antibacterial effects. In vivo experiments show that the HA‐LA granular gel eliminates excessive ROS at the wound site and up‐regulates the secretion of reparative growth factors, thus, accelerating common and diabetic wound healing significantly. Therefore, the ROS‐scavenging granular gel that can be applied to the wound surface with chronic inflammation demonstrates strong clinical utility.
## INTRODUCTION
Normal wound healing is a complex process that includes the inflammatory, proliferative, and remodeling stages. 1 These three stages affect each other and overlap during wound healing. It is difficult for chronic wounds (especially those caused by diabetes) to heal in a well‐organized and timely manner compared with normal wounds. 2 *This is* because of the imbalance of pro‐inflammatory cytokines, excessive levels of reactive oxygen species (ROS) and proteases on the wound surface, and recurrent bacterial infections at the site of long‐term wounds. 3, 4 Excessive ROS can modify or degrade extracellular matrix (ECM) proteins, promote the increase of protease and inflammatory cytokine levels, inhibit the wound from entering the proliferative stage, prolong the inflammatory response time, and damage the dermal function. All these factors combined can affect the progression of wound healing. 5, 6 Long‐term high blood glucose in diabetic patients is highly conducive to the growth of bacteria, resulting in recurrent bacterial infections on the wound surface and a low level of immune regulation. 4 The body cannot clear the local infection through the immune system, but there is an excessive inflammatory response because of the influx of immune cells, which damage normal tissues and cells. 7 Therefore, adequate clearance and regulation of the wound microenvironment is the key to treating chronic refractory diabetic wounds.
Hydrogels used as wound dressings have shown their unique features to significantly promote wound healing. Primarily through materials design, the use of hydrogels as dressings can regulate the immune responses by providing an anti‐inflammatory environment. 8 Naturally derived macromolecules such as hyaluronic acid (HA, with high molecular weight) could effectively substitute the missing constituents of the ECM and revealed intrinsic anti‐inflammatory characteristics. 9 Thus, they have been widely used to fabricate immunomodulatory hydrogels to treat chronic wounds. 10, 11 In addition to the natural characteristics of HA that can modulate the features of the immune responses, there is a need to endow the HA molecules with a stronger and more symptomatic immune regulation function towards diabetic wounds. 12 Lipoic acid (LA), a necessary cofactor of mitochondrial bioenergy enzymes, 13 contains a dithiolane structure. 14 This has significant electrophilicity and the ability to react with free radicals, scavenge superoxide and peroxide radicals, and is a potent antioxidant. 15, 16 Owing to long‐term hyperglycemia in diabetic patients, the level of lipid hydroperoxide increases, 17 leading to the stimulation of the polyol pathway, 18 the formation of advanced glycation end‐products, and the formation of ROS. 5, 19 LA is approved by the FDA for clinical use to prevent or treat a series of diabetic complications caused by ROS, 20 eliminate free radicals, and quickly remove ROS in the early stage of inflammation. Additionally, it down‐regulates pro‐inflammatory cytokines to alleviate the inflammatory response. 21 Injectable hydrogels can be more flexible in adapting to the irregular shapes of wounds and therefore have received more research interest. Significantly, the pre‐crosslinked hydrogels with injectability were attractive during application because they were more convenient and applicable. 22 The granular gel is a bulk hydrogel formed by densely assembled microparticles. 23, 24 Through the design of particle interaction, the granular gel can exhibit thixotropic properties under shear force, thus allowing for injectability. 25 Notably, the flowing granular gel can recover to a solid gel immediately after injection. 26 Thixotropy makes it easy to apply to the whole wound area. 27, 28 Moreover, the inherent porous network structure in assembled microparticles can support cell proliferation, migration, and substance transport. 29 The present study developed a granular gel system based on HA grafted with LA to regulate the microenvironment of wound inflammation, accelerating wound healing in a safe and multifunctional way. Briefly, HA‐g‐LA (HA‐LA) was synthesized and crosslinked by cystamine in a water‐in‐oil emulsion to fabricate HA‐LA microgels. The HA‐LA microgels were assembled using Ag+ through its coordination effect with disulfide in dithiolane to form a bulk granular gel. The microstructure, rheological properties, and extrusion performance were then studied. The ability of HA‐LA granular gel to scavenge intracellular ROS and the effect of antibiosis was then evaluated in vitro. The process of wound healing and changes in tissue structure, protein expression, and gene expression was then assessed to evaluate the potential of the HA‐LA granular gel to regulate the wound microenvironment and promote wound healing.
## Materials
Human umbilical vein endothelial cells (HUVECs) were cultured in ECM cell culture medium. C57BL/6 mice were fed according to the protocol approved by the Laboratory Animal Center of Shanghai Jiao Tong University. HA was purchased from Bloomage Bio. Alpha‐LA, cystamine dihydrochloride (Cys), 4‐(4,6‐dimethoxy‐1,3,5‐triazin‐2‐yl)‐4‐methyl morpholinium chloride (DMTMM), and silver nitrate (AgNO3) were purchased from Aladdin. N,N′‐carbonyldiimidazole (CDI), N,N‐dimethylformamide (DMF), formamide (FA), and Span‐80 were purchased from China National Pharmaceutical Group Corporation. ROS Assay Kit was purchased from Beyotime. Dihydroethidium (DHE) were purchased from Sigma‐Aldrich. Streptozotocin (STZ) was purchased from Solarbio. Anti‐mouse F$\frac{4}{80}$ antibody‐PE, anti‐mouse CD206 antibody‐APC, and anti‐CD31 antibody were purchased from Abcam. Anti‐α‐SMA antibody, anti‐IL‐6 antibody, and anti‐TNF‐α antibody were purchased from Boster Bio. Anti‐VEGF antibody and anti‐integrin α‐3 antibody were purchased from Proteintech. Anti‐IL‐10 antibody was purchased from Affinity. Anti‐IL‐1β antibody was purchased from Santa. All other reagents are commercially available and used directly.
## Synthesis of HA‐g‐LA
First, LA was dissolved in DMF at room temperature, followed by adding CDI. The reaction was stirred at room temperature for 1 hour to fully activate the carboxyl group of LA to obtain lipoyl imidazole (LA‐IM). Then, HA was fully dissolved in FA at 95°C and cooled to room temperature, followed by the addition of catalyst 4‐dimethylaminopyridine and the addition of LA‐IM. After stirring the reaction at room temperature for 4 hours, the crude product was neutralized with an appropriate amount of potassium dihydrogen phosphate solution. After purification by dark dialysis to remove the solvent and impurities produced by the reaction, HA‐LA was collected by freeze‐drying under the condition of avoiding light.
## Characterization of HA‐LA
Nuclear magnetic resonance spectroscopy (1H NMR): A 5 mg sample was dissolved in deuterated water (D2O, 500 μL) with tetramethylsilane (TMS) as the internal standard. The samples were qualitatively and quantitatively analyzed by nuclear magnetic resonance spectrometer (Avance 500 MHz, Bruck, Switzerland) with 128 scans. The degree of substitution was calculated by the software MESTRENOVA after processing the 1H NMR results.
## Preparation and characterization of HA‐LA microgel (HA‐LA MG)
HA‐LA microgels were prepared by a water‐in‐oil inverse emulsion method. HA‐g‐LA was dissolved in deionized water to prepare a solution with a concentration of 5 wt%, and the amidation condensing agent DMTMM and the cross‐linking agent Cys were added. Then it was poured into petroleum ether containing emulsifier Span‐80 and emulsified at 18,000 rev/min. The volume ratio of the mixture, emulsifier, and petroleum ether was 4:1:40. The emulsion was poured into a three‐necked flask containing a large amount of petroleum ether, and the reaction was terminated after 24 hours at room temperature with mechanical stirring at a rotational speed of 500 rev/min. The reaction solution was poured into an equal amount of ethanol absolute, washed 3 to 5 times, and dried in a vacuum to obtain HA‐LA microgels. The morphology of dry and wet microgels was observed by tungsten filament scanning electron microscope (SEM) and phase‐contrast microscope. The particle size and particle size distribution were determined by the software Nano Measurer. Volume swelling ratio was calculated according to the following formula: S v = (V 1 − V 0)/V 0 × $100\%$, where V 1 is the volume of wet microgels and V 0 is the volume of dry microgels.
A certain mass of dry HA‐LA microgels were weighed, the mass recorded as M 0, excess distilled water was added to fully swell the microgels, and centrifuged at 10,000 rev/min. After removing excess water, the weight was measured and recorded as M 1, then the mass swelling ratio (S m) of the sample was calculated according to the following formula: S m = (M 1 − M 0)/M 0 × $100\%$.
To evaluate the effect of LA grafting ratio and Cys content on mechanical performance of hydrogel, the corresponding bulk hydrogels with different LA grafting ratio and Cys content were prepared (cylindrical samples with a diameter of 8 mm and height of 4 mm) and subjected for compression test on an Instron 5943 testing machine at $10\%$ strain per minute and stopped when hydrogels were broken.
## Preparation of granular gels
A certain mass of dry HA‐LA microgels were weighed and placed in a mold, followed by the addition of silver nitrate solutions with different concentrations to it until the system fully absorbed water to an equilibrium swelling state. After being placed away from light for 30 minutes, HA‐LA microgels were assembled into a bulk gel. The microstructures of dry and wet granular gels were observed by SEM and stereomicroscope.
The total weight M 0 of the dry HA‐LA microgels, the total weight M 1 of the system after adding the silver nitrate solution were recorded, and the water absorption rate Qs (mass ratio) of the sample was calculated according to the following formula: S m = (M 1 − M 0)/M 0 × $100\%$.
## Rheological characterization
The rheological properties of the granular gels were tested using a rheometer with a 12 mm flat steel plate fixture. The granular gels were transferred into the rheometer fixture and tested for their modulus‐frequency relationship with a frequency sweep range of 0.1 to 100 Hz and a strain of $0.5\%$. Strain sweep measurements were performed at a constant frequency of 1 Hz with strain ranging from 0.01 to $1000\%$. The shear‐thinning behavior was investigated by testing the viscosity change of the granular gels when the shear rate was continuously increased from 0 to 300 s−1. The strain step cycled between $1\%$ and $100\%$ at 1 Hz were performed, and the changes of the G′ and G″ with time and strain were recorded, respectively.
## Extrusion performance test
To test the printability and continuity of the granular gels, the prepared granular gels were transferred into a syringe barrel, and an 18 G (inner diameter 0.9 mm) needle was used for extrusion testing. A camera records the extrusion process, and SEM was used to observe the microscopic morphology of the extruded strands. The extruded bars were placed in PBS at 37°C and the system stability after extrusion was recorded.
## Evaluation of the subcutaneous degradation of the hydrogel
To evaluate the degradation of the hydrogel, bulk hydrogels (with different Cys content), HA MGs (10 μL), and HA‐LA granular gel (10 μL) were implanted subcutaneously into C57BL/6 mice. The degradation of hydrogels was observed on days 3, 7, and 14 after treatment. Then, the wound tissue was collected, fixed with $4\%$ paraformaldehyde, and sliced with paraffin‐embedded tissue for H&E staining.
## Intracellular ROS scavenging experiments
To study the in vitro ROS scavenging ability of the hydrogel, the Reactive Oxygen Species Assay Kit (ROS Assay Kit) was used. First, HUVECs were seeded in a 24‐well plate (200 μL/well, 1 × 105 cells) and cultured for 24 hours until most cells adhered. Next, 5 and 10 mg of HA MGs and HA‐LA granular gel were weighed and mixed with ECM cell culture medium and Rosup (50 mg/mL). This was then added to a 24‐well plate and incubated for 6 hours. The cells were washed (2‐3 times) with a serum‐free cell culture medium before incubation with a ROS probe (DCFH‐DA) for 20 minutes. Fluorescence images were taken using an inverted fluorescence microscope (Nikon, Ti S).
To study the in vivo ROS scavenging ability after treatment with hydrogel, fluorescent probes were used to detect intracellular ROS in wound tissues of normal and diabetic mice collected on day 3 using DHE (Sigma‐Aldrich). Cryosections were incubated with 10 μM DHE at 37°C for 30 minutes and then observed using an inverted fluorescence microscope (Nikon, Ti S) to determine the percentage of the DHE‐stained area. Quantitative data were analyzed by Image J software.
## Antibacterial ability test of hydrogel materials
The hydrogel's antibacterial effect was tested using the stiletto implement method. A sterile cotton swab was dipped into the Gram‐positive *Staphylococcus aureus* (S aureus) suspension (200 μL; 1 × 108 CFU/mL) and applied evenly on a 9‐mm TSA nutrient Agar medium. Three wells were punched on the TSA nutrient agar medium with a 5‐mm punch. Two of the wells were supplemented with 10 mg of HA MGs and HA‐LA granular gel, and the other was the BLANK group. After a 24‐hour culture, the diameter of the inhibition zone around each well was observed.
## Diabetic mice model induced by STZ
After fasting for 12 hours, C57BL/6 mice were induced by intraperitoneal injection of STZ (50 mg/kg) dissolved in sodium citrate buffer (0.1 mol/L, pH 4.5) once a day for a total of five injections. 30 One week after the injection, the mice with fasting blood glucose levels >16.7 mmol/L for 2 successive days were defined as diabetic mice.
## Accelerating wound closure by applying hydrogel materials
C57BL/6 mice were purchased from Shanghai Jihui Experimental Animal Breeding Co., Ltd. Eight‐week‐old SPF‐grade male mice weighing around 20 to 25 g were randomized into different experimental groups. A total of six independent biological replicates were performed for each group to confirm the findings.
The in vivo wound healing induced by the hydrogel was performed on wound models in healthy C57BL/6 mice (NM) and STZ‐induced diabetic C57BL/6 mice (DM). First, the back hair of normal and diabetic C57BL/6 mice was removed, and two 5‐mm diameter full‐thickness skin defects were made on each side of the midline on the back using a 5‐mm punch. A round silicone sheet was sewn around each wound, and the HA MGs (CON.) and HA‐LA granular gel (EXP.) were applied (dressings changed every 3 days). 31 The wounds were randomly divided into six groups: NM (BLANK), NM (CON.), NM (EXP.), DM (BLANK), DM (CON.), and DM (EXP.) groups. Observations, measurements, and analyses were carried out on days 3, 7, and 14 using ImageJ software. The simulated diagram of wound healing was drawn with Adobe Illustrator [2020], depicting photos of wounds taken on different days during the general observation.
## Evaluation of changes in the wound microenvironment
Changes in the wound microenvironment were observed on days 3, 7, and 14 after treatment. First, the wound tissue was collected, fixed with $4\%$ paraformaldehyde, and sliced with paraffin‐embedded tissue for H&E and Masson's trichrome staining (MTS). Anti‐CD31 antibody (Abcam, ab182981, 1:2000), anti‐α‐SMA antibody (Boster Bio, bm0002, 1:1000), anti‐integrin α‐3 antibody (Proteintech, 66,070‐1‐ig, 1:500) were incubated on tissue sections. This was followed by staining with the corresponding fluorescence‐labeled secondary antibodies and the cell nuclear dye (DAPI). The results were observed using an inverted fluorescence microscope (Nikon, Ti S) and quantitatively analyzed using ImageJ software.
## RNA‐sequencing analysis
Total RNA was isolated from the wound tissues of normal and diabetic mice and collected on day 7. RNA sequencing samples were obtained using TRIzol reagent according to the manufacturer's instructions. Briefly, wound tissues (100 mg) were harvested, treated with TRIzol reagent (1 mL), and homogenized with a tissue homogenizer. After adding chloroform (200 μL), the mixture was shaken and mixed evenly. After centrifugation, the supernatant was removed, followed by the addition of isopropanol to precipitate RNA. Then, $75\%$ ethanol solution was added for washing, and the precipitate was retained after centrifugation. Diethyl pyrocarbonate water was added to dissolve the RNA. The RNA‐seq Library Preparation Kit (Illumina) was used to construct the cDNA library. The cDNA terminal was repaired, the connector was joined, the product was purified, and the fragment size was sorted. After the cDNA library was generated, it was sequenced on an Illumina HiSeq 4000 platform. FastQC was used for quality evaluation, HISAT2 was used for sequencing evaluation, and String Tie was used for gene structure analysis. Ballgown software was used to quantify transcription levels and identify differentially expressed genes, and $P \leq .05$ was adjusted as a cutoff value. The Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using the Database for Annotation for differentially expressed genes.
## Statistical analysis
The statistical analysis of the data was evaluated by GraphPad Prism 9 software (GraphPad Software Inc., San Diego, CA, USA). The data were expressed as mean ± standard deviation (SD) and compared by t‐test. All the experiments were repeated three times. $P \leq .05$ were considered statistically significant.
## Preparation and characterization of HA‐LA microgels (HA‐LA MGs)
HA‐LA, a hyaluronic acid macromolecule grafted with LA, was synthesized (Figure 1). 1H NMR confirmed that the HA macromolecule was successfully grafted with LA (Figure 2a,b). According to Figure S1 and Table S1, the M w of HA was 4.4 × 104 g/mol, while after LA grafting, the M w of HA‐LA reduced significantly to 2.3 × 104 g/mol.
**FIGURE 1:** *Schematic diagram of HA‐LA granular gel preparation and functions. HA‐LA, hyaluronic acid‐g‐lipoic acid* **FIGURE 2:** *Preparation and characterization of HA‐LA bulk hydrogel and microgels. (a) Modified hyaluronic acid: HA‐LA. (b) 1H NMR proved that HA‐LA was successfully synthesized. (c) Compressive moduli of hydrogels with different LA grafting rate (HA‐LA (lipoic acid grafting rate)‐Cys (cross‐linking density)). (d) Compressive moduli of hydrogels with different crosslinking density (HA‐LA(lipoic acid grafting rate)‐Cys(cross‐linking density)). (e) General observation and H&E staining of hydrogels degradation on 7 day post‐implantation (scale bar = 100 μm, n = 3). (f) The SEM image of HA‐LA microgels and the diameter statistics (scale bar = 10 μm). (g) The swelling microspheres after water absorption observed by phase‐contrast microscope and the diameter statistics (scale bar = 100 μm). (h) Water absorption and volume expansion of microgels. Cys, cystamine dihydrochloride; HA‐LA, hyaluronic acid‐g‐lipoic acid; NMR, nuclear magnetic resonance; SEM, scanning electron microscopy*
The LA grafting rate and crosslinking degree were optimized to fabricate the bulk hydrogel for the compression test. Here, synthesized HA‐LA was crosslinked by cystamine via the amidation reaction between the terminal amino group of cystamine and the carboxyl group of HA‐LA (Figure 2c). The compressive modulus was found to increase with the LA modification rate, which might be related to the hydrophobic feature of 1,2‐dithiolane. However, HA‐LA with a grafting rate of LA higher than $30\%$ was easier to precipitate during dialysis and was more challenging to dissolve in the water again. Because LA was involved in ROS scavenging to maintain water solubility with a high level of LA, the present study controlled the grafting rate of LA to $30\%$.
The effect of crosslinking density (cystamine content) on hydrogel mechanical performance was also evaluated. With the increase of cystamine content, the crosslinking density increased, leading to a significant increase of compressive moduli (Figure 2d). However, because of the high swelling behavior of HA‐based polymers, a crosslinking density lower than $30\%$ would lead to insufficient crosslinking, while higher crosslinking density (over $50\%$) significantly decreased the degradation rate of the hydrogel. Figure 2e shows that 7 days post‐implantation, a hydrogel with a crosslinking density of $30\%$ was significantly swollen. H&E staining showed hydrogel degradation with a loose internal structure. However, a hydrogel with a crosslinking density higher than $50\%$ showed slight swelling and a significantly more compact structure. Typically, the disulfide bond can be readily cleaved because glutathione is widely present in tissues. 32 However, the cystamine's high content and dithiolane presence might cause an extensive disulfide exchange reaction, leading to a prolonged degradation. Thus, to minimize the residue of materials in the body, a hydrogel with a crosslinking density of $30\%$ was used in the subsequent studies.
Based on the above optimization, HA‐LA microgels were fabricated in a water‐in‐oil inverse emulsion, which was also crosslinked by cystamine. Microgels with relatively regular spherical shapes were obtained (Figure 2f,g). The diameter distribution of the dry microspheres was 1 to 11 μm. After absorbing water, the microspheres underwent noticeable volume swelling, and the diameter range was 15 to 100 μm. The mass swelling ratio of HA‐LA microgels was about $1700\%$, while the average volume swelling ratio was about $900\%$ (Figure 2h).
## Preparation and characterization of HA‐LA granular gel
By simply treating the dry HA‐LA microgel particles with Ag+ solutions, the bulk granular gels formed by coordinating Ag+ and disulfide in dithiolane could be obtained. 33 These HA‐LA microgel particles showed good stability in PBS (Figure 3a). The microstructure of the granular gel in dry and wet states was observed through SEM and the stereoscopy microscope (Figure 3b,c). It was found that the microgels inside the granular gel were densely packed, but there were still a large number of gaps, which provided a guarantee for matter exchange and cell migration (Figure 3d,e). Image analysis (using ImageJ) showed that the area occupied by gaps was $13.2\%$ ± $1.1\%$, which was not affected by the concentration of Ag+. This was one of the characteristics of granular gel that was different from traditional injectable hydrogels. The Ag+ concentration was changed to prepare three groups of granular gel. By evaluating their water absorption rate, it was found that a higher concentration of Ag+ led to a lower water absorption rate, revealing strong and stable forces between microgels (Figure 3f). However, the higher concentrations of Ag+ might have also resulted in higher tissue toxicity. Therefore, according to the literature, an Ag+ concentration of 2 mM was selected in this study. 34
**FIGURE 3:** *Preparation and characterization of granular gel. (a) HA‐LA microgels were assembled by silver ions through coordination to form bulk granular gels, which were stable in water. (b) The SEM image of dry granular gel (scale bar: 20 μm). (c) The wet granular gel observed by stereomicroscope (scale bar: 200 μm). (d) SEM image processing to show gap among microgels at dry state. (e) Image processing to show spaces among microgels at wet state (pink lines were used to draw the outline of the microspheres; the blue area indicated the gap among microgels). (f) Water absorption of granular gel prepared with different concentrations of Ag+. (g) The rheological test confirms that it is a solid‐state hydrogel. (h) The rheological test showed yield when the shear force reaches a certain degree. (i) The rheological test showed shear‐thinning feature. (j) Evaluation of self‐recovery of the granular gels under alternating strains of 1% and 100%. (k) The extrusion test and the stability of extruded filament in PBS, and the general observation of extruded granular gel with solid state. (l) The SEM images of extruded filament with densely packed microgels (scale bar: 100 μm for left, 20 μm for right). The data represent the mean ± SD. * indicates that these data showed significant differences with other data. HA‐LA, hyaluronic acid‐g‐lipoic acid; SEM, scanning electron microscopy*
Using a rheological evaluation, it was found that the storage moduli of the granular gel samples were greater than their loss moduli when the frequency sweep range was 0.01 to 100 Hz, confirming that the granular gel was an elastic bulk gel. Additionally, the granular gel with a higher Ag+ concentration showed a greater storage modulus (Figure 3g). The results confirmed that the HA‐LA microgels could be assembled by coordinating Ag+ to construct the elastic solid bulk granular gel.
Notably, as long as there was an increase in strain, G′ began to decrease, while G″ increased rapidly. When G′ was equal to G″, the breaking points of the granular gel were achieved (Figure 3h). When the strain kept exceeding the breaking point, both G′ and G″ decreased, but G″ was greater than G′. Moreover, the granular gel showed a shear‐thinning feature. 35 According to Figure 3i, the viscosity of the granular gel significantly decreased with the increase in shear rate. Under shear force, the bulk granular gel structure was destroyed. With the friction and slippage among the internal microgels, the granular gel switched from the original “solid” state with high viscosity to the “fluid” state with low viscosity, similar to the liquid. In addition, Figure 3j shows that the three groups of granular gels maintained their moduli after cyclic low‐high strain action. Under low strain, G′ was more significant than G″. When the strain increased to $100\%$, the hydrogel was broken, G′ decreased quickly and was lower than G″. When returning to the initial $1\%$ strain, G′ immediately recovered without significant decrease, indicating the self‐healing property of the granular gels.
The above rheological test results confirmed that the granular gel was extrudable. Extrusion experiments further confirmed this property. According to Figure 3k, the granular gel could be easily extruded by injection, and the extruded filament had excellent continuity. The extruded strips immersed in PBS were found to maintain a stable shape, indicating that the granular gels maintained the elastic characteristics of the solid gel after extrusion. In addition, it was worth noting that the extruded gel immediately recovered to a solid state after the removal of shear force, performing well in plasticity and shape stability. The microstructure of the extruded filament was further observed using SEM, showing that the extruded filament was assembled and stacked by many microspheres (Figure 3l). In addition, the rheological tests and extrusion experiments illustrated another feature of the granular gel. The gel behaved as a stable solid when there was no shear force. Under the action of shear force, the microgels in the system slide relative to each other, showing flow dynamics. After removing the shear force, the gel returned to a solid state. Therefore, the present granular gel could be easily extruded and applied to wounds, making the clinical application operation more convenient.
## Evaluation of the ROS‐scavenging, antibacterial ability, and degradation tests for the HA‐LA granular gel
The ability of the HA‐LA granular gel to remove intracellular ROS was evaluated using the strong impact of ROS on tissue repair. HUVECs were cultured in 24‐well plates (200 μL/well, 1 × 105 cells) with an endothelial cell medium containing 1 μl Rosup (50 mg/ mL) that can induce ROS production in cells. HA microgels (HA MGs, 5 mg/mL, 10 mg/mL, and CON. group) and HA‐LA granular gel (5 mg/mL, 10 mg/mL, and EXP. group) were added to each well for incubation for 6 hours. Then the cells were stained with DCFH‐DA cell dye with a ROS‐specific probe. According to the representative images and quantitative detection of fluorescence intensity in Figure 4a,b, cells in the CON. group showed strong fluorescence intensity. The two concentrations of HA MGs (5 mg/mL and 10 mg/mL) showed no significant difference in fluorescence intensity. However, cells in the EXP. group showed significantly lower fluorescence intensity than those in the CON. group. In addition, compared with the 5 mg/mL HA‐LA granular gel treatment, the fluorescence intensity of cells in the EXP. group with 10 mg/mL HA‐LA granular gel treatment decreased significantly. There was also a BLANK group with no treatment, showing similar fluorescence intensity of cells with the CON. group. The comparison between the BLANK and CON. groups indicated that HA MGs showed no significant ROS‐scavenging ability in vitro. In contrast, the HA‐LA granular gel exhibited a strong ROS‐scavenging ability, which increased with the granular gel concentration.
**FIGURE 4:** *Evaluation of ROS‐scavenging, antibacterial effect, and degradation of granular gel. (a) Fluorescence images and the statistical data (b) of different concentrations of HA MGs and HA‐LA granular gel co‐cultured with HUVECs cells, with the addition of reactive oxygen stimulant (Rosup) (scale bar: 100 μm, n = 3). (c) Antibacterial test of the three groups co‐cultured with Staphylococcus aureus (S aureus) (scale bar: 5 mm, n = 3). (d) Quantification of bacterial inhibition diameter in the three groups co‐cultured with S aureus; General observation (e) and H&E staining (f) of HA MGs and HA‐LA granular gel on days 3, 7, and 14 (CON. = treated with HA MGs; EXP. = treated with HA‐LA granular gel) (scale bar = 200 μm, 50 μm, n = 3). The data represent the mean ± SD. ***P < .001, according to t‐test. HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels; HUVECs, Human umbilical vein endothelial cells; SD, standard deviation*
Ag+ was not only used to assemble HA‐LA MGs but also possessed a specific antibacterial effect. 36 The antibacterial ability of HA‐LA granular gel was evaluated using the antibacterial test. According to Figure 4c,d, it was found that the BLANK group (with no treatment) and the CON. group (treated with HA MGs) had almost no inhibition zone around the hole. The bacterium, S aureus, grew evenly around the hole, and the circle diameter was the same as the diameter of the initial hole punch (5 mm). In the EXP. group (treated with HA‐LA granular gel), an apparent inhibition zone was formed around the hole, with an average diameter of 12.5 mm. The results show that HA‐LA granular gel exhibited an apparent inhibitory effect on the growth of S aureus.
To better understand the degradation of hydrogels, 10 μL of HA MGs and HA‐LA granular gel were injected subcutaneously into C57 mice, respectively, and observed on days 3, 7, and 14. Figure 4e shows that the degradation extent of the two hydrogels was similar from day 3 to day 14, and there was no apparent absorption on day 3. On day 7, the volume of the two hydrogels significantly shrunk, and on day 14, the volume reduction was more pronounced, but residual hydrogels were still not entirely absorbed. In H&E staining, Figure 4f shows that LA was further crosslinked because of the mutual coordination of silver ions, resulting in denser compacted microspheres in the EXP. group (HA‐LA granular gel). The CON. group (HA MGs) also had more internal pores than the EXP. group on day 3. On day 7, the two hydrogels were degraded to a certain extent, and the internal structure was looser compared with day 3. On day 14, the internal structure of the HA‐LA granular gel in the EXP. group was looser compared with that on day 7. In the CON. group, the internal structure was looser than that in the EXP. group, exhibiting many lymphocytes, erythrocytes, and macrophages around the HA MGs. Most HA MGs were absorbed organically, but some were not degraded completely and remained, which was consistent with the general observation. Overall, although there was little difference between the two hydrogels in the general observation; it was apparent that the degradation rate of HA MGs was faster than that of the HA‐LA granular gel in H&E staining.
## Normal and diabetic wound healing with dressing treatment
Based on the findings that the HA‐LA granular gel possessed ROS‐scavenging and antibacterial properties, the HA‐LA granular gel was then applied to normal and diabetic C57 mice wounds (EXP. Group). The application of HA microgels (HA MGs) to the wound was defined as the CON. group. Therefore, the experimental subjects could be divided into six groups: NM (BLANK), NM (CON.), NM (EXP.), DM (BLANK), DM (CON.), and DM (EXP.) group. Then the wound‐healing effect was monitored on normal C57 mice (NM) and diabetic C57 mice (DM) in vivo.
Figure 5a‐f shows that the healing of the three groups of normal mice was similar on day 3. The scab and the wound contraction appeared at the wound site. The relative area of the wound was $85\%$ to $90\%$ of the initial wound area. While the three groups of diabetic mice had slightly worse healing than normal mice, the relative area of the wound was about $90\%$ of the initial wound area. On day 7, the scab had covered the wound of the normal mice, and there was no significant healing difference in the three groups of normal mice. The relative area of the wound was about $70\%$ of the initial wound area. The healing trend of the three groups of diabetic mice was similar to the normal mice, showing the relative area of the wound to be about $75\%$ of the initial wound area. On day 14, significant differences were observed in the normal mouse group, and the NM (EXP.) group healed better than the other two groups, which were completely healed. Additionally, the color and texture of the wound were similar to normal skin, and the relative wound area of the NM (BLANK) and the NM (CON.) was $35\%$ and $20\%$ of the initial wound area, respectively. Diabetic mice also showed better healing results in the DM (EXP.) compared with the other two groups. The relative wound area in the DM (EXP.) was about $25\%$ of the initial wound area, while the relative wound area of the DM (BLANK) and the DM (CON.) was $50\%$ and $40\%$ of the initial wound area, respectively (Figure 5a‐f). In short, applying the HA‐LA granular gel on the wound surface significantly promoted both normal and diabetic mouse wound healing.
**FIGURE 5:** *General observation of different dressings on the wound of normal and diabetic C57 mice. (a, b) General observation images of wound treatment with different dressings on days 3, 7, and 14 (NM (CON.) = normal mice wound treated with HA MGs, NM (EXP.) = normal mice wound treated with HA‐LA granular gel, DM (CON.) = diabetic mice wound treated with HA MGs, DM (EXP.) = diabetic mice wound treated with HA‐LA granular gel) (scale bar: 5 mm, n = 3). (c) Simulated diagram of wound healing. (d) Quantification of wound residual area rate on days 0, 3, 7, and 14 in normal C57 mice. (e) Quantification of wound residual area rate on days 0, 3, 7, and 14 in diabetic C57 mice. (f) Quantification of wound residual area rate on day 14 in normal and diabetic C57 mice. The data represent the mean ± SD. ***P < .001, according to t‐test. HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels; SD, standard deviation*
To further investigate the effect of different dressings on wound healing, H&E staining and MTS were carried out to evaluate the histological differences in the wound tissue. On day 3, there were apparent skin lesions in all groups. In the discontinuity of the sarcolemma under the squamous epithelium, many lymphocytes and neutrophils infiltrated the tissue, and the fat was liquefied, showing a vacuolar appearance. There were more new granulation tissues on the wounds of the NM (CON.) group and the NM (EXP.) group, indicating that the presence of HA MGs and HA‐LA granular gel positively affected normal mice wounds. However, almost no new granulation tissues were observed in the DM (BLANK) and DM (CON.) groups, while more new granulation tissue on the diabetic mice wounds treated with HA‐LA granular gel was observed (Figure 6a).
**FIGURE 6:** *Histology evaluation (H&E staining) of wound healing. (a) H&E staining of the NM (BLANK), NM (CON.), NM (EXP.), DM (BLANK), DM (CON.), and DM (EXP.) groups on day 3, (b) day 7, and (c) day14 (NM (CON.) = normal mice wound treated with HA MGs, NM (EXP.) = normal mice wound treated with HA‐LA granular gel, DM (CON.) = diabetic mice wound treated with HA MGs, DM (EXP.) = diabetic mice wound treated with HA‐LA granular gel) (scale bar: 100 μm and 200 μm, n = 3). HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels*
On day 7, there were no significant differences in the formation of granulation tissue among the groups of normal mice. In groups of diabetic mice, granulation tissue formation was observed in the DM (BLANK) and DM (CON.) groups and had more neutrophils and lymphocytes, revealing that the wounds were still at the inflammatory stage. The healing progression was delayed when compared with normal mice wounds. However, the DM (EXP.) group had more new granulation tissue, and the inflammatory reaction was milder than that in the DM (BLANK) and DM (CON.) groups. In addition, the NM (EXP.) group had already formed a completely new epidermis at the wound site, which was thicker and more complete than those in the NM (BLANK), NM (CON.), and DM (EXP.) groups. In contrast, almost no new epidermis was observed in the DM (BLANK) and DM (CON.) groups (Figure 6b). On day 14, although the thickness of the new epidermis was varied, the new epidermis was observed in all groups. Compared with the DM (BLANK) and DM (CON.) groups, the new epidermis was thicker and more complete in the DM (EXP.) group of diabetic mice. New appendages such as sweat glands and hair follicles were observed in the NM (EXP.) group. In contrast, the new epidermis of the DM (BLANK) and DM (CON.) groups were relatively thin and incomplete, and the scabs were not completely exfoliated (Figure 6c). Besides, few residual microgels could be found embedding in regenerated tissues (Figure S2).
According to Masson staining, on day 3, collagen deposition in the wounds was not dense, and there was no apparent difference in each group. The fibrous collagen structure could not be observed (Figure 7a). On day 7, the three groups of normal mice showed denser collagen deposition when compared with those on day 3. The DM (EXP.) group of diabetic mice also had denser collagen deposition than the DM (BLANK) and DM (CON.) groups. The fibrous collagen structure could be observed at the same time. Collagen fibers at the wound surface of the NM (EXP.) group dispersed more evenly with higher density than that of the NM (BLANK), NM (CON.), and DM (EXP.) groups. However, observing collagen fibers in the DM (BLANK) and DM (CON.) groups was challenging. Combined with the results of H&E staining, it can be seen that the DM (BLANK) and DM (CON.) groups were still in the inflammatory stage. The inflammatory change of ECM might interfere with the staining of collagens resulting in the relatively red and dark blue change in Masson staining (Figure 7b). On day 14, the collagen deposition in all groups increased compared with day 7. Collagen fibrous structures were observed in all wounds, and it was worth noting that the NM (EXP.) group had a higher degree of ordered structure (Figure 7c).
**FIGURE 7:** *Histology evaluation (Masson staining) of wound healing. (a) Masson staining of the NM (BLANK), NM (CON.), NM (EXP.), DM (BLANK), DM (CON.), and DM (EXP.) group on day 3, (b) day 7, and (c) day 14 (NM (CON.) = normal mice wound treated with HA MGs, NM (EXP.) = normal mice wound treated with HA‐LA granular gel, DM (CON.) = diabetic mice wound treated with HA MGs, DM (EXP.) = diabetic mice wound treated with HA‐LA granular gel) (scale bar: 100 μm and 200 μm, n = 3). HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels*
In summary, the healing of the normal mice in the three groups was better than that of the diabetic mice. The NM (EXP.) group showed the closest tissue to normal skin tissue on day 14, while the healing of the DM (EXP.) group was much better than that of the DM (BLANK) and DM (CON.) groups, which was approximately the same as that of the NM (CON.) group. The results indicated that applying the HA‐LA granular gel to the wound could significantly stimulate the repair of wound tissue and promote wound healing. Overall, the histological results were consistent with the general observation.
## Assessment of in vivo ROS‐scavenging and tissue regeneration after applying HA‐LA granular gel to wounds
The in vivo removal of ROS was further assessed. Immunofluorescence staining of DHE was performed on the skin wound tissue on day 3. Figure 8a,b shows that the fluorescence density of the NM (EXP.) group was the lowest among all groups. There was no statistical difference between the fluorescence intensity of the NM (BLANK) and NM (CON.) groups. Similarly, there was no statistical difference in the fluorescence intensity between the DM (BLANK) and DM (CON.) groups. However, the skin tissue of diabetic mice treated with HA‐LA granular gel showed a lower level of ROS, and there was a significant difference between the two groups and the DM (EXP.) group. The results indicated that HA MGs showed no significant ROS‐scavenging ability in vivo, while the HA‐LA granular gel exhibited a robust ROS‐scavenging ability. Overall, the in vivo DHE immunofluorescence staining results were consistent with the in vitro DCFH‐DA immunofluorescence staining results.
**FIGURE 8:** *In vivo evaluation of ROS‐scavenging ability of HA MGs and HA‐LA granular gel. (a) Fluorescence images and the statistical data (b) of dihydroethidium (DHE) from different groups on day 3; DAPI (blue) staining of nuclei (NM (CON.) = normal mice wound treated with HA MGs, NM (EXP.) = normal mice wound treated with HA‐LA granular gel, DM (CON.) = diabetic mice wound treated with HA MGs, DM (EXP.) = diabetic mice wound treated with HA‐LA granular gel) (scale bar: 50 μm, n = 3). The data represent the mean ± SD. ***P < .001, according to t‐test. HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels; SD, standard deviation*
It is well known that the integrin α‐3 can be used as a marker for tissue regeneration and collagen. 37 Therefore, immunofluorescence staining of wound tissue with integrin α‐3 can demonstrate tissue regeneration directly. In addition, CD31 is a marker of neovascularization, 38 and α‐SMA is a specific marker of myofibroblasts, 39 which play essential roles in wound contraction and tissue fibrosis. These three markers can also serve as indicators of tissue microenvironment repair. Therefore, CD31, integrin α‐3, and α‐SMA immunofluorescence staining were performed on the skin wound tissue. Figure 9a‐i shows that the levels of three indicators in the NM (EXP.) group increased significantly on day 7, indicating that the increase in wound neovascularization and collagen production was the most obvious after HA‐LA granular gel treatment. The significant increase of α‐SMA suggested that the NM (EXP.) group entered the proliferation stage earlier than other groups. On day 14, the expression of the three indicators decreased significantly in the NM (EXP.) group while increasing significantly in the DM (BLANK) and DM (CON.) groups, indicating that the NM (EXP.) group had passed the proliferation stage and progressed to the remodeling stage. In contrast, the DM (BLANK) and DM (CON.) groups began to show significant neovascularization and collagen generation, which was in the proliferation stage and delayed tissue repair. The fluorescence intensity of three indexes of the DM (EXP.) group was lower than that of the DM (BLANK) and DM (CON.) groups, indicating that the healing was better than the DM (BLANK) and DM (CON.) groups, but not as good as the NM (EXP.) group.
**FIGURE 9:** *Wound microenvironment changes (healing) induced by ROS‐scavenging granular gel. (A‐D) Immunofluorescence images (A‐C) and the statistical data (D) of α‐SMA (red) from different groups on days 3, 7, and 14. (E‐I) Immunofluorescence images (E‐G) and the statistical data (H and I) of CD31 (red) and integrin α‐3 (green) double‐stained sections from different groups on days 3, 7, and 14; DAPI (blue) staining of nuclei (NM (CON.) = normal mice wound treated with HA MGs, NM (EXP.) = normal mice wound treated with HA‐LA granular gel, DM (CON.) = diabetic mice wound treated with HA MGs, DM (EXP.) = diabetic mice wound treated with HA‐LA granular gel) (scale bar: 50 μm, n = 3). The data represent the mean ± SD. *P < .05, **P < .01, ***P < .001, according to t‐test. HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels; ROS, reactive oxygen species; SD, standard deviation*
Compared with nondiabetic patients, because wounds in diabetic patients are more prone to long‐term hyperglycemia and bacterial infection, this can increase the number of neutrophils and macrophages, lead to an enhanced inflammatory response, increase ROS levels, and aggravate tissue damage. Research has shown that the microenvironment of excessive ROS and bacterial infection can promote the expression of M1 macrophage markers while decreasing the expression of M2 macrophage markers. 40 Decreasing ROS levels can partially reverse the M1/M2 macrophage polarization, inhibit M1 macrophages, and restore M2 macrophages. 41 Additionally, M2 phenotype macrophages can promote angiogenesis and collagen production, thus playing important roles in wound healing. 42, 43 In the present study, excessive ROS was eliminated after applying HA‐LA granular gel to the wound surface. The polarization of macrophages might be directed from the M1 to M2 type, reducing the inflammatory response and promoting tissue regeneration. In conclusion, the HA‐LA granular gel can eliminate excessive ROS and effectively promote wound healing by increasing the levels of tissue regeneration and neovascularization markers such as CD31, integrin α‐3, and α‐SMA.
## RNA‐seq analysis in the microenvironment during wound healing
To further clarify the effect of the HA‐LA granular gel in wound healing, we collected three pieces of day 7 skin wound tissues and performed RNA sequencing in groups of normal and diabetic mice. The results showed that many genes were differentially expressed in all groups on day 7. The pro‐inflammatory cytokine‐related TNF, IL1B, and IL‐6 genes were down‐regulated in the EXP. groups, while the M2‐type macrophage‐related MRC1, anti‐inflammatory cytokine‐related IL‐10, angiogenesis‐related VEGFA, PECAM1, and collagen‐related ITGA3 genes were up‐regulated (Figure 10a,b). Nonmetric multidimensional scaling (NMDS) showed that normal and diabetic mice gene expression had significantly different characteristics. The results demonstrated that the normal and diabetic mice group was highly clustered separately. Both NMDS2 ($8.63\%$) and NMDS3 ($6.86\%$) could significantly distinguish the two groups (Figure 10c).
**FIGURE 10:** *Transcriptomics data of wound tissue in all groups on day 7. (A) Heatmap showed the differential expression of RNA sequencing data from the wound tissue in all groups. (B) Scatter map showed the differential expression of RNA sequencing data from the wound tissue in all groups. (C) NMDS analysis showed the differential expression of RNA sequencing data from the wound tissue in all groups (NM (CON.) = normal mice wound treated with HA MGs, NM (EXP.) = normal mice wound treated with HA‐LA granular gel, DM (CON.) = diabetic mice wound treated with HA MGs, DM (EXP.) = diabetic mice wound treated with HA‐LA granular gel, n = 3). HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels; NMDS, nonmetric multidimensional scaling*
The trend of different genes in each group was further analyzed through Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The MAPK signaling pathway is activated after being stimulated and transmits extracellular signals through the cell membrane to the nucleus through phosphorylation and participates in various cellular functions such as cell proliferation, differentiation, and migration. 44, 45 Growth factors, cytokines, and various stresses can activate ROS. 46 Studies have shown that excess ROS induces oxidative modification of MAPK signaling proteins, such as MAP3K, thereby activating the MAPK signaling pathway. 47 One of the branches of this pathway, ERK, is involved in the cellular inflammatory response. 48 KEGG analysis showed that in the MAPK signaling pathway, IL1B, TNF, RASA2, MAP2K7, IKBKG, Nfkbia, CD14, and JUN genes were down‐regulated in the EXP. group of normal and diabetic mice compared with the BLANK group and CON. group. There was apparent gene enrichment, with the q value <0.05. Another point of concern is the cytokine‐cytokine receptor interaction. Cytokines regulate the inflammatory response, cell growth, cell differentiation, apoptosis, and angiogenesis on the cell surface of target cells. 49 Furthermore, pancreatic β cells mediate oxidative and nitrosative stress responses in response to various stimuli, promoting the expression levels of IFN‐γ, IL‐1β, TNF‐α, IL‐1, and IL‐6. This results in the activation of transcription factors and nuclear factor‐κB in the extrinsic pathway of β cells, leading to apoptosis. 50, 51 Therefore, in the KEGG analysis, the cytokine‐cytokine receptor interaction, there was apparent gene enrichment with the q value <0.05 and the q value of apoptosis signal in the DM group was lower than that in the NM group (Figure 11a,b). Overall, sequencing results indicated that the HA‐LA granular gel could reduce the release of pro‐inflammatory cytokines and promote wound healing through its ability to scavenge ROS by affecting the MAPK signaling pathway and cytokine receptor binding.
**FIGURE 11:** *Transcriptomics data of wound tissue in all groups on day 7. (a and b) KEGG analysis of differentially expressed genes from the wound tissue in all groups (NM (CON.) = normal mice wound treated with HA MGs, NM (EXP.) = normal mice wound treated with HA‐LA granular gel, DM (CON.) = diabetic mice wound treated with HA MGs, DM (EXP.) = diabetic mice wound treated with HA‐LA granular gel, n = 3). HA‐LA, hyaluronic acid‐g‐lipoic acid; HA MGs, hyaluronic acid microgels; KEGG, Kyoto Encyclopedia of Genes and Genomes*
## CONCLUSION
A multifunctional medical dressing was designed for diabetic wound surfaces. HA‐LA was synthesized and used to fabricate the microgels. HA‐LA MGs were assembled by Ag+ via its coordination effect with disulfide in dithiolane to form an injectable granular gel. The presence of LA and Ag+ endowed the gel dressing with ROS‐scavenging and antibacterial properties. The granular gel could be easily spread onto the wound and eliminate excessive ROS, which accelerated wound healing by increasing the levels of tissue regeneration and neovascularization markers such as CD31, integrin α‐3, and α‐SMA. Therefore, the ROS‐scavenging granular gel introduced in the present study could effectively regenerate wounds under various complex conditions and was expected to be used to treat difficult wounds, including diabetic infection.
## AUTHOR CONTRIBUTIONS
Shixi Zhang: Data curation (supporting); formal analysis (supporting); investigation (supporting); methodology (supporting); visualization (supporting); writing – original draft (lead); writing – review and editing (lead). Yuqing Pan: *Formal analysis* (supporting); investigation (supporting); methodology (supporting); visualization (supporting). Zhiyuan Mao: Investigation (supporting); methodology (supporting); visualization (supporting). Jiahui Zhang: *Formal analysis* (supporting); investigation (supporting); project administration (supporting); resources (lead); supervision (supporting); validation (supporting); visualization (supporting); writing – review and editing (supporting). Kunxi Zhang: Conceptualization (lead); funding acquisition (equal); project administration (supporting); supervision (lead); writing – original draft (lead); writing – review and editing (lead).
## FUNDING INFORMATION
This research was funded by National Natural Science Foundation of China (grant number 51973108, 201901047) and Natural Science Foundation of Shanghai (No. 22ZR1424700).
## CONFLICT OF INTERESTS
The authors declare no conflict of interest regarding the publication of this article.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/btm2.10402.
## ETHICS STATEMENT
All experimental protocols in the study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SH9H‐2021‐A32‐1). Informed consent was obtained from all subjects and/or their legal guardian(s). And all methods were carried out in accordance with relevant guidelines and regulations.
## ETHICS FOR USING MICE IN STUDY
Authors reporting experiments on live vertebrates must confirm that all experiments were approved by the Institutional Ethics Committee of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, and all experiments were performed in accordance with relevant guidelines and regulations. All methods are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org) for the reporting of animal experiments.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: Oxidative stress, dysfunctional energy metabolism, and destabilizing neurotransmitters
altered the cerebral metabolic profile in a rat model of simulated heliox saturation
diving to 4.0 MPa
authors:
- Xia Liu
- Yiqun Fang
- Jiajun Xu
- Tao Yang
- Ji Xu
- Jia He
- Wenwu Liu
- Xuhua Yu
- Yukun Wen
- Naixia Zhang
- Ci Li
journal: PLOS ONE
year: 2023
pmcid: PMC10013885
doi: 10.1371/journal.pone.0282700
license: CC BY 4.0
---
# Oxidative stress, dysfunctional energy metabolism, and destabilizing neurotransmitters altered the cerebral metabolic profile in a rat model of simulated heliox saturation diving to 4.0 MPa
## Abstract
The main objective of the present study was to determine metabolic profile changes in the brains of rats after simulated heliox saturated diving (HSD) to 400 meters of sea water compared to the blank controls. Alterations in the polar metabolome in the rat brain due to HSD were investigated in cortex, hippocampus, and striatum tissue samples by applying an NMR-based metabolomic approach coupled with biochemical detection in the cortex. The reduction in glutathione and taurine levels may hypothetically boost antioxidant defenses during saturation diving, which was also proven by the increased malondialdehyde level, the decreased superoxide dismutase, and the decreased glutathione peroxidase in the cortex. The concomitant decrease in aerobic metabolic pathways and anaerobic metabolic pathways comprised downregulated energy metabolism, which was also proven by the biochemical quantification of the metabolic enzymes Na-K ATPase and LDH in cerebral cortex tissue. The significant metabolic abnormalities of amino acid neurotransmitters, such as GABA, glycine, and aspartate, decreased aromatic amino acids, including tyrosine and phenylalanine, both of which are involved in the metabolism of dopamine and noradrenaline, which are downregulated in the cortex. Particularly, a decline in the level of N-acetyl aspartate is associated with neuronal damage. In summary, hyperbaric decompression of a 400 msw HSD affected the brain metabolome in a rat model, potentially including a broad range of disturbing amino acid homeostasis, metabolites related to oxidative stress and energy metabolism, and destabilizing neurotransmitter components. These disturbances may contribute to the neurochemical and neurological phenotypes of HSD.
## Introduction
High pressure above 1.3 MPa (at approximately 120-meter seawater) is induced in humans and mammals at risk of central nervous system (CNS) changes [1, 2]. The CNS might be one of the most sensitive targets in the occurrence of excessive atmospheric pressure, gas bubbles in the body, and decompression sickness (DCS) caused by deep-sea diving. In such conditions, a series of psychomotor and cognitive manifestations are highly complex, with distal and proximal tremors, electroencephalographic abnormalities, fasciculations, myoclonus, sleep disorders, nausea, headache, dizziness, and reduced performance on cognitive tests [2–4]. Moen et al. measured regional neurological abnormalities by diffusion- and perfusion-weighted magnetic resonance imaging (MRI). Perfusion deficits in cerebral microvascular function with arterial microemboli [5] were found in North Sea divers, proven by reduced mean transition time due to reduced complexity of the microvascular or capillary system. Alvhild Alette Bjørkum et al. found disturbing protein homeostasis, e.g., in synaptic vesicles, and destabilizing cytoskeletal components after heliox saturation diving in a rat model. However, Arvid Hope et al. reported that no visible CNS injuries of morphological changes under MRI scan were observed in rats with massive neurological symptoms of decompression sickness following heliox saturation decompression [6]. Thus, we hypothesize that potential molecular profile alterations behind such significant but ambiguous physiological abnormalities after a heliox-saturation dive might be observed in the CNS tissue.
The identification of biomarkers for monitoring the status of cellular physiological mechanisms is important for understanding biochemical events. It is always a challenge considering the complexity and diversity of molecular pathways involved in the response of biological systems to diverse factors in a specific moment or condition. Previous attempts linking individual biomarkers with functional CNS perturbation have provided some insight into oxidative damage and energy metabolism postsaturation dive [3, 4, 7]. Similarly, several studies have compared the amino acid neurotransmitter profiles of rats with high-pressure neurological syndrome to those of healthy individuals [2]. Illustrating the neurological metabolic fingerprint of CNS leisure might favor etiological hypotheses [8, 9], prevention [10], and therapeutic approaches [11]. However, the integrated neurological metabolic perturbation induced by a great depth of heliox saturation diving has not been investigated. Such information output from complex biological systems can be rapidly recorded because of advances in technological means [12]. The application of high-resolution nuclear magnetic resonance (NMR) spectroscopy can provide extremely amounts of high complexity but interpretable and robust metabolic profiling data. The spectral data of biofluids and tissue metabolite extracts can be obtained by means of chemometric and bioinformatic methods to reveal physiological or pathological status information. NMR spectroscopy gives immediate qualitative and quantitative information on approximately 102 different small molecules present in a biological sample. NMR detection enables a broad unbiased approach without a priori selection of specific biochemical pathways. Additionally, NMR allows high-throughput analysis and high reproducibility, and it is an intrinsically quantitative technique over a wide dynamic range due to the linear response of NMR signals within a concentration. This technology has been successfully applied to neurological diseases such as cerebellar ataxia [13], Huntington’s disease [14, 15], and Alzheimer’s disease [16, 17] in both preclinical and clinical studies.
Despite the promising applicability of NMR-based metabolomics, its application to evaluate the central nervous metabolic perturbation effects of a heliox saturation diving has not been reported previously. The aim of the present study was to investigate metabolomic changes in different anatomic compartments of the brain (cortex, hippocampus, and striatum) and biochemical index level changes in the cortex in a rat model after simulated 400 meters of sea water (msw) heliox saturation diving, as shown by the schematic experimental design of the present research in Fig 1. The illustration of the metabolic fingerprint of the target organ might provide a set of biomarkers available that could contribute to improvements in diving procedures.
**Fig 1:** *Schematic experimental design of the present research.*
## Experimental procedures
We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.
## Reagents and materials
Analytical-grade sodium chloride, DMSO, NaN3, NaH2PO4•2H2O, and Na2HPO4•12H2O were purchased from Sinopharm Chemical Reagent Co. Ltd. (Shanghai, China). HPLC-grade CHCl3 and CH3OH were obtained from Merck (Darmstadt, Germany). D2O ($99.9\%$ in D) containing sodium 3-(trimethyl-silyl) propionate-2, 2, 3, 3, d4 (TSP) as an internal standard for chemical shift reference was provided by Sigma‒Aldrich (MO, USA). A buffer system containing 0.2 M Na2HPO4/NaH2PO4 in D2O at pH 7.4 was prepared to prevent the pH effect on the chemical shifts of metabolites at different concentrations. The assay kits for the determination of sodium-potassium ATPase (Na-K-ATPase), cholinesterase (AChE) and lactate dehydrogenase (LDH) were purchased from Abcam (USA). The assay kits for dopamine (DA) were purchased from RD, USA, the assay kits for epinephrine (E) and norepinephrine (NE) were from Abnova, Taiwan, 5-hydroxytryptamine (5HT) assay kits were from BioSource, and gamma-aminobutyric acid (GABA) assay kits were from Santa Cruz, USA. The assay kits of superoxide dismutas (SOD), malonyldialdehyde (MDA), and glutathione peroxidase (GPx) were purchased from Cayman, USA.
## Ethics approval
All experimental protocols were approved by the Animal Ethics Committee of Naval Medical Center of PLA, Naval Medical University (Approval no: SYXK(Shanghai)2017-0019, Approval year: 2020). All animals received good care according to the Guide of the Care and Use of Laboratory Animals of Naval Medical University. We confirm that all methods are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org) for the reporting of animal experiments. The participants researched in this study were rats, not humans; thus, there was no consent to participate.
## Animals and grouping design
Adult male Sprague‒Dawley rats with an average age of 8 weeks and weighing 200–220 g were included in this research. Animals were purchased from Shanghai Slac Laboratory Animal Co., Ltd. (Shanghai, China). Maximum efforts were carried out to minimize animal suffering and the number of animals necessary for the capture of reliable data. Standard rat chow and drinking water were available for all animals ad libitum. Animals with 3 rats per cage were housed in a specific pathogen-free (SPF) animal room (temperature, 22–24°C, humidity, 45–$55\%$) and controlled conditions of light ($\frac{12}{12}$ hour light-dark cycle) for one week before the experiment. Prior to commencement of the diving experiments, rats were acclimatized for five days in the lab environment, including a hyperbaric chamber.
Sixteen rats were weighed, labeled, and restricted randomly assigned to a control (exposure to normobaric air but enduring similar light and noise, named the CON group, $$n = 8$$) group and an experimental (subjected to a simulated heliox saturation diving to 4.0 MPa (400 msw), named the HSD group, $$n = 8$$).
## Stimulated saturation diving of 400 msw
Rats in the HSD group were pressurized in helium-oxygen gas in a hyperbaric chamber. In brief, four phases of compression, storage, decompression, and bendwatch were included in the saturation period. The rat’s compression rate was 1 msw/min up to 10 msw with $20\%$ He-O₂ gas mixtures and 3.54 msw/min from 10 msw to 400 msw with pure He. The time compression from the surface to 400 msw took approximately 120 minutes. The bottom phase at 400 msw was 120 minutes. The oxygen concentration was supplemented with pure oxygen to maintain the oxygen partial pressure at 35~50 kPa during the compression process and the storage depth. The period of decompression bendwatch was 40 min at 300 msw, 45 min at 200 msw, 50 min at 105 msw, 60 min at 45 msw, 75 min at 10 msw, 75 min at 3 msw, and then at the surface. The ascent rate used in this decompression model is approximately 10 meters (33 ft) per min. the oxygen partial pressure was maintained at 38~67 kPa during the decompression stage. After decompression to 10 m, oxygen concentration was maintained at 20~$24\%$. The saturation diving timeline overview is shown in Fig 2. Rats in the air control group (Group CON, named the control group, with eight rats) were bred within the atmospheric environment in the same experimental lab with the same chamber as the HSD groups. The control group did not receive any compressing or decompressing procedures but endured similar noise and light as the HSD group.
**Fig 2:** *Illustrative timeline overview of the hyperbaric decompression of stimulated heliox saturation diving to 400 msw.*
## Sample collection
After the decompression period, the rats were anesthetized with pentobarbital sodium ($0.3\%$, 1.0 ml/kg rat weight) intraperitoneally, followed by removal of the brain. The left cerebral hemispheres were rapidly dissected. Cortex, hippocampus, and striatum tissues were dissected, and abbreviated respectively as HSDC, HSDH, HSDS, CONC, CONH and CONS in two animal groups. As the largest tissue among the three compartments, each cortex sample was then cut into two pieces with approximately equal weights (one piece of sample for metabolomics analysis, the other pieces of samples for biochemical assessments). All tissues were snap-frozen in liquid nitrogen and stored at −80°C until further analysis.
## Tissue extraction procedure
The extraction procedure of polar metabolites from tissue samples was a minor modification as reported [18]. Preweighed frozen tissue samples were thawed on ice and then subjected to mechanical homogenization in an icy-cold HPLC-grade methanol-chloroform-water solvent system (400 μL, 400 μL, and 285 μL, respectively, per 100 mg brain tissue) using a tissue homogenizer (Precellys 24, Bertin Technologies, Villeurbanne, France). The resultant homogenates were retained on ice for a period of 30 min and then centrifuged at 12,000 x g for a period of 10 min at 4°C. The supernatant of each sample was then removed and lyophilized to obtain powder containing polar metabolites in a freeze dryer (FD-1A-80, BIOCOOL, China). Each powder sample was subsequently reconstituted with 550 μL PBS buffer containing $0.1\%$ TSP, and all samples were then thoroughly rotamixed and centrifuged at 12,000 x g for a period of 20 min at 4°C. An aliquot of 500 μL of supernatant was then transferred into a 5.0 mm-diameter NMR tube (Norrel, UK).
The extraction principle of protein in the cortex sample was as previously described with no modifications. The exact amount of cortex samples was poured into a homogenization buffer (HEPES 25 mmol/L, pH 7.4, MgC12 5 mmo1/L, DTT 2 mmol/L, EDTA 1.3 mmol/L, EGTA 1 mmol/L, $0.1\%$ Triton X-100, aprotinin, pepstatin A and leupeptin 10 μg/ml each) and homogenized manually in an ice bath. The mixture was centrifuged at 1,000 x g for a period of 10 min at 4°C, and the supernatant was used for the quantified protein concentrations using the Bradford test.
## Biochemical assay
A sensitive, competitive enzyme-linked immunosorbent assay (ELISA) was applied with assay kits for the quantification of the metabolic enzymes Na-KATPase and AChE and the neurotransmitters DA, E, NE, 5HT, and GABA. The levels of SOD, MDA, and GPx in cortical tissue were also determined by ELISA with assay kits according to the manufacturer’s instructions.
## NMR measurement
The efficient quantification of tissue metabolites was achieved using an analytical platform based on a liquid high-resolution Bruker Avance-III NMR spectrometer equipped with a high-sensitivity cryogenic probe operating at a frequency of 600.17 MHz for 1H observation at 298 K. A water-suppressed one-dimensional 1H ZGPR (TOPSPIN version 3.0, Bruker Biospin) pulse sequence (RD-90°-ACQ) was applied to acquire NMR data for each sample. Four dummy scans and 128 transients were recorded into a time domain of 32 K data points using a spectral width of 20 ppm with a relaxation delay of 10.0 s and an acquisition time of 2.73 s. An exponential line-broadening function of 0.3 Hz and zero-filling to 64 K data points were applied to all the free induction decays (FIDs) prior to Fourier transformation. Additional two-dimensional NMR techniques with pulsed field gradient correlation spectroscopy (gCOSY) and 2D homonuclear total correlation spectroscopy (TOCSY) were employed using standard pulse programs on selected samples to confirm the chemical shift assignments. An automated sample changer for continuous sample delivery was used in all spectra acquisition.
## Metabolite identification and confirmation
NMR signals based on the location of individual resonances on the spectra were identified in Chenomx NMR Suite v. 8.4 software package (Evaluation version). The confirmations of some metabolite assignments were carried out considering chemical shifts, coupling constants and multiplicity patterns of metabolites as information on scalar couplings extracted from 1H–1H COSY, 1H–1H TOCSY, public NMR databases such as COLMAR and Human Metabolome Database (HMDB), and the literature [18, 19].
## Multivariate statistical analysis
The preprocessing protocol used for preprocessing each 1D 1H NMR spectrum was the same as that described in our previous work [20]. The 1H NMR spectra were phased and baseline-corrected using MestReNova (Mestrelab Research, S.L., Spain), and the spectral region of each metabolite was integrated into one bucket. The resonances of the organic solvent signal and the residual H2O/HOD signal region were removed in all 1D 1H NMR spectra. A 0.003 Hz bucket procedure and normalization to the sum of the spectral intensity multiplied by 10000 were applied in all spectra. To avoid misinterpretation of the discriminant metabolites due to overlapping signals, only the largest bucket values in one peak were selected for the next-step analysis. Subsequently, integral buckets of 47 metabolites were extracted and subjected to univariate and multivariate data analysis. Indiscrimination analysis by principal component analysis (PCA) and discrimination analysis by partial least squares-discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were performed using the SIMCA-P+14.0 software package (Umetrics, Umeå, Sweden) scaled to unit variance data. The PCA and PLS-DA score plots were illustrated with the first and the second principal components (t[1], t[2]), while the OPLS-DA score plot used t[1] and the orthogonal component (to[1]). The parameters Q2 (cum), R2Y(cum), and R2X (cum) were calculated to test the robustness of the discrimination models against overfitting [21]. As previously described, a quality assessment value of Q2 ≥ 0.4 is considered a reliable model. The sevenfold cross validation strategy and permutation test 200 times with the first component were applied to guard against model overfitting and further validate the reliability and trustworthiness of the models. If the Q2 regression line had a negative intercept and Q2 values fitted in the leftmost point were greater than all the Q2 values of the right points in the permutated test, the established OPLS-DA model was robust [22]. The default 10-fold cross validation strategy was applied to guard against model overfitting. *In* general, a quality assessment statistic (Q2) ≥ 0.4 is considered a reliable model, as previously described.
The correlation coefficients (r) and VIP values extracted from the OPLS-DA models were used to identify metabolites that contributed significantly to the separation between the two groups. Additionally, the fold changes of metabolites between groups were calculated using a function. IA/IB, where IA and IB represent the mean values for the metabolite integral in the A and B groups, respectively. The heatmap and box charts were plotted to facilitate the understanding of the metabolite variations among groups.
## Univariate statistics of metabolite integrals
The bucket average of metabolites in each group is expressed as the mean ± standard deviation (S.D.). Univariate analysis was also carried out using ANOVA (analysis of variance). Statistically significant differences between groups were evaluated by an unpaired t test of two tails after log conversion in GraphPad Prism V 8.4.3 software (Graph Pad Software Inc., San Diego, CA, USA). Statistically significant differences were defined by values of p (< 0.05).
According to the three criteria, including an absolute value of r greater than 0.50, a value of VIP greater than 1.0, and a p value less than 0.05, the bucket variables with one of three features will be selected as discriminatory variables. Because more than one bucket value was listed from the same metabolite, two or more discriminative variables arising from the same metabolite will be present in the discriminatory panel. Selection will be carried out based on VIP rankings, and only the variables from the same metabolites with the highest VIP values were therefore selected as discriminatory metabolite level features, which will be included in the next-step analysis.
## System statistical metabolic correlation analysis and hierarchical cluster analysis
Pearson’s correlation coefficient calculation and hierarchical cluster analysis were carried out based on the relative integrals of metabolites in R Studio (Version 1.4.1717) with small scripts. For each group pair, the correlation matrix was illustrated in a pixel map as described in the literature [21]. Briefly, for each variable in one group, correlations with p values less than 0.05 were considered significant statistical correlations and were kept to construct the final correlation network to illustrate the latent relationships of metabolites within the group and the disturbed metabolic relationships between groups. The redder squares indicate a more significant positive correlation. The bluer squares indicate a more significant positive correlation. The white squares indicate the correlation with no significance. Analysis of the most relevant cerebral metabolic pathways and networks invoked as respondents to HSD was performed using the MetaboAnalyst 3.0 tool. For the pathway topology analysis performed, the *Rattus norvegicus* (rat) mammalian pathway library was selected. This approach was also utilized to provide estimates of pathway impact, false discovery rate (FDR), and p values.
## Cortical biochemical indexes from the HSD and CON groups
To validate the metabolic alteration effects of HSD on the brain tissues, biochemical parameters, including the energy metabolism-related metabolic enzymatic activities of Na-KATP, AChE, and LDH, the neurotransmitters of DA, E, NE, 5HT, and GABA, and the oxidative stress-related proteins of SOD, MDA, and Gpx in the ipsilateral cortex tissues of HSD and CON rats were also determined (Table 1). Compared with the control group, the levels of DA, E, NE, and MDA were increased significantly, whereas the contents of Na-KATP, AChE, LDH, 5HT, GABA, SOD, and Gpx were decreased in the HSD model. The limit of detection of assay kits used in the present work were listed in the S3 Table in the file of supplementary materials.
**Table 1**
| Groups | Metabolic enzymes | Metabolic enzymes.1 | Metabolic enzymes.2 | Metabolic enzymes.3 | Metabolic enzymes.4 | Metabolic enzymes.5 |
| --- | --- | --- | --- | --- | --- | --- |
| Groups | Na-KATP (μmol/mg protein) | Na-KATP (μmol/mg protein) | AChE (U/g protein) | AChE (U/g protein) | LDH4 (U/g protein) | LDH4 (U/g protein) |
| CON | 1.70±0.224 | 1.70±0.224 | 539.00±33.542 | 539.00±33.542 | 9250.43+425.394 | 9250.43+425.394 |
| HSD | 116±0.13* | 116±0.13* | 235.86±15.00* | 235.86±15.00* | 6544.29±873.84* | 6544.29±873.84* |
| Groups | Neurotransmitters | Neurotransmitters | Neurotransmitters | Neurotransmitters | Neurotransmitters | Neurotransmitters |
| Groups | dopamine (DA, nmol/g protein) | Epinephrine (E, nmol/g protein) | Epinephrine (E, nmol/g protein) | Norepinephrine (NE, nmol/g protein) | 5-hydroxytryptamine (5HT, nmol/g p rotein) | GABA (nmol/g protein) |
| CON | 65.64±4.82 | 8.52±0.58 | 8.52±0.58 | 7.64±1.262 | 2.81±1.00 | 6.49±0.72 |
| HSD | 96.88±7.74* | 11.42±119* | 11.42±119* | 12.02±1.78* | 1.00±0.17* | 3.93±1.08* |
| Groups | Oxidative stress indicators | Oxidative stress indicators | Oxidative stress indicators | Oxidative stress indicators | Oxidative stress indicators | Oxidative stress indicators |
| Groups | SOD (U/mg protein) | MDA (nmol/mg protein) | MDA (nmol/mg protein) | GPX+(U/g protein) | SOD (U/mg protein) | MDA (nmol/mg protein) |
| CON | 153.09+37.62 | 2.88±1.44 | 2.88±1.44 | 1042.27±248.32 | 153.09+37.62 | 2.88±1.44 |
| HSD | 114.97±23.38* | 6.23±1.96* | 6.23±1.96* | 746.77+188.13* | 114.97±23.38* | 6.23±1.96* |
## Metabolites identified in 1H NMR spectra of brain tissue samples
Fig 3 displays three representative 1H NMR spectra from the cortex (Fig 3A), hippocampus (Fig 3B), and striatum (Fig 3C) of an HSD rat. A wide range of prominent metabolites were identified according to the 1H NMR data of brain tissue samples, and they are organic acid anions (lactate (Lac), malonate (Maln), succinate (Suc), malate (Mal), fumarate (FMA), 2-hydroxybutyrate (2-HB), acetate (Ace), formate (For), taurine (Tau), ascorbate(Asc), 2-hydroxyisobutyrate (2HIB)), amino acids (leucine (Leu), isoleucine (Ile), valine (Val), alanine (Ala), glycine (Gly), tyrosine (Tyr), phenylalanine (Phe), aspartate (Asp), glutamine (Gln), glutamate (Glu), threonine (Thr), serine(Ser), lysine (Lys), asparagine (Asn)), neurotransmitters (γ-aminobutyrate (GABA)), energy-related metabolites (creatine (Cre), adenosine triphosphate (ATP), adenosine monophosphate (AMP)), phospholipid related metabolites (O-phosphoethanolamine (PEA), O-phosphocholine (Pcho), sn-glycero-3-phosphocholine (GPC)), and others (myo-inositol (MI), nicotinamide adenine dinucleotide (NAD+), nicotinuric acid (Nic), N-acetylaspartate (NAA), nicotinamide-adenine-dinucleotide phosphate (NADP+), UDP-N- galactose (UDPGa), uridine (Uri), uracil (Ura), cystidine (Cyt), uridine 5’-monophosphate (UMP), inosine 5’-monophosphate (IMP), inosine (Ino), choline (Cho), carnitine (Car), glutathione (GSH). S1 Table shows detailed information on the metabolite assignments.
**Fig 3:** *Representative 600 MHz 1H NMR spectra of the cortex (A), hippocampus (B), striatum (C) extracts obtained from the HSD rats, right part of 0.70–4.80 ppm spectral region and left part of 5.65–9.45 ppm spectral region (resonance intensity amplitude enhanced 12-fold of that of right part). The abbreviations of metabolites are shown in Additional file 1: S1 Table.*
## Metabolic profile alterations revealed by metabolomic analysis
PCA, an exploratory and unbiased analysis approach of the 1H NMR spectra from all brain extracts across different brain regions, was first made to reveal the main metabolic trends driven by hyperbaric exposure to a 400 msw heliox saturation environment. A PC1 vs. PC2 scatting plot obtained from PCA (S1A–S1C Fig) of integral bucket data revealed a certain discrimination with some overlap between the two classifications. Supervised investigations of PLS-DA (S1A’–S1C’ Fig) and OPLS-DA (Fig 4A–4C) models exhibited clear class discriminations of the metabolic profiles between groups. Fig 4 shows the score plots of the OPLS-DA model for the cortex region (A), the hippocampus region (B), and the striatum region (C), showing clear discrimination between the HSD groups and the controls. The high explained variation and the goodness of the prediction reflected by the values of R2X and Q2 (S1A’–S1C’ Fig, Fig 4A–4C) and permutation test plots (Fig 4’-4C’A) indicated the robustness of the generated supervised models. The correlation coefficient (r) extracted from the S-line plots, variable importance in projection (VIP), and the p value from the nonparametric univariate tests were collected to assess the significant metabolites responsible for the class-discriminating patterns. Therefore, meeting any one of the three criteria (the absolute value of r greater than 0.50, the value of VIP greater than 1.0, together with p value less than 0.05), we selected a panel of statistically significant metabolites (Table 1) responsible for the separation between the CON and HSD groups. In Table 1, fold-change values greater than 1 indicate an increased level in the HSD group, while fold-change values less than 1 indicate a decreased level in the HSD group. The mean SD values of discriminative metabolites are listed in S2 Table. Using the hierarchical cluster analysis of metabolites and the average linkage method, the generated heatmap (Fig 5A–5C) with dendrograms allows for a better visualization of three brain region metabolic alterations caused by hyperbaric exposure in a helium oxygen-saturated environment.
**Fig 4:** *Orthogonal partial least squares discriminant analysis (OPLS-DA) score plots and permutation test plots that discriminate the effect of 400 msw heliox-saturation exposure on 1H NMR spectra from control groups in the cortex (CONC, n = 8, HSDC, n = 8, A and A’) (R2X = 0.38; Q2 = 0.46), hippocampus (CONH, n = 8, HSDH, n = 8, B and B’) (R2X = 0.37; Q2 = 0.43), and striatum (CONS, n = 8, HSDS, n = 8, C and C’) (R2X = 0.39; Q2 = 0.40).The values of the Q2 parameter in the OPLS-DA score plots, which were equal to or greater than 0.40, coupled with the Q2 values of the leftmost point in the permutated models were greater than all the fitted Q2 values of the right points (permutation test 80 times), indicating that the established OPLS-DA models were valid.* **Fig 5:** *Heatmap and statistical correlation plots derived from the bucket values of the discriminatory metabolites from cortex (A, A’, HSDC samples lies in the upper panel and CONC lies in the lower panel), hippocampus (B, B’, HSDH group lies in the upper panel and CONH lies in the lower panel), and striatum (C, C’, HSDS samples lies in the upper panel and CONS lies in the lower panel) tissues of HSD and CON rats.Metabolites on the heatmap are organized by hierarchical clustering based on the overall similarity in level patterns. Venn diagram (D) illustrating metabolite overlap among the HSDC-CONC, HSDH-CONH, and HSDS-CONS comparisons.*
## Metabolic disorders observed in different brain regions of HSD rats
The abovementioned multivariate analysis and univariate analysis using the bucket height of metabolites in the different groups provided a great work tube to identify discriminative metabolites revealing the potential neurologic metabolic alterations associated with HSD events. Elevated AMP, FMA, Nic, and Phe and a decrease in Ala, Asn, Car, Cho, Cyt, GABA, GSH, Ino, Lac, Pcho, Phe, Tyr, Ura, and Uri were found in the cortex tissue of the HSD group compared with the CON group (Fig 5A, Table 1). Meanwhile, in the hippocampus, Ala, GSH, Lac, Uri, Cyt, GABA, Tyr, and Ura also decreased and AMP increased in the HSD group, as they did in the cortex. Moreover, the upregulated Thr and downregulated Gly, ATP, Tau, Imp, Suc, Asc, and DMA were expressed in hippocampus extracts of the HSD group relative to the controls (Fig 5B, 5D, Table 1). Compared with the CONS group, the contents of Cyt, GABA, Ura, Cho, and Thr also decreased as they did in the cortex and hippocampus. Additionally, increased levels of branched-chain amino acids (BCAAs, including Leu, Ile, Val), and Lys and decreased levels of Gln, NAA, NAD+, NADP+, Asp, a series of metabolites of ATP, Tau, IMP, and Suc, which also decreased in the hippocampus, and another two metabolites, Ino and Pcho, which also decreased in the cortex, were observed in the striatum tissues of HSD group rats (Fig 5C, 5D, Table 1). Such many metabolites always indicate implicated molecular pathways with complexity and diversity. The integral bucket values of discriminant metabolites were quantified, and statistically significant fold-changes of their concentrations between the heliox-saturation-hyperbaric-exposed and control groups are summarized in S2 Table and Table 2 for the three brain regions.
**Table 2**
| metabolites | FCa (rb, VIPc, pd) in cortex | FC (r, VIP, p) in hippocampus | FC (r, VIP, p) in striatum |
| --- | --- | --- | --- |
| ATP | / | 0.72(-0.72, 1.58, 0.03) | 0.62(-0.68, 1.38, 0.04) |
| Ala | 0.93(-0.54, 1.15, 0.13) | 0.89(-0.53, 1.4, 0.06) | / |
| AMP | 1.31(0.79, 1.59, 0.00) | 1.1(0.62, 1.34, 0.05) | / |
| Asc | / | 0.87(-0.53, 1.13, 0.04) | / |
| Asn | 0.85(-0.55, 1.12, 0.07) | / | / |
| Asp | / | / | 0.93(-0.66, 1.34, 0.06) |
| Car | 0.82(-0.59, 1.22, 0.02) | / | / |
| Cho | 0.64(-0.79, 1.66, 0.01) | 0.8(-0.62, 1.35, 0.1) | 0.63(-0.92, 1.86, 0.00) |
| Cyt | 0.8(-0.59, 1.26, 0.04) | 0.69(-0.73, 1.56, 0.01) | 0.61(-0.74, 1.49, 0.00) |
| DMA | / | 0.39(-0.56, 1.25, 0) | / |
| FMA | 1.26(0.51, 1.09, 0.08) | / | / |
| GABA | 0.89(-0.54, 1.18, 0.05) | 0.92(-0.82, 1.74, 0) | 0.86(-0.53, 1.07, 0.04) |
| Gln | / | / | 0.9(-0.5, 1.23, 0.06) |
| Gly | / | 0.89(-0.6, 1.4, 0.02) | / |
| GSH | 0.75(-0.75, 1.52, 0.01) | 0.82(-0.57, 1.21, 0.05) | / |
| Ile | / | / | 1.11(0.62, 1.33, 0.03) |
| IMP | / | 0.79(-0.72, 1.56, 0) | 0.82(-0.55, 1.13, 0.09) |
| Ino | 0.53(-0.92, 1.86, 0) | / | 0.68(-0.65, 1.3, 0.06) |
| Lac | 0.88(-0.57, 1.15, 0.02) | 0.87(-0.65, 1.6, 0.02) | / |
| Leu | / | / | 1.09(0.64, 1.35, 0.02) |
| Lys | / | / | 1.05(0.55, 1.27, 0.11) |
| NAA | / | / | 0.87(-0.51, 1.23, 0.09) |
| NAD+ | / | / | 0.84(-0.58, 1.25, 0.03) |
| NADP+ | / | / | 0.57(-0.67, 1.37, 0.02) |
| Nic | 1.33(0.67, 1.44, 0) | / | / |
| Pcho | 0.88(-0.48, 1.2, 0.07) | / | 0.87(-0.54, 1.1, 0.08) |
| Phe | 1.2(0.68, 1.48, 0.02) | / | / |
| Suc | / | 0.78(-0.5, 1.34, 0.02) | 0.83(-0.56, 1.28, 0.08) |
| Tau | / | 0.92(-0.39, 1.17, 0.21) | 0.79(-0.72, 1.5, 0.01) |
| Thr | / | 1.09(0.67, 1.57, 0.01) | / |
| Tyr | 0.78(-0.7, 1.43, 0.01) | 0.9(-0.5, 1.26, 0.18) | 0.74(-0.53, 1.19, 0.04) |
| Ura | 0.83(-0.43, 1.07, 0.21) | 0.37(-0.72, 1.55, 0.01) | 0.69(-0.52, 1.07, 0.07) |
| Uri | 0.68(-0.88, 1.8, 0) | 0.78(-0.58, 1.35, 0.11) | / |
| Val | / | / | 1.09(0.59, 1.35, 0.04) |
## Metabolite correlation analysis
The relationship between or among metabolites was so complex in Fig 5A’-5C’. For the energy metabolites, the positive correlations for Lac vs Ala in HSDC, AMP vs IMP in HSDH, ATP vs Pcho in HSDS, Suc vs GABA/Gln/Tyr/NAA in HSDS, and the negative correlations for Lac vs Thr and GSH vs Suc in HSDH, Suc vs Val in HSDS were present in the metabolite correlation plots. For the neurotransmitters, the positive correlations for GABA vs Car in HSDC, GABA vs Cho in CONH, and GABA vs Suc/Ino/Gln in HSDS were present in the metabolite correlation plots. The negative correlations for GABA vs FMA in the CONC and GABA vs NAA/Ura/NADP+ in the CONS were present in the metabolite correlation plots. For the metabolites related to oxidative stress, the positive correlations for GSH vs Asn in the HSDC and the CONC, Tau vs Lac in the HSDH, Tau vs Suc/Ala in the CONH, the negative correlations for GSH vs Suc in the HSDH, Tau vs Thr in the HSDH were present in the metabolite correlation plots.
## Metabolomics pathway analysis
Quantitative pathway analysis consisting of pathway enrichment analysis and pathway impact from pathway topology revealed highly statistically significant HSD-induced modulations to a series of metabolic pathways. Pathway impact scores, together with false discovery rate (FDR) and p values, are described in Fig 6. Pathways were considered significantly enriched if p values were lower than 0.05; the profiled metabolites (hits) relative to the total metabolites of the pathway (match status) were higher than 1; and the impact scores (indicating the impact of significantly affected metabolites in the pathway based on network topology measure of relative betweenness centrality) were higher than 0. The pathways in the cortex (Fig 6A) with the greatest metabolic impact value were phenylalanine, tyrosine, and tryptophan biosynthesis > phenylalanine metabolism > pyrimidine metabolism > alanine, aspartate, and glutamate metabolism. The pathways in the hippocampus (Fig 6B) with the greatest metabolic impact were phenylalanine, tyrosine, and tryptophan biosynthesis > glutathione metabolism > glycine, serine, and threonine metabolism > purine metabolism > pyrimidine metabolism > alanine, aspartate, and glutamate metabolism > butanoate metabolism. The pathways in the striatum (Fig 6C) with the greatest metabolic impact were alanine, aspartate, and glutamate metabolism > nicotinate and nicotinamide metabolism > purine metabolism.
**Fig 6:** *Quantitative pathway enrichment analysis.Pathway topology plots illustrated by pathway impact values (which are listed in the right tables as well as FDR and p values) and–log10(p) from statistically significant metabolites from the cortex (A), hippocampus (B), and striatum (C) in the HSD groups relative to the controls.*
## Discussion
This work aims to investigate the role of oxidative stress, energy metabolism and neurotransmitter profiles in the molecular mechanism of cerebral region-dependent metabolomics profile changes induced by heliox-saturation hyperbaric exposure. Several metabolites involved in energy metabolism, oxidative stress, and amino acid metabolism as well as metabolites that contribute to membrane integrity and neurotransmitters were significantly altered by HSD exposure. The results obtained from the NMR-based metabolomics approach together with biochemical assessment strongly suggest that hyperbaric decompression in a heliox saturation environment induces changes in several metabolites and that oxidative stress, energy metabolism and neurotransmitter alteration is a major mechanism for alterations in cerebral region-specific metabolomics profiles in HSD model rats. The data showed that multiple metabolic pathways, including glutathione metabolism, mitochondrial energy metabolism, glycolysis, BCAA metabolism, alanine, aspartate and glutamate metabolism, and neurotransmitter metabolism with brain region-specific metabolic disorders, were involved in the effects of 400msw-HSD.
## Oxidative stress analysis
During large depth (>100 msw) saturation diving, the central nervous system is continuously exposed to oxidative stress due to the excessive production of reactive oxygen species (ROS) triggered by the heliox-saturation pressurized environment [23, 24]. An imbalance between oxidant and antioxidant levels is a common metabolic regulatory element in various neurological disorders, including Alzheimer’s disease [25], autism spectrum disorder [26], ischemic brain [21], traumatic brain injury [27], and so on [18, 28, 29]. The level changes of SOD, MDA, and Gpx, as recognized indicators for oxidative stress, reflect the oxygen free radical injury in vivo. In the present study, the oxidative damage indicator (MDA) content was increased, and the antioxidant indicator (SOD and Gpx) activities were decreased in the model rats, suggesting that oxidative stress did occur in the cerebral cortex of the HSD rat model. Several other metabolites are also related to the regulation of oxidative stress. Taurine plays broader roles with antioxidant, anti-inflammatory, anti-apoptotic, osmolytic, and neuromodulator effects to ameliorate the histopathological changes in brain and neuronal activity [30–36]. The significant decline in taurine content in the cerebral hippocampus and striatum upon HSD rats as well as another oxidative stress-related metabolite, GSH, was reduced in the cortex and hippocampus, suggesting that oxidative injury was induced in the different compartments of the HSD rat brain. GSH is widely recognized as an antioxidant quencher and produces stable molecules such as GSSH- and AKA-oxidized glutathione disulfide with the reaction of ROS. The low GSH level after HSD would affect mitochondrial function and redox balance, thereby accounting for the observed strong negative correlation between GSH vs Suc in the HSDH samples (Fig 5B’ upper panel). Asc (aka Vitamin C) is considered an important antioxidant/micronutrient for its antioxidant capabilities and thus performs essential functions within brain neuronal maintenance. Herein, we report that *Asc is* at lower concentrations in HSDH samples than in controls. These findings are in agreement with each other, which suggests a direct link between oxidative stress and hyperbaric decompression in a heliox-saturated environment. Lower concentrations of antioxidant quenchers in the brain may be directly associated with decreased antioxidant capacity, thereby downregulating the generation of superoxides, including SOD and Gpx, and upregulating the levels of MDA.
## Energy metabolic pathway analysis
The statistically significant positive correlation between Ala and Lac in the HSDC and CONC groups indicated that Ala, as an amino acid, has a significantly higher link to anaerobic glycolysis [37]. LDH catalyzed the conversion of pyruvate into Lac, and the decreased activities of LDH in the cortex downregulated the expression of Lac, indicating a decrease in anaerobic pathways. However, the concentration of Lac favored energetic metabolism through the activity of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and LDH, which can also be incorporated into the glutamate, glutamine, and GABA cycles in neurons. In this study, compared with CON rats, a significantly higher concentration of FMA in the HSDX group and a decreased concentration of Suc may indicate an energy metabolism decline, which was further confirmed by an increased AMP and a decreased ATP produced mainly by glycolysis and the TCA cycle. Na-K-ATPase, a key enzyme for the maintenance of a proper electrochemical gradient of sodium ions across the cell membrane, requires approximately $50\%$ of the energy available to the brain [38, 39]. The malfunction of Na-K-ATPase has an essential role in the development of neurodegenerative diseases [40, 41]. The decreased activity of Na-K-ATPase in the cerebral cortex of the HSD group provided additional proof for the decreased energy metabolism induced by the HSD effect. Together, energy metabolism dysfunctions induced by hyperbaric HSD injury might include the collaboratively suppressed aerobic metabolism and anaerobic metabolism, which is a rare metabolic alteration phenomenon.
The levels of BCAAs (Ile, Val, and Leu) in the HSDS samples were upregulated in comparison with those in CONS samples. BCAAs can be converted into acetyl-CoA and succinyl-CoA [42] as substrates for gluconeogenesis and ATP generation. Thus, the upregulation of BCAAs was potentially induced to fulfill the energy compensation needs after HSD injury. Meanwhile, consistent with the energy compensation demand, the fatty acid β-oxidation metabolic pathway was also modulated, and a lower level of Car (a marker metabolite for fatty acid β-oxidation) in the HSDC samples was detected. The glycine, serine and threonine metabolism pathway (Fig 6B) also supplies important energy metabolism precursors to enter the citrate cycle [43]. In this pathway, Cho, Gly, and Thr are the three hits. Cho and Gly were shown to decrease with HSD exposure. Likewise, cholinergic pathways have been linked to social and behavioral abnormalities, also as the essential component of cellular membranes and necessary for the synthesis of the neurotransmitter acetylcholine. Gly is the simplest amino acid with several functions, including fat metabolism, neurological function, muscle development, and incorporation into the antioxidant glutathione [44].
## Neurotransmitter metabolism
Neurotransmitter metabolism is vital to maintain normal brain function. However, a wide range of neurotransmitter imbalances has been characterized in HSD model rats. Increased levels of the excitatory transmitters dopamine and noradrenaline were accompanied by decreased inhibitory neurotransmitters including 5HT, Gly, and GABA in the HSD group compared with the controls. GABA, as the major inhibitory neurotransmitter, is responsible for halting excitatory glutamatergic activity, so naturally, disruption to either of these metabolites will affect the other in terms of the changes in the concentrations of Asp, Gly, and Gln. The excitatory neurotransmitter ACh is involved in multiple central nervous system functions [45] mainly by modulating acetylcholine receptors and their downstream pathways. In this study, AChE activity in the ACh hydrolysis process was measured. The decreased AChE activity indicates disturbance of the cholinergic pathway. Moreover, the striatal level of the essential amino acid Lys was found to be significantly elevated in HSD rats. Lys has been reported to block serotonin receptors, and its accumulation will affect the normal function of 5HT [46]. Asp, another excitatory neurotransmitter, is directly derived by transamination from a TCA cycle intermediate, oxaloacetate. We found that the concentration of Asp was significantly decreased in the striatum of HSD rats, providing additional proof for the imbalance effect on excitability neurotoxicity, with three other hits of Ala, FMA, and GABA. These four metabolites consist of the pathways of alanine, aspartate and glutamate metabolism (Figs 6A, 7). These findings indicate a perturbation in neurotransmitter recycling/production and the imbalance between the excitatory/inhibitory neurotransmitters induced by the HSD effect.
**Fig 7:** *Schematic overview of inferred changes in the potential cerebral metabolic pathways post hyperbaric heliox saturation.*
Taken together, systematic metabolic disorders, including energy metabolism dysfunctions, oxidative stress, and neurotransmitter metabolism disturbances, were induced in the region-specific cerebral injury of HSD rats. Downregulation of NAA, which is a general indicator of neuronal health, suggested neurofunctional abnormalities. These data indicated that neuronal damage was induced by the 4.0 MPa hyperbaric decompression exposure. In agreement with this deduction, the levels of Pcho and Cho, which are precursors of phosphatidylcholine (PC) involved in cell membrane lysis, apoptosis, and inflammatory responses, were downregulated. The concentration decrease in membrane-relevant metabolites suggested the disruption of cell membrane integrity related to neuronal damage. Neuronal damage might be the primary reason for the metabolic alteration induced by HSD effects.
## Conclusions
In the present study, NMR-based metabolomics and biochemistry assessment were applied to profile the metabolic alterations in the brain region-specific metabolic alteration of HSD model rats following 400 msw hyperbaric decompression of heliox saturation exposition. We then found that the HSD significantly induced metabolic aberrations, including oxidative stress, energy metabolism disorder, neurotransmitter metabolism disturbance, and cell membrane disruption. However, the more profound molecular mechanisms of HSD exposure should be investigated in future research.
## Limitations
The potential sources of errors in this research might be the design and the analyses. Although cortex, hippocampus, and striatum dysfunctions have been strongly correlated with several symptoms of CNS leisure in animal models, these three brain regions were the ones studied herein. The series of psychomotor and cognitive manifestations of CNS leisure under pathological factors (excessive atmospheric pressure, gas bubbles in the body, and decompression sickness) were also influenced by cerebellar dysfunction, but metabolic profile disturbance of the cerebellum was not considered in this research. Furthermore, only limited brain metabolites could be detected by NMR in this study; thus, an LC‒MS (liquid chromatography tandem mass spectrometry)-based metabolomic approach should be used in future studies to detect more endogenous metabolites in the brain to fully understand the mechanism of the hyperbaric decompression effect in a heliox-saturated environment.
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|
---
title: 'How food support improves mental health among people living with HIV: A qualitative
study'
authors:
- Koharu Loulou Chayama
- Emiliano Lemus Hufstedler
- Henry J. Whittle
- Tessa M. Nápoles
- Hilary Seligman
- Kimberly Madsen
- Edward A. Frongillo
- Sheri D. Weiser
- Kartika Palar
journal: PLOS ONE
year: 2023
pmcid: PMC10013904
doi: 10.1371/journal.pone.0282857
license: CC BY 4.0
---
# How food support improves mental health among people living with HIV: A qualitative study
## Abstract
### Background
Food insecurity is associated with poor mental health among people living with HIV (PLHIV). This qualitative study explored the mental health experiences of PLHIV participating in a medically appropriate food support program.
### Methods
Semi-structured interviews were conducted post-intervention ($$n = 34$$). Interview topics included changes, or lack thereof, in mental health and reasons for changes. Interviews were audio-recorded, transcribed, and double-coded. Salient themes were identified using an inductive-deductive method.
### Results
Positive changes in mental health self-reported by PLHIV included improved mood and reduced stress, worry, and anxiety. Participants attributed these changes to: 1) increased access to sufficient and nutritious foods, 2) increased social support, 3) reduced financial hardship, 4) increased sense of control and self-esteem, and 5) reduced functional barriers to eating.
### Conclusions
Medically appropriate food support may improve mental health for some PLHIV. Further work is needed to understand and prevent possible adverse consequences on mental health after programs end.
## 1. Introduction
In the era of antiretroviral therapy (ART), there is a growing number of people living with HIV (PLHIV) as a chronic condition [1, 2]. A disproportionate number of PLHIV suffer from poor mental health compared to people not living with HIV [3, 4]. A meta-analysis found that depression is nearly two times more prevalent among PLHIV than those living without HIV [4]. Further, anxiety is experienced more commonly by PLHIV ($22.9\%$) than the general population ($18.1\%$) [5, 6]. Depression and anxiety have been linked to lower adherence to ART and greater disease progression [7–9]. Moreover, mental health issues among PLHIV have been associated with substance use and sexual behaviors that can place individuals with suboptimal ART adherence and unsuppressed HIV viral load at high risk of secondary HIV transmission [10–14]. To address the individual and public health impacts of poor mental health, attention has been directed in recent years towards integrating mental health into HIV treatment and care [15]. Mental health interventions for PLHIV to date, however, have focused mainly on symptom reduction through biological and behavioral treatments, and reflect limited consideration of social and structural stressors such as food insecurity that increase an individual’s susceptibility to poor psychological wellbeing [15].
In the United States (US), HIV disproportionately affects structurally vulnerable groups (e.g., people of color, people who use drugs, sex workers) and subsequently contributes to their social and economic vulnerability [16, 17]. About half of PLHIV in the country are food insecure [18–20]. Food insecurity is defined as “the limited or uncertain availability of nutritionally adequate, safe foods or the inability to acquire personally acceptable foods in socially acceptable ways” [21]. Food insecurity contributes to poor mental health in both the general population and PLHIV, with studies indicating associations between food insecurity and mood, stress, anxiety, and depression [22–24]. Food insecurity may be associated with mental health status through several paths [25]. Food insecurity may create uncertainty around the ability to maintain a steady food supply or obtain sufficient food in the future, inducing stress that may lead to anxiety and depression [25]. Furthermore, food insecurity may lead people to acquire food in ways that are considered socially unacceptable (e.g., asking others for food, borrowing money for food, purchasing food on credit) [26], which in turn may generate a sense of powerlessness, shame, and guilt that are linked to depression [25, 27–29]. Food insecurity may also result in feelings of deprivation and alienation, and interfere with social relationships [25, 27, 30]. Among PLHIV, food insecurity may compound the daily stressors of HIV such as feelings of uncertainty, social isolation, stigma, and discrimination and contribute to worse overall mental health [19, 31–36]. Food insecurity and scarcity more generally may deplete individuals’ psychological resources as their cognitive bandwidth is devoted to meeting basic needs [37].
Medically appropriate food support (i.e., designed to meet medical recommendations including daily caloric and nutrient intake for populations with specific health conditions) is emerging as a potential strategy to ameliorate the negative physical and mental health impacts of food insecurity [31]. In 2014–2015, we conducted an intervention study entitled Food = Medicine that provided medically appropriate food support to PLHIV in the San Francisco Bay Area [38]. Study participants received three meals per day plus snacks, which were designed to meet their nutrition and health needs. In addition, participants received case management and enhanced nutritional counseling and education. In a quantitative analysis, we found improvements in depressive symptoms, the only mental health measure included in the outcome assessments [38]. While food support has the potential to improve the mental health of PLHIV, little is known about how food support impacts mental health. Further, what other facets of mental health besides depression may be affected by a medically appropriate food support intervention is not well understood. Greater understanding of the potential impacts of medically appropriate food support will build the evidence base required to guide policy decisions related to the provision of food and nutrition services for PLHIV in the US. Thus, we conducted a qualitative study as part of the parent Food = Medicine study to explore in-depth the broad and nuanced mental health experiences of PLHIV in the San Francisco Bay Area receiving medically appropriate food support. Our goal was to enhance our understanding of how such interventions may influence various aspects of mental health in low-income, chronically ill populations.
## 2.1. Setting and population
This qualitative study was conducted by the University of California, San Francisco (UCSF) in collaboration with Project Open Hand (POH). POH is a community-based organization in the San Francisco Bay Area that provides free meals and groceries to chronically ill clients. This qualitative sub-study was conducted as part of a parent pilot intervention study, Food = Medicine, which investigated changes in physical and mental health among POH clients with HIV and/or type 2 diabetes mellitus (T2DM) before and after receiving medically appropriate food support [38]. The food support consisted of three medically appropriate meals per day every day (i.e., twenty-one meals per week) plus snacks, based on the Mediterranean diet. Participants picked up their food twice per week at designated times from POH facilities in San Francisco and Oakland. A surrogate picked up the food on behalf of participants who were unable to pick up during these times. The parent Food = Medicine study population was comprised of adults (age 18 or older) living with HIV and/or T2DM who were current POH clients (or in the process of becoming clients), English- or Spanish-speaking, and low-income (under ~$300\%$ federal poverty line, i.e., under $35,310 for a family of one and $72,750 for a family of four) [39]. If the individuals had been a client for at least six months prior to study enrollment, they were also required to have a history of good adherence to POH services (defined as picking up at least $75\%$ of meals or groceries in the past six months). While there are plans to expand the intervention to clients requiring home-delivered meals or a special diet (e.g., renal, vegetarian, vegan), these groups were excluded from the study to simplify procedures in the pilot phase. A full description of the intervention and main results are available elsewhere [38].
## 2.2. Participants and procedures
Study procedures were approved by the Institutional Review Board at UCSF. To be eligible for the qualitative sub-study, participants had to be enrolled in the parent Food = Medicine study and living with HIV. Food = Medicine participants living with HIV who had not dropped out of the study were recruited into the qualitative sub-study at six-months.
All Food = Medicine study participants were provided with a flyer describing the qualitative sub-study by POH staff. Those who expressed interest in participating were then approached by UCSF study coordinators to schedule an interview. Participants were recruited until saturation was reached.
## 2.3. Data collection
Before starting each interview, all study participants provided written informed consent. In-depth, semi-structured interviews were conducted by masters-level UCSF researchers trained in qualitative research methods (including ELH and TMN), including one English- and Spanish-speaking bilingual researcher, at POH facilities or another private location specified by the participant post-implementation of the Food = Medicine program. Aside from the participant and interviewer(s), no one else was present during the interviews. No prior relationship existed between the participants and the interviewers, and prior to each interview, participants were assured that the interviewers were independent of POH. Interviews were administered in English and Spanish and designed to last between 60 and 90 minutes. Interviews were conducted upon completion of the intervention at six months, or at study exit. Interviews explored participants’ experiences with the Food = Medicine program, including what and how food from the intervention was used and how they understood the intervention to impact their mental and physical health. If participants described changes in mental health related to the intervention, they were asked to describe what aspects of the intervention contributed to these changes. All interviews were audio-recorded with the permission of the participants. Post-interview memos and brief fieldnotes were written by the interviewer(s) to provide context on the interview for future reference and to share with the rest of the study team. In addition, participants provided answers to a demographic questionnaire including city of residence, gender, age, race/ethnicity, highest level of education completed, and current housing status. Upon completion of each interview, participants received $20 in cash in recognition of their time. Interviews were conducted between May of 2014 and May of 2015.
## 2.4. Data analysis
Audio-recordings were transcribed verbatim, and data were imported into Dedoose, a qualitative data management software program, for coding. A team of five researchers developed a codebook during data collection that used an integrative inductive-deductive approach involving techniques and procedures based in content coding but allowing room for emerging themes [40]. Researchers prepared a preliminary list of codes and sub-codes from the interview guide and an initial review of the data. This preliminary codebook was used for the remaining interviews and modified as new concepts emerged. When new concepts emerged, they were read and discussed by the study team and new codes were created when needed. Transcripts from the 34 interviews were double-coded by two coders at pre-determined intervals (every four transcripts) and discrepancies were discussed until consensus was reached, to increase reliability of coding [41]. Coded excerpts from the interviews related to mental health were extracted, organized, and reviewed. Researchers discussed ideas about the codes and relationships among the codes, and came to consensus about recurrent, salient themes that provide insight into whether and how participants experienced the Food = Medicine program to influence their mental health.
## 3. Results
Among the 45 participants living with HIV in the parent study, 34 individuals agreed to participate in the qualitative sub-study (Table 1), of whom 21 lived in San Francisco and 13 lived in Alameda County, which includes Oakland and Berkeley. Most participants were male, aged between 46 and 65, belonged to non-white racial/ethnic groups, and had started or completed a college education, a distribution broadly reflective of the client base of PLHIV at POH and participants of the parent study. At the time of interview, 25 participants were living in an apartment or house, and six were living in single room occupancy or nightly hotel. Twenty-nine participants reported at least mild depressive symptoms, with 9 participants reporting moderate to severe depressive symptoms. All participants had an annual household income of less than $36,000. Mean and median incomes were $16,247 (standard deviation 7,151) and $14,700 (interquartile range 10,882, 22146), respectively. Most participants received disability income assistance through Supplemental Security Income (SSI) and/or Social Security Disability Insurance (SSDI).
**Table 1**
| Unnamed: 0 | n | % |
| --- | --- | --- |
| Residence | | |
| San Francisco | 21.0 | 62.0 |
| Alameda County | 13.0 | 38.0 |
| Gender* | | |
| Male | 29.0 | 85.0 |
| Female | 5.0 | 15.0 |
| Other | 2.0 | 6.0 |
| Age | | |
| 39–45 | 3.0 | 9.0 |
| 46–55 | 15.0 | 44.0 |
| 56–65 | 13.0 | 38.0 |
| 66–75 | 3.0 | 9.0 |
| Race/Ethnicity * | | |
| White | 15.0 | 44.0 |
| Black/African-American | 14.0 | 41.0 |
| Asian/Pacific Islander | 1.0 | 3.0 |
| Hispanic/Latino | 7.0 | 21.0 |
| Native American | 5.0 | 15.0 |
| Highest Level of Education Completed | | |
| Less than high school/GED | 3.0 | 9.0 |
| High school/GED | 5.0 | 15.0 |
| Vocational/technical school | 1.0 | 3.0 |
| Some college | 12.0 | 35.0 |
| College degree or higher | 13.0 | 38.0 |
| Current Housing Status | | |
| Apartment or house | 28.0 | 82.0 |
| Single room occupancy or nightly hotel | 6.0 | 18.0 |
| Depressive Symptoms | | |
| | 5.0 | 15.0 |
| Mild | 20.0 | 59.0 |
| Moderate | 7.0 | 21.0 |
| Severe | 2.0 | 6.0 |
Most participants reported positive mental health experiences because of the intervention. A few participants noted negative mental health experiences upon program completion. During the interviews, participants described various mechanisms underlying the link between the intervention and changes in mental health.
## 3.1. Positive mental health experiences
Most participants described experiencing improved and stabilized mood. Further, most participants described experiencing reduced stress, worry, and anxiety. Five major themes that link these positive mental health experiences with the Food = Medicine program emerged.
## 3.1.1. Increased access to sufficient and nutritious foods improved mental health
Many participants described missing meals and restricting food quantities as a coping strategy for having inadequate food prior to program participation. For these participants, access to sufficient food through the Food = Medicine program increased satiety, and in turn improved mood. A male participant in his late fifties explained: In addition to addressing food sufficiency, the program offered nutrient-rich foods and enhanced the quality of participants’ diets. For many, eating a high-quality and varied diet led to increased energy and sense of contentment. One male participant in his early fifties noted that increasing the amount of protein, vegetables, and fruits in the diet increased his energy level and happiness: Enhanced diet also stabilized mood for a few participants. A male participant in his fifties stopped experiencing extreme mood swings and attributed this to improved diet and blood sugar levels: In addition to changes in mood, many participants identified that consistent access to nutritious food alleviated stress, worry, and anxiety. For example, one male participant in his late fifties noted that knowing the meals were available relieved stress and anxiety around accessing food:
For a few participants, access to nutrient-rich foods reduced stress and improved sleep quality. One male participant in his early fifties explained that knowing he had healthy snacks available eased stress and made it easier to sleep:
## 3.1.2. Increased social support improved mental health
For several participants, receiving sufficient food on a consistent basis led to increased feelings of being cared for and improved mental health, particularly among participants who were homebound. A male participant in his early forties noted that knowing that someone cares for him enough to provide three meals every day had a positive impact: One male participant in his late sixties living with HIV and diabetes identified that the medically appropriate meals were carefully and thoughtfully prepared and created a sense that he was receiving ample support from POH: Several participants described how increased feelings of being cared for during the program also influenced their experiences with stress. A male participant in his early sixties described how the comfort of having someone feed him relieved stress, particularly at times when he was experiencing many other challenges in his life: In addition to receiving social support, some participants described how the program enabled them to offer social support to others in their community, which in turn improved their mental health. Participants explained that access to more food promoted sharing of food with their social circles, including family, partners, and neighbors. The act of offering food to others led to increased feelings of calm and content. A male participant in his late fifties who received more than a sufficient amount of food from the program described that he shared his food with others and this brought him a sense of peace:
## 3.1.3. Reduced financial hardship alleviated stress
Improved food security during the program reduced stress from financial concerns. Many participants referred to the high cost of living in the San Francisco Bay Area and described the challenges of affording food. They noted that the program alleviated some of their financial hardships and the associated stress and worries. A female participant in her early fifties noted how receiving the food eased financial worries: Other participants not only reported reduced financial concerns related to food but also other basic necessities such as housing. For example, one male participant in his late forties highlighted that rent payment is one of his greatest concerns, and the program helped ease related stress:
## 3.1.4. Increased sense of control and self-esteem improved mood
Many participants attributed improvements in mental health to enhanced feelings of control and self-esteem. Participants demonstrated knowledge of healthy eating and a desire to consume sufficient and nutritious food. Consistent access to healthy food improved their sense of control over their diets and health choices, and in turn improved their self-confidence. One male participant in his late fifties explained that consistent access to nutritious meals allowed him to access food when he wanted, which provided a feeling of control over his life and improved self-esteem. When asked how being involved in the Food = Medicine program changed his sense of control or self-esteem, if at all, he described: For some, improved sense of control was attributed to the act of participating in the program and engaging in its activities as this provided them with a sense that they have a role in caring for their own health. A male participant in his late forties described that the feeling of being proactive about addressing issues in his life gave him a sense of control: Some participants experienced positive changes in mood because of feeling involved, and in turn changes in control. One male participant in his early seventies described that participating and adhering to the program and its processes improved his mood:
## 3.1.5. Reduced functional barriers to eating alleviated stress
Some participants linked physical and mental health. They described their experiences with physical health barriers to accessing food and its associated stress and how this was addressed by the Food = Medicine program. For example, improved access to sufficient ready-made meals reduced fatigue-related barriers to cooking and eating experienced by some participants because of living with HIV, and in turn reduced stress. This was exemplified by a male participant in his early sixties who described that knowing the food was available alleviated stress around cooking: Among participants with limited mobility, having food readily available at home reduced stress. A male participant in his early forties who was homebound highlighted how the program alleviated stress around the logistics of accessing food:
## 3.2. Negative mental health experiences associated with loss of intervention
While the program resulted in overall improved mental health for our participants, a minority of participants described negative experiences around the intervention ending, even though they were still POH clients receiving scaled-down regular services. One male participant in his early forties described experiencing negative emotions because of concerns around weaning off the program. Until realizing that his usual food support plan would still be available post-intervention, he described experiencing considerable distress over the program ending, particularly with regards to the financial aspects of acquiring food in the future if left with no food support.
While participants resumed their original food support of seven to fourteen medically appropriate meals per week (or equivalent groceries), this same participant described experiencing feelings of depression because of changes in diet after completing the Food = Medicine program:
## 4. Discussion
The Food = Medicine program affected the mental health of low-income PLHIV, with most participants reporting that the program contributed to positive changes in their mental health. We add to the growing evidence suggesting that food support interventions improve psychological wellbeing among PLHIV and other structurally vulnerable groups who are food insecure. Our findings are consistent with data from a qualitative study conducted by Czaicki et al., which found that food support may reduce stress, worry, and depression and increase sense of peace among PLHIV in Sub-Saharan Africa [42]. Quantitative studies that examined the impact of food support programs among other chronically ill patients and low-income individuals and families in the US have also reported reduced psychological distress and corroborate our finding [43, 44]. Participants in our study attributed positive changes in mental health to the Food = Medicine program’s contributions to improved food security, increased social support, reduced financial hardship, increased sense of control and self-esteem, and reduced functional barriers to eating. Our findings are consistent with and extend the existing literature, including the published findings of quantitative improvement in depressive symptoms in the Food = Medicine study [38], by providing an inductive, emergent understanding of how medically appropriate food support improves mental health status and experiences. Moreover, this study is the first to our knowledge to explore these mental health impacts among PLHIV in a resource-rich setting. Our results can be used to develop hypotheses about the potential mental health impacts of food support, which should be examined in future intervention studies. For example, future quantitative studies should examine whether the intervention improves stress, anxiety, and other mental health outcomes beyond depression, which was the only mental health indicator measured in the pilot study. Furthermore, future work should examine whether improvements in mental health mediate any potential impact of the intervention on HIV outcomes (e.g., ART adherence, viral suppression).
By addressing food insecurity, the Food = Medicine program also reduced stress, worry, and anxiety related to financial, as well as functional, concerns among participants. Receiving free, healthy meals from the program reduced stress triggered by financial barriers to food security. In the San Francisco Bay Area, the burden of gentrification and displacement has fallen largely on low-income residents as their wages and social assistance have not kept pace with the high cost of living [45]. While many low-income PLHIV in the San Francisco Bay Area receive monthly disability payments and/or housing assistance through public (e.g., Housing Choice Voucher Program) and private (e.g., San Francisco AIDS Foundation) institutions [46], food spending can be a significant portion of their monthly budget and compete with other demands, including rent costs. As such, minimizing their spending on meals also helped to temporarily alleviate other financial concerns around basic necessities, particularly housing. In addition to financial constraints, the intervention reduced stress related to functional limitations that led to negative psychological outcomes prior to program participation [47]. Many expressed difficulties shopping for food and preparing healthy meals due to physical conditions such as mobility impairments and fatigue, which are common among PLHIV [48, 49]. Mobility impairments and fatigue in PLHIV may be related to HIV or comorbid conditions, which have been increasing among PLHIV as the population ages [48, 49]. Our findings shed light on how food support can promote mental health by addressing financial and functional barriers to food security.
The shifts in personal control may be particularly crucial among PLHIV as living with a chronic illness may create feelings of uncertainty and loss of control [50]. Among PLHIV and those living with other chronic illnesses, feelings of personal control in relation to aspects of their daily lives enables them to cope with their disease with less psychological distress [50]. To our knowledge, our study is the first to suggest that food support–and specifically improved quantity, quality, and consistency of food supply–can increase sense of control and enable positive changes in mental health among PLHIV.
Social support was also fostered through the program and helped enhance mood and reduce stress for our participants. The link between social isolation and reduced psychological wellbeing has been well established [51]. PLHIV are particularly vulnerable to social isolation due to HIV-related stigma and disclosure challenges [52]. Low social support has been associated with depression, stress, and anxiety among PLHIV [53–55]. Previous literature suggests that social support has positive psychological impacts, including increased sense of purpose, belonging, security, and self-worth [51]. The Food = Medicine program increased social support and sense of “being cared for” by providing ample and thoughtfully prepared meals, and in turn improved mental health. Moreover, the program provided an avenue for social engagement, both at POH’s Grocery Center and in participants’ communities. We found that not only receiving but also giving social support helped to enhance participants’ mental health. Sharing food and feeding their social networks were commonly reported ways through which the program improved their mood. This is consistent with previous work that sharing program-provided food with neighbors allowed fuller participation in the community life [56], and suggests that the act of giving social support is a protective factor for psychological wellbeing [57]. We extend current knowledge by identifying that medically appropriate food support can improve mental health by enabling the means for individuals to both receive and provide social support. This finding is particularly significant given that previous research in San Francisco has found that mutual social support among food insecure individuals living in the city is often challenging and limited [58]. Food support may therefore have added benefits in such urban settings. Although food sharing in the context of individualized food support may run the risk of being framed by implementers and evaluators as a negative outcome (i.e., by diluting dietary effects on the individual), our findings indicate that it is important for facilitating the positive mental health impacts of food support.
While participants had positive experiences with the Food = Medicine program overall, a few participants reported negative mental health experiences with the termination of the intervention. Earlier work has identified that the degree of livelihood security and socioeconomic status affect whether or not PLHIV are able to transition off food support without adverse consequences to their health [59]. While there was a transition plan in place for participants to continue receiving food support upon completion of the study, our findings nonetheless provide insight into the negative mental health consequences related to reducing food support. This warrants additional attention if such interventions are to have durable, positive health impacts. Further investigation through randomized controlled trials should follow participants after transition to lower levels of food support and examine whether intervention benefits on mental health are sustained post-intervention.
Our study offers findings relevant to policy on food support for PLHIV. Given evidence of the positive health impacts of food support in this study and other studies [38, 42–44], we recommend that public funding for food support should be protected and expanded for PLHIV. Further, food support programs serving PLHIV should be nutritious and medically appropriate to deliver optimal impact on mental health. Finally, adequate quantities of food support for people at highest need should be ensured to support the mental health of PLHIV. This may mean making food available by prescription for people with health needs.
Although we sought to interview PLHIV with a broad demographic spectrum, our interviews were limited to English- or Spanish-speaking individuals who were current POH clients. This approach may have filtered out individuals who are particularly marginalized from mainstream systems of care, such as new immigrants or individuals who are experiencing homelessness. Further, we recruited clients with a history of good compliance to POH services and may have excluded individuals with particularly challenging or de-stabilizing life circumstances, such as those with serious psychiatric disorders or substance use issues. Additionally, as participation in our study was limited to individuals who remained enrolled in the study, the experiences of individuals who dropped out of the study were not captured in our findings. Due to the voluntary nature of participation, our sample may have also incurred a selection bias in which individuals who have a stronger opinion about the program may have been more likely to participate in the interviews. In particular, there is a possibility that participants shared relatively more positive experiences in hopes to influencing the chances of program continuation. To address the possibility of these sources of bias upfront, we implemented several strategies including having POH continue to provide services to participants after study end, along with designing the interview guide and training interviewers carefully to promote honest feedback including negative or neutral experiences in the program. Finally, data for this study was collected 7 to 8 years ago, potentially limiting applicability to a contemporary population of PLHIV. Nevertheless, this study offers in-depth insights into the ways in which medically appropriate food support impacts mental health among a population underrepresented in literature.
## 5. Conclusions
Medically appropriate food support such as the Food = Medicine program can address some of the multiple social and economic constraints that affect the daily lives of PLHIV and may be an effective means of improving their mental health. Further research is needed to understand and prevent possible negative consequences associated with the loss of short-term food support interventions. Research is also needed to estimate the nutritional benefits of such food support interventions. Nonetheless, our work amplifies the importance and potential of strategies that look beyond traditional healthcare to address the social and structural context of the lives of PLHIV to enhance their mental health. Federal policy concerned with the psychological wellbeing of PLHIV should protect funding for food support as part of comprehensive treatment and care. Program and policy efforts at the local, state, and national levels should continue to ensure that food support is designed and structured in a way to promote both physical and mental health.
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|
---
title: Examining neighborhood-level hot and cold spots of food insecurity in relation
to social vulnerability in Houston, Texas
authors:
- Ryan Ramphul
- Linda Highfield
- Shreela Sharma
journal: PLOS ONE
year: 2023
pmcid: PMC10013905
doi: 10.1371/journal.pone.0280620
license: CC BY 4.0
---
# Examining neighborhood-level hot and cold spots of food insecurity in relation to social vulnerability in Houston, Texas
## Abstract
Food insecurity is prevalent and associated with poor health outcomes, but little is known about its geographical nature. The aim of this study is to utilize geospatial modeling of individual-level food insecurity screening data ascertained in health care settings to test for neighborhood hot and cold spots of food insecurity in a large metropolitan area, and then compare these hot spot neighborhoods to cold spot neighborhoods in terms of the CDC’s Social Vulnerability Index. In this cross-sectional secondary data analysis, we geocoded the home addresses of 6,749 unique participants screened for food insecurity at health care locations participating in CMS’s Accountable Health Communities (AHC) Model, as implemented in Houston, TX. Next, we created census-tract level incidence profiles of positive food insecurity screens per 1,000 people. We used Anselin’s Local Moran’s I statistic to test for statistically significant census tract-level hot/cold spots of food insecurity. Finally, we utilized a Mann-Whitney-U test to compare hot spot tracts to cold spot tracts in relation to the CDC’s Social Vulnerability Index. We found that hot spot tracts had higher overall social vulnerability index scores ($P \leq 0.001$), higher subdomain scores, and higher percentages of individual variables like poverty ($P \leq 0.001$), unemployment ($P \leq 0.001$), limited English proficiency ($P \leq 0.001$), and more. The combination of robust food insecurity screening data, geospatial modeling, and the CDC’s Social Vulnerability Index offers a solid method to understand neighborhood food insecurity.
## Introduction
The United States Department of Agriculture (USDA) defines food insecurity as a household-level economic and social condition of limited or uncertain access to adequate food [1]. They further divide food insecurity into two levels, with “low food security” referring to reduced quality, variety, or desirability of diet, but no reduction of food intake, and “very low food security,” referring to disrupted eating patterns and reduced food intake [1]. In 2020, an estimated 13.8 million households in the US reported food insecurity at some time during the year [1]. There were an estimated 724,750 food insecure individuals in Greater Houston in 2018, and the estimated food insecurity rate was about $16.6\%$, which was roughly 4 percentage points above the national average in that year [2].
In a systematic review of literature on the impact of food insecurity on health outcomes, Gunderson and Ziliak [3] examine recent research evidence of the health consequences of food insecurity for children, non-senior adults, and seniors throughout the US. Regarding children, they found studies suggesting that food insecurity is associated with an increased risk of birth defects, anemia, lower nutrient intake, cognitive problems, aggression, and anxiety [3]. They also found research indicating that food insecurity is associated with higher risks of children being hospitalized, having asthma, behavioral problems, depression, suicide ideation, poor oral health, and poor overall health [3]. Regarding non-senior adults, they cite studies showing that food insecurity is associated with decreased nutrient intake, increased rates of mental health problems, diabetes, hypertension and hyperlipidemia, poor sleep outcomes, and poor overall health [3]. Finally, they point to studies indicating that food-insecure seniors are more likely to be in poor health, depressed, and have limited daily activities compared to their food-secure peers [3].
The aim of this study is to utilize geospatial modeling of individual-level food insecurity screening data ascertained in health care settings to test for neighborhood hot and cold spots of food insecurity in a large metropolitan area, and then compare these hot spot neighborhoods to cold spot neighborhoods in terms of the CDC’s Social Vulnerability Index and subdomains. Testing for neighborhood-level hot/cold spots of food insecurity is necessary because little is known about the geographical nature of food insecurity. While other social determinants of health like poverty and education levels are tracked extensively at the neighborhood level through surveillance mechanisms like the Census’s American Community Survey, food insecurity isn’t. With a greater understanding of the neighborhood-level factors that affect area-level food insecurity, health care providers, public health practitioners and policy makers can potentially craft interventions aimed at mitigating it.
In 2008, the CDC produced the Modified Retail Food Environment Index (mRFEI), which indicated the percentage of healthy food retailers by census tract. The USDA later demarcate “food deserts,” or areas that lack stores that sell healthy and affordable food, by introducing low income, low access (LILA) census tracts, where low income is defined by poverty rates and median family income, and low access by proximity to supermarkets or large grocery stores [4]. LILA and mREFI census tracts are widely used in food environment studies but assume that proximity to food retailers, or the presence of food retailers in a geographical area, is related to the ability to access food. Studies show, however, that food choices and purchases are not necessarily associated with proximity to food outlets but are likely a function of personal preferences, cultural factors, social norms, and the ability to afford foods [5–8]. Additional research validates this concept, showing that the introduction of low-cost grocery stores into underserved “food desert” neighborhoods does not significantly impact shopping behaviors, household food availability, or the health of residents [9–12].
Feeding America, a nationwide non-profit organization aimed at mitigating hunger and food insecurity, offers one of the few methods of identifying neighborhood food insecurity that doesn’t involve proximity to food outlets, in its Map the Meal Gap tool. Defining food insecurity as the lack of reliable access to a sufficient quantity of affordable, nutritious food, the Mapping the Meal Gap tool estimates regional and area-level food insecurity prevalence using publicly available data on neighborhood unemployment, poverty, and other household characteristics [13]. To our knowledge, however, no studies have explored the use of address-level food insecurity screening data from healthcare settings, geospatial modeling, and the CDC’s Social Vulnerability Index, to better understand neighborhood food insecurity.
## Data
This is a cross-sectional secondary data analysis of food insecurity screening data collected from Medicare and Medicaid beneficiaries who participated in the Centers for Medicare & Medicaid Service’s (CMS) Accountable Health Communities (AHC) Model, which was piloted by the University of Texas Health Science Center at Houston School of Public Health. According to CMS, the AHC Model seeks to address gaps between clinical care and community services by testing whether identifying and addressing health-related social needs through screening, referral and community navigation, impacts health care costs and reduces health care utilization [14]. Screening was offered to all community-dwelling Medicare and Medicaid beneficiaries at several clinical delivery sites across Greater Houston. These sites include five hospitals affiliated with two large Texas Medical Center (TMC) health systems, and four outpatient clinics affiliated with one of the area’s largest ambulatory groups.
## Food insecurity
Like many models that screen for food insecurity, The AHC model adopted The Hunger Vital SignTM screening tool [15]. Hunger Vital *Signs is* a two-question screening tool, suitable for clinical or community outreach use, that identifies risk for food insecurity if families answer that either or both of the following statements are “often true” or “sometimes true,” versus “never true”: Children’s Health Watch, a nonpartisan network of pediatricians, public health researchers, and policy and child health experts validated the Hunger Vital SignTM tool with a sample of 30,000 caregivers. They found excellent sensitivity ($97\%$) and specificity ($83\%$) with the Hunger Vital Signs tool compared to the much longer US Household Food Security Scale (HFSS) screening tool, which is considered the “gold standard” in assessment and identification of food security [15].
## Statistical analysis
After obtaining IRB approval from the University of Texas Health Science Center at Houston’s Committee for the Protection of Human Subjects, we pulled a total of 7,658 food insecurity screening records, collected at AHC sites in Harris County, from August 2018–January 2020. Participants’ home addresses were geocoded using ArcGIS Pro Street Map Premium. Partial addresses, unrecognizable addresses, PO Box addresses, and addresses that fall outside of Harris County were excluded, providing a sample size of 6,749 useable addresses that geocoded successfully to a street address. 3,636 participants screened positive for food insecurity and 3,113 screened negative. After addresses were mapped using ArcGIS, we created incidence profiles of people who screened positive for food insecurity per 1,000 people, by census tract, and analyzed the data spatially.
We utilized Anselin’s Local Moran’s I statistic to identify statistically significant clusters of census tracts with a high/low incidence of positive food insecurity screens. Anselin’s Local Moran’s I statistic, also known as the Local Indicators of Spatial Association (LISA) statistic, does this by creating a neighborhood around each census tract and calculating a Moran’s I score for each neighborhood, which compares the neighborhood to the study area [16]. If the Moran’s I score is positive then that census tract has similar values to its neighbors and is part of a suspected cluster, if it’s negative, it has dissimilar values from its neighborhood and is part of a suspected outlier [16]. We utilized the queen’s contiguity method, a commonly used method to model spatial relationships in public health analysis, to determine the neighborhood around each census tract, which means that census tracts that share sides or vertices with a given census tract are part of its neighborhood [16].
To test for statistical significance, the Local Moran’s I statistic randomly takes values from other census tracts in the study area, imputes them into a given neighborhood and recalculates the Moran’s I score for that neighborhood [16]. It does this 9,999 times as part of a Monte Carlo simulation aimed at creating a reference distribution to compare the observed local Moran’s I score with one created by random permutations [16]. If less than $5\%$ of the Local Moran’s I values generated from permutations display more clustering than the original data, then the data displays significant clustering [16]. Clusters of high values are known as hot spots and clusters of low values are known as cold spots [16].
In the context of this research, hot spot census tracts are census tracts with high incidences of positive food insecurity screens, surrounded by neighboring census tracts with high incidences of food insecurity screens, tested for statistical significance. Cold spots are census tracts with low incidences of positive food insecurity screens, surrounded by neighboring census tracts with low incidences of positive food insecurity screens, tested for statistical significance. Analyses were performed using the Cluster and Outlier Analysis tool in ArcGIS Pro Version 2.9.2.
Next, we compared hot spot and cold spot census tracts in terms of neighborhood characteristics, using the CDC’s Social Vulnerability Index (SVI). The CDC’s Social Vulnerability index uses 15 census tract-level variables to create subdomain scores for socioeconomic status, household composition & disability, minority status & language, and housing & transportation. Scores for each subdomain are then used to create an overall social vulnerability index score. For subdomain scores and overall scores, values close to 0 indicate low vulnerability and values close to 1 indicate high vulnerability. Since all of the variables we looked at were nonparametric, we utilized a Mann Whitney-U test, performed in SPSS Version 28.0.1.1, to compare median rank values in hot spot census tracts versus cold spot census tracts for 15 census variables, all four SVI subdomain scores, and overall SVI scores.
## Results
We Identified 66 statistically significant hot spot census tracts of food insecurity incidence and 150 cold spots (Table 1 and Fig 1). The rest of the census tracts in Harris County [570] were either not part of any statistically significant clusters or were outliers. The Mann-Whitney U test indicated that hot spot census tracts were significantly more socially vulnerable than cold spot census tracts, as measured by SVI subdomain scores and overall scores (Table 2). In terms of SVI domain 1, socioeconomic status, hot spot census tracts had higher percentages of people below the federal poverty level, unemployed people, people with no high school diploma, and lower per capita incomes.
**Fig 1:** *Hot/Cold spot map of food insecurity incidence, by tract.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 Regarding domain 2, household composition & disability, they had statistically higher percentages of disabled people and single parent households. In SVI domain 3, minority status and language, hot spot census tracts had statistically higher percentages of minorities and people with limited English proficiency than cold spot tracts. For domain 4, housing and transportation, hot spot census tracts of food insecurity had higher percentages of household crowding, no vehicle ownership, and group quarters than cold spot tracts. We created a choropleth map of overall social vulnerability, by census tract, for visual comparison (Fig 2).
**Fig 2:** *Choropleth map of Social Vulnerability Index (SVI) scores, by tract.*
## Discussion
This study utilized geospatial modeling of individual-level food insecurity screening data ascertained in health care settings to identify neighborhood hot and cold spots of food insecurity in a large metropolitan area. This adds to the knowledge base on neighborhood food insecurity by providing evidence that food insecurity exhibits a neighborhood clustering pattern versus a null hypothesis of being randomly distributed throughout the region. This is noteworthy because little is understood about the geographical nature of food insecurity, due to an absence of robust surveillance on the issue. Researchers used to postulate that food-insecure people resided in “food deserts” until a growing body of evidence suggested otherwise [9–12]. Many food insecurity researchers and advocates now estimate neighborhood food insecurity rates using extant data on poverty and unemployment, but this analysis uses more precision inputs and more robust modeling.
This analysis also provides evidence that neighborhood hot spots of food insecurity have higher levels of social vulnerability than cold spots, as measured by the CDC’s Social Vulnerability Index, it’s subdomains, and individual census-tract level variables. While the relationship between social vulnerability and food insecurity is well established in scientific literature, this analysis is unique because it is the first analysis, to our knowledge, to examine this issue using geospatial modeling and the CDC’s Social Vulnerability Index, a well-established and thorough measure of neighborhood vulnerability. This implies that even if public health practitioners and food security advocates do not know where food insecurity is more or less prevalent in a region, they can target their efforts to high SVI communities and likely reach food insecure populations.
## Strengths
We are unaware of any analysis of a universal offer to screen approach for food insecurity, such as the one used in the AHC Model, where every Medicaid or Medicare patient seen at participating clinics is offered screening. We are also unaware of any other research utilizing geospatial modeling to identify census tract-level hot/cold spots of food insecurity in a large metropolitan area–a methodology that can guide the deployment of neighborhood-level interventions in a more precise manner. Kolak, Abraham, & Talen [17] point out in their analysis of type 2 diabetes clusters in a primary care population in Chicago, that adapting spatial methods to smaller, more localized populations, may provide additional strategies for identifying localized areas of health risk for targeting interventions and improving care in smaller panel populations.
## Limitations
The primary limitation in this analysis is the sample size of participants screened for food insecurity. Ideally, there would be several more data collection sites and a much larger sample of screens. Additionally, the AHC Model only screens people enrolled in Medicaid and Medicare, excluding people who have private insurance or are uninsured or do not access the health care system. This limits the sample and adds selection bias. Further, the data sample we used in this analysis was collected before the onset of the COVID-19 pandemic, which significantly increased food insecurity despite emergency legislation that put more resources into food assistance programs, increased unemployment benefits, and provided stimulus payments [18]. A final limitation of this study is that it only examines food insecurity in binary terms, instead of considering the different levels of food insecurity.
## Conclusions
In the absence of more robust surveillance of food insecurity, using data from people screened for food insecurity in health care settings, geospatial modeling, and the CDC’s Social Vulnerability Index, offers a solid approach for understanding food insecurity at the neighborhood level. As the practice of screening for food insecurity in health care settings continues to grow, future research should examine this methodology with a larger sample of food insecurity screens, over several regions, or even throughout the entire state. Future research should also utilize this method to analyze neighborhood food insecurity in the years after the onset of the COVID-19 pandemic.
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|
---
title: 'The association between HDL‐C and stroke in the middle‐aged and elderly: A
cross‐sectional study'
authors:
- Yang Hu
- Min Song
- Dongmei Wu
- Yuqing Zhang
- Gongbo Li
- Haiyan Luo
journal: Brain and Behavior
year: 2023
pmcid: PMC10013934
doi: 10.1002/brb3.2901
license: CC BY 4.0
---
# The association between HDL‐C and stroke in the middle‐aged and elderly: A cross‐sectional study
## Abstract
[1] After adjusting for age, sex, race/ethnicity, LDL‐C, cholesterol, Triglycerides, HbA1c, income to poverty ratio, education level, smoking status, alcohol consumption, body mass index (BMI), diabetes, hypertension, We found a non‐linear relationship between HDL‐C and stroke. [ 2] HDL‐C was inversely associated with stroke when HDL‐C was less than 1.55 mmol/L (OR = 0.36, $95\%$ CI:0.21–0.62 $P \leq 0.05$) after adjusting for other confounders. [ 3] In subgroup analyses stratified by sex and race/ethnicity, HDL‐C was inversely associated with stroke in men and Whites.
### Background
Previous epidemiological studies have indicated that high‐density lipoprotein cholesterol (HDL‐C) is inversely associated with the risk of cardiovascular disease. However, this issue has aroused controversy in recent years. Besides, the relationship between HDL‐C and the risk of total stroke in sex and race is less clear. Thus, we aimed to examine the association between different ranges of HDL‐C and the risk of total stroke in adults over 40 years old.
### Methods
This cross‐sectional study was conducted on a sample of 8643 participants (4222 men and 4421 women) aged ≥40 years old from the National Health and Nutrition Examination Survey 2007–2016. HDL‐C was an independent variable and stroke was a dependent variable in this study, with the other variables as potential effect modifiers. To examine the associations between them, we used multivariate logistical regression models and smooth curve fittings, as well as subgroup analyses.
### Results
HDL‐C was inversely associated with stroke when HDL‐C was less than 1.55 mmol/L (odds ratio [OR] = 0.36, $95\%$ confidence interval [CI]:0.21–0.62, $p \leq .05$). However, above 1.55 mmol/L, the incidence of stroke was not significant (OR = 1.29, $95\%$ CI: 0.79–2.09, $p \leq .05$). Stratified by race/ethnicity and sex, the subgroup analyses demonstrated that HDL‐C was inversely associated with stroke in men and Whites, but no significant differences among women, Mexicans, blacks, and other races.
### Conclusion
We found a nonlinear relationship between HDL‐C and total stroke. Our study reveals a range of inverse associations between HDL‐C and stroke (HDL‐C<1.55 mmol/L), especially among men and Whites. This finding suggested that maintaining an appropriate HDL‐C range may be beneficial in reducing the incidence of stroke.
## INTRODUCTION
Stroke is the second leading cause of death and a major contributor to disability worldwide. It affects roughly 13.7 million people and kills around 5.5 million annually (GBD 2016 Stroke Collaborators, 2019). The most common and controllable risk factors for stroke include high blood pressure, diabetes, dyslipidemia, smoking, and obesity (O'donnell et al., 2010). High‐density lipoproteins (HDL) are a type of lipoproteins that carry cholesterol in the blood. Elevated concentration of serum high‐density lipoprotein cholesterol (HDL‐C) protects against cardiovascular disease through a variety of mechanisms, including reverse cholesterol transport, anti‐inflammatory, antioxidant, and antithrombotic effects (Navab et al., 2009; Rosenson et al., 2013). However, studies investigating the association between HDL‐C and stroke remain limited and controversial.
Several previous studies showed that higher HDL‐C was associated with a lower risk of stroke (Reina et al., 2015; Zhang et al., 2012). While other studies showed that the levels of HDL‐C were not associated with stroke (Bowman et al., 2003; Rohatgi et al., 2014). In contrast, a recent meta‐analysis reported that high levels of HDL‐C may be associated with a higher risk of stroke (Wang et al., 2013). Thus, the claim that increasing HDL‐C treatment is beneficial for stroke is being challenged.
To the best of our knowledge, few studies have reported the exact dose‐response relationship between HDL‐C and total stroke with different sex and races. Therefore, we aimed to examine the association between the different HDL‐C levels and total stroke in people older than 40 years old using a large‐scale public database from the National Health and Nutrition Examination Survey (NHANES). Our findings could facilitate the development and promotion of blood lipid prevention strategies aimed at reducing the risk of stroke.
## Data collection and study population
The NHANES is a population‐based national survey that collects health and nutrition data in the United States in biennial cycles. Besides, the NHANES is a cross‐sectional study designed to produce a nationally representative sample of the U.S. population by using a multifaceted probability design. In mobile examination centers, following a standardized home interview, a physical examination and biological specimen collection are conducted. The NHANES data are freely available to researchers throughout the world on the internet. Information on HDL‐C as well as stroke measures in five cycles (2007–2016) were combined and used in this analysis. Generally, a total of 71,714 individuals participated in the NHANES from 2007 to 2016. We defined middle‐aged and elderly adults as aged ≥40 years (Feifel & Strack, 1989). Participants with missing stroke data ($$n = 27078$$) and HDL‐C data ($$n = 29718$$), as well as participants with cancer ($$n = 1304$$), and who were under the age of 40 ($$n = 4971$$) were excluded. In our analysis, we included 8643 participants (Figure 1). The National Center of Health Statistics Ethics Review Board approved the study, and written informed consent was obtained from each participant.
**FIGURE 1:** *Flow diagram of the screening and enrollment of study participants. Total people from the National Health and Nutrition Examination Survey (NHANES) 2007−2016.*
## Definition of stroke
In the NHANES, participants were asked whether and when they had a prior stroke. Participants were asked, “*Has a* doctor or other health professional ever told you that you had a stroke?” In the United States, self‐reported measures for stroke are fairly accurate among the general population and have been previously used in epidemiological studies with the NHANES data (Lin et al., 2015).
## Assessment of HDL‐C
HDL‐C was measured by the direct immunoassay method during 2007–2012. During the 2013–2016 cycle, neither the lab method nor the lab location was changed. However, the HDL‐C lab equipment was changed. In the 2007–2012 cycle, the analyte was measured using the Roche Modular P chemistry analyzer. The analyte was measured using Roche Cobas 6000 and Roche modular P chemistry analyzers from 2013 to 2016.
Some large‐scale studies demonstrated that HDL‐C ≥2.07 mmol/L (≥80 mg/dl) or ≥2.32 mmol/L (≥90 mg/dl) may increase the risk of cardiovascular disease events (Ko et al., 2016; Madsen et al., 2017; Wilkins et al., 2014). According to a Japanese community‐based cohort study, HDL‐C levels between 1.56 and 2.06 mmol/L (60–79 mg/dl) were associated with a decreased risk of coronary heart disease morbidity compared to those with lower levels between 1.04 and 1.55 mmol/L (40−60 mg/dl). In order to examine the association between HDL‐C and stroke, we divided the HDL‐C levels into five categories: HDL‐C <1.04 mmol/l (<40 mg/dl), 1.04–1.56 mmol/l (40–59 mg/dl), 1.56–2.07 mmol/l (60–79 mg/dl), 2.07–2.32 mmol/l (80–89 mg/dl), and ≥2.32 mmol/L (≥90 mg/dl).
## Covariates
Demographic variables included age, sex, race/ethnicity, low‐density lipoprotein cholesterol (LDL‐C), cholesterol, triglycerides, glycated haemoglobinA1c (HbA1c), income‐to‐poverty ratio, smoking status, education level, body mass index (BMI), alcohol consumption, diabetes, hypertension, and cancer. Race/ethnicity was categorized into Mexican American, other Hispanic, non‐Hispanic White, non‐Hispanic Black, and other races. Education level was defined as below high school, high school, and above high school. The DxC800 measures serum cholesterol concentrations by using a timed‐endpoint method. LDL‐C was converted to categorical variables considering the impact of missing LDL data on sample size. HbA1c levels were assessed with high‐performance liquid chromatography, standardizing the procedure to the Diabetes Control and Complications assay. The poverty ratio index was used to define poverty. Income‐to‐poverty ratio was set at the following cut‐points: <1.99, 1.99−3.49, and ≥3.5 (Vieux et al., 2019), and missing data were included as a separate category. The smoking status was categorized as “non‐smokers” (lifetime use of <100 cigarettes), “current smokers” (lifetime use of ≥100 cigarettes and who currently smoke cigarettes), and missing data as a group alone. Participants were asked if they had at least 12 alcohol drinks/1 year. Alcohol consumption was categorized as more than 12 glasses in 1 year, less than 12 glasses in 1 year, and missing data as a group alone. The height and weight of the patient were measured by trained health technicians, and the BMI of the patient was calculated by dividing the weight in kilograms by the height in meters. Diabetes, hypertension, and cancer were self‐reported via a questionnaire. Diabetes, hypertension, and cancer were defined using self‐reported diagnoses.
## Statistical analysis
All statistical analyses were conducted using the R statistical package (R version 3.5.3) and EmpowerStats. All p‐values were two‐sided, and values of $p \leq .05$ were considered statistically significant. We present continuous variables as means ± standard deviations (SD) and categorical variables as totals and percentages (%). The chi‐square test was used for categorical variables. Continuous variables were first checked for normality. Whenever normal distribution criteria were met, one‐way analysis of variance (ANOVA) tests were performed, whereas when results were not normally distributed, Kruskal–Wallis tests (nonparametric ANOVA tests) were performed. Unadjusted models and multivariate adjusted models using generalized logistic regression were used to evaluate the association between HDL‐C and stroke. To explore and understand the complex relationships between HDL‐C and stroke, continuous variables were transformed into categorical variables: HDL‐C <1.04 mmol/L(40 mg/dl), 1.04–1.56 mmol/L(40–59 mg/dl), 1.56–2.07 mmol/L(60–79 mg/dl), 2.07−2.32 mmol/L (80−89 mg/dl), and ≥2.32 mmol/L (≥90 mg/dl). Furthermore, a generalized additive model was applied to find the nonlinear relationship. If a nonlinear correlation was found, we calculated the threshold effect of HDL‐C on stroke risk using a two‐piecewise linear regression model in terms of a smoothing plot. If the ratio between them appears obvious in a smooth curve, the recursive method will automatically calculate the inflection point using the maximum model likelihood. We constructed three multivariable logistic regression models: model 1, no covariates were adjusted; model 2, age, gender, and race were adjusted; and model 3, all the covariates presented in Table 1 were adjusted. Subgroup analyses stratified by gender and race were also performed.
**TABLE 1**
| HDL‐C | Q1 (<1.04) | Q2 (1.04–1.56) | Q3 (1.56–2.07) | Q4 (2.07–2.32) | Q5 (≥2.32) | p‐Value |
| --- | --- | --- | --- | --- | --- | --- |
| N | 1916 | 4253 | 1853 | 324 | 297 | |
| Age | 57.11 ± 11.67 | 58.42 ± 11.85 | 59.32 ± 11.97 | 61.04 ± 12.06 | 58.16 ± 11.63 | <.001 |
| BMI | 31.37 ± 6.20 | 29.98 ± 6.47 | 27.75 ± 6.52 | 25.83 ± 5.37 | 25.05 ± 5.67 | <.001 |
| Cholesterol | 4.93 ± 1.23 | 5.08 ± 1.12 | 5.29 ± 0.98 | 5.53 ± 0.92 | 5.89 ± 0.97 | <.001 |
| Triglycerides | 3.00 ± 2.27 | 1.76 ± 1.43 | 1.23 ± 0.64 | 0.98 ± 0.43 | 0.96 ± 0.51 | <.001 |
| HbA1c | 6.25 ± 1.36 | 6.00 ± 1.21 | 5.74 ± 0.92 | 5.66 ± 0.91 | 5.49 ± 0.48 | <.001 |
| Sex (%) | | | | | | <.001 |
| Men | 71.19 | 50.34 | 30.55 | 25.62 | 22.90 | |
| Women | 28.81 | 49.66 | 69.45 | 74.38 | 77.10 | |
| Race (%) | | | | | | <.001 |
| Mexican American | 19.99 | 18.53 | 12.20 | 11.73 | 6.73 | |
| Non‐Hispanic White | 44.42 | 40.65 | 41.55 | 42.59 | 49.49 | |
| Non‐Hispanic Black | 13.57 | 19.87 | 25.20 | 27.16 | 31.31 | |
| Other race | 22.03 | 20.95 | 21.05 | 18.52 | 12.46 | |
| Level of education (%) | | | | | | <.001 |
| Less than high school | 35.23 | 30.85 | 24.82 | 25.62 | 18.18 | |
| High school | 23.54 | 22.90 | 20.89 | 20.37 | 19.87 | |
| More than high school | 41.23 | 46.25 | 54.29 | 54.01 | 61.95 | |
| Income to poverty ratio (%) | | | | | | <.001 |
| <1.99 | 47.91 | 42.49 | 38.26 | 35.19 | 34.68 | |
| 1.99–3.49 | 18.37 | 18.88 | 18.40 | 20.37 | 17.51 | |
| >3.50 | 23.07 | 29.34 | 33.51 | 33.33 | 38.72 | |
| Missing | 10.65 | 9.29 | 9.82 | 11.11 | 9.09 | |
| LDL‐C (%) | | | | | | <.001 |
| <1.5 | 1.72 | 1.50 | 1.19 | 1.54 | 3.70 | |
| 1.5–3.0 | 19.26 | 23.07 | 25.90 | 23.15 | 25.25 | |
| 3.0–4.5 | 14.98 | 21.61 | 22.34 | 20.99 | 19.87 | |
| >4.5 | 2.14 | 3.69 | 3.24 | 2.78 | 1.68 | |
| Missing | 61.90 | 50.13 | 47.33 | 51.54 | 49.49 | |
| Smoking status (%) | | | | | | <.001 |
| Current smokers | 55.01 | 47.73 | 39.56 | 44.44 | 50.84 | |
| Non‐smokers | 44.99 | 52.27 | 60.44 | 55.56 | 49.16 | |
| Alcohol consumption (%) | | | | | | <.001 |
| More than 12 glasses in 1 year | 66.86 | 62.45 | 60.71 | 66.05 | 74.07 | |
| Less than 12 glasses in 1 year | 25.31 | 29.49 | 30.60 | 24.07 | 14.81 | |
| Missing | 7.83 | 8.06 | 8.69 | 9.88 | 11.11 | |
| Diabetes (%) | | | | | | <.001 |
| Yes | 25.37 | 18.39 | 11.23 | 11.11 | 5.72 | |
| No | 74.63 | 81.61 | 88.77 | 88.89 | 94.28 | |
| Hypertension (%) | | | | | | <.001 |
| Yes | 50.57 | 47.05 | 43.34 | 43.21 | 39.06 | |
| No | 49.43 | 52.95 | 56.66 | 56.79 | 60.94 | |
## RESULTS
Our study included 8643 participants after reviewing the data of 71,714 participants(women mean age: 58.35 ± 11.68 years; men mean age: 58.48 ± 12.04; $51.15\%$ women). The flow diagram in Figure 1 shows the selection of participants. As shown in Table 1, we categorized HDL‐C levels as follows: Q1 group <1.04 mmol/L; Q2 group 1.04–1.56 mmol/L; Q3 group 1.56–2.07 mmol/L; Q4 group 2.07–2.32 mmol/L; Q5 group ≥2.32 mmol/L. There were significant differences in baseline characteristics among the HDL‐C five groups ($p \leq .05$). The population with higher HDL‐C levels had higher values for cholesterol, women, Whites, Blacks, education levels, alcohol consumption, and income‐to‐poverty ratio, and lower values for BMI, triglycerides, and HbA1c. Participants in the lowest group are more likely to develop diabetes and hypertension.
Table 2 shows the results of the multivariate regression analyses. In the crude model (odds ratio [OR] = 0.67, $95\%$ confidence interval [CI]: 0.52–0.86, $p \leq .05$), HDL‐C was negatively correlated with stroke. After adjusting for confounding factors, this negative association was still present in the Minimally adjusted model (OR = 0.50, $95\%$ CI: 0.38–0.66, $p \leq .05$) and the Fully adjusted model (OR = 0.69, $95\%$ CI: 0.49–0.96, $p \leq .05$). After converting HDL‐C from a continuous variable to a categorical variable (five groups), compared with the reference group, the risk of stroke was reduced by $33\%$(OR = 0.67, $95\%$ CI: 0.5094–0.8860, $p \leq .05$) in the Q2, by $39\%$ (OR = 0.61, $95\%$ CI: 0.4211–0.8962, $p \leq .05$) in the Q3, by $61.0\%$ (OR = 0.39, $95\%$ CI: 0.1906–0.8141, $p \leq .05$) in the Q4, and by $32\%$ (OR = 0.68, $95\%$ CI: 0.3388–1.3771, $p \leq .05$) in the highest group. Although no significant difference was seen in the highest group, the trend was significant among the five different HDL‐C groups ($p \leq .05$).
**TABLE 2**
| Exposure | Crude model OR (95% CI), p‐value | Minimally adjusted model OR (95% CI), p‐value | Fully adjusted model OR (95% CI), p‐value |
| --- | --- | --- | --- |
| HDL‐C | 0.67 (0.52, 0.86), <.05 | 0.50 (0.38, 0.66) <.05 | 0.69 (0.49, 0.96) .298 |
| HDL‐C(quintile) | | | |
| Q1 (<1.04) | Reference | Reference | Reference |
| Q2 (1.04–1.56) | 0.68 (0.5374, 0.8621) <.05 | 0.57 (0.4478, 0.7337) <.05 | 0.67 (0.5094, 0.8860) <.05 |
| Q3 (1.56–2.07) | 0.61 (0.4555, 0.8271) <.05 | 0.45 (0.3301, 0.6254) <.05 | 0.61 (0.4211, 0.8962) <.05 |
| Q4 (2.07–2.32) | 0.47 (0.2473, 0.9187) <.05 | 0.30 (0.1541, 0.5937) <.05 | 0.39 (0.1906, 0.8141) <.05 |
| Q5 (≥2.32) | 0.63 (0.3436, 1.1557) .14 | 0.46 (0.2434, 0.8555) <.05 | 0.68 (0.3388, 1.3771) .2866 |
| p for trend | .05 | 0 <.05 | 0 <.05 |
| Subgroup analysis stratified by sex | | | |
| Men | 0.63 (0.42, 0.94) .0254 | 0.46 (0.30, 0.70) .0004 | 0.58 (0.34, 0.98) .0424 |
| Women | 0.68 (0.48, 0.95) .0263 | 0.53 (0.37, 0.75) .0005 | 0.77 (0.50, 1.18) .2316 |
| Subgroup analysis stratified by race/ethnicity | | | |
| Mexican American | 0.42 (0.17, 1.02) .0552 | 0.47 (0.19, 1.18) .1087 | 0.64 (0.21, 2.00) .4436 |
| Non‐Hispanic White | 0.45 (0.31, 0.65) <.0001 | 0.31 (0.20, 0.47) <.0001 | 0.49 (0.29, 0.82) .0070 |
| Non‐Hispanic Black | 0.92 (0.61, 1.39) .7011 | 0.86 (0.56, 1.33) .4993 | 1.03 (0.61, 1.75) .9035 |
| Other races | 0.84 (0.38, 1.85) .6575 | 0.67 (0.28, 1.60) .3697 | 0.43 (0.14, 1.35) .1463 |
Stratified by sex and race/ethnicity in subgroup analyses(Table 2), the model 3 shows that the negative association between HDL‐C and stroke remains in men (OR = 0.58, $95\%$ CI: 0.34–0.98, $p \leq .05$) and whites (OR = 0.49, $95\%$ CI: 0.29–0.82, $p \leq .05$), but not in women (OR = 0.77, $95\%$ CI: 0.50–1.18, $p \leq .05$), Blacks (OR = 1.03, $95\%$ CI: 0.61–1.75, $p \leq .05$) and Mexican American (OR = 0.64, $95\%$ CI: 0.21–2.00, $p \leq .05$). After adjusting for the above covariates, Figure 2 shows a nonlinear relationship between HDL‐C and stroke using smooth curve fitting and generalized additive models. We calculated the inflection point as 1.55 using a two‐piecewise linear regression model (Table 3). The inflection point of the study was 1.55 mmol/L. Below 1.55 mmol/L, the risk of stroke decreased by $64\%$(OR = 0.36, $95\%$ CI:0.21–0.62, $p \leq .05$) for each unit increase of HDL‐C. Above 1.55 mmol/L, the risk of stroke increased for each unit increase, but there was no statistically significant difference(OR = 1.29, $95\%$ CI: 0.79–2.09, $p \leq .05$).
**FIGURE 2:** *A nonlinear relationship between high‐density lipoprotein cholesterol (HDL‐C) and stroke using smooth curve fitting and generalized additive models was detected after adjusting for age, sex, race/ethnicity, low‐density lipoprotein cholesterol (LDL‐C), cholesterol, triglycerides, glycated haemoglobinA1c (HbA1c), income‐to‐poverty ratio, education level, smoking status, alcohol consumption, body mass index (BMI), diabetes, and hypertension. The inflection point of the study was 1.55 mmol/L. Below 1.55 mmol/L, the risk of stroke decreased by $64\%$($p \leq .05$) for each unit increase of HDL‐C. Above 1.55 mmol/L, the risk of stroke increased for each unit increase, but there was no statistically significant difference($p \leq .05$).The solid line and dashed line represent the estimated values and their corresponding $95\%$ confidence intervals, respectively.* TABLE_PLACEHOLDER:TABLE 3
## DISCUSSION
In this study, we found a negative association between HDL‐C and stroke. A nonlinear relationship between HDL‐C and stroke incidence with a point of inflection at 1.55 mmol/L was indicated. Based on subgroup analyses stratified by sex and race/ethnicity, the negative relationship of HDL‐C with stroke remained in men and Whites. This finding suggests that keeping HDL‐C levels at a slightly higher level could reduce the incidence of stroke.
Even though HDL‐C and stroke have been studied previously, the relationship between stroke and HDL‐C is limited and controversial. Three prospective studies reported an inverse association between HDL‐C levels and stroke incidence in a low level range of HDL‐C (Chei et al., 2013; Saito et al., 2017; Zhang et al., 2012). This conclusion was supported by another cohort study (Vitturi & Gagliardi, 2022). Several recent studies have shown people with extremely high HDL‐C paradoxically have high all‐cause mortality (Hirata et al., 2016, 2018; Madsen et al., 2017). Therefore, HDL‐C appears to be a double‐edged sword for atherosclerosis, possibly because both very high and low levels of HDL‐C are significantly associated with endothelial dysfunction. A prospective cohort study from China reported both low and high cumulative mean HDL‐C were associated with an increased risk of ischemic stroke and hemorrhagic stroke, a U‐shaped relationship (Li et al., 2022). Possible explanations for the U‐shaped relationship between HDL‐C and cardiovascular disease risk include genetic mutations that lead to very high HDL‐C, which contributes to adverse cardiovascular disease risk as well; extreme elevations in HDL‐C may represent dysfunctional HDL in some individuals, which in turn may increase cardiovascular risk (Singh & Rohatgi, 2018). In our research, the inverse relationship between HDL‐C and the risk of stroke at a low level range of HDL‐C was demonstrated. We did not find a positive correlation between extremely high levels of HDL‐C and stroke risk. However, extremely high levels of HDL‐C increased the height of the end curve even though the results were not statistically different. The small sample size of extremely high levels of HDL‐C could be the reason. More clinical studies are needed in the future to confirm this.
In our study, by using multiple logistic regression, stratified analysis, and trend test, HDL‐C was found to reduce the risk of stroke in both men and women. However, the benefits of HDL‐C on stroke risk attenuated after adjustment for all covariates are presented in Table 1, especially in women. Estrogen is known to contribute to cardiovascular protection by increasing HDL‐C (Tikkanen et al., 1982). HDL‐C levels are reduced to moderate levels in postmenopausal women (Matthews et al., 1989). We speculate that the larger inclusion of postmenopausal women with decreased estrogen secretion and the possible presence of residual confounders such as the current use of combined oral contraceptives, and hormone replacement therapy may be the reason. A multi‐ethnic study of people aged 45–84 in the United States showed that HDL‐C was associated with lower stroke risk; however, when interactions with race were examined, the relationship between HDL‐C and stroke was significant only in Blacks (Reina et al., 2015). We observed a stable negative correlation between HDL‐C and stroke in Whites with or without adjustment for confounders, which suggested that the HDL‐C may have an effect on stroke outcome differently in Whites than in other races. Race‐specific differences can be explained by differences in alcohol consumption, obesity, genetic factors, and other factors. Further large prospective studies are needed to elucidate the relationship between HDL‐C and stroke in the white middle‐aged and elderly people population.
The current study has several limitations. First, because of the cross‐sectional design, the causal relationship between HDL‐C and stroke was not assessed. Long‐term observational studies should be considered in future studies. Second, the study only included people over 40 years old and excluded patients with cancer, so the results cannot be used for young people and patients with cancer. Third, considering that the data sources have certain geographical and ethnic restrictions, the result of this study is only applicable to Americans. Fourth, the NHANES database does not distinguish between ischemic and hemorrhagic strokes. The relationship between stroke type and HDL‐C should be considered in future studies. Fifth, the sample size of the participants was small. In addition, our subgroup analyses and their results are exploratory since these are not established a priori. Sixth, because of the limitations of database biochemical indicators, HDL particle was not addressed in our study. Thus, further research is needed to find out the relation between HDL particles and stroke.
## CONCLUSION
Our study revealed a range of negative associations between HDL‐C and stroke among people over 40 years old, especially among men and Whites. This association followed a nonlinear curve (inflection point: 1.55 mmol/L). Measurement of HDL‐C may provide a responsive biomarker for the early identification of stroke and to guide treatment.
## AUTHOR CONTRIBUTIONS
Yang Hu, Gongbo Li, and Haiyan Luo conceived and designed the study. Yang Hu, Min Song, Dongmei Wu, and Yuqing Zhang conducted the formal analysis and developed the methodology. Yang Hu and Min Song wrote the initial drafts. Dongmei Wu and Yuqing Zhang helped draft the manuscript. Gongbo Li and Haiyan Luo are the corresponding authors of this work and supervised work on the entire manuscript. All the authors read and approved the final manuscript.
## CONFLICT OF INTEREST STATEMENT
The authors have no conflicts of interest to declare.
## FUNDING INFORMATION
No funding was received for this study.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2901.
## DATA AVAILABILITY STATEMENT
The survey data are publicly available on the internet for data users and researchers throughout the world (www.cdc.gov/nchs/nhanes/).
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|
---
title: 'Health behavior of young patients with ischemic stroke in Estonia: A score
of five factors'
authors:
- Minni Saapar
- Riina Vibo
- Siim Schneider
- Liisa Kõrv
- Sandra Mallene
- Janika Kõrv
journal: Brain and Behavior
year: 2023
pmcid: PMC10013939
doi: 10.1002/brb3.2908
license: CC BY 4.0
---
# Health behavior of young patients with ischemic stroke in Estonia: A score of five factors
## Abstract
### Background
Behavioral risk factors are common among young patients with stroke. This study aimed to compare the health behavior of patients and healthy controls and develop a combined risk score of health behavior.
### Methods
The health behavior of patients aged 18–54 years who suffered an ischemic stroke from 2013 to 2020 in Estonia was compared to the Health Behavior among Estonian Adult Population 2014 study sample. We chose five risk factors for comparison: smoking status, body mass index, physical exercise, diet (salt use and vegetable consumption), alcohol intake (quantity and frequency), and composed a summary score.
### Results
Comparing 342 patients and 1789 controls, daily smoking ($49.0\%$ vs. $22.7\%$), obesity ($33.4\%$ vs. $15.9\%$), low physical activity (< twice/week) ($72.2\%$ vs. $60.5\%$), excessive salt use ($8.6\%$ vs. $4.5\%$), and frequent alcohol use (≥ weekly) ($39.9\%$ vs. $34.0\%$) were more prevalent among patients. The differences in infrequent vegetable consumption (<6 days/week) and excessive alcohol consumption (7 days, >8 units/females, >16 units/males) were not significant. The observed differences were similar for age groups 18–44 years and 45–54 years. The average Health Behavior Stroke Risk Score (0–10) was 4.6 points (CI 4.4–4.8, SD ± 1.97) for patients and 3.5 points (CI 3.4–3.6, SD ± 1.90) for controls.
### Conclusions
Before stroke, young patients displayed significantly worse health behavior than the general population. The largest differences were found for smoking and obesity, and a cumulation of risk factors was observed via the HBSR score.
## INTRODUCTION
Lifestyle choices have a significant impact on health, and many studies have emphasized the connection between modifiable risk factors and stroke. It has been demonstrated that more than $80\%$ of strokes are related to hypertension, current smoking status, obesity, unhealthy diet, and physical inactivity (O'Donnell et al., 2010) and that $74\%$ of the stroke burden is due to behavioral factors (Feigin et al., 2016). These traditional stroke risk factors are also common in young patients with stroke (Putaala, 2016), and they are surprisingly prevalent regardless of the stroke etiology (Maaijwee et al., 2014). This may be related to the reported increase in stroke incidence in young adults over the last decades (Ekker et al., 2019). In Estonia, the incidence and case fatality of stroke in young adults are higher than in other high‐income countries (Kõrv et al., 2021), and the traditional risk factors are common (Schneider et al., 2017; Vibo et al., 2021). Previous studies on young patients with stroke mainly focused on clinical risk factors, and only a few have evaluated health behavior. More complex topics, for example, diet, are often not included, and there is no standard evaluation of all the factors.
## OBJECTIVES
The present study aimed to assess the health behavior of young patients with stroke, compare this to the general population, and develop a simple health behavior summary score to evaluate composite behavioral stroke risk.
## METHODS
The Estonian Young Stroke *Registry is* a prospective hospital‐based ongoing database, which comprises all consecutive patients with a discharge diagnosis of ischemic stroke ($94.9\%$ first‐ever, $6.1\%$ recurrent) and aged 18−54 years, as described previously (Vibo et al., 2021). While hospitalized for acute stroke, written informed consent was obtained from the participants, and they completed a self‐report questionnaire about their health behavior before the stroke. The questionnaire was designed to match the Health Behavior among Estonian Adult Population (HBEAP) study of 2014. The HBEAP study is conducted every 2 years by the National Institute for Health Development (NIHD) in Estonia, and this is a postal questionnaire with a stratified random sample of 5000 people aged 16−64 years (Tekkel & Veideman, 2015). We used responses from the 2014 survey as the control sample for our stroke cohort; individuals aged <18 years or ≥55 years or with a history of stroke were excluded. This study was approved by the Research Ethics Committee of the University of Tartu (302/M‐23).
To evaluate composite health behavior and develop the Health Behavior Stroke Risk Score (HBSR score), we analyzed five factors that have been correlated with an increased stroke risk: smoking status, body mass index (BMI), physical activity, diet, and alcohol use. High‐risk health behaviors were defined based on the recommendations of the NIHD Estonia (Tekkel & Veideman, 2015), and we included seven variables: daily smoking, BMI ≥30, exercise < twice/week, adding salt to ready‐made meals, vegetable consumption <6 days/week, alcohol use frequency once a week or more, and alcohol amount in 7 days >8 units/female or >16 units/male. To calculate the HBSR score, the health behavior factors were graded according to the risk level: low, moderate, or high. Next, the total score (0–10) for each patient was calculated, with a score of 10 corresponding to the highest risk. The description of the score is shown in Table S1. For a binary variable, scores were divided into low (0−5) and high (6−10).
## Statistical methods
Comparisons between patients with stroke and controls were performed using the Z‐test, and Bonferroni correction was used for multiple comparisons. The odds ratios (ORs) for each behavioral factor were calculated using logistic regression, presented in both crude and adjusted models, expressed with $95\%$ confidence intervals (CI). The OR for having a high HBSR score was calculated using the same method, and the mean scores were compared using a two‐tailed t‐test. All statistical analyses were performed using Stata version 16.1 (StataCorp, College Station, TX, USA).
## RESULTS
In all, 436 young patients with ischemic stroke were recruited in the registry between January 1, 2013 and December 31, 2020. Of these, 342 ($78.4\%$) completed the health behavior questionnaire. There were no statistically significant differences between the respondents and non‐respondents with respect to the distribution of sex. However, the respondents had milder strokes (mean National Institutes of Health Stroke Scale [NIHSS] 4.3 vs 8.8, $p \leq .001$) with $67.3\%$ having mild strokes (NIHSS ≤ 4), $29.5\%$ moderate (NIHSS 5−15), $2.9\%$ moderate to severe and $0.3\%$ severe strokes. They were also younger (mean age 44.5 vs. 45.7, $p \leq .001$). A total of 342 patients and 1789 general population controls aged 18−54 years were included in the analysis (Figure 1). Among the patients, there was a higher proportion of males ($62\%$ vs. $41\%$, $p \leq .001$) and a higher median age (47 years [IQR 39−51] vs. 37 years [IQR 28−46]) than among controls. There were significantly more participants with higher education among controls ($34\%$ vs. $20\%$; $p \leq .001$), and many of them were single ($22\%$ vs. $15\%$; $$p \leq .005$$).
**FIGURE 1:** *Flowchart depicting recruitment of participants.*
## Health behavior
Table 1 shows the prevalence of behavioral risk factors among patients and controls by two age groups. Overall, the proportion of daily smokers was $49.0\%$ among patients and $22.7\%$ in controls ($p \leq .001$). In addition, the patients were more often obese ($33.4\%$ vs. $15.9\%$, $p \leq .001$), physically inactive ($72.2\%$ vs. $60.5\%$, $$p \leq .000$$), used excessive salt ($8.6\%$ vs. $4.5\%$, $$p \leq .002$$), and consumed alcohol at least once a week ($39.9\%$ vs. $34.0\%$, $$p \leq .009$$). The differences in vegetable and alcohol consumption over the last 7 days were not statistically significant.
**TABLE 1**
| Unnamed: 0 | Age 18−44 | Age 18−44.1 | Age 18−44.2 | Age 45−54 | Age 45−54.1 | Age 45−54.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | Patients (n = 135) | Controls (n = 1263) | p‐Value1 | Patients (n = 206) | Controls (n = 526) | p‐Value1 |
| Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) | Smoking status, n (%) |
| Daily smoker | 62 (46.6) | 254 (20.3) | .000* | 103 (50.3) | 148 (28.5) | .000* |
| Occasional smoker | 10 (7.5) | 112 (9.0) | .576 | 16 (7.8) | 33 (6.3) | .481 |
| Ex‐smoker2 | 17 (12.8) | 344 (27.5) | .000* | 48 (23.4) | 133 (25.6) | .545 |
| Non‐smoker | 44 (33.1) | 539 (43.2) | .025 | 38 (18.5) | 206 (39.6) | .000* |
| BMI, n (%) | BMI, n (%) | BMI, n (%) | BMI, n (%) | BMI, n (%) | BMI, n (%) | BMI, n (%) |
| BMI <25.0 | 53 (40.8) | 740 (59.2) | .000* | 45 (23.1) | 197 (37.8) | .000* |
| BMI 25−29.9 | 45 (34.6) | 352 (28.1) | .120 | 74 (37.9) | 202 (38.8) | .840 |
| BMI ≥30.0 | 32 (24.6) | 159 (12.7) | .000* | 76 (39.0) | 122 (23.4) | .000* |
| Physical exercise, n (%) | Physical exercise, n (%) | Physical exercise, n (%) | Physical exercise, n (%) | Physical exercise, n (%) | Physical exercise, n (%) | Physical exercise, n (%) |
| ≥4 Times a week | 16 (12.6) | 189 (15.1) | .445 | 15 (7.9) | 66 (12.7) | .077 |
| 2−3 Times a week | 24 (18.9) | 344 (27.5) | .036 | 32 (16.9) | 99 (19.1) | .516 |
| Once a week | 18 (14.2) | 152 (12.2) | .513 | 23 (12.2) | 68 (13.1) | .743 |
| 2−3 Times a month | 17 (13.4) | 158 (12.7) | .813 | 16 (8.5) | 48 (9.2) | .748 |
| A few times a year or not at all | 52 (40.9) | 406 (32.5) | .055 | 103 (54.5) | 238 (45.9) | .042 |
| Vegetable consumption, n (%) | Vegetable consumption, n (%) | Vegetable consumption, n (%) | Vegetable consumption, n (%) | Vegetable consumption, n (%) | Vegetable consumption, n (%) | Vegetable consumption, n (%) |
| ≥6 Days a week | 51 (39.5) | 537 (42.7) | .490 | 87 (43.7) | 226 (43.5) | .950 |
| <6 Days a week | 78 (60.5) | 721 (57.3) | | 112 (56.3) | 294 (56.5) | |
| Salting meals, n (%) | Salting meals, n (%) | Salting meals, n (%) | Salting meals, n (%) | Salting meals, n (%) | Salting meals, n (%) | Salting meals, n (%) |
| Yes, mostly before tasting the food | 9 (6.9) | 66 (5.2) | .433 | 19 (9.9) | 15 (2.9) | .000* |
| Yes, when the food needs more salt | 86 (65.6) | 782 (62.1) | .426 | 118 (61.5) | 346 (66.0) | .256 |
| No, never | 36 (27.5) | 411 (32.7) | .229 | 55 (28.6) | 163 (31.1) | .526 |
| Alcohol use, n (%) | Alcohol use, n (%) | Alcohol use, n (%) | Alcohol use, n (%) | Alcohol use, n (%) | Alcohol use, n (%) | Alcohol use, n (%) |
| ≥2 Times a week | 26 (19.9) | 232 (18.5) | .713 | 54 (26.9) | 92 (17.6) | .006* |
| Once a week | 20 (15.3) | 206 (16.4) | .727 | 33 (16.4) | 73 (14.0) | .407 |
| 2−3 Times a month | 34 (25.9) | 334 (26.7) | .859 | 38 (18.9) | 145 (27.8) | .014 |
| Only a few times a year | 38 (29.0) | 379 (30.3) | .764 | 51 (25.4) | 170 (32.6) | .060 |
| | 13 (9.9) | 101 (8.1) | .462 | 25 (12.4) | 42 (8.0) | .068 |
| Alcohol use (last 7 days), n (%) | Alcohol use (last 7 days), n (%) | Alcohol use (last 7 days), n (%) | Alcohol use (last 7 days), n (%) | Alcohol use (last 7 days), n (%) | Alcohol use (last 7 days), n (%) | Alcohol use (last 7 days), n (%) |
| Excessive alcohol use3 | 23 (18.2) | 174 (13.9) | .187 | 20 (10.5) | 64 (12.4) | .491 |
| No excessive alcohol use | 103 (81.8) | 1075(86.1) | | 171 (89.5) | 454 (87.6) | |
In further analysis, the odds of having high‐risk health behavior were calculated, and crude and adjusted (age, sex, education, marital status) ORs are presented in Table S2. After adjusting for age and sex, patients with stroke had higher odds (OR [CI]) for daily smoking (2.54 [1.97−3.28]), obesity (1.94 [1.47−2.56]), lack of regular exercise (1.36 [1.03−1.79]), and an unhealthy diet (2.42 [1.34−4.38]), but not for excessive alcohol use. After further adjustments for education level and marital status, the only factors that remained significant were daily smoking (2.13 [1.62−2.80]) and obesity (1.81 [1.35−2.41]). However, a trend did exist with the previously significant factors. When applying the fully adjusted models to the two age groups (18−44 and 45−54) separately, the differences between the age groups were not statistically significant.
## Health behavior stroke risk score
In young patients with stroke, the average HBSR score was significantly higher than that in the general population (4.6 points [CI 4.4−4.8, SD ± 1.97] vs. 3.5 points [CI 3.4−3.6, SD ± 1.90]; $p \leq .001$). The distribution of HBSR scores for patients and controls is shown in Figure 2. The prevalence of a score of 6−10 was significantly higher among the patients ($52.5\%$ vs. $30.4\%$, $p \leq .001$), and this difference was significant for both males ($64.3\%$ vs. $45.2\%$, $p \leq .001$) and females ($33.9\%$ vs. $20.3\%$, $$p \leq .001$$), as well as age 18−44 ($42.2\%$ vs. $27.0\%$, $$p \leq .001$$) and age 45−54 ($59.5\%$ vs. $38.8\%$, $p \leq .001$). The total HBSR score in patients with stroke remained significantly higher after adjustment for age, sex, education, and marital status (Table S2).
**FIGURE 2:** *Distribution of the Health Behavior Stroke Risk Score in young patients with stroke and the general population of Estonia.*
## DISCUSSION
Our prospective study showed that young patients displayed significantly worse pre‐stroke health behavior than the general population in most of the studied domains. Following adjustments for confounding factors (age, sex, education, and marital status), smoking and obesity were more prevalent among patients with stroke than controls. The HBSR score provides comprehensive behavioral stroke risk assessment through an aggregate score that was higher in patients than in controls, indicating worse health behavior.
There are minimal reports that have focused on the health behavior of young patients with ischemic stroke, and there is a lack of case–control studies. Our results are in line with those of previous studies reporting smoking and physical inactivity as the most common behavioral risk factors among young patients with stroke (Putaala, 2016). However, while the earlier studies frequently assessed smoking status (Aigner et al., 2017; Goeggel Simonetti et al., 2015; Kivioja et al., 2018; Mitchell et al., 2015; Putaala et al., 2012; Renna et al., 2014; Von Sarnowski et al., 2013), physical inactivity was rarely reported. The most common risk factor in our study was physical inactivity, with more than $72\%$ of the patients and $61\%$ of controls engaging in exercise less than twice a week. Though this criterion is even less strict than that presented in stroke primary prevention guidelines (Meschia et al., 2014), it only involves physical activity during exercising and does not account for action at work or during the commute. In Germany, $49\%$ of young patients with stroke had low levels of physical activity (20−30 min <3 times a week), compared to $19\%$ of controls (Aigner et al., 2017). In addition, a recent study found that excess sedentary leisure time (≥ 8 hours/day) was associated with an increased risk of long‐term stroke in young adults (Joundi et al., 2021).
In this study, almost half of the patients were daily smokers compared with less than a quarter of controls. The difference between our patients and controls was higher than that in similar studies from Germany ($48\%$ vs. $35\%$) (Aigner et al., 2017), Finland ($44\%$ vs. $31\%$) (Kivioja et al., 2018), and the United States ($45\%$ vs. $29\%$) (Mitchell et al., 2015). In the general population, the prevalence of smoking has declined by almost half during the last two decades in Estonia, as it was $18\%$ in 2020 (Reile & Veideman, 2021); however, it remains high in young patients with stroke (Vibo et al., 2021). Based on the high prevalence of smoking among patients, regular documentation of smoking status and cessation support is vital for both primary and secondary prevention.
The weekly alcohol consumption was observed in $40\%$ of the patients and $34\%$ of controls; the difference was statistically significant only before adjusting for age and sex. While differences in the frequency of alcohol intake were noted, the average amount of alcohol consumed (in the last 7 days) was similar in the two groups. The definition of excessive alcohol consumption differs between studies. When defined as >5 alcoholic drinks per day or occasion at least once a month, heavy episodic consumption was recorded in $33\%$ of patients versus $18\%$ of controls in a German sub‐cohort of the SIFAP1 study, and the overall prevalence was very similar ($33\%$) in the multinational SIFAP1 study (Aigner et al., 2017). Heavy drinking, defined as an estimated intake of >200 g of pure alcohol per week, has been reported in $14\%$ of patients with stroke in Finland (Putaala et al., 2009). Similarly, in our previous study, $16\%$ of patients reported the constant use of alcohol (Vibo et al., 2021).
The obesity rate of our patients ($33\%$) was twice as high as that of controls, and it was higher than in most earlier studies of young patients with stroke ($11\%$−$22\%$) (Aigner et al., 2017; Putaala et al., 2012; Renna et al., 2014; Von Sarnowski et al., 2013). To date, only one study has shown a higher prevalence ($39.5\%$), but there was also notably more obesity among controls ($29\%$) (Mitchell et al., 2015). The rate of obesity may reflect the proportion of obese population in a specific country, and the prevalence of obesity is increasing in Estonia (Reile et al., 2020). While BMI is not a behavioral factor in itself but only reflects excessive caloric consumption, we decided to use this factor in our study because it seems to be critical considering the increase in obesity and there is lack of standards for measuring excessive caloric consumption itself.
It is challenging to evaluate diet using only a few questions. We analyzed frequent vegetable and low salt consumption as indicators of a healthy diet. The lack of differences in vegetable consumption between patients and controls was unexpected, as consuming more vegetables has been associated with lower stroke risk in long‐term prospective studies (Aune et al., 2017). It is possible that recall bias may lead to errors in reporting vegetable consumption frequency and that social desirability bias causes patients to report their behavior toward healthier at the hospital (compared to anonymous replies mailed by controls). As for salt use, we found no studies that focused on the habit of adding salt to ready‐made meals or any studies on salt consumption, specifically in young patients with stroke. As per an estimate, patients who add salt to food before tasting consume approximately 10 g of salt per day, while the body requires ∼0.5 g (Spence, 2019). The stroke primary prevention guidelines recommend a diet low in sodium and rich in fruits and vegetables (Meschia et al., 2014). Considering that a healthy diet could notably lower stroke risk (Spence, 2019), clear dietary advice for patients with stroke is needed. It is possible that the effects of diet could not be found in our study due to the young age of the patients, as the health effects of diet accumulate over a long time.
Behavioral risk factors in young patients with stroke have been understudied. Having simple indicator questions for complex variables such as physical activity or diet would facilitate relevant data collection in future research and clinical practice. To summarize the risk factors, we created the HBSR score that is quick and easy to administer, focusing on five important health behavior aspects of stroke in young adults. The questionnaire has only nine questions for the person to answer and is an attempt to create a tool that provides an aggregate score of health behavior that can be used for both research and clinical practice. The HBSR score could be the basis for further counselling for health behavior change as it gives the patient a clear estimate of the modifiable risk factor burden.
A population‐based observational study of behavioral risk factors was performed in Canada, and the Stroke Population Risk Tool was developed (Manuel et al., 2015). That score included the same health behavior aspects as our study, but the variables differed. Health behavior and stroke history were documented during a national survey, and individuals were followed for 5‐year stroke incidence, while we used patients with stroke from the hospital who reported their recent health behavior at the time of stroke. Additionally, they did not focus on young patients with stroke, including 20‐ to 83‐year olds (Manuel et al., 2015).
One of the strengths of our study is recording health behavior during the initial days after stroke onset; therefore, the results reflect the behavior immediately before stroke. Our sample is representative, including all consecutive patients from 8 years from a tertiary stroke center and using a population‐based control group. The aggregate score allows comparisons of overall health behaviors and assesses patients’ lifestyles.
A limitation of our study is that we did not use matched controls; however, we used adjusted models to correct for confounding factors. As patients in our sample had milder strokes than non‐respondents, our results might not represent patients with more severe strokes, but this is more likely to decrease the differences between patients and controls than increase them. Social desirability could have affected the answers, as the control subjects answered the questionnaire anonymously, but the patients with stroke did so during their hospital stay. In choosing our indicator variables, we used questions that are easy to answer and identical to the Estonian HBEAP study; however, these are also more robust and not specifically tested in stroke prevention.
In conclusion, our study demonstrated that young patients with stroke displayed significantly worse health behaviors than the general population in Estonia. As young patients have many decades to benefit from behavior change, more emphasis should be placed on informing patients of these modifiable risk factors and helping enforce these changes (e.g., medication and counselling for smoking cessation, guidelines for exercise and diet). The HBSR score can be used to provide feedback to individuals about the degree of modifiable stroke risk factors in their lifestyle or to compare groups. Further studies are required to evaluate the relationship between HBSR score and stroke risk.
## CONFLICT OF INTEREST STATEMENT
The authors declare that there are no conflicts of interest.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2908
## DATA AVAILABILITY STATEMENT
The data are available upon request from the corresponding author (Minni Saapar).
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|
---
title: 'The impact of postoperative cognitive training on health‐related quality of
life and cognitive failures in daily living after heart valve surgery: A randomized
clinical trial'
authors:
- Marius Butz
- Tibo Gerriets
- Gebhard Sammer
- Jasmin El‐Shazly
- Marlene Tschernatsch
- Patrick Schramm
- Thorsten R. Doeppner
- Tobias Braun
- Andreas Boening
- Thomas Mengden
- Yeong‐Hoon Choi
- Markus Schoenburg
- Martin Juenemann
journal: Brain and Behavior
year: 2023
pmcid: PMC10013943
doi: 10.1002/brb3.2915
license: CC BY 4.0
---
# The impact of postoperative cognitive training on health‐related quality of life and cognitive failures in daily living after heart valve surgery: A randomized clinical trial
## Abstract
The aim of this study was to investigate whether early postoperative cognitive training has an impact on health‐related quality of life and cognitive failures in daily living after cardiac surgery. The study was a 2‐arm, randomized, controlled, outcome‐blinded trial involving older patients undergoing elective heart valve surgery with extracorporeal circulation. As our cognitive training has shown beneficial effects, this intervention could be a promising method to enhance health‐related quality of life after cardiac surgery.
### Background
Heart surgery is a risk factor for objectively and subjectively assessable postoperative cognitive decline (POCD), which is relevant for everyday life. The aim of this study was to investigate whether early postoperative cognitive training has an impact on health‐related quality of life and cognitive failures in daily living after cardiac surgery.
### Methods
The study was a two‐arm, randomized, controlled, outcome‐blinded trial involving older patients undergoing elective heart valve surgery with extracorporeal circulation (ECC). Recruitment took place at the Departments of Cardiac Surgery of the Kerckhoff Clinic in Bad Nauheim (Germany) and the University Hospital in Giessen (Germany). The patients were randomized (1:1 ratio) to either a paper‐and‐pencil–based cognitive training group or a control group. We applied the Short Form Health Survey (SF‐36) and the Cognitive Failures Questionnaire (CFQ) prior to surgery and 3 months after the cognitive training. Data were analyzed in a per‐protocol fashion.
### Results
Three months after discharge from rehabilitation, the training group ($$n = 31$$) showed improvement in health‐related quality of life compared to the control group ($$n = 29$$), especially in role limitations due to emotional problems (U = −2.649, $$p \leq .008$$, η 2 = 0.121), energy and fatigue (F[2.55] = 5.72, $$p \leq .020$$, η 2 = 0.062), social functioning (U = −2.137, $$p \leq .033$$, η 2 = 0.076), the average of all SF‐36 factors (U = −2.374, $$p \leq .018$$, η 2 = 0.094), health change from the past year to the present time (U = −2.378, $$p \leq .017$$, η 2 = 0.094), and the mental component summary (U = −2.470, $$p \leq .013$$, η 2 = 0.102).
### Conclusion
As our cognitive training has shown beneficial effects, this intervention could be a promising method to enhance health‐related quality of life after cardiac surgery.
## INTRODUCTION
Neurocognitive complications have been described following cardiac surgery. These include delirium (Kotfis et al., 2018) and postoperative cognitive decline (POCD) (Greaves et al., 2019), which both potentially lead to reduced quality of life (Newman et al., 2001) or increased mortality (Steinmetz et al., 2009). POCD often appears to be subclinical and therefore remains unrecognized. Nevertheless, patients and their relatives report a decrease in cognitive abilities in daily living up to at least 3 months after cardiac surgery (Kastaun et al., 2016). Taken together, subjectively assessed POCD and health‐related quality of life represent a clinically relevant intervention target. The pathogenesis of POCD involves several preoperative risk factors, such as depression (Kadoi et al., 2011; Tully et al., 2009), anxiety (Andrew et al., 2000; Tully et al., 2009) or mild cognitive impairment (Bekker et al., 2010). Furthermore, perioperative mechanisms are thought to cause and promote POCD. These include anesthesia, particularly cerebral (micro‐ and macro‐)embolization, and neuroinflammation (Berger et al., 2018). In addition, preoperative administration of dexamethasone ameliorates the inflammatory response provoked by surgery, which is related to a reduction in the incidence of POCD (Glumac et al., 2017). As we have recently demonstrated in a prospective, randomized controlled interventional study, early postoperative paper‐and‐pencil–based cognitive training can reduce the incidence of objectively measurable POCD (Butz et al., 2022). To further elucidate the clinical relevance of our cognitive training, this report evaluates the training‐induced effects on health‐related quality of life and cognitive failure in daily living after cardiac surgery. Our hypothesis is that “there is a difference between the control group and the training group in relation to health‐related quality of life and cognitive failures in daily living.”
## trial design and enrolment
This study was a bicentered, two‐arm, 1:1 randomized, controlled trial, conducted at following locations: The Department of Cardiac Surgery of the Kerckhoff Heart and Thorax Centre in Bad Nauheim, Germany; the Department of Cardiovascular Surgery at the University Hospital in Giessen, Germany; and the Department of Rehabilitation at the Kerckhoff Clinic in Bad Nauheim, Germany. The study, including informed consent, has been approved by the Ethics Committee of the Justus‐Liebig University Giessen (Ref.: $\frac{28}{14}$), complies with the Declaration of Helsinki, and is registered with the German Clinical Trials Register (ID: DRKS00015512). The study protocol was published in advance (Butz et al., 2019).
The study coordinator screened the patient information on elective cardiac surgery for eligibility criteria. Potential participants received detailed information about the study project. Written informed consent was signed if the patient agreed.
Age, sex, education, body mass index, preexisting conditions, and the severity partition for left ventricular ejection fraction (Lang et al., 2015) were documented at baseline. Perioperatively, duration of surgery and extracorporeal circulation (ECC), cross‐clamp time, and invasive ventilation time were recorded. Postoperative complications, including delirium, were documented. After acute hospitalization, patients were directly transferred to the Department of Rehabilitation at the Kerckhoff Clinic in Bad Nauheim, Germany, where both groups received inpatient cardiac rehabilitation therapies that were individualized and based on the International Classification of Functioning. Key features of the therapeutic treatments were endurance and strength training, respiratory gymnastics, and educational lectures. Patients received 12–14 therapy sessions per week. In addition, patients of the training group underwent a multidomain cognitive intervention consisting of paper‐and‐pencil exercises that started about 1 week after surgery and lasted about 15 days until discharge from the rehabilitation clinic. A detailed description of the development and concept of the cognitive training (Butz et al., 2019) and its beneficial effects on cognition (Butz et al., 2022) have been published in advance.
## Inclusion and exclusion criteria
Inclusion criteria included elective aortic or mitral valve replacement/reconstruction with or without coronary artery bypass crafting under ECC and sufficient knowledge of German. Exclusion criteria comprised history of stroke, psychiatric or neurological diseases, and health insurance that did not support postoperative rehabilitation at the Kerckhoff Clinic.
## Randomization
A computer‐generated list with a 1:1 blocked allocation ratio was used for randomization. The randomization has randomly varied block sizes, and the study coordinator generated, sequentially numbered, and concealed it prior to the start of the study. After preoperative neuropsychological assessment, the study coordinator assigned the patients to the cognitive training group or the control group.
## Blinding
Surgeons, neurologists, and neuropsychologists involved in the outcome variables were blinded for randomization status.
## Outcome measures
The results of the primary outcome of the study have been published in advance (Butz et al., 2022). Secondary outcomes published in the present paper are the effect of cognitive training on health‐related quality of life and subjectively assessed cognitive failure in daily living at 3 months after the cognitive training.
## Questionnaires
To reveal cognitive failures in daily living, we used the Cognitive Failures Questionnaire (CFQ), which is the most widely used instrument to assess self‐reported cognitive failures (Carrigan & Barkus, 2016). Study patients completed a validated German 25‐item version of the Cognitive Failures Questionnaire for self‐assessment (s‐CFQ) (Klumb, 1995). The patients’ close relatives responded to an 8‐item Cognitive Failures Questionnaire to evaluate foreign assessment (f‐CFQ) (Broadbent et al., 1982). Both have to be answered on a 5‐point scale from “never” to “very often.” The questionnaires examine the frequency of failures in daily living related to memory, attention, action, and perception. Because memory impairment is an important element that can affect every day functioning, the s‐CFQ was supplemented by 4 additional questions related to memory failures, taken from the validated German version of the Memory Complaint Questionnaire (MCQ) (Heß, 2005). We calculated various models to assess self‐reported cognitive failures. First, we averaged all items to a one‐factor value. Since several cognitive functions are integrated into the averaged one‐factor value and we did not overlook training effects on specific cognitive factors (e.g., distractibility, memory for names, misdirected actions), we analyzed several CFQ factor models that have already been described (Larson et al., 1997; Pollina et al., 1992; Rast et al., 2009; Wallace et al., 2002). According to the CFQ factor models, we calculated a single factor of the 4 memory questions taken from the MCQ.
We assessed health‐related quality of life using the 36‐Item Short Form Health Survey (SF‐36, Version 1.0) (Bullinger & Kirchberger, 1998). The SF‐36 includes 36 items covering 8 health‐related factors, including physical functioning (10 items), role limitations due to physical health (4 items), role limitations due to emotional problems (3 items), energy/fatigue (4 items), emotional well‐being (5 items), social functioning (2 items), pain (2 items), and general health (5 items). Furthermore, we determined a total score across all 8 factors, as well as a 2‐factor model, indicating the physical component summary (physical functioning, role limitations due to physical health, pain, general health) and mental component summary (role limitations due to emotional problems, energy/fatigue, emotional well‐being, and social functioning). The answers provided by the patients within the factors refer to the last 4 weeks, except for the factor physical functions and the first question of the factor general health, which refer to the present state of health. Furthermore, it also contains a single item (item 2, health change), which gives an indication of the extent to which the present health has changed in relation to the past year. The SF‐36 was scored using the RAND scoring method (Hays et al., 1993). Each item in the questionnaire was assigned a score from 0 to 100, with a higher score indicating a better health state.
For all questionnaires, the CFQ, the MCQ, and the SF‐36 handling with missing data were as follows. If the patients answered at least $50\%$ of all items per factor, per time point, the mean score of this factor was calculated to determine the values of the factors. Items that were left blank (missing data) were not considered. Therefore, the factor values represent the average for all items of a factor that the respondent responded to.
Since worries about one`s cognition could have an impact on self‐reported cognitive failures in a way that depressed people answering themselves more conservative (Könen & Karbach, 2020; Wilhelm et al., 2010), we considered depression values as a control variable taken from the validated German version of the Hospital Anxiety and Depression Scale (HADS‐D) (Herrmann‐Lingen et al., 2011).
## Statistical analyses
We carried out a sample size calculation for the primary outcome of our study (cognitive training–related effect on objectively assessed cognition), which was published in advance (Butz et al., 2022). Therefore, we did not perform any sample size calculation for this report, which refers to the secondary outcomes of our trial, and we analyzed the data exploratively.
To determine the effect of cognitive training on cognitive failure in daily living and health‐related quality of life, we conducted analyses of covariance (ANCOVAs) with the postoperative questionnaire value as the dependent variable, groups (control group/training group) as the fixed factor, and the preoperative questionnaire value as the covariate. We tested assumptions for ANCOVAs using the Levene test (homogeneity of variance between groups) for the dependent variable and a statistically significant correlation between the dependent variable and covariate (preoperative test value) calculated using the Pearson product‐moment correlation. As a further assumption, we checked the distribution and Q‐Q plots for normality. When assumptions for ANCOVA were violated, we calculated difference values between pre‐ and posttests, followed by the Mann–Whitney U‐test for between‐subject effects. To control for the possibility of confounder variables that could affect the results, we conducted correlation analysis between potentially confounding variables (e.g., continuous demographic variables, perioperative details, changes in anxiety and depression) and changes between pre‐ and postoperative SF‐36 and CFQ assessment. In the case of these variables’ significant contributions, we implemented them additionally to the preoperative values as further covariates to the ANCOVA. We checked all data entries for inconsistent values.
Subjective‐POCD was defined as a decline and subjective‐POCI as an improvement from pre‐ to postassessment of at least 1 SD (Kastaun et al., 2016) in all considered CFQ models. To measure the difference of 1 SD between pre‐ and postassessment, we used Z‐scores, which were calculated by the difference of the individual raw values from the mean value of the total baseline data divided by the SD of the total baseline data. To reveal the training effect on cognition, frequencies of dichotomous (yes/no) subjective‐POCD and subjective‐POCI variables were compared with Pearson's χ2 test between the groups.
We give the effect size as η 2 and set the criterion for statistical significance at $p \leq .05.$ We evaluated our data set with a per‐protocol analysis, and we performed all analyses using the statistical software SPSS (version 22) and JASP (version 0.12.2).
## RESULTS
Between July 13, 2016 and January 8, 2020, a total of 130 patients were enrolled, randomized, and tested preoperatively. The last patient was tested on February 27, 2020 for the 3‐month follow‐up. The recruitment has concluded.
After randomization of 130 patients, 36 (training group $$n = 18$$, control group $$n = 18$$) were lost to follow‐up before the training or control intervention had begun. Thus, 94 patients (training group $$n = 47$$, control group $$n = 47$$) were considered for the baseline sample. Table 1 lists the baseline characteristics. No statistical significant group differences have been shown in the baseline data. Thirteen patients (training group $$n = 10$$, control group $$n = 3$$) were lost to follow‐up, whereas 81 patients (training group $$n = 37$$, control group $$n = 44$$) remained at discharge from rehabilitation. Thus, about $80\%$ of the patients completed the training. Another 21 patients (training group $$n = 6$$, control group $$n = 15$$) were lost to the 3‐month follow‐up, resulting in 60 patients (training group $$n = 31$$, control group $$n = 29$$) for analysis. Five patients (training group $$n = 2$$, control group $$n = 3$$) did not answer enough questions within the questionnaires to complete the missing data, resulting in different sample sizes in the statistical tests (see Table 2). Figure 1 outlines the reasons that patients were lost to follow‐up. The training intervention lasted 14.86 (SD = 2.507) days and did not cause any adverse events.
Three months after discharge from rehabilitation, some improvements in health‐related quality of life were evident for the training group compared to the control group. These improvements have been seen in role limitations due to emotional problems (U = −2.649, $$p \leq .008$$, η 2 = 0.121), energy and fatigue (F[2.55] = 5.72, $$p \leq .020$$, η 2 = 0.062), social functioning (U = −2.137, $$p \leq .033$$, η 2 = 0.076), the average of all SF‐36 factors (U = −2.374, $$p \leq .018$$, η 2 = 0.094), health change from the past year to the present time (U = −2.378, $$p \leq .017$$, η 2 = 0.094), and the mental component summary (U = −2.470, $$p \leq .013$$, η 2 = 0.102). Figure 2 shows the interaction effects. Table 2 provides a full description of the results of all SF‐36 factors.
**FIGURE 2:** *Interaction effects of all SF‐36 factors between the training group and control group. Shown are the mean values (higher scores indicating a better health state), including SE bars for preoperative testing and 3 months after discharge from the rehabilitation clinic. Statistical significant interaction effects with a p value of <.05 are marked with an asterisk (*).*
In the adjusted ANCOVA with potentially confounding variables, cross‐clamp time (F[3.54] = 6, $$p \leq .018$$, η 2 = 0.058) and changes in depression over time (F[3.54] = 4.19, $$p \leq .046$$, η 2 = 0.046) contributed significantly to the SF‐36 factor energy/fatigue.
There were no statistically significant or clinically relevant interaction effects between the control group and the training group in all considered s‐CFQ models, f‐CFQ, and MCQ.
## DISCUSSION
Our key findings were beneficial effects 3 months after discharge from rehabilitation in several health‐related quality‐of‐life domains. These have been found in role limitations due to emotional problems, energy and fatigue, social functioning, the average of all SF‐36 factors, health change from the past year to the present time, and the mental component summary. The treatment success of cardiac surgery is potentially based on the objective clinical or physiological status. The subjective patient‐centered changes (physical and psychological) seem to be important as well and may especially contribute to the treated patient's quality of life (Koch et al, 2008). Therefore, we used the SF‐36 questionnaire to assess postoperative patient's physical and mental processes in the context of a cognitive training program.
A computerized approach has shown enhancements for health‐related quality of life after postoperative cognitive training, which is in line with our results (Ajtahed et al., 2019). In addition, healthy older people showed various improvements in quality‐of‐life parameters (e.g., role limitations due to emotional problems, social functioning, role limitations due to functional limitations) after a controlled cognitive intervention with positive posttraining effects at a 3‐month follow‐up (Shati et al., 2021). Cognitive training–related improvements of depressive symptoms also exist in older adults with subclinical cognitive decline (Gooding et al., 2016). Reduced postoperative health‐related quality of life can increase mortality after cardiac surgery (Steinmetz et al., 2009), making the beneficial effects of cognitive training particularly important.
Cognitive abilities were evaluated as an additional outcome in this study because they contribute substantially to independence, personality, and self‐image for elderly patients, and also represent another important factor of health‐related quality of life. In the present investigation, the subjective assessment of cognitive failures in everyday living has not shown a difference between the groups. Therefore, we assumed that the CFQ‐questionnaire might not be sensitive enough to reveal alterations from the cognitive training. There is evidence that subjective assessments of cognitive ratings are unrelated to objective testing of cognitive performance (Brück et al., 2019; Carrigan & Barkus, 2016). Studies have discussed whether ICU survivors or elderly people, who may be more likely to be affected by cognitive impairment, inadequately estimate their own cognitive performance. This could lead to a lack of correlation with objective tests (Brück et al., 2019). In addition, it is possible that people underestimate cognitive deficits in the daily life of relatives who has survived a potentially life‐threatening disease or medically necessary surgery.
Nevertheless, we have shown that cognitive training is associated with psychometrically confirmed improvements in postoperative cognition (Butz et al., 2022). We assume that this effect can be transferred to psychologically relevant everyday situations and, therefore, increase health‐related quality of life.
A few limitations should be mentioned. First, a comparison with patients without the use of an ECC or a healthy control cohort does not seem possible, as we only examined patients who had been operated with ECC. Related to the results reported here and a previously published article concerning the objectively determined frequency of POCD (Butz et al., 2022), another limitation of our study emerges. We did not evaluate the incidence of “postoperative mild and major neurocognitive disorders” as defined by Evered et al. [ 2018], which is recommended for the research of POCD. This was not possible because some of the following research criteria were not implemented in our study protocol: the questions about postoperative alterations in activity of daily living (ADL) and the patient´s subjective decrease of postoperative cognition, specifically referring to heart surgery. Furthermore, the use of a healthy control group to calculate a reliable change index (controls for time and practice effects), which is also recommended to calculate POCD (Rasmussen et al., 2001), was not involved in our study. Furthermore, we did not evaluate if and how the patients practiced some cognitive‐enhancing activities (e.g., playing games, reading books, pronounced social activities) between the end of cognitive training and the 3‐month follow‐up, which could have also had an impact on cognitive plasticity (Xu et al., 2019). Since patients in the cognitive training group learned specifically what type of training material could be used for cognitive improvement, they would be more likely to come up with the idea of using similar material in the postrehabilitation period than the control group, who were only made aware of the potential of cognitive‐enhancing training through the consent form and information sheet. Compared to the control group, the training group showed shorter duration of surgery, extracorporeal circulation, and cross‐clamp time. As duration of surgery and anesthesia are reported to be risk factors for POCD (Vu & Smith, 2022), this could also affect health‐related quality of life (HQL), as an association between POCD and HQL has been found (Phillips‐Bute et al., 2006). However, the duration of surgery, extracorporeal circulation, and cross‐clamp time seems unassociated with HQL (Sanders et al., 2022). In addition, no significant group differences were shown for these factors in our sample, and a post hoc ANCOVA with these factors as control variables showed no changes in between‐group effects from statistically significant to nonsignificant results. We counted the incidence of POD using medical records. As we did not perform a standardized daily assessment of POD in the ICU and normal ward, the frequency of POD cases in our sample may be underreported. Because health insurance for patients transferred from the acute clinic to the rehabilitation center only covers patients of retirement age, the results of cognitive training are limited to this particular cohort.
Since our training concept was able to decrease POCD or maintain and improve health‐related quality of life after cardiac surgery, it could also be useful for noncardiac surgery patients potentially affected by POCD. *In* general, it may also be beneficial for patients suffering cognitive impairment after stroke or in the context of dementia. In addition, our concept is designed to be continued in an ambulatory setting after clinical implementation (e.g., in a home‐based environment) or performed in a home‐based setting prior to surgery that has the potential to impair cognition. Preoperative cognitive training might build up a so‐called cognitive reserve, which could provide prophylactic protection of the brain (Saleh et al., 2015).
## AUTHOR CONTRIBUTIONS
Marius Butz: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; supervision; validation; visualization; writing‐original draft. Tibo Gerriets: Conceptualization; funding acquisition; methodology; project administration; supervision; writing–review & editing. Gebhard Sammer: Conceptualization; methodology; project administration; supervision; validation; writing–review & editing. Jasmin El‐Shazly: Conceptualization; funding acquisition; investigation; methodology; project administration; supervision; validation; writing–review & editing. Marlene Tschernatsch: Writing–review & editing. Patrick Schramm: Writing–review & editing. Thorsten R. Doeppner: Writing–review & editing. Tobias Braun: Writing–review & editing. Andreas Boening: Resources; writing–review & editing. Thomas Mengden: Conceptualization; resources; writing–review & editing. Yeong‐Hoon Choi: Writing–review & editing. Markus Schoenburg: Conceptualization; funding acquisition; methodology; project administration; resources; supervision; writing–review & editing. Martin Juenemann: Conceptualization; funding acquisition; methodology; project administration; supervision; writing–review & editing.
## CONFLICT OF INTEREST STATEMENT
The authors declare that they have no conflict of interests.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2915.
## DATA AVAILABILITY STATEMENT
Deidentified participants’ data analyzed during the current study are available from the corresponding author on reasonable request.
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|
---
title: Predicted resting metabolic rate and prognosis in patients with ischemic stroke
authors:
- Xiaoyu Lin
- Aichun Cheng
- Yuesong Pan
- Mengxing Wang
- Xia Meng
- Yongjun Wang
journal: Brain and Behavior
year: 2023
pmcid: PMC10013948
doi: 10.1002/brb3.2911
license: CC BY 4.0
---
# Predicted resting metabolic rate and prognosis in patients with ischemic stroke
## Abstract
Predicted resting metabolic rate by equation has a reversely significant association with all‐cause mortality and poor functional outcome after is hemic stroke that indicates it seems to be a protective predictor of poor outcome of ischemic stroke as a metabolic proxy.
### Purpose
Resting metabolic rate (RMR) could represent metabolic health status. This study aims to examine the association of the predicted RMR with 1‐year poor functional outcome and all‐cause mortality in patients with ischemic stroke as a proxy of metabolic profile.
### Methods
A total of 15,166 patients with ischemic stroke or transient ischemic attack (TIA) from the Third China National Stroke Registry (CNSR‐III) were enrolled in this study. The Harris–Benedict equation based on sex, age, weight, and height was used to predict RMR. The primary endpoints were poor functional outcome defined as ≥3 modified Rankin Scale (mRS) score and all‐cause mortality within 1 year. The association between predicted RMR and prognosis was assessed by multivariable regression analysis. Besides that, subgroup analysis of age, sex, and body mass index (BMI) with predicted RMR was also performed.
### Results
$12.85\%$ [1657] individuals had poor functional outcome and $2.87\%$ [380] died of whatever causes within 1 year. An inverse association was found between predicted RMR with poor functional outcome and all‐cause mortality. Compared to the lowest quartile, the highest quartile was significantly associated with lower risk of poor functional outcome (adjusted odds ratio [OR], 0.43 [$95\%$ confidence interval (CI) 0.33–0.56]) and all‐cause mortality (adjusted hazard ratio [HR], 0.44 [$95\%$ CI 0.28–0.71]). No significant interaction was between predicted RMR and specified subgroup.
### Conclusions
Predicted RMR by the Harris–Benedict equation seems to be an independent protective predictor of poor functional outcome and all‐cause mortality after ischemic stroke as a metabolic proxy.
## INTRODUCTION
Ischemic stroke is the leading cause of physical disability and causes enormous economic burdens (Wang et al., 2020). It is imperative to recognize the related markers and factors associated with poststroke results in arrange to direct clinical stroke administration way better. Emerging evidence suggested that metabolic related factors, such as obesity, insulin resistance, type 2 diabetes mellitus, have a great implication for stroke incidence as well as its prognosis (Lau et al., 2019; Rodríguez‐Castro et al., 2019; Yang et al., 2021).
Resting Metabolic Rate (RMR), defined as the daily energy required to ensure life‐sustained functions in resting status, takes up 60–$70\%$ of the total daily energy demands (Zampino et al., 2020). Age, height, weight, sex, genetic variation, and physiology all are responsible for RMR. Previous studies indicated that RMR may represent the metabolic health status as a marker of whole body metabolism level. For example, a recent report from the European Prospective Investigation into Cancer and Nutrition (EPIC) showed that the higher basal metabolic rate estimated by WHO/FAO/UNU equation was associated with a greater risk of specific cancers (Kliemann et al., 2020). Another supportive example was that the higher metabolic rate assessed using indirect calorimetry an older adult had, the greater burden of multimorbidity he/she might have (Fabbri et al., 2015). That indicated that the RMR may be an indicator for prognosis after ischemic stroke as a proxy of metabolic profiles same as for cancer or natural death (Kliemann et al., 2020; Ruggiero et al., 2008). However, the effects of RMR on poststroke prognosis have not been investigated in depth. It is worth investigating the relationship between them in terms of better prognosis management of ischemic stroke.
This study aims to assess the association between predicted RMR as a marker of metabolic profile and the prognosis of ischemic stroke based on the Third China National Stroke Registry (CNSR‐III) database (Wang et al., 2019). Owing the initial design of the CNSR‐III database did not include the RMR variable measured by indirect calorimetry, we used the predicted RMR by the Harris–Benedict equation as an alternative.
## Study design
This study was based on baseline and follow‐up data from the CNSR‐III study. The rationale, design, and baseline participant characteristics of the CNSR‐III had been described previously (Wang et al., 2019). Briefly, the CNSR‐III, a nationwide prospective registry for patients presented to hospitals with acute ischemic cerebrovascular events, recruited 15,166 patients with ischemic stroke or transient ischemic attack (TIA) within 7 days from the onset of symptoms between August 2015 and March 2018 in China. Ischemic stroke was diagnosed according to the World Health *Organization criteria* and confirmed by Magnetic Resonance Imaging or brain Computed Tomography. In this study, participants were excluded if they presented with one of the following: [1] age < 30 or > 80 years; [2] body mass index (BMI) < 16 or > 40 kg/m2; [3] cancer at recruitment; [4] discharge diagnosis of TIA.
## Standard protocol approvals, registrations, and patient consents
The study was approved by the ethics committees of Beijing Tiantan Hospital (IRB approval number: KY2015‐001‐01) and all participating centers. Written informed consents were signed by all participants or their legal proxies before enrolling in the study.
## DATA COLLECTION
Baseline data on demographic characteristics, medical and medication history, laboratory findings as well as clinical situations were collected by trained research coordinators at admission. Height and weight were measured according to standardized methods. Height was measured to the nearest 0.1 cm and weight was measured to the nearest 0.1 kg wearing only light underwear. Self‐reported height and weight were collected if the information were impossible to be collected. BMI was calculated as weight (kg) divided by height square (m2). The functional state was evaluated using modified Rankin Scale (mRS) score. Stroke severity at admission was assessed by trained neurologists according to the National Institutes of Health Stroke Scale (NIHSS) score. The etiology of ischemic stroke was classified according to the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria. Fasting blood samples were collected within 24 h of admission and were frozen in cryotube at −80°C refrigerator. Samples were sent to the central laboratory in Beijing Tiantan Hospital and measured by laboratory technicians blinded to the baseline data.
## Assessment of the predicted resting metabolic rate
Harris–Benedict equation derived early in 1919 is one of the most frequently used to predict RMR in clinical application (Harris & Benedict, 1918). This method calculates RMR using sex‐specific equations and is also based on the participant's age, weight, and height. Accumulating evidence indicated that predicted RMR by the Harris–Benedict equation was associated well with that by indirect calorimetry in nonobesity, healthy participants and even more so in those with obesity (Bendavid et al., 2021). We chose the Harris–Benedict equation to assess RMR. Meanwhile, RMR was also calculated by using the other three common equations, Oxford, Mifflin St Jeor, and WHO/FAO/UNU for each individual for comparison according to Nathalie et al (Supplemental Table S1) (Energy & Protein Requirements, 1985; Henry, 2005; Kliemann et al., 2020; Mifflin et al., 1990).
## Outcome and follow‐up
Follow‐up time started from the day of enrollment in the registry project. Patients were followed up for 1 year after ischemic stroke by trained research coordinators over the telephone. Information on functional status and all‐cause death was collected. Each case death was confirmed according to a death certificate from the attended hospital or the local citizen registry. The primary study endpoints included poor functional outcome, defined as functional dependency based on mRS score of 3 to 5, and all‐cause mortality resulting from the index event or other causes in 1‐year follow‐up.
## Statistical analysis
The predicted RMR at baseline was calculated by the Harris–Benedict equation and also was assessed using Mifflin St Jeor, WHO/FAO/UNU, and Oxford equations. Pearson's correlation coefficients between RMR defined by the Harris–Benedict equation and other equations were derived.
Patients were dived into four groups according to predicted RMR quartiles and we chose the lowest quartile or first quartile as a reference. Continuous variables were expressed as median (interquartile range, IQR). Categorical variables were presented as proportions. Kruskal–Wallis test was used for comparisons of continuous variables. Categorical variables were compared with the χ2 statistics or Fisher's exact test as appropriate. Predicted RMR was analyzed according to a quartile or a continuous variable. The relationship between predicted RMR with the 1‐year poor functional outcome and all‐cause mortality was investigated using multivariable logistic regression analysis (odds ratio [OR] and $95\%$ confidence intervals [CI]) and Cox proportional hazards regression analysis (hazard ratio [HR] and $95\%$ CI), respectively. Additionally, the association between ordinary mRS (7 levels with 0–6 score) and predicted RMR was also assessed with logistic regression analysis. We constructed two adjusted models to evaluate the primary endpoints: sex adjusted model and multivariable adjusted model. The latter model incorporated the following variables: sex, BMI, current smoking, hypertension, dyslipidemia, diabetes mellitus, prior stroke, prior atrial fibrillation (AF), prior coronary heart disease (CHD), NIHSS and mRS at admission, TOAST subtype, recombinant tissue plasminogen activator intravenous thrombolysis (rt‐PA IVT) treatment, antithrombotic drugs, and lipid‐lowering drugs. To vigorously assess the relationship between multivariable adjusted primary endpoints and predicted RMR on a continuous scale, we depicted restricted cubic splines with five knots (at the 5th, 27.5th, 50th, 72.5th, and 95th centiles). Kaplan–Meier curves associated with predicted RMR were compared using log‐rank tests by an original unadjusted model. In addition, we performed prespecified subgroup analysis to evaluate the effects of sex (man; woman), age (<60; ≥60), BMI (<25; ≥25) on predicted RMR. In sensitivity analysis, we exclude the population with mRS of 4 to 5 at admission and conducted univariable and multivariable analysis in patients with the mRS of 0 to 3.
A two‐sided p value<.05 was considered significant. All of the analyses were performed with SAS software, version 9.4 (SAS Institute, Inc., Cary, NC)
## RESULTS
In CNSR‐III, a total of 15,166 ischemic stroke or TIA patients were finally eligible and had complete information at baseline. Therein, 769 individuals not according with age of 30–80 years old limit, 45 individuals out of BMI of 16 to 40 limit, 109 individuals with reported cancer at recruitment, and 1020 individuals diagnosed TIA when discharged. Therefore, 1943 cases were excluded and the rest of 13,223 out of 15,166 individuals ($87\%$) of ischemic stroke were eligible for analysis with median age of 62 years, $69.4\%$ male, median predicted RMR of 1408.0 kcal/day.
Patients with higher predicted RMR reported greater BMI and a tendency of current smoking. But patients with lower predicted RMR were more likely to suffer from cardiovascular and cerebrovascular diseases. Moreover, those who were in the lowest quartile were more likely to have a higher initial NIHSS score compared to other quartiles (Table 1). Predicted RMR calculated applying the Harris–Benedict equation was significantly correlated (r ≥.92) with those derived from the Mifflin St Jeor, WHO/FAO/UNU, and Oxford equations (Supplemental Table S2).
**TABLE 1**
| Unnamed: 0 | Unnamed: 1 | Resting metabolic rate (RMR) (kcal/d) | Resting metabolic rate (RMR) (kcal/d).1 | Resting metabolic rate (RMR) (kcal/d).2 | Resting metabolic rate (RMR) (kcal/d).3 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| Variables | Overall | Q 1(< 1270.9) | Q 2(1270.9–1408.0) | Q 3(1408.0–1552.8) | Q 4(>1552.8) | p Value |
| RMR, median (IQR) | 1408.0 (1270.9–1552.8) | | | | | |
| Age, median (IQR) | 62 (54–69) | 68 (63–74) | 63 (57–69) | 61 (54–67) | 53 (47–61) | <.0001 |
| Male, n (%) | 9174 (69.4) | 854 (25.9) | 2005 (60.6) | 3039 (92.0) | 3276 (99.1) | <.0001 |
| BMI, median (IQR) | 24.5 (22.7–26.6) | 22.5 (20.6–24.2) | 24.0 (22.5–26.1) | 24.5 (23.4–26.0) | 26.7 (25.3–28.4) | <.0001 |
| Current smoking, n (%) | 4367 (33.0) | 426 (12.9) | 874 (26.4) | 1388 (42.0) | 1679 (50.8) | <.0001 |
| hs‐CRP, median (IQR) | 1.77 (0.82–4.63) | 1.92 (0.84–5.46) | 1.74 (0.8–4.63) | 1.68 (0.81–4.66) | 1.73 (0.86–4.04) | .03 |
| Medical history, n (%) | | | | | | |
| Diabetes mellitus | 3108 (23.5) | 769 (23.3) | 806 (24.3) | 768 (23.2) | 765 (23.1) | .63 |
| dyslipidemia | 1017 (7.7) | 198 (6.0) | 237 (7.2) | 267 (8.1) | 315 (9.5) | <.0001 |
| Hypertension | 8312 (62.9) | 2050 (62.2) | 2065 (62.4) | 2030 (61.4) | 2167 (65.6) | .0026 |
| stroke | 2921 (22.1) | 732 (22.2) | 734 (22.2) | 784 (23.7) | 671 (20.3) | .01 |
| AF | 818 (6.2) | 290 (8.8) | 205 (6.2) | 184 (5.6) | 139 (4.2) | <.0001 |
| CHD | 1336 (10.1) | 412 (12.5) | 339 (10.2) | 340 (10.3) | 245 (7.4) | <.0001 |
| Num. of the chronic diseases, median (IQR) a | 1 (1–2) | 1 (1–2) | 1 (1–2) | 1 (1–2) | 1 (1–2) | .33 |
| Medication history, n (%) | | | | | | |
| Lipid‐lowering drugs | 1380 (10.4) | 366 (11.1) | 333 (10.1) | 387 (11.7) | 294 (8.9) | .001 |
| Antithrombotic drugs | 2276 (17.2) | 578 (17.5) | 591 (17.9) | 597(18.1) | 510 (15.4) | .02 |
| mRS at admission, n (%) | | | | | | .05 |
| 0–1 | 12027 (91.0) | 2998 (90.9) | 2981 (90.0) | 3026 (91.6) | 3022 (91.4) | |
| 2–3 | 887 (6.7) | 212 (6.4) | 238 (7.2) | 215 (6.5) | 222 (6.7) | |
| 4–5 | 304 (2.3) | 88 (2.7) | 92 (2.8) | 63 (1.9) | 61 (1.9) | |
| Initial NIHSS, median (IQR) | 3 (2–6) | 4 (2–7) | 3 (2–6) | 3 (1–6) | 3 (1–6) | <.0001 |
| TOAST subtype, n (%) | | | | | | <.0001 |
| LAD | 3405 (25.8) | 859 (26.1) | 857 (25.9) | 866 (26.2) | 823 (24.9) | |
| SVO | 761 (5.8) | 268 (8.1) | 199 (6.0) | 161 (4.9) | 133 (4.0) | |
| CE | 2999 (22.7) | 654 (19.8) | 754 (22.8) | 769 (23.3) | 822 (24.9) | |
| Others b | 6053 (45.8) | 1517 (46.0) | 1501 (45.3) | 1508 (45.6) | 1527 (46.2) | |
| rt‐PA IVT, n (%) | 1439 (10.9) | 393 (11.9) | 368 (11.1) | 325 (9.8) | 353 (10.7) | .05 |
$12.85\%$ [1657] participants had poor functional status and $2.87\%$ [380] deaths occurred during 1 year. A previous survey from the Bigdata Observatory Platform for Stroke of China (BOSC) revealed that the rate of 1‐year death and disability was $6.0\%$ ($5.7\%$−$6.3\%$) and $14.2\%$ ($13.8\%$−$14.7\%$) respectively after first‐ever stroke in Chinese population. Although compared to the BOSC our study was with higher mortality, study cohorts and inclusion criteria like whether the first‐stroke or not and the occurrence‐to‐admission time could explain the reason (Tu et al., 2021). Compared with the first quartile, the multivariable adjusted ORs and $95\%$ CIs for the risk of poor functional outcome gradually decreased in the higher quartile groups (second quartile (Q2), adjusted OR [aOR], 0.70 [$95\%$ CI, 0.58–0.83]; third quartile (Q3), aOR, 0.53 [$95\%$ CI, 0.42–0.65]; fourth quartile (Q4), aOR, 0.43 [$95\%$ CI, 0.33–0.56], p for trend <.001). Similarly, the significant association was also found when examining mRS as an ordinary variable (Q1 vs. Q4, 0.80 [$95\%$ CI 0.72–0.89] vs. 0.60 [$95\%$ CI 0.52–0.70]). A 100 kcal/day increment of predicted RMR was inversely associated with poor functional outcome (aOR, 0.78 [$95\%$ CI 0.74–0.83]) (Table 2). We also examined the relationship between predicted RMR and poor functional outcome at 3‐month, the similar results were obtained (Supplemental Table S3)
**TABLE 2**
| Unnamed: 0 | Unnamed: 1 | Unadjusted | p Value | Sex adjusted | p Value.1 | Multivariable adjusted a | p Value.2 | p for trend |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | OR/HR (95% CI) | | OR/HR (95% CI) | | OR/HR (95% CI) | | |
| mRS ≥3 | Q 1 | Ref. b (1) | | Ref. (1) | | Ref. (1) | | <.001 |
| | Q 2 | 0.74 (0.64–0.84) | <.0001 | 0.70 (0.61–0.81) | <.0001 | 0.70 (0.58–0.83) | <.0001 | |
| | Q 3 | 0.59 (0.51–0.68) | <.0001 | 0.54 (0.46–0.64) | <.0001 | 0.53 (0.42–0.65) | <.0001 | |
| | Q 4 | 0.49 (0.42–0.56) | <.0001 | 0.44 (0.37–0.52) | <.0001 | 0.43 (0.33–0.56) | <.0001 | |
| Ordinary mRS | Q 1 | Ref. (1) | | Ref. (1) | | Ref. (1) | | <.001 |
| | Q 2 | 0.82 (0.75–0.89) | <.0001 | 0.78 (0.71–0.85) | <.0001 | 0.80 (0.72–0.89) | <.0001 | |
| | Q 3 | 0.72 (0.66–0.79) | <.0001 | 0.66 (0.59–0.73) | <.0001 | 0.69 (0.61–0.78) | <.0001 | |
| | Q 4 | 0.63 (0.57–0.69) | <.0001 | 0.56 (0.50–0.63) | <.0001 | 0.60 (0.52–0.70) | <.0001 | |
| Per 100 kcal/day increment of RMR for mRS ≥3 | Per 100 kcal/day increment of RMR for mRS ≥3 | 0.85 (0.83–0.88) | <.0001 | 0.83 (0.80–0.86) | <.0001 | 0.78 (0.74–0.83) | <.0001 | |
| All‐cause mortality | Q 1 | Ref. (1) | | Ref. (1) | | Ref. (1) | | <.001 |
| | Q 2 | 0.68 (0.53–0.89) | .0042 | 0.60 (0.46–0.79) | .0003 | 0.69 (0.51–0.94) | .02 | |
| | Q 3 | 0.61 (0.47–0.80) | .0003 | 0.48 (0.35–0.66) | <.0001 | 0.58 (0.40–0.85) | .0055 | |
| | Q 4 | 0.42 (0.31–0.57) | <.0001 | 0.32 (0.23–0.50) | <.0001 | 0.44 (0.28–0.71) | .0008 | |
| Per 100 kcal/day increment of RMR for all‐cause mortality | Per 100 kcal/day increment of RMR for all‐cause mortality | 0.83 (0.79–0.88) | <.0001 | 0.78 (0.73–0.84) | <.0001 | 0.81 (0.73–0.89) | <.0001 | |
The inverse relationship was also observed between the predicted RMR and all‐cause mortality (Q2, adjusted HR [aHR], 0.69 [$95\%$ CI, 0.51–0.94]; Q3, aHR, 0.58 [$95\%$ CI, 0.40–0.85]; Q4, aHR, 0.44 [$95\%$ CI, 0.28–0.71]). An all‐cause mortality risk reduction of $19\%$ was associated with each 100 kcal/day increment in predicted RMR (aHR, 0.81 [$95\%$ CI 0.73–0.89]) (Table 2). Similarly, at 3 month the predicted RMR still showed the inverse association with all‐cause mortality, although which turned nonsignificant after adjusting the multiple confounders due to certain potential factors (Supplemental Table S3). The cumulative incidence of all‐cause mortality was significantly lower in participants with the highest predicted RMR (Q1 versus Q4, $4.2\%$ versus $1.8\%$, $p \leq .0001$) (Figure 1).
**FIGURE 1:** *Kaplan–Meier curves for incidence of all‐cause mortality by quartiles of predicted RMR.*
The restricted cubic spline model indicated a similar associated pattern to the predicted RMR as a categorical variable both in the relationship with poor functional status and all‐cause mortality (Figure 2). No significant heterogeneity was observed across specified subgroups of age, sex, and BMI on the primary endpoints (all p for interaction >.05; Supplemental Table S4). The sensitivity analysis performed in 12,914 participants of mRS of 0 to 3 did not change the above reverse associations (Supplemental Table S5).
**FIGURE 2:** *Restricted cubic spline for poor functional outcome (A) and all‐cause mortality (B) according to levels of predicted RMR. Solid dark line is a multivariable adjusted OR/HR, with a gray area indicating 95% CI derived from restricted cubic spline regression. A dashed dark line represents no association.*
## DISCUSSION
This study provided that after controlling for related confounders, predicted RMR was significantly inversely associated with poor functional status and all‐cause mortality. Furthermore, the results were consistent even after stratification for age, sex, and BMI. The results indicated that predicted RMR seemed to be an independent protective predictor for prognosis of ischemic stroke as a proxy of metabolic profile.
Given that the RMR measured in current study was only based on the age, weight, and height variable by sex, so in theory, the male subjects with bigger body size (greater weight and higher) and younger age were more likely to obtain good outcome after ischemic stroke. Although it seemed to appear that outcome status was decided on the pure sum of the variables in equation, we should note that each of the variables has different weighted factors which indicted that different RMR would be obtained by changes in same unit of every variable. Owning to measurements on RMR obtained by Harris–Benedict equation (closely associated with indirect calorimetry in nonobesity, healthy participants and most subjects in this study satisfied these conditions as presented in Table 1) instead of indirect calorimetry, with consideration of its significance in clinical practice, further testified studies on stroke and RMR with an indirect calorimetry method were expected.
Using RMR as a potential biomarker of body metabolic health has been supported by several previous studies. However, the conclusion drawn on the dangerous indicator of higher RMR for diseases or death appeared to predominate over its protective side (Drabsch et al., 2018; Hand & Blair, 2014; Jumpertz et al., 2011; Kang et al., 2021; Kliemann et al., 2020; Ruggiero et al., 2008; Schrack et al., 2014). An over 40‐year follow‐up of 1227 healthy participants conducted in the Baltimore Longitudinal Study of Aging (BLSA), mostly men, indicating those with high basal energy rate was associated with shorter longevity. ( Ruggiero et al., 2008) That was supported by an over 2‐year calorie restriction trial on 53 nonobese adults showing that decreased energy expenditure improved the rate of living. ( Redman et al., 2018) Higher incidences of urolithiasis recurrence and diabetes were observed in the individuals with higher RMR (Kang et al., 2021). Nonetheless, diabetes and metabolic syndrome were also found more likely to occur in the population with low RMR (Buscemi et al., 2007; Georgopoulos et al., 2009; Maciak et al., 2020; Olive et al., 2008). Our results seemed to disagree with the tendency of the dangerous implication of RMR for healthy state. The target population among studies, like whether they were healthy, sex‐specific dominant, obese, with ischemic stroke or not, had different demographic characteristics. In addition, difference in study design and RMR evaluating methods among studies could partly account for the discrepancy of conclusions. Our interests in terms of secondary prevention instead of primary prevention could cause the appearance of protective effect that may be similar to the relationship between obesity and ischemic stroke (Vemmos et al., 2011). The controversial relationship between RMR and oxidative stress that is considered to damage health might be one of the inconsistent causes (Frisard & Ravussin, 2006). Therefore, to confirm our findings, further studies evaluating populations affected by stroke or other diseases are warranted.
A consistent tendency between the level of predicted RMR and BMI ($p \leq .0001$) in our study may result from the common crucial variable of weight (Table 1). In the subgroup analysis of overweight/obesity, our results found predicted RMR could act as an independent protective factor for the poor functional outcome (p for interaction,.24) and all‐cause mortality (p for interaction,.84) (Supplemental Table S3). This indicated that predicted RMR could detect extra information beyond BMI, while BMI is positively associated with improved outcome after ischemic stroke (Vemmos et al., 2011).
Although scarce studies directly examined the protective predictive value of RMR on unfavorable ischemic stroke prognosis, several mechanisms could account for the inverse association. The positive relationship between RMR and cardiorespiratory fitness has been found, and the latter is treated as a target to reduce cardiovascular diseases and mortality. Higher RMR could represent better cardiorespiratory fitness and promotes functional recovery (Ebaditabar et al., 2021; Shook et al., 2014). On the other hand, higher RMR represents greater composition of skeletal muscle, the loss of which significantly influences the physical activity (Li et al., 2020; Oh et al., 2019; Soysal et al., 2019; Visser et al., 2005). Additionally, a series of spontaneous recovery processes after ischemic stroke, such as the activity of neuroglial cells and the changes of repair‐related molecular, need support of the strong reverse capacity that may be along with great RMR (Bélanger et al., 2011; Cramer, 2008; Zampino et al., 2020).
The study first applied a large‐scale cohort with ischemic stroke to examine the association between energy metabolism and ischemic stroke prognosis. However, some limitations should be considered. First, the RMR was predicted by the equation instead of a gold standard with indirect calorimetry owing to the expensive prices and complex operation. A decrease in accuracy of the Harris–Benedict equation may occur when used in individuals with low or high BMI and older age (Jésus et al., 2015). Given that, we excluded the patient whose age or BMI was beyond the range from 30 to 80 and from 16 to 40, respectively. Second, the predicted RMR was measured once at baseline regardless of its potential change over time. Third, the information on the self‐reported thyroid disease lacked in our analysis. Concerning that the prevalence of overt hyperthyroidism and hypothyroidism in a nationwide survey with an enrollment of 78, 470 Chinese adults was $0.78\%$ ($95\%$ CI, $0.69\%$ to $0.87\%$) and $1.02\%$ ($95\%$ CI, $0.88\%$ to $1.18\%$) respectively, our results might not be influenced substantially (Li et al., 2020). Fourth, there were a lot of potential confounders like nutritional status, body composition and etc. which we did not assess due to lack of necessary related data, that could interfere the outcomes. Finally, relatively short follow‐up period and small cumulative number of deaths (380, $2.87\%$) led to no death in female patient with highest predicted RMR, which could cause a lower statistical power in assessing the association between predicted RMR and outcome.
In summary, higher predicted RMR could be an independent protective indicator for the risk of poor functional outcome and all‐cause mortality of ischemic stroke as a proxy of metabolic profile. Nevertheless, it is not yet known whether the RMR is merely a proxy or plays a causal role in the relationship with poststroke outcome. Figuring it out will facilitate to improve outcome and decrease death and disability by changing the RMR. We here proposed the following potential points regarding the further study on RMR: use of the more rigorous methods to verify the association like golden standard, study of the molecular and cellular mechanisms that influence RMR so as to seek to transform it into pharmaceutical and behavioral intervention.
## AUTHOR CONTRIBUTIONS
XYL and ACC drafted the manuscript and interpreted the data. YSP and MXW contributed to revised the manuscript and statistical problems. XM revised the manuscript. YJW interpreted data and revised the manuscript. All authors read and approved the final manuscript to be published.
## CONFLICT OF INTEREST STATEMENT
The author declares that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2911
## DATA AVAILABILITY STATEMENT
All data are available to researchers on request for purposes of reproducing the results or replicating the procedure by directly contacting the corresponding author.
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|
---
title: 'Electroacupuncture alleviates pain after total knee arthroplasty through regulating
neuroplasticity: A resting‐state functional magnetic resonance imaging study'
authors:
- Bingxin Kang
- Chi Zhao
- Jie Ma
- Haiqi Wang
- Xiaoli Gu
- Hui Xu
- Sheng Zhong
- Chenxin Gao
- Xirui Xu
- Xinyu A
- Jun Xie
- Mengmeng Du
- Jun Shen
- Lianbo Xiao
journal: Brain and Behavior
year: 2023
pmcid: PMC10013951
doi: 10.1002/brb3.2913
license: CC BY 4.0
---
# Electroacupuncture alleviates pain after total knee arthroplasty through regulating neuroplasticity: A resting‐state functional magnetic resonance imaging study
## Abstract
### Introduction
We aimed to evaluate the efficacy of electroacupuncture in relieving acute pain after total knee arthroplasty (TKA) and related mechanism.
### Methods
In this randomized, single‐blind, and sham‐acupuncture controlled study. Forty patients with postoperative acute pain were recruited and randomly divided electroacupuncture group ($$n = 20$$) and sham‐acupuncture group ($$n = 20$$) from November 2020 to October 2021. All patients received electroacupuncture or sham‐acupuncture for 5 days after TKA. Their brain regions were scanned with resting‐state functional magnetic resonance imaging before and after intervention. Pain was scaled. Another 40 matched healthy controls underwent scanning once. The amplitude of low‐frequency fluctuation (ALFF) values was compared. Pearson's correlation analysis was utilized to explore the correlation of ALFF with clinical variables in patients after intervention.
### Results
Compared with the HCs, patients with acute pain following TKA had significantly decreased ALFF value in right middle frontal gyrus, right supplementary motor area, bilateral precuneus, right calcarine fissure and surrounding cortex, and left triangular part of inferior frontal gyrus (false discovery rate corrected $p \leq .05$). Patients had higher ALFF value in bilateral precuneus, right cuneus, right angular gyrus, bilateral middle occipital gyrus, and left middle temporal gyrus after electroacupuncture (AlphaSim corrected $p \leq .01$). Correlation analysis revealed that the change (postoperative day 7 to postoperative day 3) of ALFF in bilateral precuneus were negatively correlated with the change of NRS scores (r = −0.706; $$p \leq .002$$; $95\%$ CI = −0.890 to −0.323) in EA group.
### Conclusions
The functional activities of related brain regions decreased in patients with acute pain after TKA. The enhancement of the functional activity of precuneus may be the neurobiological mechanism of electroacupuncture in treating pain following TKA.
## INTRODUCTION
Total knee arthroplasty (TKA) can effectively treat end‐stage knee osteoarthritis (KOA) by reducing pain and helping patients to resume daily life activities. Acute postoperative pain after TKA prolongs rehabilitation duration, weakens the therapeutic effect, and thereby decreases the patient's quality of life. Intense postoperative pain is associated with chronic pain, and it may also prolong rehabilitation duration, weaken the therapeutic effect of TKA, and thereby decrease the patient's quality of life (Coppes et al., 2020; Hsia et al., 2018). The American Society of Anesthesiologists has recommended that multimodal pain management (administration of two or more drugs via the same route or different routes) should be implemented whenever possible to maximize the analgesia effect while minimize the potential adverse effects and reduce the consumption of opioids (American Management, 2012). But the incidence of moderate to severe pain following TKA is still as high as $58\%$, indicating that postoperative pain management is far from satisfactory (Summers et al., 2020). Conventional analgesia medicine could increase the risk of nausea, vomiting and other digestive system side effects, abnormal liver/ renal functions, and inhibit bone formation and healing (Zhao & Davis, 2019). Thus, effective and safe nonpharmacologic interventions for analgesia are necessary.
Acupuncture, a methodology of treating human diseases in China with a history of more than 3000 years, has been applied to various pain disorders, such as migraine (Tu et al., 2020), KOA(Kong et al., 2018), low back pain (Yu et al., 2020), fibromyalgia pain (Mawla et al., 2021), neck pain (Li et al., 2020), and cancer pain (Liang et al., 2020; Yang et al., 2021). Increasing evidence has proved the safety and effectiveness of acupuncture as an analgesic treatment and its advantage in decreasing the need for opioids (Michaelides & Zis, 2019). Neuroimaging studies have shown that acupuncture can achieve analgesic effect by restoring the pain processing, regulating pain perception, improving abnormal structure, and functional activities of patients (Tian et al., 2021; Wen et al., 2021). Electroacupuncture (EA), which combines the traditional acupuncture theory with electrical stimulation, has standardized intensity, frequency, duration and other parameters, making it more suitable for scientific studies than traditional manual acupuncture. EA has been found to relieve pain and reduce opioid consumption following TKA (Tedesco et al., 2017). The EA's analgesic effect on neuropathic pain may rely on the activation the brain functional connectivity between bilateral hemispheres and the sensorimotor cortex (Hou et al., 2020). The descending pain modulation systems, including the anterior cingulated cortex, the periaqueductal gray, and the rostral ventromedial medulla, play an important role in the analgesic effect of EA (Chen et al., 2020). Although the central integration and plasticity play a critical role in the analgesic mechanism of acupuncture (Xiao et al., 2018), the underlying mechanism of EA in regulating the brain central system to relieve acute pain following TKA is largely unknown.
Resting‐state functional magnetic resonance imaging (rs‐fMRI), which gauges fluctuations in the blood oxygen level‐dependent signal, is widely used to explore the neural mechanisms, assess the efficacy of acupuncture treatment, and it is also commonly used for pain research. The amplitude of low‐frequency fluctuation (ALFF), which depicts the intensity of regional spontaneous neuronal activities, strikes a good balance between test‐retest reliability and replicability (Chen et al., 2018). ALFF reflects the blood oxygenation level‐dependent (BOLD) signal fluctuations within the gray matter and the local properties of spontaneous neuronal activity. The enhancement of ALFF shows that the excitability of brain area is activated, and the BOLD signal deviated from the baseline. The weakening of ALFF indicates that neurons are inhibited and their activities are decreased. ALFF can be represented as the square root of the power spectrum in low‐frequency range (0.01–0.08 Hz), which measures evaluate the brain's pathophysiological state by computing the regional intensity of spontaneous fluctuation in BOLD signal at rest (Zang et al., 2007). Studies no various diseases such as heroin addicts (Luo et al., 2020), trigeminal neuralgia(Ge et al., 2022), migraine (Chen et al., 2021), cervical discogenic pain (Ma et al., 2020), chronic low back pain(Zhang et al., 2019), fibromyalgia (Katherine et al., 2019), suggest that ALFF, as a reliable indicator of regional spontaneous neural activity in resting‐state, can be widely used in pain disease studies.
In this study, we used rs‐fMRI to explore the brain central mechanism of EA in treating acute pain following TKA. To our knowledge, few neuroimaging research has focused on this area. We speculated that [1] the ALFF patterns in patients with acute postoperative pain might be abnormal, and [2] EA could treat acute pain after TKA by regulating the functional activities of specific brain regions.
## Standard protocol approvals, registration, and consents
The study took place at Shanghai Guanghua Hospital of Integrated Traditional Chinese and Western Medicine from November 1, 2020 to October 31, 2021. The study protocol was preregistered with ClinicalTrials.gov (No. ChiCRT2000033778, June 14, 2020, https://www.chictr.org.cn/edit.aspx?pid=54096&htm=4). All study protocols were approved by the Ethics Committee of Shanghai Guanghua Hospital of Integrated Traditional Chinese Medicine and Western Medicine (2020‐K‐44), and all participants provided written informed consent in accordance with the Declaration of Helsinki.
## Study participants
Enrolled were KOA patients having undergone primary unilateral TKA. The inclusion criteria included [1] aged 60–80 years; [2] right‐handed; [3] a pain intensity score of 5 or higher on a 0–10 Numerical Rating Scale (NRS); [4] a score > 24 on the Minimum Mental State Examination. The excluded criteria included [1] a history of neurological and psychiatric disorders, or head trauma with loss of consciousness; [2] serious renal, cardiovascular, respiratory or other organic diseases; [3] any contraindications to rs‐fMRI scanning (such as defibrillator, cardiac pacemaker, metal stents or electronic implant, intraocular mental foreign body, claustrophobia, and hyperpyrexia); [4] unwillingness to sign the consent form; [5] having received acupuncture/EA in the past three months.
The sex‐ and age‐matched healthy controls (HCs) without any illness‐induced pain sensation or psychological diseases were also recruited.
## Experimental design and sample size
A participant‐blinded, randomized and sham‐acupuncture (SA) controlled clinical trial was applied. Previous studies have shown that 15 participants should be included in each group to ensure stable statistical effect for brain fMRI analysis (Qiu et al., 2016; Szucs & Ioannidis, 2020). In this study, rs‐fMRI was conducted to explore the cerebral mechanism rather than observe the clinical efficacy. The participants were randomly assigned to either EA or SA group ($$n = 20$$ each) for rs‐fMRI scanning.
The study lasted for 5 days: from postoperative day (POD) 3 to POD 7. Patients were instructed to completed postoperative pain diary by documenting the onset time, pain intensity (measure by NRS score), rescue medication use, and rs‐fMRI scanning, which collected at POD 3 and POD 7.
## Masking and intervention
The group allocation information was sealed an envelope and given to the acupuncturist. The surgeons, principal investigators, study staff, data analysts, and the participants were blinded to grouping. Using the sham electroacupuncture design in the previous literature(Liu et al., 2017), adhesive pads were applied to both groups, thus the blunt‐tipped placebo needles with a similar appearance to conventional needles provided participant‐blinding effects but no skin penetration. Besides, the SA included a connecting cord with a broken inner wire with no actual current output. Participants in the EA group received acupuncture. Acupoint locations were selected based on the standardized acupuncture protocol for TKA (Zhong et al., 2019). Four acupoints at the surgical limb, including Futu (Stomach 32, ST32), Zusanli (Stomach 36, ST36), Yinlingquan (Spleen 9, SP9), and Yanglingquan (Gall Bladder 34, GB34), were selected. After skin disinfection, sterile adhesive pads were placed on ST32, ST36, SP9, and GB34, and the sterile disposable acupuncture needles (0.25 mm diameter, 40 mm length, stainless steel) were inserted through the adhesive pads approximately 20–35 mm in to the skin, depending on the thickness of the local tissues. The inserted needles were moved until the “Deqi” sensation (a composite of sensations including soreness, numbness, distention, heaviness) was achieved without causing a sharp pain. Then the needles, which were connected to an EA machine (SDZ‐II, Huatuo, Suzhou, China), with a pair of electrodes connecting GB 34 with ST 32, and another pair of electrodes connecting ST36 to SP9 (Zhong et al., 2019), put through a continuous wave of 2 Hz and 1 to 5 mA current intensity into the skin. The electric stimulation was enhanced gradually to the highest tolerable level for the patient without causing pain, and retained for 20 min.
In SA group, noninserted sham needles were applied to the same acupoints as in EA group. The electrodes were attached to these needles with, the same treatment setting as in the EA group. The EA device was turned on, but the electrodes were not inserted into an active port on the device, and no skin penetration or needle manipulation was achieved for “De qi.” Intervention was not performed on HCs. All the patients included in the final analysis completed 5 treatment sessions during the 5‐day treatment.
## Clinical assessment
Nonsteroidal anti‐inflammatory drug was used for analgesia, and supplementary dosage could be administered if needed. No additional analgesic medication was asked for in both EA and SA groups throughout the study. All patients underwent rs‐fMRI scan twice. The demographic information and clinical scale data (dependent variables) at POD 3 (preintervention) and POD 7 (postintervention) were analyzed. The primary outcome was mean reduction in pain intensity represented by the NRS score of patients. To obtain the NRS score, the patients were asked to circle a number ranging from 0 (no pain) to 10 (the most intense pain imaginable) that best fit their current level of pain. The reduction in Zung Self‐Rating Depression Scale (SDS) score was calculated as the secondary outcome. SDS was used to evaluate the subjectively reported depression by the patients. The scale consisted of 20 items and each item was divided into 4 levels according the frequency of symptoms, which is suitable for adults with depression. For the SDS score, patients scored on the scale according to their emotional state.
## Rs‐fMRI data acquisition
The rs‐fMRI data were acquired using a Clinical 1.5 Tesla whole body MR imager (United Imaging, Shanghai, China). Although our previous clinical trial (Kang et al., 2022) had verified the reliability of 1.5 Tesla data, we repeatedly tested the stability of the machine before the start of this study to ensure the reliability of the data. A head‐hugger and earplugs were used to minimize noise and head movement during scanning. Participants were instructed to keep their eyes closed, relax, stay awake, and not think about anything in particular. Rs‐fMRI images were obtained by a rapid‐gradient echo‐planar imaging sequence with the following setting: repetition time, 3000 ms; echo time, 30 ms; flip angle, 90°; field of view, 225 × 225 mm2; acquired matrix, 64 × 64 matrices; 43 slices with a thickness of 3.5 mm; voxel size, 3.52 × 3.52 × 3.52 mm3; bandwidth, 2250 Hz/pixel. The scanning duration was 12 min and 13 s. In response to a questionnaire after the scan, all participants stated that they had not fallen asleep.
## Statistical analysis
Clinical outcomes were analyzed using a statistical package for windows version 25 (SPSS, IBM Inc., Chicago, IL). A single‐factor ANOVA/two‐sample t‐test and a χ 2 test was applied to compare the baseline characteristics of the participants. The NRS and SDS scores were compared using a linear mixed model with group, time allocation and interaction between the two as fixed effect. The mean (standard deviation, SD) was presented for continuous variables; while frequency was used for categorical data with corresponding p and t/F values. A two‐side test was applied, with a confidence interval of $95\%$ and $p \leq .05$ indicating statistically significant.
## ALFF analysis
The data was processed by the Data Processing Assistant for rs‐fMRI (Restplus) based on Statistical Parametric Mapping 12 (SPM 12, http://www.fil.ion.ucl.ac.uk/spm/software/spm12/) and run using MATLAB R2014. Before analysis, we performed left and right head flip in patients underwent left TKA, thereby lateralizing the reflection area to the same hemisphere in all patients. The processing included (Xu et al., 2020) [1] discarding the first 10 volumes; [2] slice‐time correction; [3] head movement correction (translational or rotational motion parameters < 3 mm or 3°); [4] spatial normalization to the standard template and resampling to a 3×3×3 mm voxel size; [5] spatial smoothing with an 6 mm full width at half maximum (FWHM) kernel; [6] linear trend removal; [7] regression of nuisance covariates (including the white matter, cerebral spinal fluid, and the Firston 24 head motion parameters) (Yan et al., 2013) The blood oxygen level‐dependent time series for each voxel was converted to the frequency domain with fast Fourier transform. The square root of the power spectrum was computed and averaged across the specified frequency range (0.01–0.08 Hz) at each voxel. The averaged square root was used as the ALFF, which was transformed by Fisher's z transformation for subsequent group‐level analysis.
To investigate the alternations in HCs and patients in POD 3, we used false discovery rates (FDR) correction for multiple comparison (voxel‐$p \leq .05$, cluster‐$p \leq .05$). For patient group‐level analyses, we used AlphaSim correction for multiple comparison (voxel‐$p \leq .01$, cluster‐$p \leq .01$). The ALFF was analyzed by two‐sample t‐test and paired t‐test using SPM 12 software.
Compared with HCs, all patients at POD 3 had significantly lower ALFF in the right middle frontal gyrus (MFG), right supplementary motor area (SMA), bilateral precuneus, right calcarine fissure and surrounding cortex (CAL), and left triangular part of inferior frontal gyrus (IFGtriang) (Table 3, Figure 2).
In EA group, the patients showed significantly higher ALFF at POD 7 in bilateral precuneus, right cuneus, right angular gyrus, bilateral middle occipital gyrus (MOG), and left middle temporal gyrus (MTG), compared with those at POD 3 (Table 4, Figure 3). In SA group, no significant differences in ALFF were observed between POD 3 and POD 7.
## Correlation analysis
The EA‐stimulation difference (EA_ POD 7 vs. POD 3) and group difference (HCs vs. patients at POD 3) were chosen as the regions of interest (ROI). The mean values in the ROI between the two groups were analyzed using the receiver operating characteristic curves. Pearson's correlation coefficients were calculated to indicate the relationship between the mean ALFF values in ROI in patients, where $p \leq .05$ was considered as a significant difference.
## Baseline characteristics
Totally, 38 patients (20 in EA group, 18 in SA group) completed the two rs‐fMRI scans (baseline and after 5‐day treatment). Forty HCs completed one rs‐fMRI scan. Due to excessive head movement (>3 mm) in scan, 7 patients and 8 HCs were excluded (Figure 1). No significant differences in age, gender, and body mass index were observed between patients and HCs. Table 1 shows that there was no difference between in the EA and SA group in pain intensity and SDS at baseline (age, gender, and body mass index).
**FIGURE 1:** *Folw chart of screening, randomization and intervention. SA, sham‐acupuncture; EA, electroacupuncture; rs‐fMRI, resting‐state functional magnetic resonance imaging.* TABLE_PLACEHOLDER:TABLE 1
## Clinical outcomes
Table 2 summarizes the clinical outcome and statistics. Significant time × group interaction effects were found in pain intensity (mean reduction in NRS [$F = 15.634$, $p \leq .001$]) and emotional state (mean reduction in SDS [$F = 4.827$, $$p \leq .036$$]).
**TABLE 2**
| Unnamed: 0 | EA group, n = 16 | EA group, n = 16.1 | Unnamed: 3 | SA group, n = 15 | SA group, n = 15.1 | SA group, n = 15.2 | Interaction effect | Unnamed: 8 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Pretreatment mean (SD) | Posttreatment mean (SD) | Post‐pre (95% CI) | Pretreatment mean (SD) | Posttreatment mean (SD) | Post‐pre (95% CI) | Interaction effect | Effect size |
| NRS | 6.0 (0.6) | 3.5 (0.6) | −2.2 (−2.4, −2.5) | 6.1 (0.6) | 4.7 (0.6) | −1.4 (−1.4, −1.5) | p < .001 | 0.842 |
| SDS | 46.1 (3.9) | 39.1 (2.9) | −7.8 (−6.8, −7.1) | 46.6 (2.8) | 42.7 (3.3) | −3.9 (−3.8, −4.0) | p = .036 | 0.575 |
## Correlation between ALFF and pain intensity after intervention
The EA‐stimulation difference (EA_ POD 7 vs. POD 3) and group difference (HCs vs. patients at POD 3) as shown by ALFF overlapped in bilateral precuneus, which was chosen as the ROI. Correlation analysis revealed that the change (POD 7–POD 3) in ALFF of bilateral precuneus were negatively correlated with the change of NRS scores (r = −0.706; $$p \leq .002$$; $95\%$ CI = −0.890 to −0.323) and not significantly correlated with the change of SDS score (r = −0.012; $$p \leq .965$$; $95\%$ CI = −0.505 – −0.487) in EA group. There was no significant difference between the changed (POD7–POD3) ALFF values of bilateral precuneus correlated with the change of NRS (r = −0.346; $$p \leq .206$$; $95\%$ CI = −0.729 – −0.201) and SDS ($r = 0.196$; $$p \leq .485$$; $95\%$ CI = −0.352 – −0.644), respectively (Figure 4).
**FIGURE 4:** *Correlation between the change of ALFF in precuneus with the change of NRS scores and SDS in patients. The change (POD7–POD3) of ALFF in precuneus was negatively correlated with the change of NRS scores and not significantly correlated with the change of SDS in EA group. There was no significant difference between the changed ALFF values of precuneus with the changed NRS scores and SDS. ALFF, amplitude of low‐frequency fluctuation; PCUN, precuneus; NRS, numerical rating scale; SDS, Self‐Rating Depression Scale.*
## Complications and adverse events
All incisions were healed by the first intention. No skin ulcer, hematoma, infection, liver or kidney injury, or other adverse events was observed.
## DISCUSSION
The transmission of pain in the central nervous system is highly complicated and involves multiple brain regions (Kim et al., 2021; Qi et al., 2018). In this study, we investigated the underlying neurobiology of postoperative acute pain and the modulatory effect of EA on acute pain after TKA.
We found that patients had lower ALFF in right MFG, right SMA, bilateral precuneus, right CAL, and left IFGtriang following TKA (preintervention), compared with HCs without any pain symptoms. MFG and IFGtriang in the prefrontal cortex can modulate pain perception (Morton et al., 2016). The MFG activity is implicated in pain stimulation and modulation, perception of negative emotions, and cognitive evaluation (Boissoneault et al., 2020). Deactivating the left IFGtriang is associated with response inhibition, as well cognitive and emotional dysregulation (Liu et al., 2020). Precuneus, a cortical element in the medial surface, is involved in attention, spatial integration, self‐awareness, psychological activities, social cognition, and emotional processing (Pereira‐Pedro & Bruner, 2016). CAL, surrounded by the primary visual cortex, is related to cognitive function (Yang et al., 2020). Although all participants in this study showed normal cognitive function, the decreased functional activities in the above brain regions may lead to the experience of intense pain in patients, further reducing their brain activities and weakening cognitive function (Huang et al., 2015). We speculate that patients with pain sensitivity after TKA may have a higher risk of pain‐induced cognitive impairment, which is associated with the changed functional brain activities. The SMA in the frontal cortex can delimit the motor from the prefrontal cortical regions and link cognition to action in normal behaviors, involving movement preparation, short‐term retention of pain dynamics, and other cognitive and motor‐related processes (Khoshnejad et al., 2017). The decreased functional activities of SMA may be related to the knee motor dysfunction. In KOA patients, the pathological changes influenced the spatial patterns of intrinsic brain activity, leading to altered brain function and structure (Kang et al., 2022). The deactivation of these regions involved in pain regulation may indicate inadequate inhibition or increased facilitation of sensory information, which is associated with functional impairment in pain processing, thus contributing to acute pain after TKA.
This preliminary clinical trial has shown that EA reduced pain intensity and depression tendency after TKA. The analgesia effect of EA may be achieved through regulating the plasticity of brain functional activities, while SA cannot, exert such an effect. And this explains why the patients in EA group had higher ALFF in right cuneus, right angular gyrus, bilateral MOG, right MTG, and bilateral precuneus at POD 7 than at POD 3.
The cuneus integrates somatosensory input with other sensory stimuli and cognitive processes, involving the emotion dimension of pain (Price, 2000). The angular gyrus is implicated in cognitive functions, including attention and spatial cognition, default mode network, and social cognition (Seghier, 2013). Factors associated with cognition are likely to modulate pain perception. The surgical injury triggers a myriad of responses in the pain matrix, from sensitization of central pain pathways to anxiety and depression (Small & Laycock, 2020). Emotion affects pain perception, and patients experiencing severe acute pain are more likely to develop a negative emotion, which in turn increases pain perception, thus forming a vicious circle (Michaelides & Zis, 2019). Decreased ALFF of left MOG is observed in depressive patients (Teng et al., 2018). Temporal cortex participates in pain perception and modulation, as confirmed in human neuropathic states, and it is also closely related to emotional control and sensory process modulation, and pain sensation duration (Khoshnejad et al., 2017). The SDS score in EA group showed the patients receiving EA had a lower tendency to develop depression, which can be explained by the reduced pain severity and negative emotions after EA, and the resultant increase of the functional activities in the relevant brain regions. Another explanation is EA stimulation directly activates the relevant brain areas to regulate the adverse emotions and reduce the perception of acute pain. Pain relief could be evidenced by the reduction of NRS score in the EA group, compared with the SA group, although both received a conventional analgesic agent during the whole study. Changes of these brain functional activities indicate that EA relieves the pain by increasing the activities of the brain regions associated with the pain modulation, and thus changing the pain perception from nociceptive receptors to the pain matrix.
The EA‐stimulation (EA_POD 7 vs. POD 3) and group (HC vs. POD 3) difference as shown by ALFF overlapped in precuneus. The nucleus cuneiformist is an inhibitory region in the descending pathway, and the hypoactivity in this region indicates reduced inhibition of the pain response in patients, which produces a higher pain score (Schwedt et al., 2015). Precuneus, a key region in the neuronal network for continuous information gathering and assessment of self‐relevant sensations, mediates intrinsic activities throughout the default model network (Goffaux et al., 2014). The ALFF in precuneus decreased when acute pain is evoked in chronic low back pain patients (Zhang et al., 2019). When receiving pain stimuli, precuneus presents deactivation, and pain sensitivity is negatively correlated with the activity in the precuneus (Goffaux et al., 2014). The correlation analysis between the reduction of NRS score and the ALFF change of bilateral precuneus in the present study, we found that the activated changes in precuneus may play a crucial role in modulating postoperative pain perception. And we speculate that EA increases the functional activity of precuneus to reduce the postoperative pain sensitivity and perception, which may be a central mechanism of EA in alleviating acute pain after TKA.
The pain intensity of patients in the SA group was also gradually eased over time after TKA. This may be attributed to the fact, the patients tended to limit their limb movement on the operative side to reduce the pain sensation due to the poor analgesic effect of SA. No changes (POD 7 vs. POD 3) in brain regions related to pain regulation were found on rs‐fMRI in SA group. The different changes of brain regions in the two groups indicated the central mechanisms of EA and SA were different. Part of the therapeutic effect of SA may be produced by the physiological effects on the skin during the stimulation. It should be noted that the AlphaSim correction was applied to explore the mechanism of EA analgesia in our study. Although the precuneus functional activities in EA group were closer to those HCs, the finding did not survive the more stringent FDR correction methods, and the AlphaSim correction method is relatively loose; therefore, further studies are needed.
Taken together, we propose that EA relieves acute pain after TKA by increasing right cuneus, right angular gyrus, bilateral MOG, left MTG, and precuneus functional activity. The increased ALFF in pain‐related brain regions may exert beneficial impacts on pain‐regulating functions in patients after TKA. Besides, the EA‐stimulation and group difference overlapped in precuneus, suggesting that precuneus functional activities increase with the decreased pain intensity, which is pivotal to pain processing and regulation. Our research confirms the abnormal decrease of the precuneus functional activity can be reversed by EA, but the results need further validation.
Our study has several limitations. [ 1] The small sample size of the study may increase the likelihood of a false positive error. Ours are preliminary findings, which lack prior power calculations, and thus should to be verified by studies with larger sample sizes. [ 2] This study only verifies the EA effect at the group level, and the specificity of EA analgesia is not fully explained. Future studies are needed to confirm the immediate effect of EA and validate our findings. [ 3] Due to excessive head movements, quite a few participants were excluded; but it should be emphasized that the exclusion of the images exerts no impact on treatment response. [ 4] Due to some practical difficulties and the limitations of experimental conditions, we failed to enroll an untreated group to investigate the effect of elapsed time on pain management after TKA. Hopefully, we will overcome the disadvantages in the subsequent work.
## CONCLUSION
In conclusion, the functional activities in the right MFG, right SMA, bilateral precuneus, right CAL, and left IFGtriang in patients with acute pain after TKA decreased. EA on four acupoints (Futu, Zusanli, Yinglingquan, and Yanglingquan) can increase the functional activities of right cuneus, right angular gyrus, MOG, left MTG, and precuneus. The functional activity of the precuneus is a biomarker of pain after TKA. Enhancement of functional activity of precuneus may be the neurobiological mechanism of EA in treating acute pain following TKA.
## AUTHOR CONTRIBUTIONS
BXK, CZ, and JM conceived the study; BXK drafted the study; HQW, XLG, HX, SZ, CXG, and XRX recruited the participants. XYA and JX collected clinical data. MMD and CZ were responsible for statistical analyses and tables. LBX and JS have primary responsibility for the final content. All authors contributed to writing and revising the paper and agreed to submission.
## CONFLICT OF INTEREST STATEMENT
The authors declare that they have no conflict of interest.
## ETHICS STATEMENT
This study was approved by the Ethics Committee of Shanghai Guanghua Hospital of Integrated Traditional Chinese and Western Medicine and was in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent to participate was obtained from all of the individual participants included in the study.
## TRIAL REGISTRATION
The trial was registered in Chinese Clinical Trial Registry (ChiCTR2000033778).
## PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2913.
## DATA AVAILABILITY STATEMENT
The data sets used and analyzed during the current study are available from the corresponding author on reasonable request.
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|
---
title: Natural Morin-Based Metal Organic Framework Nanoenzymes Modulate Articular
Cavity Microenvironment to Alleviate Osteoarthritis
authors:
- Jinhong Cai
- Lian-feng Liu
- Zainen Qin
- Shuhan Liu
- Yonglin Wang
- Zhengrong Chen
- Yi Yao
- Li Zheng
- Jinmin Zhao
- Ming Gao
journal: Research
year: 2023
pmcid: PMC10013961
doi: 10.34133/research.0068
license: CC BY 4.0
---
# Natural Morin-Based Metal Organic Framework Nanoenzymes Modulate Articular Cavity Microenvironment to Alleviate Osteoarthritis
## Abstract
Osteoarthritis (OA) is always characterized as excessive reactive oxygen species (ROS) inside articular cavity. Mimicking natural metalloenzymes with metal ions as the active centers, stable metal organic framework (MOF) formed by natural polyphenols and metal ions shows great potential in alleviating inflammatory diseases. Herein, a series of novel copper-morin-based MOF (CuMHs) with different molar ratios of Cu2+ and MH were employed to serve as ROS scavengers for OA therapy. As a result, CuMHs exhibited enhanced dispersion in aqueous solution, improved biocompatibility, and efficient ROS-scavenging ability compared to MH. On the basis of H2O2-stimulated chondrocytes, intracellular ROS levels were efficiently declined and cell death was prevented after treated by Cu6MH (Cu2+ and MH molar ratio of 6:1). Meanwhile, Cu6MH also exhibited efficient antioxidant and anti-inflammation function by down-regulating the expression of IL6, MMP13, and MMP3, and up-regulating cartilage specific gene expression as well. Importantly, Cu6MH could repair mitochondrial function by increasing mitochondrial membrane potential, reducing the accumulation of calcium ions, as well as promoting ATP content production. In OA joint model, intra-articular (IA) injected Cu6MH suppressed the progression of OA. It endowed that Cu6MH might be promising nanoenzymes for the prevention and treatment of various inflammatory diseases.
## Introduction
Osteoarthritis (OA) is a common joint degenerative disease caused by aging, obesity, strain, trauma and congenital joint abnormalities, etc. [ 1,2]. It is clinically manifested by progressive joint pain, swelling, stiffness, tenderness, and deformity, seriously affecting patients’ quality of life [3]. Moreover, reactive oxygen species (ROS) like hydroxyl radical (·OH), hydrogen peroxide (H2O2), and superoxide anion (·O2−) have been confirmed to be involved in OA pathogenesis by up-regulating the expression of inflammatory cytokines, leading to degeneration and destruction of extracellular matrix, and further contributing to OA progression. Meanwhile, excessive ROS can be markedly detected in joint cavity of OA patients, which even can reach 1 mM [4–6]. Therefore, ROS have been considered as a key marker of OA, and scavenging ROS can be applied as an effective strategy for OA therapy. Current studies have reported that ROS scavengers, such as melanin [7,8], vitamins [9], and nature polyphenols [10], have shown great potential in scavenging ROS and further alleviating OA severity. Nevertheless, most potent ROS scavengers have unrealistic biocompatibility and poor stability in joint cavity and are easy to be cleared by synovial fluid, limiting their further applications in clinical therapy of OA.
Morin hydrate (MH) is a natural active substance and contains many biological activities of flavonoids with low toxicity [11]. Numerous studies have shown that MH has good antioxidant and anti-inflammatory effects through ROS scavenging [12]. MH can prolong the survival of free radical-damaged cells and substantially possess better free radicals scavenging ability than that of vitamin, mannitol, and ascorbate [13]. It was widely applied in the treatment of mastitis [14], osteoporosis [15], osteoarthritis [16], and other inflammatory diseases. However, the therapeutic application of MH is severely limited by its insolubility, poor bioavailability, and rapid elimination from the organism. The nanosized preparation of MH is a possible method to solve these problems [17]. Recent evidences showed that stable metal organic framework (MOF) formed by natural herb-derived agents and metal ions could greatly improve the utilization rate and thus therapeutic effects, mimicking the coordination structures of natural metalloenzymes where the metals act as active centers [18]. With the introduction of active sites, electron transport interaction between drugs and metals endowed the novel agent with superior catalytic activity. It had been reported that epigallocatechin-3-gallate (EGCG), a natural polyphenol compound from green tea, could be chelated with Cu2+ [19], Sm3+ [20], or Mn2+ [21] to form a stable MOF, which could accelerate the repair of diseases like myocarditis, metastatic melanoma, and sepsis. Besides, tannic acid (TA), another kind of natural herb-derived agent, could be chelated with Ce3+ [22], Cu2+ [23,24], or Fe3+ [25,26] to form nanozymes with excellent catalytic activity, showing great potential in the therapy of inflammatory diseases. Compared to polyphenols like EGCG and TA itself, new formed MOF nanoenzymes could prolong the retention time in vivo. In addition, with slow release of EGCG under physiological environment, the drug utilization rate and therapeutic effect had been greatly improved [26]. Importantly, the effective combination of metal ions and EGCG or TA shielded the interaction between phenolic hydroxyl groups and led to more exposed phenolic hydroxyl groups, which were helpful to improving surface binding energy and ROS adsorption, further promoting antioxidant effect [23].
Among various metallic elements, Cu attracts the most attention because it plays a key role in various enzymatic reactions [27]. Accumulating evidence indicated that Cu nanoparticles were helpful to strengthen electron transfer reactions to shield the activity of ROS and achieve the function of ROS scavenging and ultimately improved skin, kidney, and liver injury treatment [28–30]. Importantly, Cu can also act as the connecting point by bridging ligands to form a stable MOF and plays its synergistic enhancement effect of antioxidation and anti-inflammation [24,31].
Inspired by MOF nanoenzymes with superior catalytic activities, we designed a series of CuMHs through the coordination of Cu2+ and MH as novel ROS scavengers to regulate the microenvironment of articular cavity for OA therapy (Fig. 1). Simultaneously, MH has multiple aromatic rings, standing out as a renewable source of rigid molecules. In addition, Cu, with various valences, acts as the connecting point for bridging MH. The novel designed CuMHs exhibited synergistic enhanced superoxide dismutase (SOD) and catalase (CAT)-like activities and ROS-scavenging capacities compared to MH alone. As revealed by H2O2-induced chondrocytes and OA joint models, CuMHs exhibited favorable biosafety and efficient ROS-scavenging ability to restore mitochondrial function, leading to the suppression of OA progression. Summarily, it might be employed as an effective method to fabricate natural polyphenol-based MOF with excellent ROS-scavenging capacity for the prevention and therapy of ROS-related diseases.
**Fig. 1.:** *Schematic illustration of the preparation of natural polyphenols morin hydrate (MH)-based metal organic framework (CuMHs) by hydrothermal stirring and their in vivo therapy of OA rat model via modulating the microenvironment of articular cavity (ROS scavenging, anti-inflammation, and mitochondria repairing).*
## Basic characterization of CuMHs
As a natural flavonoid compound with polyphenol groups, MH has attracted wide attention, while its biomedical use is often limited because of poor water solubility and low bioavailability. Therefore, transition metal element Cu was employed to coordinate with MH to obtain well-dispersed MH-based complexes: CuMHs. As shown in Fig. 2A, CuMHs were prepared by the coupling assembly of Cu2+ and MH with various molar ratios after thermal reflux reaction. MH has a strong chelating ability to form stable framework structures with multivalent metals. With a droplet of yellow MH solution, obvious crystals were observed, suggesting that the complexes were achieved between Cu2+ and MH. By adjusting the moles of Cu2+, different CuMHs were obtained and their corresponding symbols were Cu1.5MH, Cu3MH, Cu6MH, Cu12MH, and Cu24MH with a Cu2+ and MH molar ratio of 1.5:1, 3:1, 6:1, 12:1, and 24:1, respectively (Table S1). As shown in Fig. S1, the color of CuCl2·2H2O and MH was blue and brown, respectively. After forming complexes, the color became dark yellow for all CuMHs.
**Fig. 2.:** *Preparation and characterization of CuMHs with different molar ratios of Cu2+ and MH. (A) Chemical synthesis of CuMHs. (B) FTIR results of MH and CuMHs. (C) XRD curves of MH and CuMHs. (D) Zeta potential of CuCl2·2H2O, MH, and CuMHs. (E) TEM images of Cu1.5MH (i), Cu3MH (ii), Cu6MH (iii), Cu12MH (iv), and Cu24MH (v). (Scale bar = 100 nm) (F) TEM-mapping images of Cu6MH. (Scale bar = 20 nm) (G) Full spectrum of CuMHs by XPS. (H) Detailed Cu spectrum of CuMHs (i) and the corresponding Cu2+/Cu+ ratio (ii) by XPS. (I) BET results of Cu1.5MH (i), Cu3MH (ii), Cu6MH (iii), Cu12MH (iv), and Cu24MH (v).*
The molecular structure of CuMHs was firstly characterized by ultraviolet-visible (UV-vis) spectrum. From Fig. S2, 2 apparent peaks at 262 and 388 nm existed for MH, while no characteristic peaks were observed for CuCl2·2H2O. After forming CuMHs, 2 weak peaks still maintained at 245 and 290 nm. The shift of peaks was possibly attributed to the formation of CuMHs.
In addition, CuMHs were also characterized by Fourier transform infrared (FTIR). As illustrated in Fig. 2B, compared to MH alone, various characteristic peaks for CuMHs weakened or disappeared, possibly because of coupling assembly of Cu2+ and MH to shield various functional groups [21]. Specifically, the characteristic peak of a carbonyl group (C=O) of MH appeared at 1,657 cm−1, while it disappeared for CuMHs. In addition, the obvious broad peak (3,650 to 2,900 nm) was observed for MH, Cu1.5MH, and Cu3MH, while the splitting sharp peaks existed for Cu6MH, Cu12MH, and Cu24MH. The coordination between Cu2+ and –OH induced the disruption of –OH vibration [32], finally resulting in the existence of splitting peaks for CuMHs with the molar ratio of Cu2+ and MH above 3.
X-ray diffraction (XRD) is always used to evaluate the crystallization change of materials. Thus, the molecular structure of CuMHs was further characterized by XRD. As shown in Fig. 2C, it was observed that a few of obvious characteristic peaks appeared for MH below 30°, while no obvious diffraction peaks were observed for Cu1.5MH, indicating that it was an amorphous structure for Cu1.5MH. However, if the amount of Cu2+ during CuMHs preparation was increased, then the characteristic peaks gradually appeared for Cu3MH, Cu6MH, Cu12MH, and Cu24MH, demonstrating that the unique crystalline structure was formed for the above complexes. In particular, the obvious peak was observed at 34.6° for Cu3MH compared to other CuMHs, showing an intermediate transition state for Cu3MH. In the meantime, the unique characteristic peaks observed for Cu6MH, Cu12MH, and Cu24MH were also not similar with other Cu-based materials (e.g., Cu, Cu2O, CuO, or Cu(OH)2). Thus, when the molar ratio of Cu:MH was above 3, it was possible to fabricate CuMHs with appropriate crystalline structures.
To investigate the dispersion stability of CuMHs, their zeta potential was investigated. The zeta potential was −23.9 mV for MH due to the fact of a lot of –OH groups for MH itself. After forming the complex, the zeta potential of CuMHs obviously increased, attributed to the reason that the introduction of Cu2+ shielded –OH groups (Fig. 2D). In addition, the morphology of CuMHs was observed by transmission electron microscopy (TEM). As shown in Fig. 2E, the apparent irregular structure happened to Cu1.5MH and Cu3MH. However, with the increase of Cu2+ amount, it gradually changed to form a certain flake structure for Cu6MH, Cu12MH, and Cu24MH. Besides, to further confirm the three-dimensional structure of CuMHs, atomic force microscopy (AFM) was applied. As illustrated in Fig. S3, the height of Cu6MH was around 61.8 nm. Meanwhile, from the mapping images, it was obviously observed that only Cu, C, and O elements existed in Cu6MH (Fig. 2F).
Besides, X-ray photoelectron spectroscopy (XPS) was utilized to characterize the element composition and chemical state of CuMHs. From Fig. 2G, obvious Cu, C, and O elements were observed for CuMHs. The atomic ratio of Cu element markedly rose with the increase of Cu2+:MH molar ratio, which was $3.4\%$ for Cu1.5MH and increased by 5.4-fold for Cu24MH. Conversely, C and O atomic ratios gradually declined with the increase of Cu2+:MH molar ratio (Table S3). Specifically, from fine spectra of Cu, it was clearly found that Cu2+ and Cu+ species existed in CuMHs. In addition, the fine spectra of Cu2p showed the obvious binding energies at 934.8 and 932.7 eV, respectively. ( i of Fig. 2H) Specifically, for Cu1.5MH, the atomic ratio of Cu2+ and Cu+ was $93.1\%$ and $6.9\%$, and Cu2+/Cu+ ratio was 13.6 due to its amorphous state. However, the Cu2+/Cu+ ratio was 3.2 for Cu3MH and increased by 2.1-, 2.5-, and 5.6-fold for Cu6MH, Cu12MH, and Cu24MH, respectively (ii of Fig. 2H). To our knowledge, the chelating Cu2+ could not be easily reduced by phenolic hydroxyl. Thus, it was little content of Cu+ for Cu1.5MH, leading to a high Cu2+/Cu+ ratio. Conversely, with the increase of Cu2+ dosage, more Cu2+ was exposed and reduced by phenolic hydroxyl, resulting in low Cu2+/Cu+ ratio for Cu3MH. If the dosage of Cu2+ continues to increase, then it also could increase Cu2+/Cu+ ratio. Thus, Cu2+/Cu+ ratio gradually rose with the increase of Cu2+ and MH molar ratio. Nevertheless, there were no obvious differences of fine spectra of C and O among different CuMHs (Fig. S4), indicating that different Cu2+:MH molar ratios could not affect the chemical state of C and O. After further calculation, the real Cu:MH molar ratio was listed in Table S1.
Furthermore, to confirm their MOF structures, Brunauer–Emmett–Teller (BET) assay was applied to explore the porosity of CuMHs. As indicated in Fig. 2I, it presented the adsorption–desorption isotherms of CuMHs. Because of extremely low specific surface area, the adsorption and desorption equilibrium of Cu1.5MH could not be established, indicating the formation of amorphous structures for Cu1.5MH. However, it displayed the typical adsorption–desorption isotherms with H3-type hysteresis for Cu3MH, Cu6MH, Cu12MH, and Cu24MH, respectively. Thus, the apparent porous structures were observed for CuMHs with the Cu2+:MH molar ratio above 3. After calculation, the pore volume and specific surface area was extremely low for Cu1.5MH. In addition, the order of pore volume was Cu3MH < Cu6MH < Cu12MH < Cu24MH, while it was in the order of Cu3MH > Cu6MH > Cu12MH > Cu24MH for specific surface area (Table S3). Combined the results of XRD and BET, it concluded that the stable MOF structures were established for Cu6MH, Cu12MH, and Cu24MH.
From the above, it confirmed the successful preparation of CuMHs with amorphous structure, intermediate state, and stable crystalline structure by adjusting the Cu2+:MH molar ratio. Significantly, when Cu:MH molar ratio was above 3, CuMHs could form a relative stable MOF with natural polyphenol MH as the ligand.
## In vitro degradation properties
For biomedical application, it was required that the materials possessed the degradation property without causing additional side effects after realizing their therapeutic effects. To evaluate its in vitro degradation property, Cu1.5MH and Cu6MH were immersed in 50 μM H2O2 (pH = 6.8) followed by investigating the concentration of Cu ions at predetermined time points (0, 3, 6, 12, 24, 48, and 72 h). In addition, 50 μM H2O2 (pH = 6.8) was applied to simulate the microenvironment of articular cavity of OA model with high ROS levels and weak acidic conditions [33]. From the results of Fig. S5, it was observed that the Cu ions release amount of Cu1.5MH was higher than that of Cu6MH. It also indicated that Cu1.5MH degraded relatively more quickly than the Cu6MH. The stability and degradability of materials are highly associated with their crystalline structures [34,35]. Because of its crystalline structure, it was relatively stable and not easy to be degraded for Cu6MH.
## ROS-scavenging ability
To explore ROS-scavenging capability of CuMHs, the ·OH, ·O2−, and H2O2 scavenging kits were applied. As shown in Fig. S6 and Fig. 3A, MH presented a certain of ·OH scavenging ability, still lower than that of CuMHs except for Cu1.5MH. Among all CuMHs, the order of ·OH scavenging ratio was Cu6MH > Cu12MH > Cu24MH > Cu1.5MH. In addition, increasing the amount of CuMHs, the corresponding ·O2− scavenging ratios were also increased. The similar tendency was also observed for ·O2− and H2O2 scavenging. It was observed that Cu6MH possessed the optimal scavenging ratios of ·O2− and H2O2, followed by Cu12MH, Cu24MH, MH, and Cu1.5MH with the same concentrations. Specifically, if increasing the weight concentration of Cu6MH from 10 to 50 μg/ml, the corresponding scavenging effect increased by 1.5-fold for ·O2− and 1.2-fold for H2O2, respectively (Fig. 3A). Besides, ROS-scavenging efficiencies were also evaluated by electron spin resonance (ESR). As illustrated in Fig. 3B, compared to the control group, all samples presented a certain of ROS-scavenging ability with obvious decreased signal observed. The order of ·OH and ·O2− signal intensity was Cu1.5MH > MH > Cu24MH > Cu12MH > Cu6MH, indicating that the order of ·OH and ·O2− scavenging capacity was Cu1.5MH < MH < Cu24MH < Cu12MH < Cu6MH. In the meantime, the O2 release rate was tested to assess H2O2 scavenging capacity by dissolved oxygen meter. It was obviously observed that O2 concentration maintained the baseline level while it rose slowly versus time for Cu1.5MH with different concentrations (10, 20, and 50 μg/ml). However, for Cu6MH, Cu12MH and Cu24MH, the O2 concentration markedly increased at the beginning and reached the equilibrium around 1,000 s (i, ii, and iii of Fig. 3C). After statistical calculation, it was possible to observe that the testing buffer could produce a certain concentration of O2 and it markedly increased for CuMHs with different concentrations. In addition, the order of O2 concentration was Cu1.5MH < Cu24MH < Cu12MH < Cu6MH. Among all groups, Cu6MH presented the optimum scavenging capacity of ·O2−, ·OH, and H2O2. The excellent ROS-scavenging capacity of Cu6MH was attributed to its high specific surface area and regular MOF structure [36,37]. Thus, Cu6MH was considered as artificial MOF nanoenzyme for further investigation.
**Fig. 3.:** *ROS-scavenging effect evaluation. (A) ·OH, ·O2−, and H2O2 scavenging ratio of MH and CuMHs with different concentrations (10, 20, and 50 μg/ml) by ROS testing kits. (B) ·OH and ·O2− scavenging capacity of MH and CuMHs by ESR (20 μg/ml). (C) O2 release rate of 100 mM H2O2 incubated with CuMHs of different concentrations: 10 (i), 20 (ii), and 50 (iii) μg/ml and the corresponding quantification of O2 concentration (iv).*
## Biocompatibility in vitro
To determine chondrocyte cytotoxicity of CuMHs, cell counting kit-8 (CCK-8) assay was initially applied. As indicated in Fig. 4A, MH exhibited low cytotoxicity with the cell viability above $90\%$ from 0 to 200 μg/ml. However, Cu1.5MH and Cu6MH exhibited a certain level of cytotoxicity with the increase of concentration due to the original cytotoxicity of Cu ions. The cell viability was above $80\%$ for Cu6MH with the concentration of 20 μg/ml. Thus, 20 μg/ml was considered as the use concentration of all CuMHs for further experiments. Besides, to investigate their protection ability, the live/dead staining was applied by incubating H2O2-treated chondrocytes with 20 μg/ml CuMHs. From Fig. 4B, a lot of dead chondrocytes (red signal) were observed compared to normal chondrocytes. After incubation, the number of dead cells decreased for MH and Cu15MH, while only a few of dead cells were observed for Cu6MH. After statistical analysis, the live/dead ratio markedly decreased to $7.6\%$ for chondrocytes after H2O2 stimulation compared to the normal group, and MH and Cu1.5MH improved the cell viability for a certain degree with increased live/dead ratio. Importantly, it was obviously observed that the live/dead ratio increased after Cu6MH treatment (Fig. 4C). Among all groups, Cu6MH possessed the optimum protection ability of chondrocytes away from the toxicity of H2O2, due to its excellent antioxidant ability.
**Fig. 4.:** *(A) Cell viability of MH, Cu1.5MH, and Cu6MH with the concentration ranging from 0 to 200 μg/ml by CCK-8 kit. (B) Live/dead staining of H2O2 (400 μM, 30 min)-induced chondrocytes after incubation by fluorescent microscopy, and the corresponding quantification of live/dead ratio (C). The samples were normal chondrocytes (Normal), H2O2-induced chondrocytes (Control), H2O2-induced chondrocytes after incubating with 20 μg/ml MH, Cu1.5MH, and Cu6MH, respectively. (D) Cellular uptake of Cy5.5-Cu1.5MH and Cy5.5-Cu6MH after 24 h by confocal microscopy (Cy5.5-Cu1.5MH or Cy5.5-Cu6MH: red, actin: green, and DAPI: blue) and the corresponding quantification (E). (“*” symbol compared with normal group, **P < 0.01, ***P < 0.001, and ****P < 0.0001, and “#” symbol compared between groups, #P < 0.05, ##P < 0.01, ###P < 0.001, and ####P < 0.0001).*
## Cellular uptake
To give full play to their roles, it was also expected that nanoenzymes could be effectively uptake by the cells. Therefore, the nanoenzymes could scavenge not only the extracellular ROS but also intracellular ROS. As indicated in Fig. 4D, after 24 h of incubation, it was observed that fluorescent signal (red) existed inside the chondrocytes, indicating that Cy5.5-Cu1.5MH and Cy5.5-Cu6MH were taken up by the cells. Most importantly, it displayed higher fluorescence intensity for Cy5.5-Cu6MH than that of Cy5.5-Cu1.5MH (Fig. 4E). Therefore, the chondrocytes could take up more Cu6MH, leading to better therapeutic effect. Besides, the cells were also observed by TEM after 12 h. As illustrated by Fig. S6, Cu6MH was obviously observed inside the chondrocytes. In summary, it was possible for Cu6MH to be taken up by cells, finally to achieve its efficacy in intracellular levels.
## Antioxidant and anti-inflammatory capacity
To confirm the antioxidant capacity of CuMHs, intracellular ROS levels were evaluated by a ROS fluorescent probe. As illustrated in Fig. 5A, compared to normal chondrocytes, a lot of green fluorescence was observed for the control group by a 2′, 7′-dichlorofluorescin diacetate (DCF) probe, indicating high intracellular ROS levels after H2O2 stimulation. In addition, MH decreased the total ROS levels for a certain degree with slightly decreased green signal, while the green signal was significantly declined for Cu6MH, indicating high ROS-scavenging efficacy of Cu6MH. It also presented the same tendency for intracellular ·O2− and ·OH levels by dihydroethidium (DHE) (red signal) and hydroxyphenyl fluorescein (HPF) (green signal) probes, respectively. Compared to normal chondrocytes, it presented high ·O2− and ·OH levels for the control group with markedly increased fluorescent intensity, while Cu6MH presented close fluorescent intensity to the normal group, indicating the optimum ·O2− and ·OH scavenging efficiencies. After statistical calculation, the mean fluorescent intensity (MFI) obviously increased for the control group after H2O2 stimulation compared to the normal group. Importantly, the MFI of Cu6MH was declined by $73.0\%$, $56.3\%$, and $63.3\%$ compared to the control group for total ROS, ·O2−, and ·OH, respectively (Fig. 5B). From the above, H2O2 stimulation could increase the intracellular ROS levels, and Cu6MH presented the excellent scavenging ability of intracellular ROS.
**Fig. 5.:** *Antioxidant and anti-inflammatory capacity in cellular levels. (A) Intracellular ROS levels of H2O2 (400 μM, 30 min)-induced chondrocytes after incubation by fluorescent microscopy: total ROS levels (DCF), ·O2− (DHE), and ·OH (HPF), and the corresponding quantification of mean fluorescent intensity (MFI) (B). (C) Genes (IL6, MMP13, MMP3, and Col2α1) expression of H2O2 (400 μM, 30 min)-induced chondrocytes after incubation by qRT-PCR. (D) Immunofluorescent images of IL6 and MMP13 expression of H2O2 (400 μM, 30 min)-induced chondrocytes after incubation by fluorescent microscopy and the corresponding quantification (E). The samples were normal chondrocytes (Normal), H2O2-induced chondrocytes (Control), and H2O2-induced chondrocytes after incubating with 20 μg/ml MH, Cu1.5MH and Cu6MH, respectively. (“*” symbol compared with normal group, **P < 0.01, ***P < 0.001, and ****P < 0.0001, and “#” symbol compared between groups, #P < 0.05, ##P < 0.01, ###P < 0.001, and ####P < 0.0001).*
Inflammation-induced degeneration was a typical feature of OA progression [38]. Thus, the inflammatory gene expression levels were quantified and analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) to examine anti-inflammation ability of CuMHs. Particularly, interleukin 6 (IL6) played the key role in inducing matrix-degrading enzymes [39]. In addition, it was well known that matrix metalloproteinase 13 (MMP13) and MMP3 participated in cartilage degeneration and metastasis, and their up-regulation always resulted in the progression of cartilage degeneration [40]. Conversely, Col2α1 was an important constituent of extracellular matrix and possessed great necessity for cartilage regeneration [41]. From Fig. 5C, H2O2 stimulation markedly increased the levels of inflammation-related genes comparing with normal chondrocytes, where it was 3.5, 6.8, and 10.3 for IL6, MMP13, and MMP3, respectively, for the control group. Conversely, the expression of Col2α1, which was 0.4, was markedly reduced for the control group after H2O2 treatment. Most importantly, Cu6MH exhibited a significant decrease of the expression level of IL6, MMP13, and MMP3 compared to the control group, while there was a limited decrease for MH and Cu1.5MH. Meanwhile, Cu6MH could increase the expression of Col2α1 to some extent, while it did not show obvious changes for Cu1.5MH compared with the control group (Fig. 5C).
Besides, the inflammatory factors (IL6, MMP13, and MMP3) expression of cell supernatant was detected by enzyme-linked immunosorbent assay (ELISA). As shown in Fig. S8, all inflammatory factors markedly increased for chondrocytes after H2O2 stimulation. In addition, MH and Cu1.5MH slightly reduced their expression levels, in agreement with the results of qRT-PCR. However, it presented the similar condition that Cu6MH could most effectively reduce the expression of IL6, MMP13, and MMP3. The above results revealed that Cu6MH could reduce the expression levels of inflammation-related genes and promote the expression levels of chondrogenic gene, thus protecting cartilage to a certain extent.
Furthermore, the expression of inflammatory factors was also verified by immunofluorescent staining. As shown in Fig. 5D and E, after H2O2 treatment, the fluorescent intensity of the control group markedly increased more than that of normal chondrocytes, indicating an increased expression of IL6 and MMP13. However, MH, Cu1.5MH, and Cu6MH decreased the fluorescent intensity of H2O2-induced chondrocytes, especially for Cu6MH. Meanwhile, the IL6 and MMP13 expression levels increased by $389.9\%$ and $308.4\%$, respectively, for the control group compared to normal chondrocytes. Importantly, Cu6MH greatly down-regulated IL6 and MMP13 expression by $54.0\%$ and $54.5\%$, respectively. However, MH and Cu1.5MH only slightly lowered their expressions compared to the control group, showing similar results as those of qRT-PCR and ELISA.
Above all, these results showed that Cu6MH had superior antioxidant and anti-inflammation capacity, as well as could protect chondrocytes away from the attack of oxidative stress by maximally removing intracellular ROS, compared to MH and Cu1.5MH.
## Mitochondrial function repair
ROS is mainly produced by mitochondria, and excessive ROS often lead to the damage of mitochondria [42,43]. Numerous studies confirmed that osteoarthritis was often caused by overproduced ROS [44,45]. Thus, scavenging endogenous ROS and repairing the function of mitochondria were helpful to achieving OA therapy with high efficacy. To confirm the above functions, the ROS levels inside mitochondria were assessed by a MitoSOX red mitochondrial superoxide indicator, which specifically targeted mitochondria to detect ·O2− levels inside mitochondria. As shown in Fig. 6A, for normal chondrocytes, the ·O2− levels were relatively low and almost no red signal was observed. After H2O2 stimulation, red signal markedly increased, indicating that high endogenous ROS levels appeared for the control group. In addition, it was observed that MH and Cu1.5MH could lower red signal to a certain level, equal to slightly decreasing the endogenous ROS levels. Nevertheless, Cu6MH markedly lowered the endogenous ROS levels with obvious decreased red signal observed. After statistical analysis, the MFI of the control group markedly increased compared to normal chondrocytes, while Cu6MH reduced the MFI by the most (Fig. 6D).
**Fig. 6.:** *Mitochondrial function repair effects. (A) Endogenous ROS levels of H2O2 (400 μM, 30 min)-induced chondrocytes after incubation by fluorescent microscopy (MitoSOX: red and DAPI: blue), and the corresponding quantification (D). (B) Mitochondrial membrane potential of H2O2 (400 μM, 30 min)-induced chondrocytes after incubation by fluorescent microscopy (JC-1 aggregates: red and JC-1 monomers: green) and their corresponding quantification (E). (C) Mitochondrial Ca2+ level of H2O2 (400 μM, 30 min)- induced chondrocytes after incubation by fluorescent microscopy and the corresponding quantification (F). (G) ATP content of H2O2 (400 μM, 30 min)-induced chondrocytes after incubation. The samples were normal chondrocytes (Normal), H2O2-induced chondrocytes (Control), and H2O2-induced chondrocytes after incubating with 20 μg/ml MH, Cu1.5MH, and Cu6MH, respectively. (“*” symbol compared with normal group, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001, and “#” symbol compared between groups, #P < 0.05, ##P < 0.01, ###P < 0.001, and ####P < 0.0001).*
In the meantime, the membrane potential of mitochondria was also investigated by a JC-1 probe to reflect the function of mitochondria. The change of membrane potential was revealed by the transition between JC-1 aggregate (red) and JC-1 monomer (green) [46]. As indicated in Fig. 6B, a lot of red signal and little green signal were observed, meaning that a lot of JC-1 aggregates and few JC-1 monomers existed for normal chondrocytes; most of JC-1 aggregates disappeared and lots of JC-1 monomers appeared for normal chondrocytes after stimulation (control group). However, MH, Cu1.5MH, and Cu6MH increased the number of JC-1 aggregates and declined the amount of JC-1 monomers compared to the control group. After statistical calculation, the red/green ratio was 0.2 for the control group and increased by 5.0-, 6.3-, and 12.4-fold for MH, Cu1.5MH, and Cu6MH, respectively (Fig. 6E). Higher red/green ratio was equaled to higher mitochondrial membrane potential, indicating that more energy was produced by mitochondria, which promoted the energy conversion of cells. Thus, Cu6MH could most effectively reserve depolarization of mitochondrial membrane potential caused by oxidative stress compared to MH and Cu1.5MH, showing the excellent mitochondrial protection ability.
In addition, mitochondria plays a significant role in chondrocytes metabolism, participating in the regulation of cellular processes, such as redox homeostasis regulation and cellular Ca2+ balance [47]. Rhod2/Acetoxymethyl ester (AM), a high-affinity Ca2+ indicator excited by visible light, was applied to reflect the level of Ca2+ in cells. As indicated in Fig. 6C, compared to normal chondrocytes, lots of red signal was observed for the control group, indicating that Ca2+ level was markedly enhanced and Ca2+ balance was disordered for chondrocytes after H2O2 stimulation. Nevertheless, MH, Cu1.5MH, and Cu6MH could effectively decrease the Ca2+ level with reduced red signal, especially for Cu6MH. After statistical analysis, the order of MFI was normal group < Cu6MH < Cu1.5MH < MH < control group; corresponding to the order of Ca2+ level was control group > MH > Cu1.5MH > Cu6MH > normal group (Fig. 6F). It also confirmed that Ca2+ balance was redistributed after CuMHs or MH treatment, especially for Cu6MH.
Furthermore, adenosine triphosphate (ATP) is the main energy substance in cells and produced by mitochondria [48–50]. Thus, ATP content was applied to reflect the function of mitochondria. As shown in Fig. 6G, the relative ATP content was 12.6 nmol/mg for normal chondrocytes, which markedly decreased by $73.0\%$ for the control group. Moreover, Cu6MH most effectively rescued the function of mitochondria with the relative ATP content of 8.7 nmol/mg, compared to MH and Cu1.5MH.
Above all, H2O2 stimulation markedly increased the endogenous ROS levels and Ca2+ level and reduced the mitochondrial membrane potential and relative ATP content. Among MH and CuMHs, Cu6MH presented the best effects of scavenging endogenous ROS, down-regulating Ca2+ level, improving mitochondrial membrane potential as well as promoting the ATP production, further protecting mitochondria away from oxidative damage, and activating the mitochondrial function of ROS-induced chondrocytes to achieve OA therapy.
## In vivo therapeutic effect
To evaluate in vivo therapeutic effect, Sprague-Dawley rates were initially treated with anterior cruciate ligament-deficient surgery for 4 weeks followed by intra-articular (IA) injection of CuMHs (20 μg/ml, 100 μl) twice per week until another 4 weeks. The knee joints and synovial fluid of each rats were collected at 4 and 8 weeks to evaluate the therapeutic effect (Fig. 7A). The macroscopic observation of dissected knee joints was illustrated in Fig. 7B. From Fig. 7B, it was observed that the femur and tibia of articular surfaces were not damaged for the sham group and the corresponding cartilage surfaces were smooth. However, the obvious bone erosion and fissures appeared, and cartilage was deteriorated versus time for the OA group. In addition, the deterioration became weak for MH and Cu1.5MH at 4 weeks. Importantly, the apparent reduction of cartilage lesion and erosion existed for Cu6MH at 4 weeks. Meanwhile, the cartilage surface for MH and Cu1.5MH was uneven, with a certain of inflammation observed on the erosion area at 8 weeks. Specifically, Cu6MH markedly improved the inflammation degrees with no obvious erosion and defect areas existed. By Pelletier scoring, it was 3.3 at 4 weeks and 3.7 at 8 weeks, respectively, for the OA group, and it slightly decreased for MH and Cu1.5MH at both weeks. Most importantly, Cu6MH obviously reduced the scores by $80.0\%$ at 4 weeks and $63.6\%$ at 8 weeks, respectively, compared to the OA group, indicating the optimum OA therapeutic effect (Fig. 7C).
**Fig. 7.:** *In vivo OA therapy effect. (A) Time schedule of in vivo experimental design. (B) Macroscopic observation of OA joints after IA injection at 4 and 8 weeks and their corresponding macroscopic scoring (C) (“*” symbol compared with OA group, *P < 0.05, **P < 0.01, and “#” symbol compared between groups, #P < 0.05). (D) IL6 and MMP13 expression of joint fluid by ELISA. The corresponding groups were as follows: normal rats (Sham), OA rats by PBS treatment (OA), OA rats by MH treatment (MH), OA rats by Cu1.5MH treatment (Cu1.5MH), and OA rats by Cu6MH treatment (Cu6MH), respectively. (“*” symbol compared with sham group, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001, and “#” symbol compared between groups, #P < 0.05, ##P < 0.01, ###P < 0.001, and ####P < 0.0001) ACLD, anterior cruciate ligament-deficient.*
Next, the inflammatory factors expression of synovial fluid were also assessed to reflect OA therapeutic effects. As indicated in Fig. 7D, the IL6 and MMP13 expression was markedly ascended for the OA group compared with the sham group, confirming the successful establishment of OA models. In addition, MH and Cu1.5MH could reduce IL6 and MMP13 expression to some extent, with almost close variance analysis to OA group. However, their expressions were markedly declined for Cu6MH, decreased by $29.3\%$ and $43.2\%$ at 4 and 8 weeks for IL6 and by $44.7\%$ and $57.1\%$ at 4 and 8 weeks for MMP13, respectively. Meanwhile, the ROS levels of articular cartilage were also evaluated by a ROS testing kit. At 8 weeks, it increased by 12.0-fold for the OA group compared to the sham group, indicating high ROS levels in articular cartilage for OA rats. Notably, the ROS levels decreased by $63.5\%$ for Cu6MH (Fig. S9). During OA progression, the inflammatory factors and ROS levels were always ascended versus time. However, Cu6MH could effectively reduce the inflammatory factors expression and ROS levels, corresponding to the optimum OA therapeutic effect.
Besides, hematoxylin and eosin (H&E) and Safranine O-fast green staining were applied to evaluate OA therapeutic effect. As illustrated in Fig. 8A, compared to the sham group, the obvious fissures, fibrillation, and matrix loss were observed for the OA group at 4 and 8 weeks. However, slight restoration of matrix happened to MH and Cu1.5MH. Especially for Cu6MH, it displayed obvious cartilage matrix restoration at both weeks. The similar tendency was also observed for Safranine O-fast green staining. For the OA group, thin and irregular cartilage layers were observed together with a lot of destroyed cartilage layers. However, the obvious restoration of cartilage layer and proteoglycan can appeared for Cu6MH, indicating the efficient OA therapy effect (Fig. 8B). After histological scoring, the Mankin score was 10.0 for the OA group, decreased by $37.0\%$, $27.0\%$, and $67.0\%$, respectively, for MH, Cu1.5MH, and Cu6MH at 4 weeks (Fig. 8C). Similarly, at 8 weeks, the Mankin score was 12.0, 8.0, 7.7, and 4.3, respectively, for the OA group, MH, Cu1.5MH, and Cu6MH (Fig. 8C). From the above, it revealed that Cu6MH displayed excellent cartilage protection on OA joints.
**Fig. 8.:** *Histological evaluation of OA therapy. (A) H&E staining images of OA joints after therapy. (B) Safranine O-fast green staining images of OA joints after therapy and the corresponding Mankin scoring at 4 and 8 weeks (C). Immunohistochemical staining results of OA joints after treatment: IL6 (D) and MMP13 (E). The corresponding groups were as follows: normal rats (Sham), OA rats by PBS treatment (OA), OA rats by MH treatment (MH), OA rats by Cu1.5MH treatment (Cu1.5MH), and OA rats by Cu6MH treatment (Cu6MH). (“*” symbol compared with OA group, *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001, and “#” symbol compared between groups, #P < 0.05 and ##P < 0.01).*
Furthermore, the immunohistochemical staining was applied to evaluate the expression of inflammatory factors further to reflect the anti-inflammatory effect. As shown in Fig. 8D, compared to the sham group, the brown staining markedly increased, indicating high expression of IL6 for the OA group. In the meantime, MH and Cu1.5MH could reduce the expression of IL6 to some extent with slightly decreased brown staining. However, apart from the sham group, the lowest positive staining was observed for Cu6MH, verifying the efficient in vivo anti-inflammatory effect. It also presented the same trend for the expression of MMP13 (Fig. 8E). The above results also indicated that Cu6MH optimally reduced the inflammatory factors expression and improved OA microenvironment, finally delaying the progression of OA.
Finally, the in vivo cytotoxicity was further evaluated by the histological analysis of major organs of Sprague-Dawley rats after treatment. From H&E staining of major organs, there were no obvious histopathological necrosis and inflammation lesions observed, close to the sham group, indicating no in vivo cytotoxicity during OA therapy (Fig. S7). Moreover, the content of Cu element in major organs was quantified detected by an inductively coupled plasma-mass spectrometer (ICP-MS). As illustrated in Fig. S8, the Cu contents in Cu1.5MH and Cu6MH groups were close to the sham group for all organs, meaning that Cu elements were removed from the rats and it would not cause additional cytotoxicity to major organs.
From all of the above, it had demonstrated that Cu6MH could effectively suppress the progression of OA with optimum effect. It was known to all that MH possessed a certain level of antioxidant and anti-inflammatory capacity, limited by its poor water solubility and low bioavailability. In addition, researches showed that Cu-based MOF could act as nanoenzymes with excellent ROS-scavenging ability [31]. However, it was expected that Cu-based MOF had favorable biodegradability so that it was possible to give full play to its ability of ROS scavenging for biomedical application. Thus, Cu6MH presented excellent capacity of scavenging both exogenous and endogenous ROS, and repairing the function of mitochondria, so as to regulate the microenvironment of articular cavity and further achieve most effectively protecting cartilage tissue and suppressing OA progression. In addition, it also could maintain relative stability and gradually degraded inside articular cavity, finally resulting in long-term effective OA therapy with no cytotoxicity.
## Conclusion
Avoid intrinsic cytotoxicity of nanoenzymes and bring into full play its ROS-scavenging activity are of high importance for the therapy of OA. Herein, the novel Cu6MH with Cu2+ and MH molar ratio of 6:1 was explored to act as nanoenzyme to alleviate OA. It had confirmed that Cu6MH could effectively scavenge ROS in all directions and activate the function of mitochondria, further regulating the microenvironment of articular cavity. The excellent antioxidant and anti-inflammatory capacity of Cu6MH indicated a very promising strategy for protecting articular cartilage and attenuating OA progression, leading to the remarkable OA therapeutic effects.
## Materials
MH (>$90\%$) and copper (II) chloride dihydrate (CuCl2·2H2O) were commercially obtained from Aladdin Biochemical Technology Co., Ltd (Shanghai, China). Sodium hydroxide (NaOH) was commercially purchased from Chron Chemical Co., Ltd (Chengdu, China). Hydrogen peroxide (H2O2, $30\%$) and ethanol were purchased from Sinopharm Chemical Reagent Co., Ltd (Shanghai, China). All reagents and chemicals were directly used without further treatment.
## Preparation of CuMHs
CuMHs were fabricated through the coupling assembly strategy as previously described [23,32]. In a typical synthesis, 0.04 g of MH and different amounts of CuCl2·2H2O were dissolved in 30 ml of mixed solution containing deionized (DI) water and ethanol with the volume ratio of 1:9. The mixture reacted at 60 °C for 3 h after adjusting pH = 7.4. The final products were obtained after centrifuge and DI water washing for 3 times, and finally vacuum drying overnight for further experiments. The detailed recipe for the preparation of CuMHs and their corresponding symbols are listed in Table S1.
## Basic characterization
The above prepared complexes were respectively characterized by using UV-vis spectroscopy (UV-2700, Shimadzu, Japan), FTIR spectrometer (IRAffinity-1S, Shimadzu, Japan) and XRD (MiniFlex 600, Japan). Their microstructure and element composition were also tested and analyzed by TEM (Hitachi, Japan) coupling with energy dispersive X-ray spectroscopy, and XPS (Thermo ESCALAB 250Xi, USA). Besides, their zeta potential in DI water was investigated by Nano-ZS (Malvern Panalytical, China). The three-dimensional morphology of samples was implemented by AFM (Bruker, USA). Finally, to evaluate the porosity of samples, the BET (Micromeritics ASAP, USA) was applied.
Meanwhile, the in vitro degradability of Cu1.5MH and Cu6MH was also evaluated. In brief, Cu1.5MH and Cu6MH (5 mg/ml) were immersed in 50 μM H2O2 (pH = 6.8) with a shaker (100 rpm, 37 °C) to simulate the OA microenvironment. The solutions at predetermined time points (0, 3, 6, 12, 24, 48, and 72 h) were collected and centrifuged at a high speed (10,000 rpm, 30 min). Cu concentration of the collected solutions were evaluated by using the dithizone chromogenic method (Macklin, China) according to the previous work with slight adjustment [51]. Briefly, 50 μl of sample solution was mixed with 600 μl of dimethyl sulfoxide (DMSO) solution containing $0.05\%$ dithizone and $1\%$ sodium acetate (Aladdin, China) in DMSO. After sufficiently mixing, 100 μl of solution was taken out and added to a 96-well plate, and the absorbance was detected by a microplate reader at 500 nm. Combing with Cu contents by ICP-MS, the Cu release rate of Cu1.5MH and Cu6MH was obtained.
## ROS-scavenging capacity investigation
The ROS-scavenging abilities of CuMHs were investigated by ·OH (Beijing baiaolaibo biotechnology, China), ·O2− (Nanjing Jiancheng Bioengineering Institute, China) and H2O2 (Beyotime Biotechnology, China)-scavenging capacity assay kits, respectively after following the manufacturer’s instruction. Briefly, for ·OH scavenging ability, different concentrations of Cu1.5MH, Cu6MH, Cu12MH, Cu24MH, and MH (10, 20, and 50 μg/ml) were dispersed in the corresponding working solutions. The absorbance was observed at 550 nm by a microplate reader (Thermo Scientific, USA) after reacting for 1 min. Similarly, the absorbance was respectively obtained at 550 and 520 nm by using the microplate reader for investigating ·O2− and H2O2 scavenging capacity.
Meanwhile, the ROS-scavenging effect of CuMHs was evaluated by ESR spectroscopy (ESR, Bruker A300, Germany) including ·OH and ·O2−. Briefly, 5-tert-butoxycarbonyl 5-methyl-1-pyrroline-N-oxide (BMPO) could react with ·OH and form stable adduct (BMPO/·OH) [52]. The signal of solution was recorded after mixing 5 mg/ml FeSO4, 100 mM BMPO, 10 M H2O2, and 20 μg/ml MH or CuMHs. Specifically, for ·O2− testing, ·O2− was initially generated by mixing xanthione (10 mM) and xanthione oxidase (1 U/ml) in phosphate buffer saline (PBS) buffer followed by the addition of 20 μg/ml MH or CuMHs. BMPO was applied to trap ·O2− and form the spin adduct (BMPO/·OOH) [53].
Besides, O2 release concentration was determined by a dissolved oxygen meter (INSEA, China). In brief, different concentrations of CuMHs (10, 20, and 50 μg/ml) were added to 100 mM H2O2 and stirred for a certain of time. The data was recorded every 60 s by dissolved oxygen meter.
## Isolation and culture of chondrocytes
The femur and tibia from Sprague-Dawley rats (male, 3 to 5 d old) were applied to extract chondrocytes as previously described [40]. Briefly, the tissue was treated with Dulbecco’s modified eagle’s medium (Gibco, USA) containing $0.2\%$ type II collagenase (Col2, Biofox, China) for a certain of time (6 h, 37 °C). And then, the undissociated substance was filtered by cell strainer (pore size of 0.45 μm). Chondrocytes were cultured with Dulbecco’s modified eagle’s medium containing $10\%$ fetal bovine serum (FBS, Every Green, China), and $1\%$ penicillin-streptomycin solution (Solarbio, China) in the incubator (37 °C, $5\%$ CO2). At last, the medium was replaced every 2 days and the chondrocytes were passaged only 2 generations for further experiments.
## Cell viability evaluation
The cell viability of CuMHs was tested by CCK-8 (Biosharp, China) method. In detail, chondrocytes were cultured into 96-well plate (8,000 cells per well, 100 μl) and incubated for 24 h (37 °C, $5\%$ CO2). The cultured medium was replaced with fresh medium containing different concentrations of MH or CuMHs (0, 1, 2, 5, 10, 20, 50, 100, and 200 μg/ml) respectively. After 24 h of incubation, chondrocytes were treated with fresh culture medium (100 μl, $10\%$ CCK-8) after gently washing with PBS 3 times. After incubated for another 2 h, the absorbance was quantified measured (450 nm) by the microplate reader.
## Live/dead staining assay
Chondrocytes were seeded into 6-well plates (3 × 105 cells per well) for further experiments. The inflammatory cell model was built by stimulating the chondrocytes with H2O2 solution (400 μM) for 30 min and then cocultured with 20 μg/ml MH, Cu1.5MH, or Cu6MH for another 24 h. Next, the cells were washed with PBS buffer 3 times followed by adding 500 μl of Calcein-AM/Propidium Iodide (AM/PI) staining solution (Beyotime Biotechnology, China) in the dark. Finally, the chondrocytes were washed again with PBS and photographed by using a fluorescent microscopy (OLYMPUS BX53F, Japan), and the number of live and dead cells was quantified by ImageJ.
## Intracellular ROS level testing
To verify intracellular ROS levels, chondrocytes were observed by fluorescent microscopy after treatment and staining. Details were as follows: The chondrocytes (2 × 105 cells per well) were seeded in 6-well plates and incubated for 24 h. The chondrocytes were stimulated by H2O2 solution (400 μM) for 30 min before incubating with 20 μg/ml MH, Cu1.5MH, or Cu6MH overnight, respectively. Subsequently, the chondrocytes were respectively cocultured with 20 μM DCF (Solarbio, China) for total ROS testing, 10 μM HPF (maokangbio, China) for ·OH testing, or 5 μM DHE (Beyotime Biotechnology, China) for ·O2− testing before PBS washing 3 times. Finally, the fluorescent intensity of chondrocytes was observed by fluorescent microscopy and the corresponding quantification was analyzed by ImageJ.
## Cellular uptake investigation
Chondrocytes were seeded in confocal dishes (1 × 105 cells), and then incubated with Cy5.5-Cu1.5MH or Cy5.5-Cu6MH (20 μg/ml) in the dark for 24 h. After incubation, chondrocytes were washed with PBS 3 times and then fixed with paraformaldehyde ($4\%$ PFA, Biosharp, China) for 10 min. Later, the cytoskeleton was stained with the actin-tracker green-488 (Beyotime Biotechnology, China) following the protocols, and the nuclei was stained with 4, 6-diamidino-2-phenyindole dilactate (DAPI, Beyotime Biotechnology, China) for 10 min before PBS washing 3 times. Finally, the images were captured with a confocal scanning microscope (Leica, Germany), and the corresponding fluorescence intensity was quantified by ImageJ. Specifically, Cy5.5-Cu1.5MH and Cy5.5-Cu6MH were prepared by the following procedures. In detail, 50 mg of Cu1.5MH or Cu6MH was dispersed in DMSO followed by the addition of 50 mg/ml Cy5.5 N-hydroxysuccinimide ester (Cy5.5 NHS ester, Lumiprobe, USA) overnight. The final product was collected by vacuum drying after methanol washing. Besides, chondrocytes were incubated with Cu6MH (20 μg/ml) for 12 h, and chondrocytes were also embedded in paraffin and cut into slice for observation by TEM.
## Inflammatory factor evaluation
The inflammation factors expression levels of chondrocytes were initially investigated by qRT-PCR. Similarly, the chondrocytes were cultured into 6-well plates (2 × 105 per well). After H2O2 (400 μM) stimulation for 30 min, the chondrocytes were treated with different samples (20 μg/ml) overnight. And then, total RNA of chondrocytes was extracted by an RNA extraction kit (Magen, China) following the protocols. The corresponding cDNA was synthesized according to reverse transcription kit (TaKaRa, China), and qRT-PCR was implemented with a LightCycler® system (Roche, Switzerland). Finally, the gene expression levels were analyzed by a 2−ΔΔCT method and normalized with glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and the corresponding detailed primer sequences are listed in Table S2. Besides, the supernatant of the corresponding chondrocytes was collected, and the expression levels of inflammatory factors (IL6, MMP13, and MMP3) were also detected using ELISA kits (Meinian, China) according to the manufacturer’s instructions.
To further verify IL6 and MMP13 expression levels, the treated chondrocytes (2 × 105 per well) were fixed with PFA ($4\%$, 15 min), stimulated with $3\%$ H2O2, and blocked with bovine serum albumin working solution (ZS-Bio, China) for 15 min. Later, chondrocytes were stained against the antibody of anti-IL6 or anti-MMP13 (1:200 dilution, Bioss, China) overnight after washing with PBS buffer 3 times. Next, the cells were reacted against the specific fluorescein isothiocyanate-labeled secondary antibody (1:100 dilution, Boster, China) in the dark (37 °C, 1 h) before PBS buffer washing. Meanwhile, the nuclei of cells was costained using DAPI in the dark (15 min). Finally, the images were captured by fluorescent microscopy, and their corresponding quantification was analyzed by ImageJ.
## Endogenous ROS levels
The endogenous ROS levels from mitochondria were evaluated by MitoSOX red mitochondrial superoxide indicator (Yeason, China) by following the procedure of manufacture. In brief, chondrocytes were seeded in 6-well plates (2 × 105 per well) and stimulated with H2O2 (400 μM) for 30 min. Chondrocytes were cocultured with different types of samples (20 μg/ml, 24 h) before PBS washing. After incubation with MitoSOX (5 μM, 30 min) at 37 °C, chondrocytes were washed with PBS another 3 times. And then, the cells were fixed with $4\%$ PFA, and the nuclei was stained with DAPI solution (15 min). Ultimately, the images were photographed by fluorescent microscopy and their corresponding intensity was quantified analyzed by ImageJ.
## Mitochondrial membrane potential
The JC-1 probe (Beyotime Biotechnology, China) was applied to evaluate the mitochondrial membrane potential of chondrocytes. In brief, chondrocytes (2 × 105 per well) were treated with 400 μM H2O2 for 30 min followed by incubating with samples (20 μg/ml) for another 24 h. After incubating with JC-1 probe (20 min, 37 °C), the mitochondrial membrane potential was observed using by fluorescent microscopy, and the red/green fluorescence ratio was analyzed by ImageJ.
## Mitochondrial calcium levels
The mitochondrial calcium levels were investigated by Rhod2/AM cell permeable calcium ion (Ca2+) fluorescent probe (Yeasen, China). In brief, H2O2 (400 μM, 30 min) treated chondrocytes (3 × 104 per well) were incubated with different kinds of samples (20 μg/ml) for 24 h and stained with Rhod2/AM probe (4 μM, $0.02\%$ Pluronic F127, Sigma, USA) at 37 °C for another 30 min. After PBS washing for 3 times, the images were obtained by fluorescent microscopy and the corresponding fluorescent intensity was quantified analyzed by ImageJ.
## ATP production
The ATP content of chondrocytes was tested by an ATP content detection kit (Beyotime biotechnology, China). In detail, chondrocytes (3 × 105 per well) were treated with 400 μM H2O2 (30 min) and then cocultured with samples (20 μg/ml) for another 24 h. The supernatant was saved and tested by the microplate reader (BioTek, USA) following the protocol. In order to eliminate errors of protein concentration, the final results were denoted as nM/mg protein based on a BCA protein quantification kit (Beyotime biotechnology, China).
## Establishment of OA models
Sprague-Dawley rats (male, 150~200g) were obtained from Guangxi Medical University Experimental Animal Center, and all experiments were implemented in accordance with institutional guidelines and approved by Animal Ethics Committee of Guangxi Medical University. The OA model was established by anterior cruciate ligament-deficient surgery of Sprague-Dawley rats and fed for 1 month before further experiments. The rats were separately divided into 5 groups: [1] Sham: rats were only treated with incising the joint capsule, [2] OA: OA rats with PBS injection (100 μl), [3] MH: OA rats with MH injection (20 μg/ml, 100 μl), [4] Cu1.5MH: OA rats with Cu1.5MH injection (20 μg/ml, 100 μl), and [5] Cu6MH: OA rats with Cu6MH injection (20 μg/ml, 100 μl). The rats were treated with IA injection twice per week until 4 or 8 weeks before overdose of pentobarbital sodium, and the corresponding knee joints and other major organs were collected for further analysis. Specifically, the knee joints were imaged and evaluated by Pelletier’s macroscopic scoring.
## Inflammatory factors expression of synovial fluid
IL6 and MMP13 expression of joint fluid was measured by ELISA kits by following the protocols. The corresponding absorbance was obtained by the microplate reader at 450 nm.
## ROS levels of articular cartilage
The ROS levels of articular cartilage were measured by a tissue ROS detection kit (Bestbio, China) according to the manufacturer’s instruction. In brief, 50 mg of cartilage tissue was made into homogenate with 1 ml of PBS buffer, and the supernatant of homogenate was centrifuged (1,000 rpm, 4 °C) for 3 min and collected for further analysis. Then, 1 μl of ROS probe (BBoxiProbe) was added to 200 μl of collected solutions, and the mixture was incubated at 37 °C for 30 min. Finally, the absorbance of mixture was detected by the microplate reader (BioTek, USA) at an excitation wavelength of 488 nm and an emission wavelength of 610 nm.
## Histological staining of joint section
After euthanasia at 4 and 8 weeks, the knee joins were fixed with $4\%$ PFA and decalcified in ethylene diamine tetra acetic acid (EDTA, pH = 7.2, Macklin, China) by Ultrasonic Decalcifying Unit (USE 33, Germany) for 1 month. The joint samples were embedded and sectioned in paraffin (5-μm thickness) for further staining. The joint sections were respectively stained with H&E (Solarbio, China) and Safranine O-fast green (Safranine O, Solarbio, China) for histological analysis, and OA progression was evaluated according to Mankin scoring as previously described by 2 blinded observers [41]. Specifically, for immunohistochemical staining, the sections were initially incubated with rabbit polyclonal anti-IL6 or anti-MMP13 (Boster, China) antibodies overnight at 4 °C followed by binding with biotinylated secondary antibodies (ZS-Bio, China). The slides were photographed by optical microscopy (OLYMPUS BX53F, Japan).
## In vivo cytotoxicity evaluation
Major organs including heart, liver, spleen, lung, and kidney from Sprague-Dawley rats were initially fixed in $4\%$ PFA for 2 days and processed as paraffin sections followed by H&E staining. Besides, the Cu2+ contents of each organs were quantified analyzed by ICP-MS (Varian, 720-ES, USA).
## Statistical analysis
All data were expressed as means ± SD and analyzed by one-way analysis variance (ANOVA) or Student t test for comparisons by GraphPad Prism 8.0. The differences with $P \leq 0.05$ indicate significance.
## Data Availability
All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.
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|
---
title: Content validation of a questionnaire to assess the knowledge of pediatricians,
family, and community physicians on obesity
authors:
- Daniel Servigia Domingos
- Eduardo Juan Troster
- Teresa Cristina Alfinito Vieira
journal: Revista Paulista de Pediatria
year: 2023
pmcid: PMC10013993
doi: 10.1590/1984-0462/2023/41/2022063
license: CC BY 4.0
---
# Content validation of a questionnaire to assess the knowledge of pediatricians, family, and community physicians on obesity
## ABSTRACT
### Objective:
The aim of this study was to validate the content of a questionnaire in order to assess the attitudes and practices in childcare consultations, knowledge on overweight and obesity, their risk factors, and barriers in addressing the issue by pediatricians and family physicians.
### Methods:
The Delphi technique was used, with the objective of reaching a consensus on a certain subject, through experts’ opinions. The content validity index (CVI) of each item, axis, and questionnaire was calculated. The inter-rater reliability was calculated using an agreement coefficient suitable for the answer distribution such as Gwet's AC2 with ordinal weight.
### Results:
A total of 63 experts were invited to assess and give their opinion on the questionnaire. In all, 52 accepted the invitation and analyzed the instrument. After two rounds, the questionnaire reached the proper CVI for the study and was considered complete, with its final version having 40 questions, a final index of $95\%$, and an inter-rate reliability of 0.905.
### Conclusions:
This instrument, developed to assess attitudes and practices, knowledge, and barriers found in addressing the obesity by primary care physicians, obtained a CVI greater than 0.8 and an excellent agreement coefficient of the 52 judges. Therefore, its content can be considered validated.
## Objetivo:
Validar o conteúdo de um questionário para a avaliação das atitudes e práticas nas consultas de puericultura, para o reconhecimento do sobrepeso e da obesidade, seus fatores de risco e barreiras encontradas para abordar o tema por pediatras e médicos da família.
## Métodos:
Foi utilizada a técnica de Delphi, com o objetivo de alcançar um consenso sobre determinado assunto, por meio da opinião dos especialistas. Foi mensurado o índice de validade de conteúdo por item, por eixo e para o questionário geral. A concordância entre os avaliadores foi calculada utilizando-se coeficiente de concordância adequado à distribuição de respostas, tal como o AC2 de Gwet com ponderação ordinal.
## Resultados:
Foram convidados 63 juízes para avaliar e opinar sobre o questionário. Cinquenta e dois aceitaram o convite e analisaram o instrumento. Após duas rodadas, o questionário foi finalizado por atingir o índice de validação de conteúdo (IVC) adequado para o presente estudo. O questionário final terminou com 40 questões, e o índice final do questionário atingiu $95\%$. O índice de concordância geral entre os juízes foi de 0,905.
## Conclusões:
Este instrumento, construído para avaliar as atitudes e práticas, conhecimento e barreiras encontrados na abordagem do problema da obesidade por médicos da atenção básica, obteve IVC maior que 0,8 e excelente índice de concordância dos 52 juízes. Assim, seu conteúdo pode ser considerado validado.
## INTRODUCTION
Obesity has become a global public health problem and was classified as the epidemic of the 21st century. In approximately 40 years, the global prevalence of overweight and obesity has increased eightfold in the 5–19 age group. 1 This has worsened in the past 20 years, with the global prevalence of children with excessive weight having jumped from 4.9 to $5.6\%$ in 2016, reaching 340 million children aged between 5 and 19 years and 38 million children aged under 5 years in 2019, according to the World Health Organization (WHO). 2 In Brazil, the 2019 National Health Survey showed that the percentage of obese adults increased from 12.2 to $26.8\%$ (from 2003 to 2019), while $26.1\%$ of adolescents aged 15–19 years were overweight. 3 In the United States, obesity is associated with an annual cost increase of $36\%$ with health and $77\%$ with medication, compared to the expenses of people with normal weight. 4 In Brazil, in 2018, $16\%$ of the total hospital admissions through the Unified Health System were due to comorbidities associated with obesity, such as arterial hypertension and diabetes, at a cost of BRL 3.84 billion. 5 According to a 2016 WHO survey, obesity is the fifth leading cause of death, accounting for $23\%$ of ischemic diseases and $44\%$ of diabetes cases globally. 6 Studies have shown that obese children and adolescents are more likely to remain obese throughout their lives. 7
Certain periods of childhood are considered critical for the development of obesity, such as the neonatal period, the first year of life, the ages between 3 and 7 years, and adolescence. 8 Evidence for the period known as “1000 days,” which covers from the day of conception to the second year of life, is increasing. This phase is a window of opportunity for environmental and nutritional interventions in children at risk of obesity, which, if successful, could result in improving their body composition in the long term.
Family physicians and pediatricians are primary care professionals who, by monitoring children and their families regularly from prenatal care to adulthood, can identify overweight and obesity, thus being able to carry out an early intervention that may result in positive changes in the child's weight gain. 9 The question is whether these professionals find it difficult to detect overweight and obesity and their risk factors at an early stage. An American study applied a questionnaire to primary care physicians and found out that only $26\%$ of them diagnosed obesity correctly, $56\%$ thought they had received sufficient training to prevent and treat obesity, and $63\%$ believe that they have limited time to talk about nutrition. 10 In 2006, another American study applied a questionnaire to pediatricians with the objective of evaluating attitudes, practices, and barriers found by pediatricians related to obesity. Once identified, a series of policies were proposed to address it. In 2017, the same questionnaire was applied, and an improvement was observed in most of the flaws pointed out in 2006. 11 *The hypothesis* raised is whether a questionnaire would be able to measure the knowledge of pediatricians and family physicians regarding the diagnosis of overweight, obesity, and their risk factors, in addition to identifying the professionals’ difficulties in facing the issue. As there is no validated instrument that evaluates the practices, knowledge, and barriers related to the obesity of primary sector physicians, in Portuguese, it was decided to develop an instrument to validate its content and thus identify the aspects that, in primary health care attention, need to be improved in terms of approach, diagnosis, and prevention of overweight, with the objective of contributing to the decrease in obesity prevalence in children and adolescents in Brazil.
## METHOD
This is a study with the subsequent application of questionnaires, aiming to validate an analysis instrument. The Delphi technique was used to judge the information and reach a consensus among experts on the given subject. This technique consists of a series of rounds of questionnaires with controlled feedback, allowing experts (judges) to express their opinions on a given topic, in order to find the most reliable consensus on the topic. It is a useful technique when a group's judgment is more reliable than individual opinions and has the advantage of including geographically distant participants in a simple, quick, and low-cost manner. 12 To validate the content, some questionnaires were submitted to a group of judges considered experts on the topic in question. The following inclusion criteria were applied: pediatric endocrinologist or pediatric nutrologist, as they are professionals used to deal with obesity. Professionals who do not have qualifications as specialized physicians in the area were excluded from the sample. The sample included professionals from different regions of Brazil with expertise in the area and availability to participate in the study. A convenience sample was used. We met experts known to the authors and asked for indication of other professionals. Those with the highest academic titles and used to treating patients with obesity were prioritized. A minimum number of 20 was initially stipulated to start the study. Due to the fear of dropouts during the project, 63 judges were invited.
An e-mail invitation was sent to each judge with explanations about the research objectives and the Informed Consent Form. The instrument was applied through the online tool Google Forms, and the experts had 10 days to reply with their answers.
A Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) was used to classify the experts’ opinions. 12 Each question, each topic axis, and the questionnaire as a whole were evaluated as follows.
Each topic was evaluated using four questions created by the authors in order to assess the representativeness, relevance, clarity, and coherence of the content, as these items are the most commonly used criteria in the content validity assessment sheet. 12 *Each axis* was evaluated through two close-ended questions and an open-ended one: The questionnaire as a whole was evaluated through two close-ended questions and an open-ended one: The initial questionnaire had 35 questions distributed in three axes: Axis 1: “Attitudes and practices of pediatricians or family physicians during the childcare”; Axis 2: “Theoretical knowledge about obesity diagnosis and identification of its risk factors”; and Axis 3: “Barriers found by family physicians and pediatricians to address the obesity issue during the appointments.”
Scientific articles, guidelines from the Brazilian Society of Pediatrics, and the WHO were used as references for the preparation of the questions. 13,14,15,16,17,18,19,20,21 At the end of each round, the content validation index (CVI) was measured. To finish the Delphi technique rounds and validate the questionnaire, the CVI should be greater than $80\%$.
The CVI measures the percentage of experts who agree on certain aspects of the instrument. The score is calculated by adding up items that were marked with a “4” or “5” by the experts, dividing this number by the total number of responses, and multiplying the result by 100. New Delphi rounds were carried out until reaching $80\%$ CVI.
In the characterization of the experts, the categorical variables were described by absolute and relative frequencies and the numerical variables by means or medians and standard deviations or interquartile range.
In the evaluation phase using the Delphi technique, the experts’ answers were classified by absolute frequencies and percentages, to verify the clarity of alternatives and statements, the coherence between statements and alternatives, and the relevance and pertinence of the questions.
The questionnaire evaluations were described by the percentages of each type of answer. The CVI was calculated per item (CVI-i), per axis (CVI-a), and for the full questionnaire (CVI-q), according to the aspect evaluated. The CVI-a is the average of the CVI-i of each item of the questions referring to the analyzed axis, and the CVI-q is the average of the CVI-i of each item evaluated in all the questions. The analysis was performed using the SPSS software, version 24. Data were collected and stored in Google Drive and exported to Microsoft Excel, where the analyses were performed.
The agreement among the experts was calculated using an inter-rater reliability measurement appropriate to the answer distribution, such as Gwet's AC2 with ordinal weight, 22 with the coefficients followed by $95\%$ confidence intervals and p-values for the test of hypotheses. The agreement coefficients were compared to the classification presented by Altman, 23 which considers poor the coefficients lower than 0.2, reasonable between 0.2 and 0.4, moderate between 0.4 and 0.6, good between 0.6 and 0.8, and excellent those above 0.8. For this study, CVI values or agreement coefficients over 0.80 were considered satisfactory. The analyses were performed using the irrCAC, an R package. 24 The survey project was submitted for analysis by the Survey Ethics Committee and approved by Statement No. 4.636.789, CAAE: 43156021.4.0000.0068, issued on April 8, 2021.
## RESULTS
A total of 63 experts were invited to assess and give their opinion on the questionnaire. In all, 52 accepted the invitation and analyzed the instrument, and 11 accepted the invitation but were unable to respond within the deadline. At the beginning of the questionnaire, the experts were asked the following question: “Do you think obesity is handled well by family physicians or pediatricians?” $65.4\%$ disagreed. The data of the 52 experts are presented in Table 1.
**Table 1.**
| Number of specialists | 52 |
| --- | --- |
| Median age | 47.4 years |
| Minimum and maximum age | 32–79 years |
| Female | 75% |
| Regions of Brazil | Regions of Brazil |
| South | 9.6% |
| Southeast | 53.8% |
| Central-West | 13.5% |
| Northeast | 13.5% |
| North | 9.6% |
| Area of expertise | Area of expertise |
| Endocrinology | 82.7% |
| Nutrology | 17.3% |
| Experience in the area of expertise (years) | Experience in the area of expertise (years) |
| <5 | 7.7% |
| 5–15 | 28.8% |
| 16–25 | 38.5% |
| 26–35 | 15.4% |
| >35 | 9.6% |
| Workplace | Workplace |
| Public service only | 11.5% |
| Private service only | 5.8% |
| Private and public services | 82.7% |
| Title | Title |
| Specialist title | 21.2% |
| Master's degree | 28.8% |
| Doctorate degree | 42.3% |
| Postdoctoral degree | 7.7% |
| Percentage of overweight patients seen in the week | Percentage of overweight patients seen in the week |
| <10 | 27% |
| 11–20 | 48% |
| 21–30 | 19% |
| >30 | 6% |
In the first round, the experts expressed their opinions for each item of the 35 questions, according to the Likert scale. Questions 8 and 9 did not reach the minimum CVI of $80\%$ for clarity of alternatives, and question 31 did not reach the CVI for clarity of statement. Also, $100\%$ of the experts found the three axes relevant, and $98\%$ found them comprehensive. Regarding the questionnaire, $98\%$ rated it as relevant and comprehensive.
After the statistical analysis and reading experts’ comments, the questionnaire was reformulated. In addition to the questions with CVI<$80\%$, questions 1, 29, and 30 were modified based on the experts’ opinions, even having reached CVI>$80\%$. The Likert scale of axis 1 was changed based on suggestions, and four new questions were added. The changes were submitted for analysis by the experts in a second round.
In the second round, the same 52 experts analyzed and answered the questionnaire. In total, $86\%$ of them agreed to modify the Likert scale of axis 1. All modified questions achieved a CVI greater than $80\%$, and the four added questions obtained a CVI above $90\%$.
After two rounds, the questionnaire reached the CVI appropriate for the study, with its final version containing 40 questions. The final CVI of all questions was over $80\%$. The CVI of the axis and the final questionnaire can be found in Table 2.
**Table 2.**
| Axis | CVI |
| --- | --- |
| 1 (questions 1–15) | 94.26 |
| 2 (questions 16–31) | 95.16 |
| 3 (questions 32–40) | 95.99 |
| Final questionnaire | 95.01 |
After the two rounds, the expert agreement index was calculated using Gwet's AC2 inter-rater reliability coefficient. In the second round, the agreement indexes for clarity of the statements, clarity of the alternatives, coherence, relevance, and general agreement were excellent. After the final changes, the inter-rater reliability was calculated again with 40 questions, and the data are shown in Table 3.
**Table 3.**
| Aspect | Agreement coefficient (95% CI) | p-value |
| --- | --- | --- |
| Clarity of the statement | 0.896 (0.880; 0.913) | <0.001 |
| Clarity of the alternatives | 0.843 (0.807; 0.878) | <0.001 |
| Coherence | 0.896 (0.877; 0.916) | <0.001 |
| Relevance | 0.970 (0.958; 0.981) | <0.001 |
| General | 0.905 (0.893; 0.918) | <0.001 |
## DISCUSSION
The minimum requirement for the development of a measurement instrument is to have its content validated as relevant and representative. 25 The validation process is one of the most common challenges in the preparation of this type of instrument. This study faced this challenge and validated the content of a questionnaire that will serve as an instrument to assess the attitudes, the practices, and the knowledge of family physicians and pediatricians in relation to the diagnosis, prevention, and treatment of obesity, as well as to identify the barriers found by these professionals to address this topic.
The validity of a measurement tool is a critical factor in its selection and application in both professional practice and academic research. An instrument is valid when it measures what it was supposed to measure. 26 The content is validated by a panel of experts who judge the instrument elements and categorize the tool according to the relevance and representativeness of its content. 27 *There is* no consensus in the literature on the minimum number of experts. Grant and Davis do not specify an exact number, but often the studies use a group of up to 10 experts. These authors also propose that the decision on the number of experts depends on their level of specialization, their experience, and their knowledge on the subject being evaluated. 28 This study invited 63 experts. There was also a concern about the quality of the experts. Half of them had a doctorate or a postdoctoral degree, setting up a panel of experts with a high degree of specialization. Additionally, they came from all regions of Brazil, contributing opinions based on their different regional realities.
At the end of the study, more than $80\%$ of the experts responded not only to the first round but also to the second. This high number of experts could have been a problem for the study, as it is known that the higher the number of people evaluating a topic, the harder it is to reach a consensus, which could lead to many evaluation rounds. Nevertheless, this was not the case, and a consensus was reached after just two rounds.
The low abstention observed in this study can be explained by the use of an online form that facilitated the experts’ answers and reminded them about the evaluation each week. Another reason that may justify such adherence is the fact that the prevalence of obesity has been increasing in recent years and that its negative impact on individual and collective health has encouraged the experts to contribute with an instrument that can improve the management of such disease in the health system. 1,10,11 Upon the receipt of the experts’ answers, the questionnaires were subjected to quantitative and qualitative analyses. There is more than one approach to verify the validity of an instrument content, as well as several statistical methods for data analysis. Some studies classify these methods into two categories: indexes related to content validity and indexes of general agreement. 29 This study used both methods for the analysis.
Polit and Beck proposed a CVI>0.78 for each item of the questionnaire and a mean CVI>0.9 for the content to be considered validated. 30 This study required an index >0.8 for both measures. At the end of the study, the CVI of all instrument items was above 0.8. The mean CVI of all axes and the mean CVI of the final questionnaire were both above 0.9, which allowed the content of the instrument to be considered validated and representative.
There are several ways to calculate the agreement coefficient. This study chose to use Gwet's AC2, which is more appropriate when there are concentrations of responses in one direction, that is, when the proportion of favorable and unfavorable responses is not the same. 22 After all the modifications, the experts agreed that the statements and alternatives were clear and that the questionnaire was coherent and relevant, as all these items had an index greater than 0.8 with statistical significance. The final agreement coefficient was excellent, reaching 0.905 in the general analysis, indicating that, despite the high number of experts, they agreed with each other.
As limitations of the study, we can mention the general scope of the questionnaire, which was 0.78 in the first round. A possible justification for this index lower than 0.8 may be the fact that not all the causes of obesity were addressed in the questionnaire — only the main ones, because obesity is multifactorial, and it was not possible to include all causes. 13 *Conducting a* pilot test with the application of the final instrument to the target audience could also bring improvements to the tool. The lack of face-to-face meetings with the experts to clarify doubts regarding the evaluation steps could negatively contribute to the results. Therefore, to minimize this risk, the guidelines of some authors were followed for the instrument validation process, such as sending an invitation letter, the references, the study objective, and a detailed instruction manual on how to proceed with the evaluation. The validation process is more comprehensive than content validation. 26 Therefore, it is necessary to subject the questionnaire to other psychometric tests, such as reliability or construct validation, for example, before applying it in research.
The development of a questionnaire that can assess whether family physicians and pediatricians know how to diagnose obesity and overweight, whether they know how to identify their risk factors, and what are the barriers they find in addressing the issue can be useful in improving pediatric patient care and adopting effective preventive measures against obesity, as these professionals are inserted in a strategic position in the public health system, which is the primary sector. 9 These physicians are essential in the detection of obesity, the application of preventive measures, and the assistance in and implementation of public policies to fight the increased prevalence of obesity.
This developed instrument had a CVI>0.8, and an excellent agreement coefficient from the judges, which validate the instrument. Therefore, the instrument can be considered relevant to identify aspects that need to be improved with regard to the approach, diagnosis, and prevention of overweight in primary health care attention.
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|
---
title: Prevalence of dyslipidemia, atherogenic and cardiovascular risk in overweight
and obese adolescents
authors:
- Clara dos Santos de Andrades
- Victória Volkweis Poletti
- Vera Elizabeth Closs
- Andreia da Silva Gustavo
- Margareth da Silva Oliveira
- Márcio Vinícius Fagundes Donadio
- Ana Maria Pandolfo Feoli
journal: Revista Paulista de Pediatria
year: 2023
pmcid: PMC10014021
doi: 10.1590/1984-0462/2023/41/2021312
license: CC BY 4.0
---
# Prevalence of dyslipidemia, atherogenic and cardiovascular risk in overweight and obese adolescents
## Abstract
### Objective:
To analyze the lipid profile and cardiovascular risk of overweight and obese adolescents and correlate the findings with anthropometric measurements.
### Methods:
This is a cross-sectional study on overweight and obese adolescents of both sexes (aged 14 to 18 years old). The collected variables were sex, weight, height, age, total cholesterol, triglycerides, High-density lipoprotein (HDL) and low-density lipoprotein (LDL). The Atherogenic Index of Plasma and Castelli Risk Indices I and II were calculated. These indices were classified into cutoff points to stratify cardiovascular risk. The anthropometric profile was evaluated by Z score according to Body Mass Index for age. Significance level was considered as p≤0.05.
### Results:
A total of 146 adolescents participated in the study; the mean age was 16.4±1.1 years and most of them were girls ($74.7\%$) and obese ($52.7\%$). The prevalent dyslipidemias were high triglycerides ($47.9\%$), LDL ($26.7\%$), total cholesterol ($37.7\%$), and low HDL ($46.6\%$). Most adolescents presented increased atherogenic risk according to the Atherogenic Index of Plasma ($55.5\%$); $15.1\%$ presented high cardiovascular risk according to Castelli Risk Index I; and $13.7\%$, according to Castelli Risk Index II. Boys presented higher values of anthropometric measurements and Castelli Risk Indices I and II in relation to girls — who, conversely, presented higher values of HDL. There was a positive correlation of the Z score with Atherogenic Index of Plasma and a negative correlation with HDL.
### Conclusions:
The adolescents of the study presented high prevalence of cardiovascular and atherogenic risk according to the evaluated indices. In addition, the increased cardiovascular risk was correlated with higher Body Mass Index.
## Objetivo:
Analisar o perfil lipídico e os índices de risco cardiovascular de adolescentes com sobrepeso e obesidade e correlacionar os achados com medidas antropométricas.
## Métodos:
Estudo transversal com adolescentes com sobrepeso ou obesidade de ambos os sexos (14 a 18 anos). Foram coletadas as variáveis: sexo, peso, altura, idade, colesterol total, triglicerídeos, lipoproteína de alta densidade (HDL-c) e lipoproteína de baixa densidade (LDL-c). Calcularam-se o índice aterogênico plasmático e os índices de Castelli I e II. Eles foram classificados em pontos de corte para estratificar o risco cardiovascular. O perfil antropométrico foi avaliado por meio do escore Z com base no índice de massa corporal para a idade. Considerou-se o nível de significância p≤0,05.
## Resultados:
Foram incluídos 146 adolescentes, com média de idade de 16,4±1,1 anos, a maioria do sexo feminino (74,$7\%$) e obesa (52,$7\%$). As dislipidemias prevalentes foram: triglicerídeos (47,$9\%$), LDL-c (26,$7\%$), colesterol total (37,$7\%$) elevado e HDL-c baixo (46,$6\%$). A maioria apresentou risco aterogênico aumentado pelo índice aterôgenico plasmático (55,$5\%$); 15,$1\%$ apresentaram alto risco cardiovascular segundo o índice de Castelli I e 13,$7\%$, segundo o índice de Castelli II. Os meninos apresentaram valores superiores de medidas antropométricas e índices de Castelli I e II em relação às meninas, que, por outro lado, apresentaram valores superiores de HDL-c. Houve correlação positiva do escore Z com o índice aterôgenico plasmático e negativa com HDL-c.
## Conclusões:
Os adolescentes do estudo apresentaram alta prevalência de risco cardiovascular e aterogênico conforme os índices avaliados. Além disso, o risco cardiovascular aumentado foi correlacionado com maior índice de massa corporal.
## INTRODUCTION
Currently, there is a high prevalence of overweight or obese adolescents in Brazil. 1 *Obesity is* a multifactorial disease characterized by excess weight and accumulation of body fat, caused by the imbalance between energy consumption and expenditure. 2 *Obesity is* a serious public health issue, as it falls within the most important risk factors for cardiovascular diseases (CVDs). According to the Pan American Health Organization (PAHO/WHO), CVDs are the leading cause of death in Brazil. 3 In addition, their severity extends to the socioeconomic sphere, and the level of expenditures in this context is worrisome. Recently, an analysis of the economic impact in Brazil was published, which found that costs have significantly increased in the last five years. In addition, these costs are estimated to increase more and more as the population of young people with risk factors ages and the prevalence of cardiovascular events increases. 4 Although the clinical manifestations of CVDs occur only in adulthood, asymptomatic manifestations may still be present in adolescence. 5 Excess body adiposity is related to the presence of dyslipidemia, identified by increases in the concentration of total cholesterol (TC), triglycerides (TG), and low-density lipoprotein (LDL) and by the decrease in high-density lipoprotein (HDL). 6 Indices based on lipid markers have been studied to stratify cardiovascular risk (CVR). Castelli Risk Indices I and II (CI-1 and CI-2) prove to be effective in evaluating CVR. 7 CI-1 is estimated by the ratio between TC and HDL; and CI-2 is calculated by the ratio between LDL and HDL. 8,9 Another indicator that can be used as a predictor of atherogenic risk is the Atherogenic Index of Plasma (AIP), estimated using the formula log10 (TC/HDL). 10 The use of indices for this population represents a simple investigation of certain risk factors, as well as a prior diagnosis, which guarantees better management in combating the occurrence of CVD. It should be noted that early detection of cardiovascular risk factors can reduce the chances of future complications and the serious consequences of CVD.
In view of the importance of early evaluating CVR, as well as the lack of studies that use CVR indices in adolescents, the objective of the present study is to analyze the lipid profile and CVR according to CI-1, CI-2, and AIP and to correlate the findings with anthropometric measurements of overweight and obese adolescents.
## METHOD
This is a cross-sectional study, conducted with secondary data from a larger study entitled Randomized clinical trial of a motivational interdisciplinary intervention based on the transtheoretical model of change for lifestyle modification in overweight/obese adolescents: MERC study protocol. 11 The sample derives from the database of the main study, which included adolescents of both sexes aged between 14 and 18 years and who are overweight or obese (≥Z score +1). The sample was recruited by convenience, by disclosing the research in print media, social networks, television, and the radio. The present study included only participants who had complete data in the original database for the analysis of the studied outcome, as specified in the flowchart (Figure 1).
**Figure 1.:** *Flowchart of study participants.*
The study was registered in the Clinical Trial Registry (NCT02455973) and in the Brazilian Registry of Clinical Trials (RBR-234nb5), and it was approved by the Research Ethics Committee of Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), registered under Certificate of Presentation for Ethical Consideration (CAAE) 36209814.6.0000.5336. All parents and/or legal guardians signed the Informed Consent Form, and the adolescents signed the Assent Form. A declaration of confidentiality of the data was signed by the authors to carry out the present study.
Sociodemographic (age and sex) and anthropometric (weight and height) variables, in addition to biochemical data (serum levels of TC, TG, and HDL), were collected. Blood collection to record biochemical data was performed by trained professionals, through venipuncture after 8 hours of fasting, and blood samples were analyzed at the biochemical laboratory of Hospital São Lucas at PUCRS (HSL). LDL was estimated by the Friedewald equation. 12 Weight was obtained with the individuals in the orthostatic position, with minimal clothing, without shoes, using a digital scale (G-Tech, Glass 1 FW, Rio de Janeiro, Brazil) with an accuracy of 100 g. Height was collected with the participants barefoot, with their feet in a parallel position and their ankles together. Height measurements were obtained using a portable stadiometer (AlturaExata, TBW, São Paulo, Brazil) with an accuracy of 1 mm. Once these measurements were made, the assessment of anthropometric characteristics was performed based on body mass index for age (BMI-for-age), according to the age group of the sample. For interpretation, the Z score was used: overweight (≥Z score +1 and <Z score +2), obesity (≥Z score +2 and ≤Z score +3), and severe obesity (>Z score +3). 13 For the survey of cardiovascular risk scores, the following indices were calculated: CI-1, CI-2, and AIP. CI-1 was estimated using the formula TC/HDL, and CI-2 by using the formula LDL/HDL. Cutoff points were adopted for the pediatric population based on the large study conducted by Navarra, with high risk above 3.5 for CI-1 and above 2.2 for CI-2. 14 AIP was estimated by the formula log10 (TG/HDL). Risk classification according to AIP was divided into three categories: low risk (<0.11); intermediate risk (between ≥0.11 and ≤0.21); and increased risk (>0.21). 15 For the classification of dyslipidemias, the reference values for adolescents according to Faludi were adopted. 16 Analyses were performed using the Statistical Package for the Social Sciences (SPSS) software, version 21.0 (Inc., Chicago, Illinois, USA). The normality of the distribution of numerical data was verified by the Kolmogorov-Smirnov Test and by analyzing the histogram of these data. Quantitative variables with normal distribution were described by mean and standard deviation, and the nonparametric AIP variable, by median and interquartile range. Pearson’s chi-square and Fischer’s exact tests were used to assess the association between categorical variables, and Student’s t-test, Mann-Whitney test, and one-way analysis of variance (ANOVA) were used to compare quantitative measures. The continuous data were correlated with the Pearson’s and Spearman’s correlation tests, according to the normality or not of the data, and classified according to Mukaka. 17 Analyses were performed considering a $95\%$ confidence interval (CI) ($p \leq 0.05$).
## RESULTS
The characteristics of the sample were described in Table 1. A total of 146 adolescents were evaluated, with mean age of 16.4±1.1 years, and most of them were girls ($74.7\%$) and obese ($52.7\%$). Boys presented higher Z scores and, more frequently, were severely obese, while girls were more frequently overweight or obese (Table 1).
**Table 1.**
| Characteristics | Total sample | Sex | Sex.1 | p-value |
| --- | --- | --- | --- | --- |
| Characteristics | Total sample | Boys n=37 | Girls n=109 | p-value |
| Sociodemographic | Sociodemographic | Sociodemographic | Sociodemographic | Sociodemographic |
| Age (years) | 16.4±1.1 | 16.1±1.2 | 16.5±1.1 | 0.085* |
| Anthropometric | Anthropometric | Anthropometric | Anthropometric | Anthropometric |
| Z score | 2.9±0.9 | 3.4±0.9 | 2.8±0.8 | <0.001* |
| Classification | Classification | Classification | Classification | Classification |
| Overweight | 14.0 (9.6) | 1.0 (2.7) | 13.0 (11.9) | 0.013† |
| Obesity | 77.0 (52.7) | 15.0 (40.5) | 62.0 (56.9) | 0.013† |
| Severe obesity | 55.0 (37.7) | 21.0 (56.8) | 34.0 (31.2) | 0.013† |
| Clinical | Clinical | Clinical | Clinical | Clinical |
| Triglycerides (mg/dL) | 95.3±45.0 | 86.2±30.9 | 98.4±48.6 | 0.079* |
| Classification | Classification | Classification | Classification | Classification |
| Adequate | 76.0 (52.1) | 21.0 (56.8) | 55.0 (50.5) | 0.508‡ |
| Altered | 70.0 (47.9) | 16.0 (43.2) | 54.0 (49.5) | 0.508‡ |
| HDL (mg/dL) | 48.8±13.7 | 41.8±8.5 | 51.1±14.4 | <0.001* |
| Classification | Classification | Classification | Classification | Classification |
| Adequate | 78.0 (53.4) | 11.0 (29.7) | 67.0 (61.5) | 0.001‡ |
| Altered | 68.0 (46.6) | 26.0 (70.3) | 42.0 (38.5) | 0.001‡ |
| LDL (mg/dL) | 96.0±27.8 | 96.9±28.8 | 95.7±27.6 | 0.825* |
| Classification | Classification | Classification | Classification | Classification |
| Adequate | 107.0 (73.3) | 26.0 (70.3) | 81.0 (74.3) | 0.631‡ |
| Altered | 39.0 (26.7) | 11.0 (29.7) | 28.0 (25.7) | 0.631‡ |
| Total cholesterol (mg/dL) | 163.9±33.3 | 155.9±32.4 | 166.6±33.3 | 0.093* |
| Classification | Classification | Classification | Classification | Classification |
| Adequate | 91.0 (62.3) | 26.0 (70.3) | 65.0 (59.6) | 0.249‡ |
| Altered | 55.0 (37.7) | 11.0 (29.7) | 44.0 (40.4) | 0.249‡ |
When comparing the lipid profile, girls presented higher values than boys for HDL, with a higher percentage of adequacy, and lower values for CI-1 and CI-2. The prevalence of dyslipidemia among the investigated participants was $47.9\%$ ($95\%$CI 36.2 and $53.9\%$) for high TG values; $26.7\%$ ($95\%$CI 12.8 and $33.8\%$) for LDL; and $37.7\%$ ($95\%$CI 24.9 and $44.2\%$) for TC. As for HDL, $46.6\%$ ($95\%$CI 34.7 and $52.6\%$) of the adolescents presented values below those recommended. Regarding the indices, most adolescents presented increased atherogenic risk according to AIP ($55.5\%$; $95\%$CI 44.7 and $61.0\%$). Regarding the Castelli Risk Indices, $15.1\%$ ($95\%$CI 0.1 and $22.7\%$) and $13.7\%$ ($95\%$CI -1.4 and $21.4\%$) presented high cardiovascular risk according to CI-1 and CI-2, respectively (Tables 1 and 2).
**Table 2.**
| Risk parameters | Total sample | Sex | Sex.1 | p-value |
| --- | --- | --- | --- | --- |
| Risk parameters | Total sample | Boysn=37 | Girlsn=109 | p-value |
| Castelli Risk Index 1 | 3.5±1.0 | 3.8±1.0 | 3.4±1.0 | 0.048* |
| Classification | Classification | Classification | Classification | Classification |
| No cardiovascular risk | 124.0 (84.9) | 34.0 (91.9) | 90.0 (82.6) | 0.171† |
| High cardiovascular risk | 22.0 (15.1) | 3.0 (8.1) | 19.0 (17.4) | 0.171† |
| Castelli Risk Index 2 | 2.1±0.9 | 2.4±0.8 | 2.0±0.9 | 0.026* |
| Classification | Classification | Classification | Classification | Classification |
| No cardiovascular risk | 126.0 (86.3) | 32.0 (86.5) | 94.0 (86.2) | 0.970† |
| High cardiovascular risk | 20.0 (13.7) | 5.0 (13.5) | 15.0 (13.8) | 0.970† |
| Atherogenic Index of Plasma | 0.2 (0.1-0.4) | 0.3 (0.1-0.4) | 0.2 (0.1-0.4) | 0.162‡ |
| Classification | Classification | Classification | Classification | Classification |
| Low risk | 36.0 (24.7) | 6.0 (16.2) | 30.0 (27.5) | 0.216† |
| Intermediate risk | 29.0 (19.9) | 6.0 (16.2) | 23.0 (21.1) | 0.216† |
| Increased risk | 81.0 (55.5) | 25.0 (67.6) | 56.0 (51.4) | 0.216† |
The results of the correlation analyses between the Z score and the cardiovascular risk parameters are described in Table 2. In the total sample, the Z score showed a positive correlation with AIP, and a negative correlation with HDL, significantly. There was a positive correlation with the Castelli Risk Indices I and II, although negligible. Among boys, the Z score was positively correlated with AIP and TG.
The results of the comparison between the Z score and the other cardiovascular risk classifications, according to the indices, are presented in Tables 3 and 4. In the total sample, the Z score was higher in individuals with low HDL and increased risk according to AIP. The Z score was lower in individuals with high TC. Among boys, the Z score was higher in those with increased risk according to AIP. Conversely, among girls, it was higher in those with low HDL.
## DISCUSSION
The results of the present study demonstrated that the prevalence of cardiovascular and atherogenic risk among adolescents was high. Furthermore, it was possible to observe, with the use of the indices to stratify the risk, that the risk is high according to the BMI classification, even in a sample exclusively composed of overweight and obese individuals. This statement can be evidenced by the results of correlation between the anthropometric characteristic and the indices’ ratings.
Nationwide data, represented by the Study of Cardiovascular Risk in Adolescents (ERICA), which evaluated 38,069 adolescents, showed that a significant proportion of Brazilian adolescents present changes in plasma lipids. With emphasis on the south region, in the same study, low HDL ($36.9\%$) and high TC ($22.8\%$), TG ($8.2\%$), and LDL ($3.5\%$) were reported. 5 The present study demonstrated a significantly higher percentage of prevalence of lipid alterations compared with the ERICA findings. Even considering the differences in the samples, such as, mainly, the age interval and the number of participants, it is worth noting the fact that, in this study, the participants are exclusively overweight or obese adolescents. This reinforces the possibility that overweight and obesity are important factors of lipid changes in adolescence.
Research shows that the main dyslipidemia associated with obesity is characterized by increases in TG and decreased HDL. 18 *It is* noteworthy that low HDL and hypertriglyceridemia were the most prevalent dyslipidemias in this study, which is in line with this statement. HDL is an important protective factor against the development of atherosclerosis. 16 It has already been observed, in practice, that patients with atherosclerotic coronary heart disease did not have high LDL, but had low HDL. 19 Hence, the application of indices that relate plasma lipid levels becomes relevant to estimate CVR, rather than their isolated assessment.
Regarding the assessed indices, the results indicate a significant proportion of adolescents with CVR. Most of them had an increased atherogenic risk, according to AIP. In addition, 15.1 and $13.7\%$ presented high CVR according to CI-1 and CI-2, respectively. Concerning atherogenic risk, a study that evaluated atherogenic indices in a population of Spanish adolescents found that AIP was strongly associated with the prognosis of metabolic syndrome. 20 Another investigation conducted in northeastern Brazil, on 448 adolescents, correlated AIP with CI-1 and CI-2 and other risk predictors such as dyslipidemia, physical inactivity, overweight, and obesity. The authors found a prevalence of 36.2 and $31\%$ of high risk according to CI-1 and CI-2, respectively. In addition, the results of this study showed a positive correlation of AIP with most of the evaluated CVR predictors. These data suggest that the indices prove to be efficient as markers of cardiovascular risk in overweight adolescents. 21 Accordingly, the findings of the present study demonstrated that higher AIP values were significantly associated with overweight and obesity and low HDL. In other words, these data also indicate a worrisome association between the nutritional profile of the studied population and the presence of dyslipidemia and CVR.
A cross-sectional study conducted on 807 Brazilian adolescents evaluated the prevalence of dyslipidemia and CVR according to CI-1 and CI-2. The results showed that the indices were more prevalent markers in adolescents who were overweight, in both sexes and at all ages. Thus, they emphasize that CI-1 and CI-2 are effective predictors of CVR in adolescents who are overweight. Therefore, the outcome of the study is in line with the present results. 22 In southern Brazil, Reuter et al. also found a significant association between dyslipidemia and obesity in the 1,254 adolescents evaluated. 23 We can state that obesity is among the most prevalent association factors of dyslipidemia in the young population. It is worth mentioning that dyslipidemia is responsible for the incidence of atherosclerosis, which can begin in childhood and gradually progress into adulthood. 20 The Brazilian Society of Cardiology emphasizes that the population is at risk of a premature CVD epidemic in the future, due to the increase in risk factors in the pediatric age group. 16 It should be noted that the main limitation of the study is the limited number of adolescents included. Nonetheless, more research is needed, which includes larger samples and, especially, other regions of Brazil, seeking to reinforce the findings of this study.
Lipid changes are an important health issue among young people, and CVD prevention should be focused on this stage. 24 Therefore, measures that modify risk factors to reduce or avoid future complications should be implemented in this specific population. For this purpose, early screening of CVR by using scores allows a better understanding of the pathogenesis and how to appropriately intervene.
The results of our study pointed to the high prevalence of CVR and atherogenic risk according to the indices in the studied population. In addition, increased risk was correlated with higher BMI.
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15. Vrablík M, Dobiášová M, Zlatohlávek L, Urbanová Z, Češka R. **Biomarkers of cardiometabolic risk in obese/overweight children: effect of lifestyle intervention**. *Physiol Res.* (2014.0) **63** 743-52. DOI: 10.33549/physiolres.932895
16. Faludi AA, Izar MC, Saraiva JF, Chacra AP, Bianco HT, Afiune A. **Atualização da Diretriz Brasileira de Dislipidemais e Prevenção da Aterosclerose**. *Arq Bras Cardiol.* (2017.0) **109**. DOI: 10.5935/abc.20170121
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23. Reuter CP, Brand C, Silva PT, Reuter ÉM, Renner P, Franke SI. **Relação entre dislipidemia, fatores culturais e aptidão cardiorrespiratória em escolares**. *Arq Bras Cardiol.* (2019.0) **112** 729-36. DOI: 10.5935/abc.20190068
24. Hovsepian S, Kelishadi R, Djalalinia S, Farzadfar F, Naderimagham S, Qorbani M. **Prevalence of dyslipidemia in Iranian children and adolescents: a systematic review**. *J Res Med Sci.* (2015.0) **20** 503-21. DOI: 10.4103/1735-1995.163979
|
---
title: Changes in lifestyle behaviors during the COVID-19 pandemic in children and
adolescents with congenital heart disease
authors:
- Michele Honicky
- Silvia Meyer Cardoso
- Luiz Rodrigo Augustemak de Lima
- Juliana Nicolodi Souza
- Francilene Gracieli Kunradi Vieira
- Isabela de Carlos Back
- Yara Maria Franco Moreno
journal: Revista Paulista de Pediatria
year: 2023
pmcid: PMC10014030
doi: 10.1590/1984-0462/2023/41/2022023
license: CC BY 4.0
---
# Changes in lifestyle behaviors during the COVID-19 pandemic in children and adolescents with congenital heart disease
## Abstract
### Objective:
To describe the changes in lifestyle behaviors during the COVID-19 pandemic in children and adolescents with congenital heart disease and to investigate the association of congenital heart disease complexity with lifestyle behavior changes.
### Methods:
Cross-sectional study with 127 children and adolescents with congenital heart disease, who underwent cardiac procedure (mean postoperative time: 10.11±3.13 years), conducted between December 2020 and January 2021. Lifestyle behaviors, such as dietary intake, physical activity, sedentary behavior, and sleep, were assessed through telephone interview based on validated questionnaires. Dietary patterns were identified using principal component analysis. Frequency of general and specific combinations of healthy and unhealthy lifestyle behavior changes was evaluated. Multinomial logistic regressions were used to test the association between congenital heart disease complexity and changes in lifestyle behavior.
### Results:
The main lifestyle behaviors acquired during pandemic were: $83.5\%$ decreased physical activity; $37.0\%$ increased sedentary behavior; $26.0\%$ slept more than usual; and $23.6\%$ adopted a less-healthy dietary pattern. Almost half of the participants ($41.8\%$) had at least one unhealthy change in lifestyle behavior. Complex congenital heart diseases were associated with increased sedentary behavior (OR 3.49, $95\%$CI 1.23–9.90).
### Conclusions:
Children and adolescents with congenital heart disease had unhealthy lifestyle behavior during the pandemic, mainly in the form of reduced physical activity and increased sedentary behavior.
## Objetivo:
Descrever as mudanças nos estilos de vida durante a pandemia em crianças e adolescentes com cardiopatia congênita e investigar a associação da complexidade da cardiopatia congênita com as mudanças de estilo de vida.
## Métodos:
Estudo transversal com 127 crianças e adolescentes com cardiopatia congênita, que realizaram procedimento cardíaco (tempo médio de pós-operatório: 10,11 [3,13] anos), realizado entre dezembro de 2020 e janeiro de 2021. O estilo de vida (alimentação, atividade física, comportamento sedentário e sono) foi avaliado por entrevista telefônica, com base em questionários validados. Padrões alimentares foram identificados por meio da análise de componentes principais. Frequência de combinações gerais e específicas de mudanças de estilo de vida saudável e não saudável foram avaliadas. Regressões logísticas multinominais foram utilizadas para testar associações.
## Resultados:
Os principais comportamentos de estilo de vida adquiridos durante a pandemia foram: 83,$5\%$ reduziram a atividade física, 37,$0\%$ aumentaram o comportamento sedentário, 26,$0\%$ dormiram mais e 23,$6\%$ mudaram para um padrão alimentar menos saudável. Quase metade (41,$8\%$) dos participantes teve pelo menos uma mudança não saudável no estilo de vida. Cardiopatias congênitas complexas foram associadas ao aumento do comportamento sedentário durante a pandemia (odds ratio 3,49, IC$95\%$ 1,23–9,90).
## Conclusões:
Crianças e adolescentes com cardiopatia congênita apresentaram estilo de vida não saudável durante a pandemia, principalmente na forma de redução da atividade física e aumento do comportamento sedentário.
## INTRODUCTION
COVID-19 infection may also be associated with long-term cardiac complications. 1 This fact is particularly important in congenital heart disease (CHD) patients since they are also known to be at higher risk of secondary cardiovascular disease (CVD) in early adulthood. 2 Lifestyle behavior (LB) plays an important role in the development of CVD, 3 especially in patients with CHD. In Brazil, the preventive measures adopted during the pandemic, particularly at the beginning in February 2020, provoked sudden LB changes 4-6 that may be a crucial point in the cardiovascular health of children and adolescents with CHD, as they can exacerbate unhealthy LBs. Previous studies found that children and adolescents with CHD already had unhealthy LB before the pandemic, such as unhealthy diet, physical inactivity and sedentary behavior (SB). 7 Moreover, some studies with healthy children described that the COVID-19 home confinement could be associated with LB changes, both in healthy and unhealthy patterns. 8-10 However, few studies have examined the LB changes during the pandemic in children and adolescents with CHD. A cross-sectional study with German children and adolescents with CHD found a decrease in physical activity (PA) level during the pandemic, compared to the period before the pandemic. 11 Thus, LB changes (i.e., dietary patterns [DPs], PA, SB and sleep) during the pandemic remain unclear in children and adolescents with CHD. Besides, there is a lack of studies investigating whether the complexity of CHD influences LB changes during the pandemic. Identifying the effects of the COVID-19 pandemic may help to develop healthy lifestyle promotion strategies for children and adolescents with CHD during periods of home confinement, as well as cardiovascular health promotion strategies post-COVID-19 pandemic. This study aimed to describe the changes in LBs during the pandemic in children and adolescents with CHD and to investigate the association of CHD complexity with LB changes.
## METHOD
Cross-sectional study derives data from, which includes children and adolescents with CHD aged 5-18 years, who underwent surgery or interventional catheterization for CHD, and postoperative time over 6 months. For the present study, data were collected through telephone interview, between December 2, 2020 and January 15, 2021, which was a period of self-isolation and increasing cases of COVID-19 diagnoses in Brazil. Figure 1A illustrates the timeline of events and survey context during the COVID-19 pandemic in Brazil. The present study recruited children and adolescents with CHD, who underwent surgery or interventional catheterization for CHD from the baseline. 12 Inclusion criterion was: age between 5 and 18 years. Exclusion criteria were: the presence of genetic syndromes and the presence of chronic or acute inflammatory disease. This study was approved by the Ethics Committee for and complied with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from the parents/legal guardians of all the participants enrolled in the study.
**Figure 1.:** *Timeline of events during the COVID-19 pandemic in Brazil and the level of social isolation in children and adolescents with congenital heart disease and their families.*
Sociodemographic and clinical characteristics were recorded, including age, sex, household income, cardiac procedure, postoperative time and complexity of CHD, 13 according to the CHD diagnoses (complex, moderate and mild lesions), and classified as simple/moderate or complex.
COVID-19 questionnaire consisted of questions relating to the impact of the pandemic on LBs, specifically dietary intake, PA, SB and sleep, and was adapted based on previous studies on LB changes during the pandemic in children and adolescents. 5,8,9,14 The structured questionnaire was applied through telephone with an average call duration of 40 minutes. It was applied to the parents/legal guardians and children/adolescents together, in which the children and adolescents helped with the answers. All interviewers received prior training to perform the interview in order to standardize data collection and avoid errors and biases. COVID-19 questionnaire was divided into six sections: COVID-19 diagnosis and level of isolation: Test positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in either participant or family member, symptoms, and severity of symptoms were collected. Level of social isolation was also assessed in three periods of the pandemic: March 2020, October 2020 and December 2020/January 2021. Participants also reported on their routine pediatric cardiologist follow-up during the pandemic.
Financial issues: Reported financial concern and food insecurity during past month were evaluated. Also, the receipt of government assistance during the pandemic was assessed.
Dietary intake: Frequency of 10 food groups intake were obtained: beans/chickpeas/lentils, fried pastries, vegetables, sweets, fruits, milk, meat, eggs, soft drink and ultra-processed foods. In addition, changes in eating habits during the pandemic were also assessed.
PA: PA questions were based on the International Physical Activity Questionnaire (IPAQ), which assesses walking, as well as, moderate and vigorous PA in children and adolescents. Active was defined as ≥300 minutes/week of PA. 15 PA was categorized into: active before and during the pandemic, active during the pandemic, inactive before and during the pandemic and inactive during the pandemic. Additionally, PA changes compared to the period before the pandemic were categorized as decreased, increased and constant.
SB: Sedentary time was assessed by the sum of hours spent on computers, smartphone and television per day. SB was considered at >2 hours/day 16 and was categorized into: SB before and during the pandemic, SB during the pandemic, no SB before and during the pandemic and no SB during the pandemic, and also categorized in increased and constant, compared to the period before the pandemic.
Sleep: Sleep duration and quality of sleep during the pandemic were evaluated compared to before the pandemic, and categorized in: better, similar and worse. Also, changes in hours of sleep per night were assessed and categorized into: unchanged, more than usual and less than usual.
Participant’s general characteristics were expressed as means (standard deviation [SD]) or medians (interquartile range [IQR]) for continuous variables and as frequency and percentages for categorical variables. To compare differences in eating habits and sleep before and during the pandemic, the chi-square or Fisher’s exact test were applied. DPs were derived from principal component analysis (PCA) based on the 10 food groups. Input variable was the frequency of food intake categorized in <5 days/week and ≥5 days/week, which uses a tetrachoric correlation matrix from binary data. Factor loadings >0.30 were considered representative of DP. Subsequently, DPs were named according to the food groups in each DP. After extracting the DP, individual scores of each were obtained and divided into: low adherence to DP (<75(th) percentile) and high adherence to DP (≥75(th) percentile). Next, DP changes compared to before the pandemic was categorized as more-healthy and less-healthy according to the change from low adherence to “unhealthy” or “healthy” DP to high adherence to “unhealthy” or “healthy” DP, and vice versa.
To facilitate interpretations, each change in LBs during the pandemic were categorized into two groups based on healthy and unhealthy LB. Changes in healthy LB were defined as: healthy DP (i.e., change to high adherence to healthy DP and change to low adherence to unhealthy DP); healthy PA (i.e., increased PA); healthy SB (i.e., reduced SB); and healthy sleep (increased sleep duration). Conversely, changes in unhealthy LB were defined as: unhealthy DP (i.e., change to high adherence to unhealthy DP and change to low adherence to healthy DP); unhealthy PA (i.e., reduced PA); unhealthy SB (i.e., increased SB); and unhealthy sleep (i.e., reduced sleep duration). Thereafter, the frequency of general (i.e., all four, three out of four, two out of four, one out of four, none) and specific (i.e., DP, SB, sleep and other specific combinations) combinations of LB changes during the pandemic were assessed.
Multinomial logistic regression model was used to test the association of CHD complexity (exposure) with changes in LB (outcomes). Subsequently, multinomial logistic regressions were adjusted for potential confounding: age, sex and household income. The results were expressed as odds ratio (OR) and respective $95\%$ confidence interval ($95\%$CI).
All statistical tests were conducted using the Statistical Package for Social Sciences SPSS version 23.0 (IBM SPSS Inc.), except for the DPs that were performed by Stata version 13.0 (Stata Corporation). p-values <0.05 were considered statistically significant.
## RESULTS
A total of 127 children and adolescents with CHD were included in the present study. The majority were female ($55.9\%$), and the median age was 12.1 years (10.1–15.0). Only $2.4\%$ ($$n = 3$$) of participants tested positive for SARS-CoV-2 and all had very mild symptoms. The main symptoms reported were: loss of taste, diarrhea, loss of smell, fatigue, sore throat and fever. Among the participants, $23.6\%$ had parents, siblings or other residents of the same home that tested positive for SARS-CoV-2. Regarding routine pediatric cardiologist follow-up during the pandemic, $40.9\%$ did not consult at all and the main reasons were the following: scheduling difficulties ($14.2\%$); the patient was asymptomatic ($8.5\%$); fear of the COVID-19 infection ($7.9\%$); postponement of routine follow-up until next year ($6.3\%$); and cancellation of appointment due to largely increased COVID-19 cases ($3.9\%$). Regarding the cardiac procedure, $82.7\%$ underwent cardiac surgery and $17.3\%$ underwent cardiac catheterization. The mean postoperative time was 10.1±3.1 years and $77.2\%$ had simple/moderate CHD complexity. Of all the 127 participants’ parents/guardians, $45.7\%$ reported financial concern, $19.7\%$ reported food insecurity, and $46.5\%$ received government assistance during the pandemic.
The majority of participants ($94.5\%$) followed the rules of social isolation from March 2020 to January 2021. Figure 1B describes the levels of social isolation during the COVID-19 pandemic.
Two DPs were identified before and during the pandemic (DP 1 is unhealthy and DP 2 is healthy), which explained $29.0\%$ and $29.3\%$ of the total variance, respectively. Before the pandemic, DP 1 was characterized by “unhealthy”, with high intake of fried pastries, soft drinks, sweets, and ultra-processed foods and low intake of eggs (Figure 2A), and DP 2 was represented by “healthy”, with high intake of meat, vegetables, fruits, and low intake of milk and ultra-processed foods (Figure 2B). During the pandemic, DP 1 was characterized by “unhealthy”, with high intake of soft drinks, ultra-processed foods, sweets, fried pastries and low intake of beans/chickpeas/lentils (Figure 2C), and DP 2 was represented by “healthy”, with high intake of meat, vegetables, fruits and low intake of milk and fried pastries (Figure 2D). According to the change in DPs during the pandemic, $23.6\%$ of the participants shifted to a less-healthy DP, and $16.5\%$ shifted to a more-healthy DP.
**Figure 2.:** *Dietary patterns and factor loadings of food groups before and during the pandemic in children and adolescents with congenital heart disease.*
In addition, significant increases were observed before pandemic vs during pandemic in the frequency of the behaviors as follow: having breakfast (every day: $67.7\%$ vs $74.0\%$; 1−5 days/week $12.5\%$ vs $8.6\%$; rarely $7.1\%$ vs $7.1\%$; no: $16.6\%$ vs $10.2\%$; $p \leq 0.001$); meals had together with the family (every day: $88.2\%$ vs $91.3\%$; 1−5 days/week: $11.8\%$ vs $4\%$; rarely: $3.1\%$ vs $3.1\%$; no: $0\%$ vs $1.6\%$; $p \leq 0.001$); cooking at home (<5 days/week: $85.8\%$ vs $92.1\%$; 1−4 days/week: $11.9\%$ vs $7.9\%$; no: $2.4\%$ vs $0\%$; $p \leq 0.001$); and watching TV while eating (every day: $16.5\%$ vs $24.4\%$; 1−5 days/week: $23.7\%$ vs $30\%$; rarely: $21.3\%$ vs $14.2\%$; no: $38.6\%$ vs $31.5\%$; $p \leq 0.001$). Significant decreases were observed before pandemic vs during pandemic in the frequency of the following behaviors: consumption at fast-food restaurants (1−5 days/week: $32.3\%$ vs $18.9\%$; rarely: $45.7\%$ vs $7.9\%$; no: $22.0\%$ vs $73.2\%$; $p \leq 0.001$); and use of apps to order food (<3 days/week: $22\%$ vs $2.6\%$; 1−2 days/week: $53.6\%$ vs $28.3\%$; rarely: $0\%$ vs $39.4\%$; no: $24.4\%$ vs $29.9\%$; $p \leq 0.001$).
During the pandemic, $83.5\%$ of the participants reported decreased PA levels, of which, $62.2\%$ were inactive before and during the pandemic and $21.3\%$ were inactive during the pandemic. SB was increased by $37.0\%$ in the participants, $51.2\%$ had ≥2 hours/day of SB before and during the pandemic and $33.8\%$ had ≥2 hours of SB during the pandemic (Figure 3). Moreover, the screen time for school activities was 90 minutes/day during the pandemic. Regarding school activities, $33.0\%$ of participants received only printed/textbook school activities and $2.4\%$ returned to school in person to receive assistance with school activities.
**Figure 3.:** *Physical activity and sedentary behavior in children and adolescents with congenital heart disease during pandemic.*
During the pandemic, the reported sleep duration increased by $26.0\%$ in participants, still, the quality of sleep worsened by $26.8\%$.
Among the 127 participants, $34.6\%$ had one of four healthy behavior changes, and $58.3\%$ had no healthy behavior change during the pandemic (Figure 4A and 4B). Of the $34.6\%$, sleeping more than usual was the most prevalent behavior ($21.3\%$). As for the unhealthy changes, $41.8\%$ had one of four unhealthy behavior changes and $11.8\%$ had three of four. Of the $41.8\%$ that had one of four unhealthy changes, PA decrease was the most prevalent ($33.9\%$). Moreover, the unhealthy specific combination of DP, PA and SB was identified in $9.4\%$ of the participants (Figure 4C).
**Figure 4.:** *Proportion of participants with healthy and unhealthy lifestyle behavior changes and combinations of healthy and unhealthy lifestyle behavior changes during the pandemic in children and adolescents with congenital heart disease.*
In multivariable-adjusted multinomial logistic regressions, complex CHD were associated with increased SB vs constant SB during the pandemic, compared with simple and moderate CHD. CHD complexity was not associated with other LB changes during the pandemic (Table 1).
**Table 1.**
| Outcomes | CHD complexity | CHD complexity.1 |
| --- | --- | --- |
| Changes in lifestyle behaviors | Simple/moderate CHD | Complex CHD |
| Changes in lifestyle behaviors | OR (95%CI); p-value* | OR (95%CI); p-value* |
| Change in DPs | Change in DPs | Change in DPs |
| Healthy vs unchanged | 1 | 3.00 (1.00–8.97); 0.049 |
| Unhealthy vs unchanged | 1 | 1.54 (0.48–4.90); 0.468 |
| Change in physical activity | Change in physical activity | Change in physical activity |
| Increased vs constant | 1 | 0.65 (0.08–5.04); 0.677 |
| Decreased vs constant | 1 | 1.00 (0.22–4.53); 0.997 |
| Change in sedentary behavior | Change in sedentary behavior | Change in sedentary behavior |
| Increased vs constant | 1 | 3.49 (1.23–9.90); 0.019 |
| Decreased vs constant | - | - |
| Change in sleep duration | Change in sleep duration | Change in sleep duration |
| More than usual vs constant | 1 | 0.41 (0.15–1.11); 0.080 |
| Less than usual vs constant | 1 | 0.21 (0.05–1.00); 0.051 |
| Change in sleep quality | Change in sleep quality | Change in sleep quality |
| Better vs unchanged | 1 | 0.33 (0.11–1.00); 0.049 |
| Worse vs unchanged | 1 | 0.51 (0.17–1.52); 0.226 |
## DISCUSSION
In this cross-sectional study, the main unhealthy LBs changes were physical inactivity and SB. Almost half of the children and adolescents with CHD had at least one unhealthy change in LB during the pandemic. These results reinforce the need to promote effective healthy LBs during and after the COVID-19 pandemic in this population.
One of the behavioral changes during the pandemic in children and adolescents with CHD was dietary intake, some healthy and others unhealthy. In line with previous studies that showed healthy 10,14 and unhealthy 5,17 eating habits during the pandemic in the pediatric population, Massin 7 reported that before the pandemic, children with CHD had a high intake of sugary drinks and foods high in fats and low intake of fruit and vegetables. Our findings suggest that home confinement exacerbated these unhealthy eating habits. In the present study, a shift of a less-healthy DP was found in $23.6\%$ of participants during the pandemic, they acquired other unhealthy eating habits, such as watching TV while eating. This behavior is associated with high ultra-processed foods intake. 18 Although some participants increased unhealthy eating habits, there are also notable changes in healthy eating habit reported during the pandemic in this study. For example, frequency increase of having breakfast, having meals together with the family and cooking at home, which are positively associated with overall diet quality in healthy children. 19 Moreover, our finding on increased cooking at home is similar to the results of a study in Brazilian adults 4 and Canadian families. 8 However, it is worth noting that confinement may have increased the availability of time to cook at home, but this may not necessarily represent a healthier diet. Furthermore, a previous study with children and adolescents with CHD found that approximately $36\%$ went to fast-food restaurants once a month before the pandemic. 7 In the present study, as expected, home confinement resulted in a decreased in the frequency of consumption at fast-food restaurants and in the use of app to order food. It is also noteworthy that the present study found that healthy DP during the pandemic was composed by low intake of fried pastries. In contrast, adolescents from South America and Europe showed an increase of fried foods intake during the pandemic. 20 Before the pandemic, previous studies already described high prevalence of physical inactivity in CHD patients. 7,12 Our findings suggest that self-isolation measures have presented new challenges in practicing moderate to vigorous intensity PA, resulting in a decrease of $83.5\%$ of PA levels, whereas, only $3.9\%$ were active during the pandemic. These results are in line with a German study that found a reduction of almost a quarter in daily step count in children and adolescents with CHD during the pandemic. 11 Similar results were described in healthy children 6,9,10,21 and in children with obesity. 17 Moreover, self-isolation resulted in increased leisure time and SBs such as prolonged sitting and excess of screen time in healthy children and adolescents around the world. 6,9,10 Results in the same direction were observed in the present study. Additionally, it was observed that the participants used 90 minutes/day of school activities on screen time. However, a study with American parents suggested that remote education is not the greatest contributor to SB during the pandemic, as children spend less time doing school-related video calls than sedentary leisure behaviors such as watching television/videos/movies. 21 Furthermore, a study with children and adolescents found that the perceived capability of parents to restrict their children’s screen time is one of the main factors of SB during the pandemic. 22 This suggests the important role of the family in reducing SB, especially inactive leisure, which should be taken into account in the management of these patients by the association between SB and CVD, regardless of PA. 23 In addition, patients with complex CHD had a higher risk of intensifying SB during the pandemic in the present study, which indicates that this group are advised to pay closer attention to the guidelines in order to promote a healthy LB.
In the present study, the sleep duration increased in $26.0\%$ of the participants during the pandemic. Similar results were described in healthy children. 6,9,14 A study in Brazil showed that $32\%$ of healthy children increased sleep time 6 and, a longitudinal study in Italy reported an average growth of one hour. 17 By contrast, a study from Canada showed that sleep remained the same in young children in middle and high-income families, 8 which differs from our predominantly low-income population. The school closures may have reflected in children’s sleep changes. Another possible explanation for the growth in sleep time during the pandemic would be due to the lack of establishing hours for bedtime and waking-up. 24 A combination of LBs should be evaluated to make more effective LB interventions. 25 The Canadian 24-hour movement guidelines for children and youth reinforces the importance of recommendations regarding LB combinations. 26 In the present study, $21.3\%$ of participants presented the combination of two unhealthy LBs (i.e., physical inactivity and SB) and $9.4\%$ of participants presented the combination of three unhealthy LBs (i.e., DP, PA and SB), highlighting the importance of interventions in a set of LBs during the pandemic, not just isolated behaviors.
COVID-19 restrictive policies had a predominantly unhealthy effect on PA, SB and DP in children and adolescents with CHD, those changes together can lead to the development of CVD. Previous studies with healthy children and adolescents had already suggested recommendations for promoting healthy lifestyles during the pandemic, such as performing home-based leisure activities with the family, who should support their children to be active, set routines, supervise screen time, curb SB, impose regular bedtime and waking-up times, use social media to connect with friends to minimize distance, and practice mindfulness among others. 9,27 These recommendations were also imposed on CHD patients to avoid long-term unfavorable cardiovascular outcomes. In addition, encouraging the maintenance of routine care with a pediatric cardiologist and nutritional guidelines for healthy DP are essential components for a healthy LB during and after the pandemic, especially in low-income CHD families, as food insecurity is associated with unhealthy LB and cardiovascular risk. 28 The present study has some limitations: the cross-sectional design does not allow inferring causality and real changes in LB during the pandemic; self- and parent-reported LB through questionnaire may be underestimated or overestimated due to memory recall bias; food frequency assessment due to difficulty in estimating portions through telephone interviews; data collection during the holiday season. However interviews were conducted a week after the holiday season to avoid bias. Among the strengths: isolated and combined LBs were evaluated.
In conclusion, children and adolescents with CHD had unhealthy lifestyle changes during the pandemic, mainly reduced PA and increased SB. Likewise, patients with complex CHD were associated to higher odds of increase SB. These unhealthy LBs acquired during the pandemic may have an impact on cardiovascular health in the long-term. Thus, our results emphasize the importance of strategies for the CVD prevention in post-COVID-19 pandemic to avoid keeping the unhealthy lifestyle acquired during home confinement, especially in the high-risk group for CVD, such as that with CHD.
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|
---
title: FOXO3A‐short is a novel regulator of non‐oxidative glucose metabolism associated
with human longevity
authors:
- Evan E. Santo
- Rasmus Ribel‐Madsen
- Peter J. Stroeken
- Vincent C. J. de Boer
- Ninna S. Hansen
- Maaike Commandeur
- Allan A. Vaag
- Rogier Versteeg
- Jihye Paik
- Ellen M. Westerhout
journal: Aging Cell
year: 2023
pmcid: PMC10014046
doi: 10.1111/acel.13763
license: CC BY 4.0
---
# FOXO3A‐short is a novel regulator of non‐oxidative glucose metabolism associated with human longevity
## Abstract
Intronic single‐nucleotide polymorphisms (SNPs) in FOXO3A are associated with human longevity. Currently, it is unclear how these SNPs alter FOXO3A functionality and human physiology, thereby influencing lifespan. Here, we identify a primate‐specific FOXO3A transcriptional isoform, FOXO3A‐Short (FOXO3A‐S), encoding a major longevity‐associated SNP, rs9400239 (C or T), within its 5′ untranslated region. The FOXO3A‐S mRNA is highly expressed in the skeletal muscle and has very limited expression in other tissues. We find that the rs9400239 variant influences the stability and functionality of the primarily nuclear protein(s) encoded by the FOXO3A‐S mRNA. Assessment of the relationship between the FOXO3A‐S polymorphism and peripheral glucose clearance during insulin infusion (Rd clamp) in a cohort of Danish twins revealed that longevity T‐allele carriers have markedly faster peripheral glucose clearance rates than normal lifespan C‐allele carriers. In vitro experiments in human myotube cultures utilizing overexpression of each allele showed that the C‐allele represses glycolysis independently of PI3K signaling, while overexpression of the T‐allele represses glycolysis only in a PI3K‐inactive background. Supporting this finding inducible knockdown of the FOXO3A‐S C‐allele in cultured myotubes increases the glycolytic rate. We conclude that the rs9400239 polymorphism acts as a molecular switch which changes the identity of the FOXO3A‐S‐derived protein(s), which in turn alters the relationship between FOXO3A‐S and insulin/PI3K signaling and glycolytic flux in the skeletal muscle. This critical difference endows carriers of the FOXO3A‐S T‐allele with consistently higher insulin‐stimulated peripheral glucose clearance rates, which may contribute to their longer and healthier lifespans.
Genetic studies of humans have identified the gene FOXO3A to be associated with longevity. Here, we describe how a primate‐specific form of FOXO3A which is regulated by a FOXO3A locus longevity variant functions to control insulin sensitivity in the skeletal muscle. This discovery demonstrates a potentially causative relationship between increased glucose clearance and longer life in humans and provides a molecular tool for further discovery of genes regulating human longevity.
## INTRODUCTION
The insulin/PI3K/Akt/FOXO signaling pathway is a powerful regulator of longevity in model organisms and has also been implicated in the determination of human lifespan. Suppression of insulin signaling in Caenorhabditis elegans and Drosophila causes FOXO activation and robust FOXO‐dependent lifespan extension (van Heemst, 2010). In mice, suppression of IGF/insulin signaling has resulted in lifespan extension as well, confirming the evolutionarily‐conserved importance of this pathway in longevity (Bluher et al., 2003; Selman et al., 2008). More directly, it has recently been shown that deletion of FOXO$\frac{1}{3}$/4 in the mouse nervous system results in accelerated age‐dependent axonal degeneration (Hwang et al., 2018). This finding highlights the importance of FOXO in the suppression of aging in the mammalian nervous system. In humans, multiple genetic association studies have linked SNPs at the FOXO3A locus to human longevity (Bae et al., 2018; Broer et al., 2015; Flachsbart et al., 2009, 2017; Joshi et al., 2017; Li et al., 2009; Soerensen et al., 2010; Sun et al., 2015; Willcox et al., 2008). Significantly, all of the longevity‐relevant FOXO3A variants are outside of the known protein‐coding sequences of FOXO3A (Flachsbart et al., 2013). Additionally, SNPs in FOXO1, FOXO4, and FOXO6 are not associated with longevity (Kleindorp et al., 2011). These previous findings suggest that the FOXO3A locus and/or gene products may perform a non‐redundant function in human longevity.
As a downstream mediator of insulin signaling, FOXOs are also of central importance in the maintenance of whole‐body glucose homeostasis. FOXO1 acts cooperatively with FOXO3 and FOXO4 in the liver to activate gluconeogenesis when insulin signaling is reduced, thereby maintaining blood glucose levels (Haeusler et al., 2014). Recently, the FOXO3A pro‐longevity genotype has been associated with increased insulin sensitivity (Banasik et al., 2011; Sun et al., 2015). In further support of this association between insulin sensitivity and longevity in humans, The Leiden Longevity Study found that peripheral but not hepatic insulin sensitivity is positively associated with familial longevity (Wijsman et al., 2011). Additionally, increased insulin sensitivity among centenarians has been observed when compared to their younger controls (Paolisso et al., 2001). In a recent GWAS study for body mass index (BMI) and associated traits, the pro‐longevity rs9400239 genotype was associated with lower BMI ($$p \leq 1.61$$e‐08), smaller waist circumference ($$p \leq 1.66$$e‐07), smaller hip circumference ($$p \leq 1.14$$e‐03), less coronary artery disease ($$p \leq 2.32$$e‐03), and lower systolic blood pressure ($$p \leq 4.97$$e‐03) (Locke et al., 2015). This lower BMI may help explain why the FOXO3A pro‐longevity genotype has also been associated with improved self‐rated health at old age, reduced coronary heart disease, and reduced all‐cause mortality (Willcox et al., 2016, 2017; Zettergren et al., 2018). The FOXO3A pro‐longevity genotype has also been associated with increased activity of daily living (ADL) and decreased bone fracture within a Danish oldest‐old cohort; this is particularly true for rs9400239 (ADL $$p \leq 0.02$$) (Soerensen et al., 2015). Taken together, these previous findings demonstrate that FOXO3A longevity variants and rs9400239, in particular, are not only associated with increased lifespan but improved healthspan and that enhanced insulin sensitivity might be a central phenotype underlying the health benefits associated with the pro‐longevity FOXO3A genotype.
So far, no mechanisms have been discovered that link any of the FOXO3A longevity variants to tissue‐specific FOXO3A functions of relevance to human longevity. Here, we describe a primate‐specific transcript arising from the human FOXO3A locus, FOXO3A‐Short (FOXO3A‐S), which contains the rs9400239 longevity SNP within its 5’ UTR and is primarily expressed in the skeletal muscle. Although both alleles of the FOXO3A‐S mRNA encode constitutively nuclear n‐terminally truncated forms of the FOXO3A protein, the protein products have allele‐dependent differences in stability and functionality. Specifically, the pro‐longevity FOXO3A‐S T‐allele (3A‐S(T)) produces a protein product of reduced stability which is unable to suppress glycolytic flux in the skeletal muscle in the presence of active PI3K signaling, while the non‐longevity FOXO3A‐S C‐allele (3A‐S(C)) is more stable and able to suppress glycolytic flux in both the presence and absence of active PI3K signaling. We find these differences between the alleles to be of great consequence in a cohort of human subjects where insulin‐stimulated peripheral glucose clearance is markedly higher in subjects heterozygous and homozygous for the pro‐longevity 3A‐S(T) allele. Collectively, these findings identify non‐oxidative glucose metabolism within the skeletal muscle as a particularly important metabolic pathway influencing human longevity.
## FOXO3A‐Short encodes a human longevity SNP and is highly expressed in the skeletal muscle
To gain insight into potential alternative functions for the FOXO3A longevity SNPs, we utilized the UCSC Genome Browser to visualize the mapping of the longevity SNPs and transcripts to the FOXO3A locus (Figure 1a). A short transcript consisting of an uncharacterized first exon (subsequently called exon 2A) which is transcribed from within intron 2 and spliced into exon 3 of canonical FOXO3A was evaluated. Interestingly, the longevity SNP rs9400239 was found to map within exon 2A (Figure 1a; Figure 1b). The minor T‐allele of rs9400239 has been strongly associated with longevity in two Caucasian cohorts (Flachsbart et al., 2009; Soerensen et al., 2010) but has not been examined in Asian longevity studies. To gain a better understanding of the association of rs9400239 with human longevity, we assessed its linkage disequilibrium (LD) with other lead FOXO3A longevity SNPs by using haplotype data from the 1000 Genomes Project. A longevity GWAS meta‐analysis identified rs10457180 as the lead FOXO3A SNP in Caucasians (Broer et al., 2015). Additional studies independently identified rs4946935 as their lead FOXO3A SNP in Caucasian cohorts (Bae et al., 2018; Flachsbart et al., 2017). A large parent‐based longevity GWAS discovered rs3800231 in a cohort of primarily Caucasian background (Joshi et al., 2017). In Asian populations, rs2802292 has been well‐studied but is of less significance in Caucasian populations (Li et al., 2009; Sun et al., 2015; Willcox et al., 2008). The lead SNP in Asians seems to be rs13217795 (Sun et al., 2015). We find rs9400239 to be in remarkably tight LD with all of these lead SNPs in Asians (r 2 > 0.9 for all comparisons); in Caucasians, this is true for all (r 2 > 0.87) except rs2802292 (r 2 = 0.718, Figure 1c). From this analysis, we conclude that the rs9400239 SNP encoded within exon 2A is strongly associated with human longevity across populations.
**FIGURE 1:** *The FOXO3A locus encodes a novel transcript which contains a longevity SNP. (a) A genomic view of the FOXO3A locus rendered by the UCSC Genome Browser with FOXO3A transcripts and lead longevity SNPs annotated (Hg38). (b) A non‐scaled cartoon representation of the known FOXO3A coding exons and splice junction in blue, UTRs in red and the novel exon and splice junction that comprise FOXO3A‐short in green. Start codons (ATG) for each isoform are indicated. (c) Linkage disequilibrium (LD) analysis of the FOXO3A longevity SNPs using data supplied from the 1000 Genomes Project (Phase 3 Version 5) and represented as correlation coefficients (r
2) generated using the LDmatrix tool (https://ldlink.nci.nih.gov/?tab=ldmatrix). Linkages for Europeans are highlighted in blue, East Asians are highlighted in green.*
In order to determine the mRNA expression pattern of this novel transcript throughout human tissues, we utilized data from the GTEx project. Using the “transcript browser” tool, we were able to query the expression of both canonical FOXO3A (exon 2/exon 3 splice junction) and the novel transcript (exon 2A/exon 3 splice junction) individually (GTEx version 8; https://gtexportal.org). This analysis revealed that the novel transcript is predominantly expressed in the skeletal muscle while being nearly absent in all other tissues (Figure 2a). In contrast, FOXO3A is ubiquitously expressed (Figure 2a). We further tested expression of the transcript in the immortalized human myoblast cell line LHCNM2, which can be differentiated into myotubes in vitro (Figure 2b) (Zhu et al., 2007). RT‐qPCR of a differentiation time course of these cells for FOXO3A, the differentiation marker MYH8, and the exon 2A transcript showed robust FOXO3A expression in both myoblasts and myotubes. In contrast, MYH8 and the exon 2A transcript were nearly absent in myoblasts but became robustly expressed in myotubes (Figure 2c). Temporally, the exon 2A transcript was expressed early in myoblast differentiation and remained high in mature myotubes, corroborating the observed expression in primary human skeletal muscle. 5’ RACE using the LHCNM2 myotube mRNA as a template confirmed that the novel mRNA has a 5′ m7g cap and that exon 2A is 242 bp long with the entire spliced product being 1643 bp (exclusive of the 3’ UTR). Having noticed that the sequence conservation between exon 2A and the mouse genome was very minimal (data not shown), we designed RT‐PCR primers to the available conserved sequence between mouse and human within exon 2A and exon 3. Performing RT‐PCR in pooled skeletal muscle cDNA from human, monkey, and mouse revealed that the exon 2A‐initiated transcript most likely does not exist in mice but does exist in monkeys (Figure 2d). From these findings, we conclude that the exon 2A‐initiated transcript represents a genuine primate‐specific transcript which we refer to as FOXO3A‐Short (FOXO3A‐S).
**FIGURE 2:** *FOXO3A‐S is predominantly expressed in the skeletal muscle. (a) GTEx version 8 (https://www.gtexportal.org) human tissue mRNA expression data for both FOXO3A and FOXO3A‐S depicted in TPM (transcripts per million). Data labels above each bar are the exact TPM value. (b) Representative light microscopy images from a differentiation time series of the human myoblast line LHCNM2. Day 0 is under normal myoblast growth conditions and Day 6 is under differentiation conditions. (c) RT‐qPCR measurement of FOXO3A, FOXO3A‐S, and MYH8 expression from the LHCNM2 differentiation time course imaged in (b) represented as ∆Ct. Error bars are SD. (d) RT‐PCR of pooled skeletal muscle mRNA from multiple individuals in multiple species for FOXO3A‐S transcript using primers designed to conserved genomic sequence. 18S rRNA served as a loading control.*
## The two rs9400239 variants of the FOXO3A‐S transcript encode proteins of differential stability
We next investigated the protein coding potential of the FOXO3A‐S transcript. Inspection of the full‐length FOXO3A‐S cDNA identified the first ATG 282 bp downstream of the transcriptional start site (TSS). This ATG is Met221 of canonical FOXO3A; therefore, in‐frame with the canonical FOXO3A stop codon (Figure 3a). The rs9400239 SNP is located 164 bp downstream of the TSS and 4 bp upstream of a stop codon that is in‐frame with the ATG (Figure 3a). This stop codon ensures that the rs9400239 SNP is part of the FOXO3A‐S 5’ UTR because it prevents rs9400239 from participating in any FOXO3A‐related protein coding sequence. Translation from this ATG yields a predicted protein of 453 aa with a predicted mW of 48 KDa. The predicted FOXO3A‐S protein encodes a partially truncated forkhead domain but still retains the nuclear localization and export signals as well as the transactivation domain (Figure 3b) (Greer & Brunet, 2005). Despite the partial forkhead domain loss, the crystal structure of DNA‐bound FOXO3A suggests that FOXO3A‐S may bind to DNA via a retained C‐terminal coil (amino acids 236–255 in FOXO3A) (Tsai et al., 2007).
**FIGURE 3:** *The FOXO3A‐S transcript encodes different FOXO3A‐related proteins depending on the genotype of the rs9400239 SNP. (a) The complete sequence of exon 2A of FOXO3A‐S and partial sequence of the common FOXO3A/FOXO3A‐S exon 3. Highlighted in red is the rs9400239 SNP (C/T), in green the putative ATG start codon for FOXO3A‐S and in blue the splice junction between exon 2A and exon 3. In bold/underline are stop codons in‐frame with the putative ATG start/canonical FOXO3A reading frame. (b) Annotation of the FOXO3A‐S protein sequence relative to FOXO3A. The forkhead domain is colored blue, nuclear localization signals are red, nuclear export signals are green, the transactivation domain is yellow, and the three Akt phosphorylation sites are purple. (c) Western blot of the products of in vitro transcription/translation reactions performed with FOXO3A‐S clones. Two of the clones are full‐length FOXO3A‐S transcripts containing either rs9400239 variant (3A‐S(C) and 3A‐S(T)) ‐ the rs9400239 variant being the only difference between them. The third clone is one starting exactly from the first ATG (3A‐S(ATG)) which truncates the rs9400239 position and remaining 3’ UTR. (d) Western blot from HEK293T transfections of the same clones used in (c). (e) Western blot of LHCNM2 myotube lysates (Day 7) derived from either control (NT) or a FOXO3A/FOXO3A‐S‐targeting spJCRISPR construct single‐cell clone that was further transduced with Dox‐inducible empty vector (EV), 3A‐S(C) or 3A‐S(T) clones and then treated or not with 0.1 μg/ml Dox on Day 5. Nuclear Lamin A/C is shown as a loading control. (f) Western blot of immunoprecipitations of endogenous FOXO3A and FOXO3A‐S from LHCNM2 myotube lysates (Day 7) collected from spJCRISPR single‐cell clones. All myotube cultures were treated with 200 nM of the proteasome inhibitor Bortezomib for 18 h (starting Day 6) prior to harvest. (g) Western blots of lysates from cycloheximide (CHX) pulse‐chase experiments performed with the LHCNM2 lines from (e). Myoblasts were treated with 0.1 μg/ml Dox for 48 h post‐seeding, pulsed with 300 μM CHX then harvested at indicated time points (T = 0 no CHX). Western blots are representative of three independent experiments. (h) Plot of the band densitometry quantification of Western blots from (g). FOXO3A‐S protein levels were first normalized to corresponding Lamin A/C controls which were then normalized to T = 0 within each series. Plots are average of three replicate experiments with error bars being SD. (i) FOXO3A‐S protein half life calculated by averaging the computed half lives from each time point within (h). Error bars are SD.*
To assess the protein‐coding potential experimentally, full‐length FOXO3A‐S transcripts containing both rs9400239 variants (3A‐S(C) and 3A‐S(T)) were cloned as well as a clone starting from the first ATG (3A‐S(ATG)). These clones were first tested in in vitro transcription/translation reactions. Western blot analysis with an antibody raised against the far c‐terminus of FOXO3A yielded primarily single‐protein bands for all clones running around 56 KDa (Figure 3c). Notably, the bands derived from the full‐length clones were much less intense than that achieved with the 3A‐S(ATG) clone. These same clones were also transfected into HEK293T cells, which gave the same band intensity pattern on Western blot as the cell‐free system (Figure 3d). However, multiple bands were observed for each clone in the HEK293T lysates, suggesting post‐translational modification of the FOXO3A‐S protein (Figure 3d). The large amount of protein produced using the 3A‐S(ATG) clone as opposed to either full‐length FOXO3A‐S clone strongly suggests that the 5′ end of the mRNA may be greatly inhibitory of translation.
We next sought to identify the endogenous FOXO3A‐S protein. To facilitate these experiments, we first deleted all endogenous FOXO3A and FOXO3A‐S using a spJCRISPR construct targeting the splice acceptor site of exon 3 that is common to both FOXO3A and FOXO3A‐S. We also generated a construct to specifically delete FOXO3A‐S while sparing FOXO3A; this construct targeted the splice donor site of FOXO3A‐S exon 2A. LHCNM2 myoblasts were infected with both constructs, and single‐cell clones for each construct were isolated, differentiated, and screened by Western blot for FOXO3A knockout and RT‐qPCR for FOXO3A‐S knockout. We succeeded in generating a clone that was completely null for both FOXO3A and FOXO3A‐S (3A/3A‐S KO; Figure 3e; Figure S1). The FOXO3A‐S‐specific construct only yielded a clone with partial FOXO3A‐S knockdown (3A‐S KD; Figure S1). To ensure that the FOXO3A‐S protein could be unambiguously identified on Western blot in the LHCNM2 myotube background, we established two sub‐lines by infecting the 3A/3A‐S KO clone with either the 3A‐S(C) or 3A‐S(T) transcripts under the control of a doxycycline‐inducible promoter. With Dox addition, three bands were apparent in both lines with the primary band running at 56 KDa (Figure 3e). We then performed immunoprecipitations (IPs) from myotube lysates derived from lines “no target” (NT) control, 3A‐S KD and 3A/3A‐S KO using crosslinked FOXO3A c‐terminal antibody and detecting with rabbit conformation‐specific secondary antibody to avoid heavy chain detection. These IPs resulted in the identification of an endogenous band running at 56 KDa that was greatly reduced in the 3A‐S KD line and completely absent in the 3A/3A‐S KO line; corroborating the FOXO3A‐S RT‐qPCR in these lines (Figure 3f; Figure S1). It is notable that this band is not detectable by direct Western blotting of myotube lysates (Figure 3e & data not shown) and can only be visualized by IP/Western blot. This corroborates our findings that FOXO3A‐S is a poorly‐translated protein from the full‐length transcripts (Figure 3c,d) and therefore of relatively low abundance. From these experiments, we concluded that we successfully identified the endogenous FOXO3A‐S protein; demonstrating that the FOXO3A‐S transcript is a bona fide protein‐coding gene.
We next assessed the stability of the FOXO3A‐S protein to see if there were mechanisms additional to poor translation regulating FOXO3A‐S protein abundance. For this, we performed cycloheximide (CHX) chase assays in LHCNM2 myoblasts overexpressing either the 3A‐S(C) or 3A‐S(T) transcripts in the 3A/3A‐S KO background (Figure 3g,h). Incredibly, we found the half life of the 3A‐S(C)‐derived proteoform to be on average 154 min, while the half life of the 3A‐S(T)‐derived proteoform was 54 min (Figure 3i; $$p \leq 0.0007$$). This remarkable 2.8‐fold stability difference strongly indicates that the proteoforms derived from each allele might be distinct proteins. Another possibility is that there are multiple proteoforms encoded by both transcripts, perhaps produced at different ratios to each other depending on the allelic variant. Although the 3A‐S(C)‐derived proteoform is relatively more stable than that from 3A‐S(T), both are highly unstable proteins; as the median half life of all proteins in mouse C2C12 myotubes is 43 h (Cambridge et al., 2011). From these analyses, we conclude that the rs9400239 longevity SNP directly alters the identity and/or composition of the FOXO3A‐S translation products and that all forms are highly unstable and inefficiently translated.
## The FOXO3A‐S‐encoded SNP rs9400239 is associated with peripheral glucose clearance in vivo
A recent study showed that familial longevity was positively associated with the rate of peripheral glucose clearance (Wijsman et al., 2011). In humans, peripheral glucose clearance is primarily a function of the skeletal muscle (Shulman et al., 1990). Given the near‐exclusive expression of FOXO3A‐S in the skeletal muscle and the known roles of FOXOs in insulin signaling, we hypothesized that FOXO3A‐S may regulate peripheral glucose clearance and that this function may be influenced by the rs9400239 longevity SNP. Previously, a study of a metabolically well‐characterized cohort of Danish twins found a moderate association (β = 0.85; $$p \leq 0.04$$) between one of the FOXO3A longevity SNPs, rs2802292, and peripheral glucose clearance as measured by Rd clamp (Banasik et al., 2011). SNP rs9400239 was not included in this study, so we decided to genotype the same study cohort of 186 individuals for rs9400239 and repeated the analysis. The rs9400239 SNP showed a much stronger association (β = 1.2; $$p \leq 0.003$$) with Rd clamp, the longevity T‐allele being associated with faster clearance (Figure 4a). Notably, these associations with glucose clearance parallel the associations of these SNPs with longevity in Caucasians: rs9400239 is more strongly associated with both peripheral glucose clearance and longevity in Caucasians than rs2802292 (Figure 1c; average r 2 = 0.938 for all lead FOXO3A longevity SNPs except rs2802292 when compared with rs9400239 in Caucasians; average r 2 = 0.67 for rs2802292 when compared to all lead SNPs except rs9400239 in Caucasians). This suggests a genetic co‐mapping of these traits within the FOXO3A locus and raises the possibility that the increased peripheral glucose clearance of rs9400239 T‐allele carriers may be at least partly responsible for the increase in longevity.
**FIGURE 4:** *The FOXO3A‐S T‐allele is positively associated with peripheral glucose clearance in vivo. (a) Association of the rs9400239 genotype with the Rd clamp glucose disposal parameter. The association was calculated using a mixed‐ANOVA regression analysis correcting for age, sex, BMI, twin pair, and zygosity. (b) RT‐qPCR for FOXO3A‐S mRNA from muscle biopsy pairs harvested before (Basal) and during insulin infusion (insulin) in 139 healthy individuals. Expression was calculated relative to 18S rRNA using the ∆Ct method. Error bars are expressed as SEM, and the significance was calculated using the two‐tailed paired Student's t‐test. (c) Allele‐specific RT‐qPCR of FOXO3A‐S mRNA under basal and insulin‐stimulated conditions correlated with Rd clamp. The expression of each allele was corrected to 18S rRNA using the ∆Ct method. A mixed ANOVA regression was used to assess the correlation between allele‐specific values and Rd clamp correcting for age, sex, and BMI. Error bars are 95% CI.*
Within a subset of this cohort (139 individuals), paired muscle biopsies had been harvested from all individuals before (basal) and during insulin infusion, which allowed us to explore the possibility of FOXO3A‐S mRNA expression being associated with the genotype and/or peripheral glucose clearance. We first checked for a correlation between FOXO3A and FOXO3A‐S mRNA levels and found a strong correlation under both basal and insulin‐infused conditions (Figure S2a; basal r 2 = 0.8088 $p \leq 0.00001$, insulin r 2 = 0.7515 $p \leq 0.00001$). Previously, analysis of mRNA isolated from these samples using a FOXO3A‐specific Taqman probe showed that FOXO3A mRNA levels were down‐regulated during insulin infusion ($p \leq 0.0001$) (Banasik et al., 2011). A similar down‐regulation was observed for FOXO3A‐S mRNA levels ($p \leq 0.0001$; Figure 4b). Despite this acute regulation between conditions, we did not find a significant association between FOXO3A or FOXO3A‐S mRNA levels and Rd clamp (data not shown). Furthermore, we did not find a significant correlation between the genotype of SNP rs9400239 and the levels of FOXO3A or FOXO3A‐S mRNA, neither before or during insulin infusion (data not shown). To corroborate this lack of correlation between the rs9400239 genotype and canonical FOXO3A expression, we queried FOXO3A mRNA expression vs. the rs9400239 SNP in the skeletal muscle using the publicly available GTEx resource and found no relationship (GTEx version 8; https://gtexportal.org, $$p \leq 1$$). This finding highlights that the rs9400239 SNP functions differently than those FOXO3A longevity SNPs previously reported to be regulating FOXO3A mRNA expression (Flachsbart et al., 2017; Grossi et al., 2018).
Although we did not find a correlation between total FOXO3A‐S mRNA levels and Rd clamp, we decided to quantitate FOXO3A‐S allele expression specifically across the cohort by developing an allele‐specific RT‐qPCR method (Figure S2b–d). We hypothesized this might reveal quantitative relationships between the FOXO3A‐S mRNA variants and phenotypic traits as we suspected the proteins encoded by each allele to be biochemically distinct. Again, we found no significant correlation between the genotype of SNP rs9400239 and the mRNA levels of the C or T‐alleles of FOXO3A‐S (Figure S2e), corroborating our findings from the total FOXO3A‐S RT‐qPCR. We next tested the relationship between the mRNA levels of the individual FOXO3A‐S C and T‐alleles and glucose clearance rates. Multivariate analysis correcting for age, sex, and BMI showed that the mRNA levels of the FOXO3A‐S C‐allele during insulin challenge were inversely correlated with the rate of glucose clearance (β = −0.46, $$p \leq 0.005$$); under basal conditions, there was no correlation (Figure 4c). In contrast, the T‐allele transcript levels did not show any correlation to the rate of glucose clearance either before or during insulin challenge (Figure 4c). An attractive interpretation of these data is that under insulin stimulation and PI3K activation, the protein product of the C‐allele suppresses the rate of peripheral glucose clearance, while the protein product of the T‐allele has no effect under these conditions. From these analyses, we concluded that the rs9400239 FOXO3A‐S longevity genotype is positively associated with peripheral insulin‐stimulated glucose clearance but does not influence the mRNA expression of either FOXO3A or FOXO3A‐S in the skeletal muscle.
## FOXO3A‐S suppresses glycolytic flux in myotubes with PI3K‐dependency altered by the rs9400239 genotype
From the cohort results, we hypothesized that each of the FOXO3A‐S variants might have a differential effect on muscle‐mediated glucose clearance, with this difference possibly being insulin/PI3K‐dependent. To begin evaluating the role of the FOXO3A‐S transcripts in the regulation of glucose clearance, we chose to measure the extracellular acidification rate (ECAR, a measure of glycolytic flux) in LHCNM2‐derived myotubes. We constructed doxycycline‐inducible overexpression lines with the 3A‐S(C) and 3A‐S(T) clones in a FOXO3A and FOXO3A‐S wild‐type background (Figure 5a). We observed no perturbation of PI3K signaling when either of these lines was induced by Dox and observed complete inhibition of PI3K signaling with GDC‐0941 treatment (Figure S3). Overexpression of the 3A‐S(C) clone in myotubes resulted in repression of basal glycolytic flux (Figure 5b; −Dox/DMSO vs. +Dox/DMSO $p \leq 0.0001$). Overexpression of 3A‐S(C) also enhanced the effect of PI3K inhibition causing maximal repression of basal glycolytic flux (Figure 5b; −Dox/PI3Ki vs. +Dox/PI3Ki $$p \leq 0.0055$$). Overexpression of the 3A‐S(T) clone did not repress basal glycolytic flux on its own (Figure 5b). However, 3A‐S(T) overexpression enhanced the repressive effect on basal glycolytic flux caused by inhibition of PI3K (Figure 5b; −Dox/PI3Ki vs. +Dox/PI3Ki, $$p \leq 0.0031$$). We also assessed glycolytic capacity in these same assays following oligomycin injection. Overexpression of 3A‐S(C) alone repressed glycolytic capacity (Figure 5c; −Dox/DMSO vs. +Dox/DMSO, $p \leq 0.0001$) and enhanced the repressive effect of PI3K inhibition on glycolytic capacity (Figure 5c; −Dox/PI3Ki vs. +Dox/PI3Ki, $$p \leq 0.0007$$). 3A‐S(T) alone had no effect on glycolytic capacity, while it did enhance the repressive effect of PI3K inhibition on glycolytic capacity (Figure 5c; −Dox/PI3Ki vs. +Dox/PI3Ki, $$p \leq 0.01$$). These assays indicated that the C‐allele was capable of repressing glycolysis independently of PI3K signaling, while the T‐allele only repressed glycolysis when PI3K signaling was inactivated.
**FIGURE 5:** *FOXO3A‐S suppresses glycolysis in myotubes with PI3K‐dependency altered by the rs9400239 genotype. (a) Western blot of the inducible overexpression of 3A‐S(C) or 3A‐S(T) constructs in LHCNM2 myotubes. Dox (0.1 μg/ml) was applied on Day 5 of differentiation, and on Day 7, the myotube lysates were harvested. (b) Seahorse assays performed following the same differentiation and Dox treatment as in (a) with the addition of DMSO or 5 μM GDC‐0941 (PI3Ki) for 1 h prior to the assay. All basal ECAR values were calculated as a percentage of the −Dox/DMSO control for each respective assay. The presented assay is representative of three independent replicates. Error bars are SEM. (c) Comparison of the maximal ECAR values upon oligomycin injection attained from the same overexpression assays performed in (b). The max ECAR was calculated as a percentage of the −Dox/DMSO basal ECAR within each assay. Error bars are SEM. (d) RT‐qPCR for inducible shRNA‐mediated knockdown of FOXO3A and FOXO3A‐S. Myoblasts were differentiated to myotubes and on Day 2 2 mM IPTG was applied with RNA harvested on Day 6. Both FOXO3A and FOXO3A‐S expression in the +IPTG condition was quantified relative to the −IPTG control. Error bars are SD. (e) ECAR measurement in myotubes where FOXO3A and FOXO3A‐S was inducibly knocked down (IPTG Day 2 measured Day 6). Basal ECAR values were calculated as a percentage of the −IPTG control. The presented assay is representative of three independent replicates. Error bars are SEM.*
To further confirm FOXO3A‐S as a glycolytic repressor, we constructed IPTG‐inducible shRNA knockdown lines of FOXO3A and FOXO3A‐S (Figure 5d). Importantly, LHCNM2 is homozygous for the C‐allele of FOXO3A‐S as it is the only detectable form of FOXO3A‐S on the mRNA level (Figure S2f). Knockdown of FOXO3A in LHCNM2 myotubes had no effect on basal glycolytic flux (Figure 5e). A $50\%$ knockdown of FOXO3A‐S caused a $30\%$ increase in basal glycolytic flux (Figure 5e; $$p \leq 0.022$$). This confirmed that the C‐allele of FOXO3A‐S is a repressor of glycolytic flux in myotubes. Collectively, these results demonstrate that the alleles are functionally different in the regulation of glycolysis and that this difference is in accordance with the in vivo genotype association: the T‐allele may allow for higher insulin‐stimulated glucose clearance levels in vivo due to its inability to inhibit glycolysis under insulin‐stimulated (PI3K active) conditions. In contrast, the C‐allele is capable of glycolytic repression even in the presence of insulin/PI3K signaling; explaining the inverse correlation between C‐allele transcript levels and insulin‐stimulated peripheral glucose clearance.
## FOXO3A‐S subcellular localization is unaltered by insulin/PI3K signaling in myotubes
Since FOXO3A cytosolic/nuclear localization is known to be regulated by PI3K/Akt signaling (Greer & Brunet, 2005), we checked to see where the FOXO3A‐S proteins localize in myotubes when the PI3K pathway is manipulated. We hypothesized this could explain the conditional repression of glycolytic flux by 3A‐S(T). To work in a clean background, we again utilized our 3A/3A‐S KO cells that were reconstituted with the 3A‐S(C) and 3A‐S(T) constructs for immunofluorescence (IF). We first tested IF staining with a c‐terminal FOXO3A antibody in LHCNM2 NT and 3A/3A‐S KO myotubes to validate that there was low background staining under either insulin or PI3K‐inhibited (GDC‐0941 treated) conditions (Figure S4). We then stained 3A/3A‐S KO myotubes induced to overexpress either canonical FOXO3A, 3A‐S(C), or 3A‐S(T) under these conditions. As expected, FOXO3A was restricted from the nucleus under insulin and shuttled into the nucleus with PI3K inhibition (Figure S5). In contrast, the FOXO3A‐S proteins exhibited both cytosolic and nuclear staining that was unaltered by PI3K pathway manipulation (Figure S5). This led us to rule out subcellular localization as the PI3K‐mediated regulatory mechanism modulating conditional 3A‐S(T) glycolytic flux suppression.
## DISCUSSION
Our renewed genetic analysis of the FOXO3A locus has identified FOXO3A‐Short and its regulatory polymorphism rs9400239 as novel mediators of the effect of the FOXO3A genotype on both insulin sensitivity and presumably human longevity. Given the coincident genetic mapping of these phenotypes within the FOXO3A locus, it seems likely that enhanced peripheral insulin‐stimulated glucose clearance may be strongly contributing to extraordinary human lifespan extension. Our model posits that due to the ability of PI3K signaling to inactivate the protein product of the longevity T‐allele and the inability of PI3K to inactivate that of the non‐longevity C‐allele glucose clearance is enhanced in longevity allele carriers during insulin challenge (Figure 6). Furthermore, the highly restricted expression pattern of FOXO3A‐S suggests that the skeletal muscle is the primary mediator of these phenotypes, implicating the skeletal muscle as a particularly important tissue modulating human healthspan and lifespan. Indeed, increased muscle mass is positively correlated with insulin sensitivity and inversely correlated with all‐cause mortality in humans (Srikanthan & Karlamangla, 2011, 2014).
**FIGURE 6:** *Model of FOXO3A‐S action in longevity and peripheral glucose clearance. The FOXO3A‐S proteoforms either constitutively or conditionally limit skeletal muscle‐mediated glucose clearance under insulin‐stimulated conditions. Longevity (T‐allele) carriers express a PI3K‐repressible form of the protein allowing for higher glucose clearance rates with insulin challenge while C‐allele carriers have more restricted insulin‐stimulated glucose clearance because the C‐allele proteoform is refractory to PI3K signaling. Increased peripheral glucose clearance confers health benefits to T‐allele carriers, which allows for longer and healthier lives.*
Our model that enhanced peripheral glucose clearance is positively associated with longevity in humans is supported by other human studies as well as data from primates and model organisms. The Leiden Longevity Study found that peripheral glucose clearance is positively associated with familial longevity, while hepatic glucose clearance was not (Wijsman et al., 2011). Additionally, a low insulin resistance index (HOMA‐IR) among centenarians has been observed when compared to their younger controls (Paolisso et al., 2001). In mouse models, both caloric restriction as well as growth hormone receptor knockout (GHRKO) caused increased lifespan and insulin sensitivity (Masternak et al., 2009). GHRKO mice with genetically normalized insulin sensitivity exhibited reversion of GHRKO benefits in various age‐related parameters, demonstrating a causative role for insulin sensitivity in the modulation of aging phenotypes (Arum et al., 2014). Corroborating this finding, total ablation of the insulin receptor in all the peripheral tissues of adult mice shortened lifespan (Merry et al., 2017). In monkeys, a causative association between caloric restriction, increased insulin‐stimulated PI3K activity in skeletal muscle, increased insulin sensitivity, and enhanced peripheral glucose clearance has also been observed (Wang et al., 2009). In a separate cohort of monkeys, caloric restriction was shown to significantly increase lifespan (Colman et al., 2014); this was also confirmed in gray mouse lemurs (Pifferi et al., 2018). In humans, caloric restriction was found to have no effect on insulin‐stimulated oxidative glucose metabolism in skeletal muscle while enhancing glucose disposal by increasing non‐oxidative glucose metabolism (Johnson et al., 2016). Furthermore, increased non‐oxidative glucose metabolism concurrent with strongly suppressed oxidative glucose metabolism in the skeletal muscle has been found to increase skeletal muscle glucose disposal and reduce age‐ and diet‐dependent adiposity in mice (Sharma et al., 2019). The diabetes medication metformin also increases non‐oxidative glucose metabolism in human skeletal muscle while having no or perhaps even a negative effect on oxidative glucose metabolism (Musi et al., 2002). Importantly, metformin reduces all‐cause mortality in both healthy and diabetic humans, making it a general geroprotective compound (Campbell et al., 2017). Interestingly, oxygen consumption rate (OCR) which was measured simultaneously with ECAR on the seahorse was not significantly altered by FOXO3A‐S overexpression, either in combination with PI3K inhibition or alone (data not shown). This strongly suggests that FOXO3A‐S primarily regulates non‐oxidative glucose metabolism, pinpointing this pathway as being of specific importance to human longevity. Furthermore, this mechanism may be conserved as recent work on fruit flies demonstrates that specific genetic enhancement of glycolysis was found to extend lifespan (Ma et al., 2018). It is also notable that mitochondrial function and OXPHOS complexes decline with age in the skeletal muscle of primates and humans, but not in rats (Mercken et al., 2017). This age‐dependent decline in skeletal muscle oxidative capacity may render skeletal muscle non‐oxidative glucose metabolism increasingly crucial for the maintenance of whole‐body glucose homeostasis in primates and humans as they age, paralleling the evolution of FOXO3A‐S as a novel regulator of this physiology. Taken together, our findings that the non‐longevity C‐allele suppresses glycolytic flux in skeletal muscle independently of insulin/PI3K signaling while the longevity‐conferring T‐allele is suppressed by insulin/PI3K signaling allowing for maximal glycolytic flux is highly consistent with these previous reports.
The PI3K/Akt pathway is of central importance in human glycemic regulation (George et al., 2004). Mechanistically, our findings suggest that FOXO3A‐S may be acting as another arm of insulin signaling that both interacts with and parallels PI3K signaling in the regulation of glycolysis in humans. Both alleles of FOXO3A‐S encode a protein capable of suppressing glycolysis additively with PI3K inhibition, which on its own completely inhibited both Akt and mTORC1 signaling in these myotubes (Figure S3). This observation strongly suggests that FOXO3A‐S regulates aspects of glycolysis that the PI3K/Akt/mTORC1 pathway does not control. Indeed, the potency of these FOXO3A‐S proteoforms in glycolytic regulation may explain why they exhibit exceptionally tight transcriptional, translational, and post‐translational regulatory mechanisms. In the future, it will be of great interest to discern exactly how FOXO3A‐S exerts its effects on glycolysis and how the T‐allele proteoform is conditionally regulated by insulin/PI3K signaling.
Our findings also reinforce the tissue specificity by which insulin signaling influences lifespan in mammals. The SNP and PI3K‐dependent differences in FOXO3A‐S functionality argue that increased insulin signaling in skeletal muscle is specifically beneficial for longevity in humans; otherwise no glucose metabolism and presumably longevity advantage would accrue to T‐allele carriers. This conclusion extends previous findings that increased insulin signaling in adipose tissue and decreased insulin signaling in all peripheral tissues are both situations detrimental to longevity (Bluher et al., 2003; Merry et al., 2017). Last, our results implicate FOXO3A‐S as essentially an anti‐longevity molecule, fundamentally challenging the prevailing view that increased FOXO activity will almost always be geroprotective. This makes the minimally conserved FOXO3A‐S isoform a cautionary tale in the translation of concepts from model organisms to humans, highlighting how rapid evolution in a single family member can upset a paradigm. In the future, deeper study of FOXO3A‐S will be of great utility in the elucidation of new genes and pathways directly influencing glucose metabolism and human longevity.
## Danish twin cohort
The cohort used here has been previously described and characterized (Banasik et al., 2011). The twin population included 196 monozygotic (MZ; $$n = 108$$) and same‐sex dizygotic (DZ; $$n = 88$$) Danish twins without known type 2 diabetes. The population consisted of two age groups, young (28 ± 2 year, $$n = 110$$) and elderly (62 ± 2 year, $$n = 86$$) participants. All participants were Danes by self‐report, and written informed consent was obtained from all individuals before participation. There was no significant difference in glucose tolerance status between MZ and DZ twins within each age group. Zygosity was determined by polymorphic genetic markers. The study was approved by the regional ethical committees, and the study was conducted according to the principles of the Helsinki Declaration.
## Cell lines, culture conditions & reagents
All lines were cultured on Greiner BioOne (CELLSTAR) plasticware – this was especially relevant for LHCNM2 myoblasts. HEK293T cells were cultured in high‐glucose Dulbecco's Modified Eagle Medium (DMEM) (Gibco cat. # 11965) supplemented with $10\%$ fetal bovine serum, 20 mM L‐glutamine, 10 U/ml penicillin, and 10 μg/ml streptomycin and maintained at 37°C under $5\%$ CO2. The immortalized human myoblast line LHCNM2 was previously described (Zhu et al., 2007). All cell lines were repeatedly tested for mycoplasma contamination by PCR and found to be mycoplasma‐free. These cells were cultured on collagen‐coated plates (plates pre‐incubated at 37°C for at least 1 h with a PBS solution of 10 μg/ml collagen from rat tail, Sigma) at 37°C under $5\%$ CO2. The base of the growth medium was a 4:1 mixture of high‐glucose DMEM (Gibco cat. # 11965) and Medium 199 (Gibco cat. # 11150–059) buffered with bicarbonate. Additional to this was 0.02 M HEPES, $20\%$ fetal bovine serum, 0.03 μg/ml zinc sulfate (Sigma), 1.4 μg/ml vitamin B12 (Sigma), 0.055 μg/ml dexamethasone (Sigma), 2.5 ng/ml human HGF (Peprotech), 10 ng/ml human basic FGF (Peprotech), 10 U/ml penicillin, and 10 μg/ml streptomycin. For differentiation, the cells were grown to maximal confluency, and then the medium was changed to serum‐free differentiation medium after washing 1x with PBS to remove residual growth medium. This medium contained 4:1 DMEM:M199, 0.02 M HEPES, 0.03 μg/ml zinc sulfate, 1.4 μg/ml vitamin B12, 10 μg/ml human insulin (Sigma), 10 U/ml penicillin, and 10 μg/ml streptomycin. Following the initial medium change, the cells were switched to $3\%$ CO2 and then the medium was changed every 2 days with differentiation usually becoming apparent on Day 5 with maturation being reached on Days 6 & 7. The compounds GDC‐0941 and bortezomib were purchased from Selleckchem and dissolved to 5 mM and 1 mM stocks, respectively, in Hybri‐Max DMSO (Sigma). Doxycycline and cycloheximide were from Sigma. Blasticidin and puromycin were from Invitrogen.
## Statistical analysis
Unpaired Student's t tests were used to evaluate statistical significance. Values are expressed as means ± SEM or means ± SD, as indicated. To assess correlations Pearson's correlation coefficients were calculated on normally distributed data. The Danish cohort data were analyzed using mixed‐ANOVA regressions correcting for appropriate parameters as described. All p‐values were calculated based on $95\%$ confidence intervals. See figure legends for sample size and replicate details.
See Supporting Information for additional experimental procedures.
## AUTHOR CONTRIBUTIONS
Evan E. Santo discovered FOXO3A‐Short. Evan E. Santo, Jihye Paik, Ellen M. Westerhout, and Rogier Versteeg designed experiments. Allan A. Vaag provided the Danish cohort data. Rasmus Ribel‐Madsen, Evan E. Santo, and Allan A. Vaag analyzed the Danish cohort data. Evan E. Santo, Ellen M. Westerhout, Peter J. Stroeken, Ninna S. Hansen, and Maaike Commandeur performed experiments. Vincent C. J. de Boer aided with seahorse experiments. The manuscript was written by Evan E. Santo and edited by Rasmus Ribel‐Madsen, Allan A. Vaag, Jihye Paik, and Ellen M. Westerhout.
## CONFLICT OF INTEREST
The authors declare no competing interests.
## DATA AVAILABILITY STATEMENT
The full dataset (genotyping, RT‐qPCR and glucose clearance measurements) for the Danish cohort is available on request from the corresponding author E.E.S. The cDNA sequences for the full‐length FOXO3A‐Short clones have been deposited in GenBank under submission IDs MK390614 and MK390615. All constructs and cell lines used in this paper can be requested from E.E.S. and J.P.
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|
---
title: N‐homocysteinylation of α‐synuclein promotes its aggregation and neurotoxicity
authors:
- Lingyan Zhou
- Tao Guo
- Lanxia Meng
- Xingyu Zhang
- Ye Tian
- Lijun Dai
- Xuan Niu
- Yiming Li
- Congcong Liu
- Guiqin Chen
- Chaoyang Liu
- Wei Ke
- Zhaohui Zhang
- Anyu Bao
- Zhentao Zhang
journal: Aging Cell
year: 2022
pmcid: PMC10014048
doi: 10.1111/acel.13745
license: CC BY 4.0
---
# N‐homocysteinylation of α‐synuclein promotes its aggregation and neurotoxicity
## Abstract
The aggregation of α‐synuclein plays a pivotal role in the pathogenesis of Parkinson's disease (PD). Epidemiological evidence indicates that high level of homocysteine (Hcy) is associated with an increased risk of PD. However, the molecular mechanisms remain elusive. Here, we report that homocysteine thiolactone (HTL), a reactive thioester of Hcy, covalently modifies α‐synuclein on the K80 residue. The levels of α‐synuclein K80Hcy in the brain are increased in an age‐dependent manner in the TgA53T mice, correlating with elevated levels of Hcy and HTL in the brain during aging. The N‐homocysteinylation of α‐synuclein stimulates its aggregation and forms fibrils with enhanced seeding activity and neurotoxicity. Intrastriatal injection of homocysteinylated α‐synuclein fibrils induces more severe α‐synuclein pathology and motor deficits when compared with unmodified α‐synuclein fibrils. Increasing the levels of Hcy aggravates α‐synuclein neuropathology in a mouse model of PD. In contrast, blocking the N‐homocysteinylation of α‐synuclein ameliorates α‐synuclein pathology and degeneration of dopaminergic neurons. These findings suggest that the covalent modification of α‐synuclein by HTL promotes its aggregation. Targeting the N‐homocysteinylation of α‐synuclein could be a novel therapeutic strategy against PD.
Hyperhomocysteinemia is associated with an increased risk of PD. However, the molecular mechanisms remain elusive. HTL, a reactive thioester of Hcy, covalently modifies a‐synuclein at the K80 residue. The N‐homocysteinylation of a‐synuclein stimulates its aggregation and neurotoxicity. Our findings suggest that targeting the N‐homocysteinylation of a‐synuclein could be a novel therapeutic strategy against PD.
## INTRODUCTION
Parkinson's disease (PD) is one of the most common neurodegenerative diseases caused by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc). Pathologically, PD is characterized by the formation of intraneuronal inclusions called Lewy bodies (LBs) and Lewy neurites (LNs), which are mainly composed of aggregated α‐synuclein (α‐syn) (Fares et al., 2021). Under physiological conditions, α‐syn is a natively unstructured synaptic protein that is mainly expressed in the presynaptic nerve terminals (Bartels et al., 2011). Converging evidence indicates that the toxicity of α‐syn is related to its aggregation (Araki et al., 2019). However, the molecular mechanisms that drive the aggregation of α‐syn during the onset of PD have not been completely identified.
Multiple epidemiological and clinical studies reported that high level of homocysteine (Hcy) is associated with an increased risk of PD (Bakeberg et al., 2019; Licking et al., 2017). Interestingly, the levels of Hcy gradually increase with age (Mattson et al., 2002). High level of Hcy has been implicated in several human diseases including stroke (Carlsson, 2007), coronary artery disease (Foody et al., 2000), and Alzheimer's disease (Zhuo & Pratico, 2010). Several potential mechanisms have been proposed to explain the biological links between Hcy elevation and the onset of diseases. Hcy has been reported to induce inflammation (Elsherbiny et al., 2020), microvascular damage (Muzurovic et al., 2021), and autoimmune responses (Lazzerini et al., 2007). However, the causative role of Hcy in PD pathogenesis remains to be elucidated.
Hcy is a byproduct of the methionine metabolism pathway. Methionine supplements boost Hcy levels (Zhang et al., 2019). Homocysteine thiolactone (HTL) is a reactive intermediate of *Hcy* generated by methionine‐tRNA synthetase (MARS). Recently, several studies reported that HTL covalently modifies certain lysine residues in proteins, a process known as N‐homocysteinylation. The N‐homocysteinylation of proteins results in the alteration of their structure and function (Jakubowski, 2011; Sikora et al., 2010). For example, N‐homocysteinylation of bovine serum albumin (BSA) induces it to form amyloid‐like structures (Paoli et al., 2010). α‐*Synuclein is* subjected to extensive post‐transcriptional modifications, including O‐GlcNAcylation (Levine et al., 2019), oxidation (Ponzini et al., 2019), and glycation (Vicente Miranda et al., 2017). These modifications regulate its oligomerization, polymerization, and toxicity in vivo. Since the levels of Hcy are increased in PD patients (Saadat et al., 2018), we speculated that HTL might modify α‐syn to bring about the onset of PD.
In the current work, we identified that the K80 residue of α‐syn undergoes N‐homocysteinylation, which enhances α‐syn aggregation and forms aggregates with enhanced seeding activity and neurotoxicity in vitro and in vivo. Blocking the N‐homocysteinylation of α‐syn at K80 attenuates α‐syn pathology. Therefore, we demonstrate that N‐homocysteinylation mediates α‐syn pathology, promoting the onset and progression of PD.
## α‐Syn is covalently modified by HTL
Protein N‐homocysteinylation can be specifically labeled with azide probes (Chen et al., 2019). To explore whether α‐syn undergoes N‐homocysteinylation, we treated HEK293 cells expressing HA‐tagged α‐syn with HTL and performed chemoselective reaction using a biotin‐azide probe. Immunoblotting revealed that α‐syn was homocysteinylated in the presence of HTL (Figure 1a). No signal was detected in the absence of HTL or biotin‐azide probe, confirming the specificity and selectivity of the probe (Figure 1b). Furthermore, HTL induced the homocysteinylation of α‐syn in a concentration‐dependent manner (Figure 1c,d). HTL is the reactive thioester of Hcy produced by the enzyme MARS in an error editing reaction. We further tested whether Hcy also causes the homocysteinylation of α‐syn in cells. Treatment with Hcy induced α‐syn homocysteinylation (KHcy) in a concentration‐dependent manner (Figure 1e,f). Interestingly, knockdown of MARS abolished α‐syn homocysteinylation induced by Hcy, but not that induced by HTL, indicating the conversion of Hcy to HTL is required for α‐syn homocysteinylation (Figure 1g,h). Together, our findings suggest that the Hcy metabolite HTL mediates α‐syn homocysteinylation.
**FIGURE 1:** *α‐Syn is N‐homocysteinylated. HEK293 cells were transfected with HA‐α‐syn, followed by treatment with vehicle, Hcy, or HTL. HA‐α‐syn were purified using HA beads from the cell lysates and labeled by the chemoselective reactions. (a) Chemoselective labeling of HA‐α‐syn‐transfected HEK293 cells incubated with vehicle or HTL (0.1 mM) for 12 h. (b) Verification of the selectivity of the biotin‐azide probe. (c, d) HTL induces α‐syn homocysteinylation in a concentration‐dependent manner. (e, f) Hcy induces α‐syn homocysteinylation in a concentration‐dependent manner. (g, h) Knockdown of MARS abolishes α‐syn homocysteinylation induced by Hcy. Data are shown as mean ± SEM. n = 4 independent experiments. ****p < 0.0001, NS, not significant*
## Lysine 80 is the major residue of α‐syn homocysteinylation
To identify the N‐homocysteinylation site on α‐syn, we transfected GST‐tagged α‐syn into HEK293 cells, treated with HTL, and purified GST‐α‐syn. MS identified multiple homocysteinylated lysine residues throughout the α‐syn sequence (Table S1), with lysine 80 (K80) as the major modified residue (Figure 2a). To confirm the modification of lysine residues, we generated point mutations that replace lysine with arginine (K58R, K60R, K80R, K96R, K97R, K102R). K80R mutation substantially abolished α‐syn homocysteinylation, implying that K80 is the major site of α‐syn homocysteinylation (Figure 2b). *We* generated a polyclonal antibody (anti‐K80Hcy) by immunizing the rabbits with synthetic α‐syn peptide (amino acids 75–84) containing homocysteinylated lysine at K80. Dot blots showed that anti‐K80Hcy antibody preferentially recognized α‐syn K80Hcy peptide (Figure S1). The K80R mutation completely abolished the K80Hcy signals, further confirming the specificity of the anti‐K80Hcy antibody (Figure 2b). In HEK293 cells transfected with GST‐α‐syn, both Hcy and HTL induced homocysteinylation of the K80 residue as detected with the anti‐K80Hcy antibody (Figure 2c–f). In agreement with the results using azide probes (Figure 1g,h), knockdown of the enzyme MARS abrogated the modification of K80 by Hcy, but not that by HTL (Figure 2g,h). These results indicate that K80 is the major homocysteinylation site on α‐syn.
**FIGURE 2:** *K80 is the major homocysteinylation site on α‐syn. (a) A representative spectrum of LC–MS/MS fragmentation containing K80 homocystylation. (b) K80R mutation abolishes the homocysteinylation of α‐syn. (c, d) Levels of α‐syn K80Hcy in HEK293 cells treated with different concentrations of Hcy. (e, f) Levels of α‐syn K80Hcy in HEK293 cells treated with different concentrations of HTL. (g, h) Knockdown of MARS abolishes Hcy‐induced α‐syn K80Hcy in HEK293 cells. Data are shown as mean ± SEM. n = 4 independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, NS, not significant*
## α‐Syn K80Hcy is increased in the brain in an age‐dependent manner
To test whether α‐syn K80Hcy is present in the brain, we performed immunohistochemistry (IHC) using anti‐K80Hcy antibody. α‐Syn K80Hcy was detected in the α‐syn A53T transgenic (TgA53T) mice. The signals were blocked by pre‐incubation with the K80Hcy peptide. No signal was detected in brain sections from Snca KO mice, further confirming the specificity of the anti‐K80Hcy antibody (Figure 3a). Moreover, immunofluorescence found that K80Hcy colocalized with phosphorylated α‐syn (pS129), the marker of α‐syn inclusions (Figure 3b). Furthermore, the K80Hcy signals were also positive for thioflavin S (ThS) staining, suggesting the aggregated α‐syn is homocysteinylated (Figure 3c). Remarkably, immunofluorescent staining of the SN sections from PD patients also confirmed that K80Hcy colocalized with pS129 (Figure 3d). Immunoblotting with α‐syn K80Hcy antibody revealed that α‐syn K80Hcy was elevated in the SN from PD brains (Figure 3e). ELISA showed that K80 modification in standard was recognized by anti‐K80Hcy antibody in a concentration‐dependent manner, while the anti‐K80Hcy antibody did not react with the α‐syn K80R mutant or unmodified α‐syn (Figure S2a). ELISA showed that the percentage of α‐syn K80Hcy in control subjects was $1.63\%$, while that in PD patients was about $5.57\%$ (Figure S2b). Aging is the most important risk factor for PD. We found that the concentrations of both Hcy and HTL in the mouse brain samples increased in an age‐dependent manner (Figure 3f,g). Consistently, both α‐syn K80Hcy and pS129 immunoreactivity were escalated in the TgA53T mice in an age‐dependent manner (Figure 3h). These observations were recapitulated by Western blot analysis (Figure 3i–k). ELISA showed that $1.99\%$, $3.47\%$, and $6.05\%$ of α‐syn was modified in TgA53T mice at 8, 10, and 12 months of age, respectively (Figure S2c). Thus, the age‐dependent increase of Hcy and HTL in the brain is accompanied by the accumulation of homocysteinylated α‐syn.
**FIGURE 3:** *α‐Syn K80Hcy is increased in TgA53T mice in an age‐dependent manner. (a) Immunohistochemistry of the α‐syn K80Hcy in SN sections from 12‐month‐old WT, TgA53T, and Snca KO mice. (b) Double immunofluorescence of K80Hcy and pS129 on SN sections from 12‐month‐old TgA53T mice. (c) Colocalization of K80Hcy with Thioflavin S (ThS) on SN sections from 12‐month‐old TgA53T mice. (d) Immunofluorescent staining of K80Hcy and pS129 on the SN sections from PD patients. (e) Western blot of K80Hcy in the SN tissues from PD and control subjects. (f, g) LC/MS analysis of Hcy (f) or GC/MS analysis of HTL (g) in the striatum of TgA53T mice and their wild‐type littermates at different ages. (h) Immunostaining of α‐syn K80Hcy or pS129 in the SN, red nucleus, and spinal cord from different‐age TgA53T mice. (i–k) Western blot quantification of α‐syn K80Hcy and pS129 in the SN of TgA53T and Snca KO mice at different ages. Data are shown as mean ± SEM. n = 3 (f, g), 4 (i–k) independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Scale bar is 20 μm.*
## Homocysteinylation of α‐syn facilitates its fibrillization
To explore the effect of homocysteinylation on α‐syn fibrillization, we monitored the kinetics of α‐syn fibrillation via Thioflavin T (ThT) assay. HTL dramatically facilitated α‐syn fibrillization with decreased lag time. However, the fibrillization of K80R mutant α‐syn was not affected by HTL (Figure 4a, Table S2). Electron microscopy revealed that the fibrils formed in the presence of HTL were more condensed than the control fibrils (Figure 4b). To determine the properties of α‐syn pre‐formed fibrils (PFFs), we conducted limited proteolysis of fibrils with proteinase K (PK) and pronase. HTL‐modified α‐syn PFFs were more resistant to digestion with protease K or pronase than control α‐syn PFFs (Figure 4c,d, Figure S3a).
**FIGURE 4:** *HTL potentiates α‐syn aggregation. (a) Kinetics of α‐syn aggregation in the presence or absence of HTL. Data were normalized to the highest signal. (b) Electron microscopy images of α‐syn fibrils formed by WT and K80R mutant α‐syn in the presence or absence of HTL. Scale bar is 200 nm. (c, d) Proteinase K digestion of WT and K80R mutant α‐syn fibrils formed in the presence or absence of HTL. Quantification represents the ratio of remaining protein to total PFFs. (e–h) α‐Syn‐HEK293 cells were exposed to HTL for 12 h, then transduced with α‐syn PFFs (140 ng/ml final concentration) and incubated for another 24 h. (e, f) Images and quantification of insoluble α‐syn inclusions. (g, h) Western blot analysis of Syn211 and pS129 in Triton X‐100‐soluble and SDS‐soluble fractions. (i–l) WT or K80R mutant α‐syn PFFs were induced to aggregate into PFFs in the presence or absence of HTL. (i) The seeding activity of different PFFs was tested in α‐syn‐HEK293 cells. (j) Quantification of the percentage of the cells with insoluble α‐syn inclusions. (k, l) Western blot analysis of Syn211 and pS129 in Triton X‐100‐soluble and SDS‐soluble fractions. (m, n) Primary neurons were treated with α‐syn PFFs or HTL‐α‐syn PFFs. Immunofluorescence shows α‐syn phosphorylation. n = 3 (a), 4 (c, d), 8 (e, f), 4 (g, h), 8 (i, j), 4 (k, l), 6 (m, n) independent experiments. All data are shown as mean ± SEM. ***p < 0.001, ****p < 0.0001. Scale bar is 20 μm.*
To further investigate the impact of K80 homocysteinylation on α‐syn fibrillization in cells, we used HEK293 cells stably transfected with α‐synuclein‐GFP (α‐syn‐HEK293 cells) as reporter cells. Transduction of the reporter cells with α‐syn PFFs induces the formation of insoluble α‐syn inclusions (Yan et al., 2022). ELISA analysis using the anti‐K80Hcy antibody showed that $4.94\%$ of α‐syn was modified in α‐syn‐HEK293 cells exposure to 0.1 mM HTL (Figure S3b). Pre‐treatment of the reporter cells with HTL dose‐dependently potentiated the formation of α‐syn aggregates induced by α‐syn PFFs (Figure 4e,f). The aggregates colocalized with ubiquitin, a marker of Lewy bodies (Figure S3c). Fractionation analysis revealed that HTL treatment decreased the levels of α‐syn in the Triton X‐100‐soluble fraction, while increased that in the insoluble fraction (Figure 4g,h). Moreover, HTL induced α‐syn phosphorylation in the absence of α‐syn PFFs in α‐syn‐HEK293 cells and neurons (Figure S3d‐g). Similar to HTL, Hcy also resulted in an elevation of α‐syn aggregation in α‐syn‐HEK293 cells (Figure S4a‐d). Knocking down of MARS in α‐syn‐HEK293 cells prevented the elevation of α‐syn aggregation induced by Hcy treatment, but not by HTL (Figure S4e‐h), indicating that the conversion of Hcy to HTL is required for it to promote α‐syn aggregation in cells.
To further confirm the effect of α‐syn K80 modification on its aggregation, we treated HEK293 cells expressing wild‐type or K80R mutant α‐syn with Hcy or HTL, and then transduced the cells with α‐syn PFFs. Hcy and HTL promoted the aggregation of wild‐type α‐syn, but not the K80R mutant α‐syn (Figure S5a,b). The results were further validated by Western blot analysis of the soluble and insoluble α‐syn species (Figure S5c‐f).
## Homocysteinylated α‐syn PFFs are more prone to seed soluble α‐syn in vitro
To investigate the effect of K80Hcy on the seeding activity of α‐syn fibrils, we generated PFFs from wild‐type and K80R mutant α‐syn in the presence or absence of HTL, and then transduced α‐syn‐HEK293 cells with these PFFs. ELISA assay showed that $10.63\%$ of α‐syn was modified by K80Hcy in α‐syn PFFs generated from wild‐type α‐syn in the presence of HTL (Figure S6a). Coomassie Blue staining confirmed that equal amounts of fibrils were used (Figure S6b). While the PFFs generated from K80R mutant α‐syn displayed comparable seeding capability to α‐syn PFFs. They were resistant to HTL's increasing effect in seeding α‐syn aggregation (Figure 4i,j). Fractionation analysis found that HTL‐modified α‐syn PFFs show enhanced seeding activity, which was abolished by K80R mutation (Figure 4k,l). When transduced to primary neurons, the homocysteinylated α‐syn PFFs were more potent to induce α‐syn phosphorylation (Figure 4m,n). These results indicate that K80Hcy potentiates α‐syn aggregation, and results in α‐syn fibrils with enhanced seeding activity.
## Homocysteinylated α‐syn fibrils show enhanced seeding activity and neurotoxicity in vivo
To investigate the effect of homocysteinylation on the neurotoxicity of α‐syn fibrils, we injected α‐syn PFFs and HTL‐modified PFFs into the striatum of wild‐type mice brains and monitored α‐syn pathology 6 months after injection. Compared with α‐syn PFFs, HTL‐PFFs induced more deposition of pS129 (Figure S7a). HTL‐α‐syn PFFs caused an increased accumulation of high molecular weight α‐syn species (Figure S7b‐g). Furthermore, the expression of the microglial marker IBA1 and astrocyte marker GFAP in the striatum was higher in mice injected with HTL‐α‐syn PFFs than these in mice injected with α‐syn PFFs (Figure S7h‐j). The number of tyrosine hydroxylase (TH)‐positive neurons in the SNpc ipsilateral to the injection site was slightly decreased compared with that in mice injected with α‐syn PFFs (Figure S7k,l). Moreover, the loss of TH‐positive nerve terminals in the striatum was also more drastic in mice injected with HTL‐α‐syn PFFs (Figure S7k,m). Western blot analysis found that the levels of TH in the striatum were decreased in mice injected with HTL‐α‐syn PFFs compared with mice injected with α‐syn PFFs (Figure S7n,o). Consistently, the mice injected with HTL‐PFFs showed more severe motor deficits in behavioral tests including the rotarod test, wire hang test, pole test, and balance beam test (Figure S7p‐t). Thus, the homocysteinylated α‐syn PFFs induce more severe α‐syn pathology, loss of dopaminergic neurons, and PD‐like motor impairments.
## Elevated levels of brain Hcy exacerbates α‐syn pathology in a mouse model of PD
We further tested the effect of hyperhomocysteinemia on α‐syn pathology in TgA53T mice. As expected, the levels of Hcy and HTL in the mouse brain were increased after l‐methionine (Met) administration (Figure 5a). The percentage of α‐syn K80Hcy increased from $3.58\%$ in vehicle‐treated TgA53T mice to $6.88\%$ in Met‐treated TgA53T mice (Figure S8a). Six months after the administration of Met, IHC showed that the levels of α‐syn K80Hcy increased in both wild‐type mice and TgA53T mice. The levels of pS129 were higher in Met‐treated TgA53T mice than that in vehicle‐treated TgA53T mice. Met did not trigger the appearance of pS129 in wild‐type mice (Figure S8b). These results were confirmed by Western blot analysis (Figure 5c–j). Met administration also induced an increased IBA1 and GFAP immunoreactivity in TgA53T mice, but not in wild‐type mice (Figure S8c‐j). Furthermore, the number of TH‐positive dopaminergic neurons in the SNpc was decreased in Met‐treated TgA53T mice when compared with vehicle‐treated TgA53T mice (Figure 5k,l). Treatment with Met also induced more severe loss of TH‐positive nerve terminals in the striatum of TgA53T mice (Figure 5m,n). Met treatment did not induce the degeneration of the nigrostriatal pathway in wild‐type mice (Figure 5k–n). The levels of TH were also decreased in the striatum of TgA53T mice treated with Met (Figure 5o,p). Behavioral analysis found that the vehicle‐treated TgA53T mice showed impaired motor function compared with the vehicle‐treated WT mice. The TgA53T mice treated with Met exhibited exacerbated motor deficits in the behavioral tests than the vehicle‐treated TgA53T mice (Figure 5q–u). Thus, elevated levels of Hcy promote α‐syn pathology in vivo.
**FIGURE 5:** *Diet‐induced elevated Hcy worsens α‐syn pathology in TgA53T mice. (a) Brain tissues were assayed by LC–MS for levels of Hcy and GC–MS for levels of HTL. (b) Representative α‐syn K80Hcy (green) and TH (red) double‐immunostaining in the SN, α‐syn K80Hcy immunostaining in the cortex (M1), red nucleus, spinal cord, and hippocampus (CA1). Scale bar is 20 μm. (c–j) Levels of α‐syn K80Hcy, pS129, and human α‐syn levels in the SN (c–f) and cortex (g–j) of TgA53T mice. (k, l) TH immunohistochemistry images in the SNpc. Scale bar is 100 μm. (m, n) TH immunohistochemistry images in the striatum. Scale bar is 200 μm. (o, p) Levels of TH in the striatum. (q–u) Behavioral tests. Shown are the results of rotarod test (q), wire hang test (r), pole test (s, t), and balance beam test (u). n = 3 (a), 4 (c–f, g–j, p), 6 (k–n), 12 (q–u) mice per group. All Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, NS, not significant*
## Blockade of α‐syn K80Hcy ameliorates the toxicity of Hcy in vivo
AAV‐mediated α‐syn overexpression in the SN has been shown to induce α‐syn pathology in the brain (Oliveras‐Salva et al., 2013). To determine whether α‐syn K80Hcy modification mediates the toxic effect of Hcy, we injected adeno‐associated viruses (AAVs) encoding GFP, human α‐syn A53T, and human α‐syn A53T K80R, respectively, into the SN of three‐month‐old WT mice and fed the mice with vehicle or Met for 6 months. The expression of exogenous α‐syn in the SN was confirmed by immunostaining with an antibody specific to human α‐syn (Syn211) (Figure S9a). Immunofluorescence confirmed that TH‐positive neurons in the SN were efficiently infected by AAVs (Figure S9b). Six months after injection, Western blot showed that the levels of human α‐syn A53T and human α‐syn A53T K80R were comparable. Met treatment enhanced the levels of α‐syn K80Hcy and pS129 in mice injected with AAV‐α‐syn A53T, but not in mice injected with AAV‐α‐syn A53T K80R (Figure 6a–d). The results were further confirmed by immunostaining (Figure 6e and Figure S10a). The deposition of α‐syn K80Hcy and pS129 was accompanied by increased IBA1 and GFAP immunoreactivity (Figure S10b‐e). Histological analysis showed that the overexpression of α‐syn A53T and α‐syn A53T K80R decreased the number of TH‐positive neurons in the SNpc ipsilateral to the injection site. The administration of Met caused a more severe loss of TH‐positive cells in mice injected with AAV‐α‐syn A53T, but not in mice injected with AAV‐α‐syn A53T K80R (Figure 6f,g). Met exacerbated the loss of TH‐positive neuronal terminals and the decrease of TH levels in the striatum in mice expressing α‐syn A53T, but not in mice expressing α‐syn A53T K80R (Figure 6h,i and Figure S10f,g). Consistently, Met promoted behavioral deficits in mice expressing α‐syn A53T, but not in mice expressing α‐syn A53T K80R (Figure 6j–n). Collectively, inhibition of α‐syn K80Hcy antagonizes the detrimental effect of Hcy, supporting the role of α‐syn K80Hcy in the development of α‐syn pathology.
**FIGURE 6:** *Blockade of α‐syn K80Hcy attenuates α‐syn pathology induced by Hcy. (a–d) Levels of human α‐syn, pS129, and α‐syn K80Hcy in the ipsilateral SN. (e) Representative double‐immunostaining for α‐syn K80Hcy (green) and TH (red) in the SNpc. Scale bar is 50 μm. (f) Representative TH immunohistochemistry images in the SNpc. Scale bar is 100 μm. (g) Stereological counting of the number of TH‐positive neurons. (h, i) Levels of TH in the ipsilateral striatum. (j–n) Behavioral assessment. Shown are the results of the rotarod test (j), wire hang test (k), pole test (l, m), and balance beam test (n). Data are shown as mean ± SEM. n = 4 (a–d, i), 6 (g), 12 (j–n) mice per group. *p < 0.05, **p < 0.01, ****p < 0.0001, NS, not significant*
## DISCUSSION
In the current study, we report that N‐homocysteinylation of α‐syn on the K80 residue triggers α‐syn pathology. The levels of α‐syn K80Hcy are increased in an age‐dependent manner, correlating with elevated Hcy and HTL in the brain during aging. α‐Syn K80Hcy is more prone to aggregate and form fibrils with enhanced seeding activity and neurotoxicity both in vitro and in vivo. Chronic exposure to higher Hcy levels promoted the deposition of α‐syn K80Hcy in the brain and induced dopaminergic neuronal degeneration and behavioral deficits in the TgA53T mouse model of PD. Blockade of α‐syn homocysteinylation by mutating the K80 residue to R suppressed α‐syn pathology and behavioral deficits induced by Hcy. These results strongly support the role of N‐homocysteinylation on the onset and progression of α‐syn pathology.
Abnormal Hcy metabolism is a well‐known condition linked to a higher risk of several neurological diseases including stroke, AD, and multiple sclerosis (Chen et al., 2017; Wang et al., 2019). Recent epidemiological evidence indicates that elevated Hcy level is a metabolic risk factor for PD independent of other confounders (Licking et al., 2017; Sapkota et al., 2014). Subjects with serum Hcy levels higher than 20 μmol/L show an 8.64‐fold increased chance of having PD (Saadat et al., 2018). However, the molecular mechanisms underlying the detrimental effect of Hcy remain unclear. Hcy is converted to HTL in error‐editing reactions catalyzed by MARS (Jakubowski, 2011). HTL has been reported to form isopeptide bonds with protein lysine residues, which is known as N‐homocysteinylation (Jakubowski, 1999). N‐homocysteinylation is an emerging posttranslational modification that impairs the protein's structure/function (Jakubowski, 2019).
Genetic or nutritional deficiencies in one‐carbon and Hcy metabolism cause the accumulation of protein N‐homocysteinylation. In the present study, we first used a biorthogonal azide probe to confirm that α‐syn can be homocysteinylated (Figure 1). LC–MS/MS together with point mutations identified that K80 is the major homocysteinylation site of α‐syn. The presence of K80Hcy in the brain of PD mouse models was validated by a polyclonal antibody against α‐syn K80Hcy (Figure 3). K80 resides in the NAC region, which is critical for α‐syn fibrillization and LB formation. Consistently, we found that homocysteinylation of α‐syn promotes its fibrillization, while K80R mutation that blocks homocysteinylation on K80 abolishes the effect of HTL on α‐syn fibrillization in vitro and in vivo. These data strongly support that α‐syn homocysteinylation on the K80 residue facilitates its aggregation, S129 phosphorylation, and neurotoxicity. It is very interesting that the K80 is the major residue modified by N‐homocysteinylation when α‐syn contains various lysine residues. The surrounding amino acids may affect the selectivity of N‐homocysteinylation. Even though N‐homocysteinylation has been reported in many proteins (Bossenmeyer‐Pourié et al., 2019; Zhang et al., 2018), it is hard to predict the exact modification sites until now. No specific enzyme has been reported to mediate the modification of lysine residues.
PD is a heterogeneous disease with many different subtypes. Different patients show distinct patterns of progression, outcomes, and symptoms. Increased levels of Hcy are associated with more severe motor impairment, depression, and cognitive dysfunction in PD patients (O'Suilleabhain et al., 2004). Many factors may contribute to the heterogeneity of PD (Armstrong & Okun, 2020). We found that the homocysteinylated α‐syn fibrils are more potent to seed the aggregation of α‐syn in α‐syn‐HEK293 cells, primary neurons, and mouse brains. Furthermore, we found that increasing the levels of Hcy and HTL in the brain of TgA53T mice enhanced the deposition of α‐syn K80Hcy and pS129, and exacerbated PD‐like motor impairments. This is consistent with the clinical observation that higher Hcy levels are associated with worse motor and non‐motor symptoms of PD (Bakeberg et al., 2019; Christine et al., 2018). Here, we found more severe α‐syn pathology and neurodegeneration in SN of TgA53T mice when compared with previous reports. This might be ascribed to the fact that the background of M83 mice in our study is different from that in the previous reports. The mice in our study were on B6 background, but not B6:C3 background. Thus, the different backgrounds of M83 mice may contribute to the difference in the degree of dopaminergic degeneration.
In patients with elevated Hcy due to mutations in the cystathionine β‐synthase (CBS) gene, HTL concentration also increases 72 folds above its reference value (Chwatko et al., 2007). Increased HTL induces N‐homocysteinylation of the substrate proteins, which leads to a variety of diseases (Bossenmeyer‐Pourié et al., 2019; Wang et al., 2018). Many studies found that target proteins are subjected to extensive N‐homocysteinylation of lysine residues, the only known target of HTL reaction in proteins (Jakubowski, 1999, 2000). Protein N‐homocysteinylation results in the formation of new free thiols. These thiols are prone to oxidation with the consequent production of disulfide bridges, leading to protein oligomerization. This may explain how the homocysteinylation of α‐syn promotes its aggregation. Several types of posttranslational modifications contribute to pathological protein aggregation (Levine et al., 2019; Vicente Miranda et al., 2017). Based on the results reported in this work, we suggest that protein N‐homocysteinylation should also be considered a risk factor for protein conformational diseases including PD.
In conclusion, we present comprehensive evidence that α‐syn homocysteinylation is a mechanism by which Hcy regulates the onset and progression of α‐syn pathology. α‐Syn homocysteinylation contributes to PD‐related conformational change of α‐syn, fibril formation, and neurotoxicity. Thus, monitoring Hcy and homocysteinylated α‐syn levels may represent a promising biomarker for the recognition and diagnosis of PD. Moreover, the discovery of α‐syn homocysteinylation in PD provides a framework for therapeutic intervention.
## Animals
Adult C57BL/6J mice, Snca‐knockout mice, and human A53T variant α‐syn transgenic line M83 were from the Jackson Laboratory (stock number: 000664, 003692 and 004479, respectively). M83 mice were backcrossed with C57BL/6J mice for at least 10 generations to obtain TgA53T mice with C57BL6/J congenic strain. Then the heterozygous male and female TgA53T mice were bred to obtain homozygous offspring and wild‐type littermates. The genotypes were identified using RT‐PCR. Animal maintenance and experiments were performed in accordance with the Declaration of Helsinki and guidelines of Renmin Hospital of Wuhan University. The protocol was reviewed and approved by the Animal Care and Use Committee of Renmin Hospital of Wuhan University [20210103].
## Diet treatments of PD mouse models
4‐month‐old TgA53T mice and their wild‐type littermates were given $0.5\%$ l‐methionine (wt/vol, dissolved in drinking water) for 6 months. The mice in the vehicle group received normal drinking water.
## Cell culture and treatment
HEK293 cells were from the American Type Culture Collection (ATCC) and tested for mycoplasma contamination before use. The cells were stably expressed with wild‐type α‐syn with a GFP tag directly fused to the C‐terminal of α‐syn (α‐syn‐HEK293 cells). HEK293 and α‐syn‐HEK293 cells were cultured in Dulbecco's modified Eagle's medium (DMEM) containing $10\%$ fetal bovine serum (FBS) and 100 × Penicillin–Streptomycin. Cell lines were maintained at 37°C and $5\%$ CO2. L‐Homocysteine (Hcy) (Sigma, 69453) and HTL (Sigma, H6503) were freshly prepared before use. Hcy or HTL were added to the culture media to reach the final indicated concentration (0.1, 0.5, and 1 mM) after the cells were starved in $1\%$ FBS medium for 24 h.
## Human tissue samples
Post‐mortem brain samples were from the frozen brain samples from the Emory Alzheimer's Disease Research Center. PD cases were clinically diagnosed and neuropathologically confirmed. The average age of the control and PD patients was 71.8 ($$n = 6$$) and 71.2 ($$n = 6$$), respectively. The average disease duration was 6.8 years. Informed consent was obtained from all subjects. The study was approved by the biospecimen committee at Emory University. For immunofluorescent staining, the brain sections were incubated in $0.1\%$ Sudan Black B (SSB) and $70\%$ ethanol to eliminate the autofluorescence signal.
## Plasmid constructs and transfection
Cells were transfected with plasmids encoding WT or point‐mutant α‐syn using polyethyleneimine (PEI). For MARS knockdown, HEK293, and α‐syn‐HEK293 cells were transfected with siRNAs using Lipofectamine 2000 (Invitrogen, 11668019). The siRNA sequences used were as follows: sense: 5'‐CCGCUGGUUUAACAUUUCGUU‐3′, antisense: 5′‐ ACGAAAUGUUAAACCAGCGG‐3′.
## Protein purification
cDNAs corresponding to his‐tagged α‐syn and K80R mutant were cloned into a PRK172 plasmid and transformed into the E. coli BL21 DE3 strain. The expression and purification were performed as previously described (Dai et al., 2021). Briefly, the pellet from 1 L culture was resuspended in 100 ml osmotic shock buffer (30 mM Tris–HCl, $40\%$ sucrose, and 2 mM EDTA, pH 7.2) and incubated for 10 min at room temperature. The pellet collected by centrifugation at 12,000 rpm for 20 min was resuspended quickly with 90 ml cold water followed by adding 37.5 μl of saturated MgCl2, and kept on ice for 3 min. His‐tagged proteins were purified through Ni‐chelating affinity chromatography and eluted at around 125 mM imidazole. The protein was dialyzed, lyophilized, and stored at −80°C before use.
## Preparation and transduction of α‐syn fibrils
PFFs were generated by incubating purified protein (1 mg/ml) at 37°C with constant agitation (1000 rpm) for 5–7 days. 1 mg/ml WT or K80R mutant α‐syn was mixed with vehicle or HTL at 37°C, rotated at 250 rpm for 8 h, and then dialyzed against PBS. The samples were shaken at 37°C at 1000 rpm for 5–7 days in an Eppendorf thermomixer C and monitored by ThT fluorescence at various time points. Briefly, aliquots of 10 μl incubation samples were diluted to 100 μl with 20 μM ThT in PBS and tested at 450 nm excitation and 510 nm emission using Spectra Max plate reader (Molecular Devices). The fibrils were collected by centrifugation at 100,000 g for 20 min and resuspended in sterile PBS. Before transduction, the fibrils were sonicated with 60 pulses at $10\%$ power (total of 30, 0.5 sec on, 0.5 sec off), and then quantified using an ELISA kit. α‐Syn fibrils (140 ng/ml, final concentration) were transduced using Lipofectamine 2000. HTL was added 12 h before transduction with fibrils and incubated during the whole experiment. The cells were then transfected with fibrils and incubated for another 24 h.
## Transmission electron microscopy (TEM)
A 20 μl droplet from each sample was dropped onto the copper grid with carbon film for 3–5 min. Two percentage of phosphotungstic acid was dropped on the copper grid to stain for 1–2 min. Grids were allowed to dry at room temperature. Images were obtained using TEM.
## Proteinase K and pronase digestion
The PFFs samples (19 μl, 19 μg) were mixed with 1 μl of Proteinase K (2.5 μg/ml final concentration) or pronase (50 μg/ml final concentration), and incubated at 37°C for 30, 60, and 90 min, after which, 5 μl of 5× SDS loading buffer were added to quench the reactions, and then boiled for 10 min at 95°C. Afterward, 6 μl of each digested product was separated by SDS‐PAGE. Gels were stained with Coomassie brilliant blue R‐250 (BioFroxx, 1912GR025).
## Sequential extraction
Soluble and insoluble cell fractions were prepared as previously described (Volpicelli‐Daley et al., 2014). In brief, cells were scraped into $1\%$ Triton X‐100 (TX‐100) in Tris‐buffered saline (TBS) (50 mM Tris, 150 mM NaCl, pH 7.4) with protease and phosphatase inhibitor cocktail, and incubated on ice for 30 min. Lysates were then sonicated and centrifuged at 100,000 g for 30 min. The supernatant was collected as the soluble fraction. The pellet was washed with $1\%$ TX‐100 in TBS, sonicated, and centrifuged at 100,000 g for 30 min. The supernatant was discarded. The pellet was suspended in $2\%$ SDS in TBS as the insoluble fraction.
## Western blot analysis
Cells were lysed with ice‐cold NP‐40 lysis buffer containing a cocktail of protease inhibitors and phosphatase inhibitors. Dissection of SN from mice was performed as previously described (Salvatore et al., 2012). Tissues were homogenized in ice‐cold RIPA lysis buffer containing protease and phosphatase inhibitors. The lysates were then sonicated briefly and centrifuged at 21,130 g for 20 min. The protein concentration was measured using the BCA assay. Proteins extracts were separated by $10\%$ Bis‐Tris SDS‐PAGE gels. The following primary antibodies were used: HA (1:5000, Proteintech, 51064‐2‐AP), GAPDH (1:8000, Proteintech, 60004‐1‐Ig), Streptavidin‐HRP (1:5000, Proteintech, SA00001‐0), MARS (1:10000, Proteintech, 14829‐1‐AP), GST (1:10000, Proteintech, 66001‐2‐Ig), K80Hcy (1:1000, Abmart), α‐Synuclein (D37A6) (1:1000, Cell Signaling Technology, #4179), Syn211 (1:1000, Thermo Fisher Scientific, MA5‐12272), pS129 (1:1000, Cell Signaling Technology, #23706), GFP (1:10,000, Proteintech, 66002‐1‐Ig), TH (1:1000, Millipore, #AB152). The following secondary antibodies conjugated to horseradish peroxidase (HRP) were used: goat anti‐rabbit IgG (H + L)‐HRP (1:8000, Bio‐Rad, 1706515), goat anti‐mouse IgG (H + L)‐HRP (1:8000, Bio‐Rad, 1706516). Signals were developed by detecting enhanced chemiluminescent (ECL) with Imaging System (Bio‐Rad, ChemiDoc™ Touch). Blot intensity was analyzed and quantified using Image J.
## Immunohistochemistry
IHC Detection System Kit (ZSGB‐BIO, PV‐6001/PV‐6002) was used. The sections were incubated with primary antibodies against TH (1:2000, Abcam, ab117112), pS129 (1:1000, Biolegend, 825701), IBA1 (1:500, Wako, 019–19741), GFAP (1:500, Thermo Fisher Scientific, PA5‐16291), Syn211 (1:1000, Thermo Fisher Scientific, MA5‐12272), or K80Hcy antibody (1:500, Abmart) at 4°C for overnight. The signal was developed using DAB. The levels of immunoreactivity were determined by optical density analysis using Image J, plus the IHC Profiler plugin.
## Immunofluorescence
Neurons were fixed with $4\%$ paraformaldehyde (PFA) and $0.1\%$ TX‐100 in PBS for 15 min. To detect the insoluble α‐syn aggregates, α‐syn‐HEK293 cells were fixed with $4\%$ PFA in PBS followed by permeabilization with $1\%$ TX‐100. Cells were incubated with anti‐pS129 (1:1000, Biolegend, 825701), anti‐Ubiquitin (1:500, Cell Signaling Technology, #3936) overnight at 4°C. The samples were stained with corresponding secondary antibodies Alexa Fluor 594 or 488 (1:1000, Invitrogen). Nuclei were visualized with DAPI (1 μg/ml, BioFroxx, 1155MG010) for 5 min. To quantify the percentage of positive cells, a total of 8 fields, each with 100+ cells, were analyzed per condition. The primary cultured neurons and brain sections were double stained for MAP2/pS129, pS129/K80Hcy, Thioflavin S (ThS)/K80Hcy, TH/pS129, TH/K80Hcy, or GFP/TH.
## Primary neuron cultures
Primary cortical neurons dissected from E18 embryos were cultured as previously described (Zhang et al., 2017). PFFs were added to the culture medium at 7 days in vitro (DIV). Seven days later, the neurons were fixed in $4\%$ PFA, permeabilized, and immuno‐stained with MAP2/pS129 antibodies.
## Stereological quantification of TH‐positive cells and striatal terminals
The number of TH‐positive cells in the SN was estimated with a random‐sampling stereological counting method. Every sixth section from the caudal to rostral boundaries of the SN was incorporated into the counting procedure. TH‐positive fiber densities in the striatum were measured by optical density analysis using Image J, plus the IHC Profiler plugin.
## Behavioral tests
In the rotarod test, mice were placed on an accelerating rotarod cylinder, from 4 rpm up to 40 rpm within 300 s, and the latency time to fall off was documented. In the pole test, the pole was made up of a 75 cm long wooden rod that was wrapped with bandage gauze. Mice were placed on the top of the pole facing the head‐up. The time taken to orient downward and the total time taken to reach the base were recorded. In the wire hang test, the mice were placed on a horizontal wire grid. The wire was lightly shaken to make the mice grab the wire and then turned upside down. The latency of mice to fall off was measured. Trials were stopped if the mice remained on the grid for 5 min. In the balance beam test, the mice walked along a narrow beam suspended between a start platform and a dark resting box. The time to cross the beam (2 × 100 cm) was recorded.
## Stereotaxic injection of α‐syn PFFs
Two‐month‐old C57BL/6J mice were anesthetized and stereotaxically injected in one hemisphere with sonicated fibrils (5 μg each mouse) at the following coordinates: anteroposterior (AP) +0.2 mm; mediolateral (ML) −2.0 mm; dorsoventral (DV) −2.7 mm relative to Bregma. The inoculums were performed using a 10 μl Hamilton syringe at a rate of 200 nl per min with the needle in place for 5 min. Animals were monitored regularly following recovery from surgery.
## Viral construction and stereotaxic injection
AAV particles encoding human WT and K80R mutant α‐syn with the human synapsin I (hSyn I) promoter (Kügler et al., 2001) were prepared by BrainVTA (BrainVTA Co., Ltd.). Unilateral intracerebral injection of AAVs was performed stereotaxically at coordinates anteroposterior (AP) −3.1 mm and mediolateral (ML) −1.2 mm relative to the bregma, and dorsoventral (DV) −4.0 mm from the dural surface in three‐month‐old C57BL/6J mice. A total of 300 nl of viral suspension was injected into each site with a 10 μl glass syringe with a fixed needle at a rate of 40 nl/min. The needle remained in place for an additional 5 min before it was removed slowly.
## Mass spectrometry analysis
The LC–MS/MS identification and data analysis were performed by SpecAlly Life Technology (SpecAlly Life Technology Co., Ltd.). The target protein bands in the gel were cut into pieces, washed three times with $50\%$ acetonitrile/100 mM NH4HCO3, and digested in 50 mM NH4HCO3 solution (pH 8.0) with MS‐grade trypsin overnight at 37°C after reduction and alkylation of cysteines. The tryptic digests were injected into an Easy‐nLC 1200 system (Thermo Scientific) and analyzed by a Q *Exactive plus* mass spectrometer (Thermo Scientific). Peptides were first loaded onto a C18 trap column (Thermo, 75 μm × 2 cm, 3 μm particle size, 100 Å pore size) and then separated in a C18 analytical column (Thermo, 75 μm × 250 mm, 2 μm particle size, 100 Å pore size). Mobile phase A ($0.1\%$ formic acid) and mobile phase B ($80\%$ acetonitrile, $0.1\%$ formic acid) were used to establish the separation gradient. A constant flow rate was set at 300 nl/min. For the data‐dependent acquisition (DDA) mode analysis, each scan cycle was consisted of one full‐scan mass spectrum ($R = 70$ K, AGC = 3E6, max IT = 50 ms, scan range = 350–1800 m/z) followed by 15 MS/MS events ($R = 15$ K, AGC = 1E5, max IT = 50 ms). The collision energy of high energy capture dissociation (HCD) was set to 27. The isolation window for precursor selection was set to 1.6 Da. Former target ion exclusion was set for 45 s. The raw files were analyzed with Proteome Discoverer (version 2.4) using the Sequest HT search algorithm. Spectra files were searched against the human Uniprot Proteome FASTA database using the following parameters: type, identification; variable modifications: oxidation of methionine (Met), protein N‐terminal acetylation, and N‐homocysteinylation of lysine (KHcy, +174.04600 Da, lysine) (Zhang et al., 2018); fixed modification: carbamidomethyl of cysteine (Cys); digestion, trypsin. The MS1 match tolerance was set as 10 ppm; the MS2 tolerance was set as 0.02 Da. Minimal peptide length was set to six amino acids, and a maximum of three miscleavages was allowed. Search results were filtered with $1\%$ FDR at both protein and peptide levels.
## Generation of K80Hcy antibody
*To* generate the K80Hcy antibody, the synthesized peptide TAVAQKTVEG containing K80 homocysteinylation was used as an antigen to immunize rabbits. The antibody was produced by Abmart Shanghai Co., Ltd. Antiserum was collected after five sessions of immunization. The titers against the immunizing peptide were determined by ELISA.
## Detection of Hcy and HTL levels in the brain tissue
All detections were performed by Sensichip Biotech Company (Sensichip Biotech Co., Ltd.). For Hcy level detection, brain tissues were added with 20 μl of 500 mM DTT, 380 μl of extraction solution (40:40:20 acetonitrile: methanol: water). All the samples were vortexed, ground, incubated at room temperature for 30 min, sonicated for 10 min at 4°C, and then centrifuged at 13,523 g, 4°C for 10 min. Then, the supernatants were evaporated, reconstituted with 150 μl of 60:40 acetonitrile: water, vortexed well, and clarified by centrifugation at 13,523 g for 10 min at 4°C. The supernatants were transferred to an injection vial for liquid chromatography‐mass spectrometry (LC–MS) analysis. The LC–MS/MS method involved Waters Acquity ultraperformance LC (UPLC) coupled to the AB Sciex 5500 QQQ‐MS. The LC separation was performed using an Acquity UPLC BEH Amide (1.7 μm, 2.1 mm × 100 mm). Solvent A was 90:10 water: acetonitrile with 10 mM ammonium acetate and $0.2\%$ acetic acid, and solvent B was 10:90 water: acetonitrile with 10 mM ammonium acetate and $0.2\%$ acetic acid. The flow rate was 0.30 ml/min. The column temperature was 40°C. The injection volume was 10 μl. MS parameters were as follows: curtain gas, 35 arb (arbitrary units); collision gas, 9 arb; IonSpray voltage, 4500 V; IonSource temperature, 450°C; IonSource gas1, 55 arb; and IonSource gas2, 55 arb. According to the conditions described above, the prepared standard Hcy solution was added to the sample vial to quantify and identify the Hcy peaks at Rt = 2.52 min.
The concentration of HTL was performed as previously described (Piechocka et al., 2020) by using Thermo Trace 1300 gas chromatography system coupled with ISQ7000 mass spectrometry (GC–MS). Briefly, brain samples were mixed with 100 μl of 0.2 mol/L phosphate buffer (pH 7.8). The mixtures were extracted by grinding for 5 min with 800 μl of 2:1 chloroform: methanol (v/v). After centrifugation (12,000 rpm, 4°C, 5 min), the organic layer was transferred and dried under a vacuum. The residues were derivatized with 30 μl MSTFA (with $1\%$ TMCS, 50°C, 10 min) for samples. After cooling to room temperature, reaction mixtures were transferred to HPLC vials and an aliquot (1 μl) was injected into the GC–MS system. The capillary column was DB‐5MS (60 m × 0.25 mm × 0.25 μm). The instrument parameter settings were as follows: Inlet temperature, 280°C; EI temperature, 230°C; carrier gas, helium (purity > $99.999\%$); splitless injection, 1 μl sample. Temperature program: maintain at 146°C for 5 min, ramp from 146°C to 200°C at 5°C/min, the ramp from 200°C to 300°C at 50°C/min, then hold at 300°C for 3 min. Scanning from 50 to 500 (m/z). The MS detector was focused on the trimethylsilyl‐HTL derivative ions with m/z 73.0, m/z 100.0, and m/z 128.0, while m/z 100.0 was selected for quantification. All values multiplied volume and were converted to that relative to weight by dividing tissue weights.
## ELISA for K80Hcy quantification
We developed an ELISA‐based method for quantifying the K80Hcy modification. Nunc MaxiSorp plates were coated with K80Hcy antibodies and incubated overnight at 4°C. The plates were washed with washing buffer, and blocked using blocking buffer at room temperature for 1 h. Then, the plates were incubated with the samples at 4°C overnight. The plates were then washed and biotin‐labeled anti‐α‐syn (Biolegend, 807808) was added and incubated at 4°C overnight. After washing, avidin‐HRP (Biolegend, 405103) was added and incubated for 2 h at room temperature. Lastly, the plates were developed using 1‐Step Ultra TMB‐ELISA substrate solution (Thermo Fisher Scientific, 34028) for 15 min. The reaction was quenched with stop solution and plates were read at 450 nm. A standard was generated using K80Hcy‐modified α‐syn, which was prepared in vitro by incubating 60 mM HTL and 1 mg/ml α‐syn overnight. MS analysis showed that $86.27\%$ of α‐syn was modified at K80 in the standard. Thus, this ELISA system slightly overestimates the degree of modification.
## Chemoselective labeling of N‐homocysteinylated α‐syn
The reactions were performed as previously reported (Chen et al., 2019). Reaction solutions were labeled by Biotin‐azide (200 μM, final concentration, MedChemExpress, HY‐129832). Freshly made hemin (50 μM, final concentration), β‐Mercaptoethanol (100 mM, final concentration), and SDS ($0.4\%$, final concentration) were added together. The mixtures were heated at 75°C for 10 min. Then, 5× loading buffer was added, and the samples were heated at 95°C for 10 min, followed by $10\%$ Bis‐Tris SDS‐PAGE.
## Statistical analysis
All data were shown as mean ± standard error (SEM) of the mean if not mentioned otherwise. Statistical analysis was performed using either an independent sample t test (two‐group comparison) or one‐way ANOVA among more than two groups followed by Tukey's post hoc test using GraphPad Prism 8.0.1 (GraphPad Software Inc.). Normality of the data was tested with the Shapiro–Wilk test. For the data that was non‐normally distributed, Mann–Whitney U test (two groups), and Kruskal–Wallis test with Dunn's multiple comparisons (three or more groups) were applied. Statistical tests were two‐tailed, and differences with $p \leq 0.05$ were considered statistically significant.
## AUTHOR CONTRIBUTIONS
Z.Z. and B.A. conceived the project. Z.L. performed most of the experiments, analyzed the data, and wrote the manuscript. G.T. performed some of the in vitro experiments. M.L., Z.X., T.Y., and D.L. helped in designing the methodology. L.Y. helped with cell culture. L.C. helped in behavioral tests. N.X., C.G., L.C., K.W., and Z.Z. helped with the animal experiments. Z.Z. supervised the entire project.
## FUNDING INFORMATION
This work was supported by grants from the National Key Research and Development Program of China (2019YFE0115900) to Z.Z., the National Natural Science Foundation of China (No. 82271447 to Z.Z., No. 81901090 to L.M), and Medical Science Advancement Program of Wuhan University (No. TFLC2018001).
## CONFLICT OF INTEREST
The authors declare no competing interests.
## DATA AVAILABILITY STATEMENT
The authors declare that all data supporting the findings of this study are available within the article and its supplementary information files.
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|
---
title: Notch activation shifts the fate decision of senescent progenitors toward myofibrogenesis
in human adipose tissue
authors:
- Nathalie Boulet
- Anaïs Briot
- Valentin Jargaud
- David Estève
- Anne Rémaury
- Chloé Belles
- Pénélope Viana
- Jessica Fontaine
- Lucie Murphy
- Catherine Déon
- Marie Guillemot
- Catherine Pech
- Yaligara Veeranagouda
- Michel Didier
- Pauline Decaunes
- Etienne Mouisel
- Christian Carpéné
- Jason S. Iacovoni
- Alexia Zakaroff‐Girard
- Jean‐Louis Grolleau
- Jean Galitzky
- Séverine Ledoux
- Jean‐Claude Guillemot
- Anne Bouloumié
journal: Aging Cell
year: 2023
pmcid: PMC10014050
doi: 10.1111/acel.13776
license: CC BY 4.0
---
# Notch activation shifts the fate decision of senescent progenitors toward myofibrogenesis in human adipose tissue
## Abstract
Senescence is a key event in the impairment of adipose tissue (AT) function with obesity and aging but the underlying molecular and cellular players remain to be fully defined, particularly with respect to the human AT progenitors. We have found distinct profiles of senescent progenitors based on AT location between stroma from visceral versus subcutaneous AT. In addition to flow cytometry, we characterized the location differences with transcriptomic and proteomic approaches, uncovering the genes and developmental pathways that are underlying replicative senescence. We identified key components to include INBHA as well as SFRP4 and GREM1, antagonists for the WNT and BMP pathways, in the senescence‐associated secretory phenotype and NOTCH3 in the senescence‐associated intrinsic phenotype. Notch activation in AT progenitors inhibits adipogenesis and promotes myofibrogenesis independently of TGFβ. In addition, we demonstrate that NOTCH3 is enriched in the premyofibroblast progenitor subset, which preferentially accumulates in the visceral AT of patients with an early obesity trajectory. Herein, we reveal that NOTCH3 plays a role in the balance of progenitor fate determination preferring myofibrogenesis at the expense of adipogenesis. Progenitor NOTCH3 may constitute a tool to monitor replicative senescence and to limit AT dysfunction in obesity and aging.
Obese individuals accumulate senescent progenitors preferentially in visceral adipose tissue (AT). Replicative senescence of AT progenitors increases NOTCH3 expression. Notch activation enhances SFRP4 and INHBA in the senescence‐associated secretory phenotype and promotes myofibrogenic fate while inhibiting adipogenesis.
## INTRODUCTION
Central obesity and aging share similarities in terms of their metabolic and inflammatory alterations, such as the accumulation of visceral fat depots, insulin resistance, chronic low‐grade inflammatory state (Trim et al., 2018), and elevated risk to develop severe chronic pathologies, including type 2 diabetes and cardiovascular diseases. The fact that obesity shares features with aging led to the concept that excessive fat mass accelerates the onset of aging‐related disorders (Burton & Faragher, 2018). Cellular senescence, known to be involved in both aging and age‐related disorders (van Deursen, 2014), has recently also been found to play a role in obesity‐associated pathologies (Palmer, Gustafson, et al., 2019). While senescent cells can be protective against cancer development (Campisi, 2013), their accumulation in metabolically active organs, such as the liver (Ogrodnik et al., 2017) and adipose tissue (AT; Minamino et al., 2009), shows negative effects with aging and obesity. Cell cycle arrest triggered by external or internal stresses including telomere shortening (Shay, 2016), DNA damage, oxidative stress, and the excess of metabolic substrates including ATP (Pini et al., 2021) through the activation of p16Ink4a‐ and p53‐dependent pathways, together with the senescence‐associated beta‐galactosidase (SA‐β‐gal) activity, are hallmarks of AT senescent stroma‐vascular cells. In addition, mature adipocytes also express a form of cell cycle‐independent senescence (Ishaq et al., 2022). The causal role of senescence on impaired energy homeostasis is demonstrated by targeting senescent cells with drug‐inducible “suicide” genes driven by the p16(Ink4a; Palmer, Xu, et al., 2019) or p21 (Wang et al., 2022) promoter or by senolytic drugs in obese and aged mice, strategies associated with reduction of age‐ and obesity‐related metabolic abnormalities. Many of the detrimental effects of senescence are thought to be mediated by the senescence‐associated secretory phenotype (SASP) that affects the healthy neighbor cells by promoting low‐grade inflammation and fibrosis. SASP can spread senescence itself, leading to marked alterations in tissue homeostasis and function and thereby lead to systemic insulin resistance (Xu, Tchkonia, et al., 2015). Whether cell‐intrinsic mechanisms are also involved in this process remains to be established.
Progenitors, identified as CD45−/CD34+/CD31− stromal cells, are major contributors in the maintenance of AT homeostasis through the renewal of dysfunctional hypertrophic adipocytes and extracellular matrix. Progenitor cells are also important in the regulation of AT growth by providing additional adipocytes leading to AT hyperplasia. Progenitors constitute a heterogeneous mesenchymal stromal cell population of specialized subsets as shown by single‐cell RNA sequencing approaches (Emont et al., 2022). Using single‐cell flow cytometry approaches, we previously identified tissue nonspecific alkaline phosphatase (ALPL or MSCA1) and NGFR (or CD271) as respective markers of the preadipocyte and the premyofibroblast in human fat depots (Esteve et al., 2019). Impaired adipogenesis has been associated with senescence in aged/obese mice and in human AT stromal cells (Le Pelletier et al., 2021; Xu, Palmer, et al., 2015). Several data are in line with an environmental impact of the SASP‐derived factors including antiadipogenic Activin A (Xu, Palmer, et al., 2015) and pro‐fibrotic Osteopontin (Sawaki et al., 2018). But integration of senescent environmental cues, together with progenitor intrinsic mechanisms controlling their fate, have yet to be defined. In addition, a better understanding of the molecular basis of progenitor aging is of interest to monitor and target senescence of mesenchymal stromal cells for future cell‐based therapy in regenerative medicine.
The anatomical repartition of fat depots and the maintenance of AT expandability are major determinants of human metabolic health. Accumulation of visceral AT (VsAT), as observed in central obesity and with aging, is deleterious and associated with increased risk to develop cardiometabolic pathologies. In the present study, we performed flow cytometry analyses to investigate the stromal senescent state according to fat depot location using a human cohort of obese subjects and unbiased large‐scale transcriptomic and proteomic approaches on human immuno‐selected CD45−/CD34+/CD31− AT progenitors. Our observations show that TGFbeta/BMP, WNT, and NOTCH developmental pathways are dysregulated in these senescent progenitors. GREM1 and SFRP4, endogenous antagonists to the BMP and WNT pathways, and INHBA were found to be preferentially upregulated in SASP from VsAT. Cells forced into replicative senescence upregulated intrinsic NOTCH3 receptor expression. Modulation of NOTCH activity by immobilized ligand and by gamma‐secretase inhibitor demonstrates that Notch signaling is central in the pro‐myofibrogenic/antiadipogenic fate triggered by replicative senescence. The link between NOTCH3 and myofibrogenesis is further supported by gene expression studies on both human and mouse AT and analyses of human progenitor subsets.
## Human adipose tissues
Subcutaneous human AT was obtained from healthy adult women undergoing dermolipectomy (mean body mass index 27.5 kg/m2 ± SD 3.324, mean age 41.9 years ± SD 9.8). The protocol was approved by Ministère de la Recherche (DC2008‐452). Paired subcutaneous abdominal and visceral (omental) AT was obtained from patients, candidates for bariatric surgery through a clinical study approved by the Institutional Review Boards registered at ClinicalTrials.gov (SENAPID NCT01525472). All donors gave their informed consent.
## Cell isolation and culture
AT was digested with dispase (2.4 U/ml in PBS, pH 7.4, volume/volume, Gibco) and then type I collagenase (250 U/ml in PBS, $2\%$ BSA, pH 7.4, volume/volume, Sigma‐Aldrich) for 30 min at 37°C for the dermolipectomy, or with collagenase only for the SENADIP AT. Cell suspensions were filtered through a 250 μm filter. The stroma‐vascular cells (SVC) were obtained after centrifugation and treatment with erythrocyte lysis buffer (155 mmol/L NH4Cl; 5.7 mmol/L K2HPO4; 0.1 mmol/L EDTA; pH 7.3) for 10 min. Finally, matrix fragments were removed using successive filtrations through 100, 70, and 40 μm nylon meshes. The viable recovered cells were counted and further analyzed by flow cytometry or used to isolate the cell subsets by either magnetic beads‐based selection or cell sorter.
The CD45−/CD34+/CD31− progenitor cells were obtained from SVC by elimination of the immune and endothelial cells using CD45+ depletion kit (Stemcell Technologies) followed by CD31+ depletion (R&D Systems or Dynal, Thermofisher), followed by CD34+ selection kit (Stemcell Technologies) according to the manufacturer's instructions. Purity of the progenitor cells was assessed by flow cytometry.
Cells were cultured at 37°C under $21\%$ O2 and $5\%$ CO2. For adipogenic differentiation, progenitor cells were seeded at high density (120,000 cells/cm2) in ECGM‐MV (Promocell) with 50 mg/ml penicillin–streptomycin for 48 h, trypsinated, and seeded in culture plates containing basal defined adipogenic medium (ECBM (Promocell) with 66 nmol/L insulin, 1 nmol/L triiodothyronine, 0.1 μg/ml transferrin, 100 nmol/L cortisol) supplemented with 3 μmol/L rosiglitazone and isobuthylmethylxantine (IBMX, 0.25 mmol/L) and precoated with human recombinant NOTCH ligands: IgG1‐Fc, JAG1‐Fc and DLL4‐F (Invitrogen) diluted in PBS and coated at 2 μg/cm2 overnight at 4°C or 3 h at room temperature (RT). After 3 days, the medium was replaced by basal defined adipogenic medium for the following 7 days. At day 10, cells were either fixed in $4\%$ PFA solution at RT for 10 min or lysed for RNA extraction. For myofibrogenic differentiation, progenitor cells were seeded at high density (120,000 cells/cm2) in ECGM‐MV for 48 h, trypsinated, and seeded on coated recombinant NOTCH ligands in ECBM $1\%$ fetal calf serum (FCS), supplemented or not with recombinant TGFβ1 (5 ng/ml, Peprotech), and treated or not with γ‐secretase inhibitor LY411575 (10 μmol/L, Sigma‐Aldrich) or TGFβ1 receptor inhibitor SB431542 (10 μmol/L, Sigma‐Aldrich) for 4 days. At day 4, cells were either fixed with $4\%$ PFA or lysed for RNA extraction. For serial passaging, progenitor cells were seeded at low density (5000 cells/cm2) in ECGM‐MV, and then at $80\%$ confluency, cells were trypsinated and seeded at low density for next passage, or at high density on coated ligand in ECBM $1\%$ FCS. Immuno‐selected P1 progenitor cells were treated with hydrogen peroxide (H2O2) 0.1 μmol/L during 24 h, then lysed for RNA extraction or by doxorubicin (0.2 μM, Sigma‐Aldrich) or etoposide (1 μM, Sigma‐Aldrich) for 72 h, then either lysed for RNA extraction, or fixed with $4\%$ PFA. For senolytic treatment, P3 and P6 progenitor cells were seeded at high density on IGG or DLL4 in ECGM‐MV and treated with dasatinib (50 nM, Sigma‐Aldrich) and quercetin (20 μM, Sigma‐Aldrich) for 4 days then lysed for RNA extraction. For conditioned media analyses, progenitor cells (native or passaged) were seeded at high density (120,000 cells/cm2) in ECGM‐MV for 24 h, followed by three washes with ECBM and incubated for 24 h within ECBM. Media were then collected and frozen at −80°C before mass spectrometry analyses. The progenitor cell subsets, that is, MSCA1+, −/CD271+ (MSCA1−/CD271+), and −/− (MSCA1−/CD271−) were isolated using a cell‐sorting approach as described previously (Esteve et al., 2019) and lysed for RNA extraction or cultured under myofibrogenic condition.
## Senescence‐associated β‐galactosidase colorimetric staining
After fixation with $0.5\%$ glutaraldehyde (5 min for cells and 10 min for tissue), tissue pieces or cells were incubated for the next 16 h with 4 mM K3Fe(CN)6, 4 mM K4Fe(CN)6, 2 mM MgCl2, and 400 μg/ml X‐Gal in C3H7NO (dimethylformamide) in PBS/MgCl2 pH 6.0 and after washes, examined by microscopy (microscope Nikon Eclipse TE300).
## Senescence‐associated β‐galactosidase fluorescence staining and flow cytometry approaches
100,000 SVC were incubated with fluorescent‐labeled antibodies (V500‐CD45, PerCP‐CD34, and V450‐CD31 (BD Biosciences), PE‐MSCA1, APC‐CD271, and PE‐Vio770‐CD14 (Miltenyi Biotec)) or appropriate isotype control for 30 min at 4°C in PBS $0.5\%$ BSA and 2 mmol/L EDTA. Cells were washed with PBS and analyzed using a FACS Canto™ II flow cytometer. Beta‐galactosidase activity was performed using ImaGene Green C12FDG lacZ Gene Expression Kit (Molecular Probes). 250,000 cells in ECBM $0.1\%$ BSA without phenol red, pretreated with chloroquine (30 μmol/L) for 1 h at 37°C under $5\%$ CO2, were further incubated for the next 3 h with 33 μmol/L FITC C12FDG. After washing steps, antibodies or appropriate isotype control were added (PerCP‐CD34, PE‐Vio770‐CD14, V450‐CD31, and BV510‐CD45) for 20 min at 4°C. Cells were washed with PBS and analyzed using a FACS Canto™ II flow cytometer. Analyses were performed with Diva Pro software (BD Biosciences) or FlowJo (BD Biosciences).
## Scratch wound assay
p96 plate (Incucyte® Imagelock 96‐well plate, Sartorius, France) was coated overnight at 4°C with IgG and DLL4. Immuno‐selected progenitor cells were plated in ECGM‐MV at 30–40,000 cells/well on IgG/DLL4‐coated wells in the presence or not of 10 μmol/L of LY411575 or of 10 μmol/L of SB431542 or on uncoated wells, and left overnight. Scratch wound was performed according to the manufacturer's instructions using the 96‐well WoundMaker (Incucyte, Sartorius). After PBS washes, media were changed to ECBM $1\%$ FCS in the presence or not of LY411575 (10 μmol/L), or in the presence of TGFβ1 (5 ng/ml), or SB431542 (10 μmol/L) and recorded every 3 h using the Incucyte SX5 during 72 h. Wound closure was analyzed using the Incucyte Scratch Wound Analysis Software Module (Sartorius, France).
## Isolation of SVC from mouse AT
Four‐month‐old male C3H/HeOuJ mice (Charles River Laboratories France) were fed a $45\%$ high‐fat diet ($45\%$ energy as fat, Research Diets D12451) up to 6 weeks. Mice were housed in accordance with French and European Animal Care Facility guidelines at 21 °C with food and water provided ad libitum, and maintained on a 12‐h light, 12‐h dark cycle. Mice were euthanized after an overnight fasting. Perigonadal AT was excised and directly digested as described previously (Vila et al., 2014). SVC were lysed in QIAzol (Qiagen) for RNA extraction.
## Immunofluorescence staining
Fixed cells were blocked and permeabilized in PBS $0.1\%$ Triton X‐100 and $5\%$ serum for 30 min at RT and then incubated overnight at 4°C with primary antibodies (mouse anti‐αSMA, Dako 1A4, 1:100; rabbit anti‐NOTCH3, Abcam ab23426, 1:200, mouse anti‐γH2AX, EMD Millipore JBW301, 1:200). After washing steps, cells were incubated for 1 h at RT with appropriate Alexa Fluor‐conjugated secondary antibodies (Invitrogen). Nuclei were stained with DAPI (0.5 μg/ml, Invitrogen), and lipid accumulation was visualized with BODIPY $\frac{493}{503}$ staining (10 μg/ml, Invitrogen) for 15 min at RT. Images were taken with a fluorescent (Nikon Eclipse TE300, software NIS‐Elements 2.5 BR, Nikon®) or confocal microscope (ZEISS LSM780, ZEN software). Image analysis was performed with ImageJ software. Nuclei were counted from the DAPI staining, and the results were the mean of 5 images for each sample.
## Western blot analysis
Cells were lysed with RIPA buffer supplemented with antiproteases, and 2.5 μg of protein extract quantified by DC protein assay (Bio‐Rad) was used for the western approach. Primary antibodies rabbit anti‐NOTCH3 (Abcam ab23426, 1:1000), mouse anti‐β‐tubulin (Cell Signaling D3U1W, 1:1000, as loading control) were incubated overnight at 4°C in TBS buffer with $5\%$ BSA or $5\%$ milk. Detection was performed with an appropriate HRP‐coupled secondary antibody (Cell Signaling Technology) incubated for 1 h at RT and ECL reagent (Amersham). Densitometry was quantified using Chemidoc detection system (Bio‐Rad laboratories).
## Transcriptional analysis
Total RNA was isolated from human cells using Quick‐RNA Microprep kit (Zymo Research) or from murine SVC using Direct‐zol RNA Miniprep Plus kit (Zymo Research) according to the manufacturer's instructions. cDNA synthesis was performed on 100–200 ng of total RNAs with Superscript III (Thermofisher) and random hexamer primers. *Relative* gene expression levels were assessed using Taqman® Probes and TaqMan Fast Advanced Applied mastermix (Thermofisher) on a QuantStudio™ 5 (QS5; Thermofisher). Human and murine Assay‐On‐Demand are listed in Table S1. Each of the samples was run in duplicate, and the relative amount was normalized to 18 S or PPIB housekeeping gene. Data were analyzed using QuantStudio Real‐Time PCR software (Thermofisher).
## Label‐free quantitative mass spectrometry
Adsorption of proteins on the surface of Nanozeolite LTL (NanoScape AG, Germany) was carried out for 90 min at 4°C by incubation of 0.1 mg protein/ml conditioned media and 0.1 mg/ml nanoparticles in 50 mmol/L ammonium bicarbonate buffer, pH 8.0 (ABF). After centrifugation at 16,000 g for 20 min and washes in ABF, captured proteins were suspended in 100 μl of ABF containing $0.05\%$ AALS (Anionic Acid Labile Surfactants from Protea Biosciences). After reduction (10 mM DTT at 56°C for 30 min) and alkylation (20 mmol/L iodoacetamide for 30 min at RT in dark), bound proteins were digested with LysC (0.5 μg) for 4 h at 37°c followed by trypsin (1 μg) for 18 h at 37°C. After centrifugation, protein digests are collected and AALS hydrolyzed with $1\%$ TFA at 37°C for 45 min. For LC–MSMS analysis of the native Sc and Vs progenitor cell secretome, Ultimate 3500 RSLC dual system (Thermo Scientific) coupled to hybrid LTQ Orbitrap Elite mass spectrometer (Thermo Scientific) equipped with a nanoelectrospray source was used. Tryptic digests were loaded onto a C18 trap column (Thermo Scientific) and washed with $0.2\%$ HCOOH at 5 μl/min for 10 min. Peptides were eluted on a C18 reverse‐phase column (Thermo Scientific) with a linear gradient of $4\%$–$30\%$ solvent B (H2O/CH3CN/HCOOH, $\frac{10}{90}$/0.2 volumes) for 120 min, $30\%$–$90\%$ solvent B for 20 min, and $90\%$ solvent B for 5 min, at a flow rate of 250 nl/min. The mass spectrometer was operated in the data‐dependent mode to automatically switch between MS and MS/MS acquisition. Survey full‐scan MS spectra (m/z 310–1600) were acquired in the Orbitrap with a resolution of 120,000 at m/z 400. For serial passaging secretome, the nanoAcquity UPLC (Waters) coupled to a Q Exactive Plus mass spectrometer (Thermo Scientific) equipped with a nanoelectrospray source was used. Protein digests were loaded onto a nanoAcquity UPLC Trap column (Waters) and washed with $0.2\%$ formic acid at 20 μl/min for 3 min. Peptides were then eluted on a C18 reverse‐phase nanoAcquity column (Waters) with a linear gradient of 8–$31\%$ solvent B (H2O/CH3CN/HCOOH, $\frac{10}{90}$/0.2, by vol.) for 120 min, 31–$91\%$ solvent B for 20 min, and $91\%$ solvent B for 5 min, at a flow rate of 250 nl/min. The mass spectrometer was operated in the data‐dependent mode to automatically switch between MS and MS/MS acquisition. Survey full‐scan MS spectra (from m/z 325–1300) were acquired with a resolution of 70,000 at m/z 200. MSMS spectra were recorded in profile type with a resolution of 17′500. All samples were injected in triplicate. The LC–MS/MS data, acquired using the Xcalibur software (Thermo Fisher Scientific), were processed using a homemade Visual Basic program software developed using XRawfile libraries (Thermo Fisher Scientific) to generate an MS/MS peak list used for database searching and a file used for quantitative analysis. Database searches were performed using internal MASCOT server (Matrix Science) using the Swiss‐Prot human database. Mascot results were imported into Scaffold software (version 4.4.1.1) and also used for XTandem parallel Database Search. Peptide identifications were accepted if they could be established at greater than $7.0\%$ probability to achieve an FDR less than $1.0\%$ by the Scaffold Local FDR algorithm. Protein identifications were accepted if they could be established at greater than $97.0\%$ probability to achieve an FDR less than $1.0\%$ and contained at least 2 identified peptides. Quantitative differential analysis of proteins was realized using a label‐free analysis with an in‐house DIFFTAL (DIFferential Fourier Transform Analysis) software algorithm as described previously (Autelitano et al., 2014). Statistical analyses were realized with DanteR program, an R‐based software. The peptides quantification was normalized by the median intensity value of the detected feature population over the 102 runs, which includes 34 conditions (17 patients × 2 AT depots) and over the 36 runs, which includes 12 conditions (3 patients × 4 levels of serial passaging), in triplicate injections. Only peptides detected at least 2 times (over replicates) are kept, and an average intensity value per sample is calculated for each peptide. A threshold value representing the minimum detectable signal level is used. The protein abundance is determined as the average of the top three most abundant peptides per protein.
## Ampliseq RNA sequencing
RNA was isolated using RNeasy Protect Mini kit (Qiagen) and suspended in RNAprotect cell reagent. RNA quality was assessed with Agilent RNA 6000 Pico Kit (Agilent) and quantified using Qubit™ RNA HS Assay Kit (Invitrogen) and Qubit 2 Fluorometer (Invitrogen). 10 ng of RNA was used for AmpliSeq transcriptome library construction. For AmpliSeq transcriptome sequencing library construction AmpliSeq™ Library PLUS, AmpliSeq Transcriptome Human Gene Expression Panel and AmpliSeq CD indexes SetA kits were purchased from Illumina and sequencing libraries were constructed as described in AmpliSeq for Illumina Transcriptome Human Gene Expression Panel reference guide (Illumina). Sequencing libraries were quantified using Qubit dsDNA HS Assay Kit and Fluorometer (Invitrogen) and converted to molar concentration using 285‐bp peak. Equimolar concentrations of libraries were pooled at 4 nmol/L and used for sequencing. Pooled library was denatured and diluted as described in Denature and Dilute Libraries Guide (Illumina) and adjusted to final concentration of 1.4 pmol/L. Resulting library was sequenced on NextSeq 500 using NextSeq $\frac{500}{550}$ High Output v2 kit with 2 × 151 bp cycle. Generated raw files were converted to FASTQ files and used for data analysis. AmpliSeq transcriptome FASTQ files were analyzed on Array studio V10.0 (Omicsoft, Qiagen). Following raw read QC, first and last 10 bases were trimmed and mapped to reference genome Human B38. The read count data were generated using GeneModel RefGene20170606. Resulting data were normalized by DESeq package, transformed to log2 value, and used for statistical analysis using NetworkAnalyst (Zhou et al., 2019).
## Statistical analyses
Statistical analyses were performed using Prism (GraphPad Software). Comparisons between 2 groups were analyzed either paired or unpaired analysis and two‐tailed Student's t test or Wilcoxon test depending on data distribution. Comparison between more than 2 groups was analyzed by either one‐way ANOVA or two‐way ANOVA or nonparametric Kruskal–Wallis test, followed by Dunn's, Dunnet's, Tukey's, or Sidak's multiple comparison test for (n) independent experiments. Differences were considered statistically significant when $p \leq 0.05.$
## Fat depot location from obese patients impacts senescence of progenitors
Cellular senescence in both ScAT and VsAT from obese patients was first assessed through colorimetric staining of senescence‐associated beta‐galactosidase (SA‐β‐gal) activity. As shown in Figure 1a, SA‐β‐gal staining showed interindividual and interdepot heterogeneity, exhibiting a range of distinct qualitative profiles, from a diffuse low‐level staining in ScAT biopsies to much higher staining in VsAT (left panel), with positive cells widely distributed in the stroma‐vascular compartment or concentrated in crown‐like structures surrounding adipocytes (right panel, top and bottom, respectively). Although some mature adipocytes exhibited light SA‐β‐Gal staining, more intense SA‐β‐Gal staining was found in stromal cells. To further identify and quantify SA‐β‐gal‐positive stromal cells in human AT, stroma‐vascular cells obtained after collagenase digestion of both ScAT and VsAT from 115 obese subjects were stained for fluorescent SA‐β‐gal activity in combination with cell surface markers (CD45, CD14, CD34, and CD31) and analyzed by flow cytometry. Unsupervised tSNE (t‐distributed stochastic neighbor embedding) performed after downsampling and concatenation of ScAT and VsAT data from a panel of 55 donors highlighted macrophage, lymphocyte, endothelial cell and progenitor cell clusters based on cell surface marker expression (Figure S1a,b left panel). SA‐β‐gal+ cells were identified in macrophages, endothelial cells, and progenitor cells (Figure 1b right panel). Within the total SA‐β‐gal+ stromal cells, VsAT had higher percentage of progenitors and lower of macrophages than ScAT (Figure 1c). Per tissue mass, VsAT contained a higher number of SA‐β‐gal+ senescent progenitors when compared to ScAT, with no corresponding difference in the number of senescent macrophages (Figure 1D). In the progenitor cluster, back‐gating on the fat depot origin revealed higher SA‐β‐gal activity together with larger cell size in VsAT compared with ScAT (Figure S1b–d). Principal component analysis using clinical and anthropometric variables in addition to ScAT and VsAT progenitor senescence variables was performed (Figure 1e). Component 1 clearly separated two groups of variables. One group was constituted by the variables relative to progenitor senescence and BMI (at the age of 20, actual and maximal BMI). The other group was composed of parameters related to metabolic syndrome (MS) including age, waist‐to‐hip ratio (WHR), insulin resistance (HbA1c and HOMA‐IR), triglycerides, and systolic blood pressure (SBP). HDL was clearly discriminated by component 2 and, as expected, was inversely correlated with the MS‐related variables. Interestingly, the associations between BMI and senescent parameters were clearly more marked and statistically relevant in VsAT compared with ScAT (Figure 1e).
**FIGURE 1:** *Senescence in progenitor cells defines a fat depot‐specific pattern in obesity. (a) Colorimetric SA‐β‐gal staining on paired biopsies of subcutaneous and visceral adipose tissue (ScAT and VsAT). Representative views of whole AT pieces and light microscope images are shown. (b) Paired ScAT and VsAT stroma‐vascular fraction (SVF) cells from 55 subjects were analyzed by flow cytometry. tSNE of data after downsampling and concatenation enabled the identification of clusters by cell type: purple macrophages (CD45+/CD34−/CD31int/highFSC/HighSSC), red endothelial cells (CD45−/CD34+/CD31+/HighFSC/HighSSC), green progenitor cells (CD45−/CD34+/CD31−/HighFSC/HighSSC), and blue lymphocytes (CD45+/CD34−/CD31−/lowFSC/LowSSC; left panel) and same tSNE colored by SA‐β‐gal fluorescence intensity (right panel). (c) Pie charts showing the percentage of cell types within the SA‐β‐gal‐positive cells in ScAT and VsAT. (d) Supervised analysis of the SA‐β‐gal+ cells, histograms represent means ± sem of SA‐β‐gal+ cells per gram of tissue for progenitor cells (left) and macrophages (right) from ScAT (blue) and VsAT (red; n = 115 donors, two‐way ANOVA, Tukey's post‐test). (e) Principal component analyses performed on clinical and anthropometrical parameters of obese AT donors and ScAT (upper panel, blue) and VsAT (lower panel, red) progenitor senescence variables. (f) Immuno‐selected progenitor cells from paired ScAT and VsAT biopsies of 17 obese subjects were maintained for 24 h in basal medium, and the conditioned media were harvested and analyzed by mass spectrometry. Heatmap of most relevant differentially expressed proteins from the 17 secretomes of ScAT (blue) and VsAT (red) progenitors (deep blue corresponding to the smallest value and yellow to the highest value)*
Analysis of the secretome of native immuno‐selected ScAT and VsAT progenitor cells from 17 patients by large‐scale mass spectrometry identified 428 secreted proteins. Paired analysis of ScAT and VsAT progenitor secretomes highlighted 161 differentially expressed proteins (DEPs) upregulated in VsAT progenitors and 65 in ScAT progenitors; the DEPs with the highest fold changes are shown in Figure 1f. Comparison of ScAT and VsAT DEPs with the freely available secretome database, the SASP atlas (Basisty et al., 2020), the SenMayo geneset (Saul et al., 2022) and factors identified in human AT senescent progenitors (Xu, Palmer, et al., 2015; Xu, Tchkonia, et al., 2015) were performed. VsAT progenitor secretome was mainly enriched in common canonical SASP factors including IGFBP7, IL6, and CXCL8 (cellular senescence‐enriched reactome pathway), the CXCR2 Ligands (CXCL1, CXCL2, CXCL3, and CXCL8), as well as inflammatory, growth, and remodeling factors (MIF, FGF7, and INHBA) and factors of the complement and coagulation pathways (PLAT, PLAU, SERPINB2, E2 and E1, and TF; Table 1).
**TABLE 1**
| Gene Id | p Value | Ratio | SASP Atlas | SASP Atlas.1 | SASP Atlas.2 | SASP Atlas.3 | SenMayo | Irradiated preadipocytes |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | Irradiated fibroblasts | Protease inhibitor fibroblasts | Inducible RAS Fibroblast | Irradiated epithelial cells | | |
| ACE | 0.001953 | 0.65 | | | | | | |
| ADAMTS1 | 0.000427 | 57.57 | | | | | | |
| ADAMTS5 | 0.03125 | 0.82 | | | | | | |
| AEBP1 | 0.044769 | 0.88 | | | | | | |
| AGRN | 0.03125 | 29.43 | + | + | + | | | |
| ALB | 0.003845 | 1.96 | + | | + | | | |
| ANXA2 | 0.001678 | 0.81 | | + | + | + | | |
| APCS | 0.012207 | 1.75 | | | | | | |
| APLP2 | 0.005569 | 2.25 | | | | | | |
| APOA1 | 0.00293 | 55.76 | | | + | | | |
| APOC3 | 0.009766 | 1.6 | | | + | | | |
| APOD | 0.000153 | 0.52 | | | | | | |
| ARPC1A | 0.03418 | 22.82 | | | | | | |
| B4GALT1 | 0.028992 | 1.39 | | + | | | | |
| BGN | 0.003159 | 1.76 | | + | + | | | |
| C1QBP | 0.000381 | 25.15 | | | | | | |
| C4A | 0.007813 | 2.19 | | | | | | |
| C4B | 0.003906 | 4.58 | | | | | | |
| C7 | 3.1e-05 | 65.24 | | | | | | |
| CAB39L | 0.000488 | 0.44 | | | | | | |
| CALR | 1.5e-05 | 1.96 | + | + | + | | | |
| CCDC80 | 0.00029 | 2.35 | | | | | | |
| CD59 | 0.006653 | 1.28 | | | + | | | |
| CFD | 0.000107 | 0.44 | | | | | | |
| CFH | 3.1e-05 | 0.39 | | + | | | | |
| CFI | 0.000488 | 230.9 | | | + | | | |
| CHI3L1 | 0.000122 | 294.27 | | | | | | |
| CHID1 | 0.002441 | 20.9 | | | | | | |
| CILP | 0.002441 | 0.6 | | | | | | |
| CLEC3B | 3.1e-05 | 0.25 | | + | + | | | |
| CMA1 | 0.04187 | 37.97 | | | | | | |
| COL12A1 | 3.1e-05 | 0.5 | + | + | + | | | |
| COL14A1 | 0.003906 | 0.6 | | | | | | |
| COL18A1 | 0.010742 | 23.76 | | + | | | | |
| COL1A1 | 0.000381 | 0.65 | | + | | | | |
| COL1A2 | 0.003845 | 0.71 | | + | | | | |
| COL3A1 | 0.000107 | 0.48 | | + | | | | |
| COL5A2 | 0.028992 | 0.91 | | | + | | | |
| COL6A1 | 0.00029 | 0.61 | + | + | + | | | |
| COL6A2 | 0.000153 | 0.59 | + | + | + | | | |
| COL6A3 | 0.000153 | 0.45 | + | + | + | | | |
| CPA3 | 0.010986 | 0.8 | | | | | | |
| CREG1 | 0.00029 | 1.92 | | | | | | |
| CTHRC1 | 0.006836 | 0.67 | + | | + | | | |
| CXCL1 | 0.003845 | 6.44 | + | + | + | | + | + |
| CXCL2 | 0.027344 | 1.83 | + | + | + | | + | |
| CXCL3 | 0.008545 | 3.07 | + | + | + | | | |
| CXCL6 | 9.2e-05 | 735.04 | | | | | + | |
| CXCL8 | 0.000854 | 3.11 | + | | + | | + | + |
| CYR61 | 0.038635 | 233.45 | | + | + | | | |
| DCN | 0.001343 | 0.6 | | + | | + | | |
| ECM1 | 3.1e-05 | 0.44 | + | + | + | | | |
| EMILIN2 | 7.6e-05 | 2.12 | | | | | | |
| ENPP2 | 0.000488 | 0.39 | | + | | | | |
| F2 | 6.1e-05 | 195.33 | | | + | | | |
| F5 | 0.000214 | 32.05 | | + | | | | |
| FAM114A1 | 0.03125 | 0.85 | | | | | | |
| FBLN1 | 0.000381 | 0.5 | | + | + | | | |
| FBLN2 | 0.000381 | 0.59 | | + | | | | |
| FBN1 | 0.000504 | 0.59 | | + | + | | | |
| FGF7 | 0.001953 | 2.01 | | | | | + | |
| FGG | 0.03125 | 1.44 | | | | | | |
| FN1 | 0.020157 | 0.79 | + | + | + | | | |
| FST | 9.2e-05 | 0.41 | | | | | | |
| FSTL1 | 0.001343 | 0.67 | | + | | | | |
| GALNT2 | 0.037109 | 48.98 | + | | | | | |
| GC | 0.003906 | 53.89 | + | | + | | | |
| GGH | 0.020996 | 0.87 | | | | | | |
| GNAS | 0.005157 | 1.29 | | | | | | |
| GREM1 | 0.000977 | 324.39 | | | | | | |
| GSN | 1.5e-05 | 0.37 | + | + | + | + | | |
| HMCN1 | 0.013672 | 0.75 | | | | | | |
| HP | 0.010986 | 1.83 | | | | | | |
| HPX | 0.001678 | 48.49 | | | | | | |
| IGF2 | 6.1e-05 | 2.42 | | + | + | | | |
| IGFBP3 | 1.5e-05 | 24.82 | + | + | | + | + | |
| IGFBP4 | 0.000504 | 2.79 | + | + | + | | + | |
| IGFBP7 | 3.1e-05 | 2.54 | + | + | | | + | |
| IGHG1 | 0.000214 | 85.05 | | | | | | |
| IGHG3 | 0.023438 | 22.28 | | | | | | |
| IGHG4 | 0.015625 | 54.44 | | | | | | |
| IGKC | 0.006653 | 1.96 | | | | | | |
| IL6 | 0.000732 | 93.41 | | | | | + | + |
| INHBA | 3.1e-05 | 338.34 | | | | | + | + |
| ISLR | 0.023438 | 0.83 | | | | | | |
| ITGBL1 | 0.000977 | 0.37 | | | | | | |
| ITIH4 | 0.020996 | 19.22 | | | | | | |
| ITLN1 | 0.000244 | 1087.89 | | | | | | |
| KRT85 | 0.027344 | 510.83 | | | | | | |
| LAMB1 | 0.000656 | 1.65 | + | + | + | | | |
| LAMB2 | 0.000381 | 0.63 | | + | + | + | | |
| LBP | 0.03125 | 330.45 | | | | | | |
| LGALS1 | 0.000656 | 0.74 | + | + | + | | | |
| LGALS3 | 1.5e-05 | 0.54 | + | + | | + | | |
| LGALS3BP | 0.039536 | 1.47 | + | + | + | | | |
| LIF | 0.000122 | 402.14 | | | | | | |
| LOX | 0.003357 | 58.52 | | + | | | | |
| LOXL2 | 0.023438 | 0.84 | + | + | + | | | |
| LTBP2 | 0.001007 | 1.78 | | | + | | | |
| LTBP4 | 0.047913 | 28.77 | | | + | | | |
| MASP1 | 0.007813 | 0.58 | | + | | | | |
| METRNL | 0.024414 | 0.87 | | | | | | |
| MFAP5 | 0.015625 | 0.69 | | | | | | |
| MIF | 4.6e-05 | 1.69 | + | + | + | | + | |
| MMP10 | 0.001678 | 0.56 | | | | | + | |
| MMP14 | 0.029541 | 1.98 | | | + | | + | |
| MMP3 | 1.5e-05 | 0.18 | | | + | | + | + |
| MMP9 | 0.003159 | 45.56 | | | + | | + | |
| MSLN | 0.03125 | 249.28 | | | | | | |
| MYDGF | 0.003357 | 1.48 | + | | + | | | |
| NAMPT | 0.014999 | 0.81 | + | | + | | | |
| NID2 | 0.005859 | 1.69 | + | | + | | | |
| NRP2 | 0.001953 | 147.98 | | | | | | |
| OGN | 0.000122 | 2.59 | | | | | | |
| PAM | 0.044312 | 0.9 | | | | | | |
| PAMR1 | 0.01825 | 1.75 | | | | | | |
| PAPPA | 3.1e-05 | 2.51 | | | | | + | |
| PCOLCE | 0.007904 | 0.74 | | + | + | | | |
| PCOLCE2 | 0.000153 | 0.57 | | | | | | |
| PDGFRL | 0.002441 | 0.69 | | | | | | |
| PI3 | 0.044769 | 0.97 | | | | | | |
| PLAT | 3.1e-05 | 972.35 | | | + | | + | |
| PLAU | 3.1e-05 | 176.63 | + | | + | | + | |
| PLG | 0.013672 | 51.15 | | + | | | | |
| PLXDC2 | 0.027344 | 41.6 | | | | | | |
| POSTN | 1.5e-05 | 0.24 | + | + | + | | | |
| PRG4 | 0.00209 | 0.64 | | | | | | |
| PSAP | 0.026672 | 1.32 | + | + | + | | | |
| PTGDS | 0.001068 | 268.45 | | | | | | |
| PXDN | 0.047913 | 1.3 | + | + | + | | | |
| QSOX1 | 0.001678 | 1.39 | + | + | + | | | |
| RNASE4 | 0.024414 | 0.79 | | + | + | | | |
| S100A13 | 3.1e-05 | 0.52 | | | | | | |
| SDC4 | 0.001678 | 1.74 | | | | | | |
| SEPT8 | 0.039795 | 1.27 | | | | | | |
| SERPINB2 | 0.000107 | 2.75 | | | + | | | |
| SERPINC1 | 0.001068 | 125.93 | | | | | | |
| SERPINE1 | 1.5e-05 | 3.3 | + | + | + | | + | |
| SERPINE2 | 0.000504 | 2.39 | | + | | | + | |
| SERPINF1 | 3.1e-05 | 0.43 | | + | + | | | |
| SERPING1 | 0.030518 | 0.82 | | + | | | | |
| SFRP2 | 0.038635 | 2.02 | | | | | | |
| SFRP4 | 0.047913 | 83.82 | | | | | | |
| SLIT2 | 0.007813 | 0.72 | | | | | | |
| SLPI | 0.010254 | 70.58 | | | | | | |
| SOD3 | 1.5e-05 | 0.25 | | + | | | | |
| SPON1 | 0.01825 | 2.53 | | | | + | | |
| STC2 | 0.006714 | 1.84 | | | | + | | |
| TF | 0.002136 | 34.82 | | | | | | |
| TGFBI | 0.03479 | 28.33 | + | + | | | | |
| TGFBR3 | 0.026672 | 1.78 | | | | | | |
| THBS1 | 0.000153 | 2.35 | + | + | + | | | |
| THBS2 | 0.000839 | 1.93 | | + | + | | | |
| TNXB | 7.6e-05 | 0.48 | | | | | | |
| TPSAB1/TPSB2 | 6.1e-05 | 0.25 | | | | | | |
| TXN | 0.001068 | 0.75 | | | + | | | |
| TYMP | 1.5e-05 | 0.26 | | | | | | |
| VCAN | 4.6e-05 | 0.55 | + | + | + | | | |
| VTN | 0.000763 | 1.82 | | | | | | |
| WISP2 | 6.1e-05 | 0.25 | | | | | | |
| YBX1 | 0.030151 | 0.88 | + | | + | | | |
## The BMP, WNT, and NOTCH developmental pathways are markers of the senescence‐associated secretory and intrinsic phenotypes of progenitors
To investigate whether the VsAT progenitor senescence could be in part related to replicative growth pressure promoted by obesity, AT progenitors from nonobese individuals were submitted to serial passaging with low‐density cell seeding. Due to the low amount of VsAT biopsies retrieved from nonobese individuals precluding immunoselection approaches, progenitors were immuno‐selected from ScAT. A marked increase in the number of SA‐β‐gal+ progenitor cells was observed as early as P3 in both nonconfluent and confluent cells (Figure 2a). In addition, increases in cell size and doubling time and in the expression levels of CDKN2A (p16) and CCND1 (Cyclin D1) were observed while CDKN1A levels (p21) were not altered (Figure 2b). Paired analysis of the DEPs between P3 and P6 secretomes with the one of nonsenescent P1 progenitor cells identified 34 proteins upregulated either in P3 or P6 secretomes (Figure 2c) among the 342 identified secretory factors. In parallel, the comparative paired analysis of the RNA sequencing data of P6 and P1 progenitors highlighted 518 DEGs upregulated at P6 (Figure 2d). The P6 DEGs were enriched in pathways related to tissue remodeling including epithelial‐to‐mesenchymal transition (EMT), TGFβ signaling, and hypoxia as well as inflammation. Interestingly, the Notch signaling including NOTCH3 was also part of the AT progenitors senescent transcriptome (Figure 2e). Merging of the P6 DEGs and P3P6 DEPs with the native ScAT and VsAT progenitor‐specific secretomes from obese patients (from Figure 1f) highlighted a marked enrichment in senescence‐related proteins in VsAT compared with ScAT secretomes (21 proteins vs. 4, respectively; Figure 2f). Among the 21 VsAT SASP factors, 16 proteins were common with other SASP (Table 1). The remaining 5 proteins were specific of the AT progenitor replicative SASP and enriched in complement and coagulation pathways (C7 and C4B) as well as in TGFβ/BMP and Wnt/β‐catenin signaling, as GREM1 and SFRP4 are both secreted antagonists of BMP and WNT, respectively, and in adipogenesis‐related pathways (SFRP4 and LIF). RTqPCR analysis further confirmed the upregulation of GREM1, SFRP4, and LIF as well as INHBA with AT progenitor replicative senescence (Figure 1g).
**FIGURE 2:** *Replicative senescence‐associated secretory phenotype of AT progenitor cells highlights components of the TGFβ/BMP and WNT developmental pathways. (a) SA‐βgal activity by colorimetric staining on nonconfluent or confluent progenitors at Passage 0 (P0), 3 (P3), and 6 (P6). Representative photomicrographs from n = 3 donors are shown. Scale bars: 50 μm. (b) Changes in cell size according to increasing passages. Results are expressed as fold change over P1, means ± SEM of n = 11–17 independent experiments (one‐way ANOVA, Dunnett's post‐test, *p < 0.05, **p < 0.01, ***p < 0.001 compared with P1); changes in doubling time according to increasing passages. Values are means ± SEM of n = 11–17 independent experiments (one‐way ANOVA, Dunnett's post‐test, **p < 0.01, ***p < 0.001 compared with P2, changes in mRNA expression of CDKN2A, CDKN1A, and CCND1 at P3 and P6. Results are expressed as fold change over P1, means ± SEM of n = 15 independent experiments (one‐way ANOVA, Dunn's post‐test, ***p < 0.001 compared with P1). (c) Heatmap of differentially expressed proteins in paired analysis between P1 and P3 and P1 and P6 secretomes from three donors, (deep blue corresponding to the smallest value and yellow to the highest value). (d) Volcano plot of the twofold and more differentially expressed genes between P1 and P6 RNA sequencing transcriptomes performed in three donors. (e) Statistically significantly enriched pathways in the 518 DEGs upregulated in P6 transcriptome in the Human Molecular Signatures Database (MSigDB) hallmark gene sets MSigDB Hallmark (GSEA). (f) Venn diagram of DEGs upregulated in the P6 transcriptome, P3 and P6P3 secretome and ScAT (left) and VsAT (right) native progenitor‐specific secretomes. (g) mRNA levels of SASP proteins GREM1, SFRP4, LIF, and INHBA determined in P1, P3, and P6 progenitor cells by RTqPCR. Results are expressed as fold change over P1, means ± SEM of n = 7 donors (*p < 0.05, **p < 0.01, ***p < 0.001 compared with P1)*
## The activity of Notch modulates the replicative SASP of human AT progenitors
To further investigate the Notch‐dependent pathway highlighted in the replicative senescent AT progenitor transcriptome, ScAT progenitors were submitted to additional senescence‐promoting stimuli, including oxidative stress induced by H2O2 treatment and DNA damage induced by doxorubicin or etoposide treatments. The marked upregulation of NOTCH3 transcript levels, detected by RTqPCR (confirming the RNA sequencing data), was only observed in P6 progenitors although both doxorubicin and etoposide increased the expression of senescent markers including CDKN1A and CCND1 and γH2AX nuclear foci (Figure 3a and Figure S2). Interestingly, the effect of replicative senescence was specific for NOTCH3, as NOTCH1 remained unchanged (Figure 3a). The increased level of NOTCH3 mRNA with replicative senescence was associated with an increased level of the cleaved form of NOTCH3 protein with the apparent molecular weight of 100 kDa (Figure 3b). The basal levels of the canonical Notch target genes HEYL and HES1 were not changed with replicative senescence (Figure 3c). To investigate the potential involvement of Notch pathway in the modulation of the replicative SASP, pharmacological approaches were performed to activate or inhibit Notch signaling in nonsenescent and senescent progenitors. Activation of Notch signaling was achieved by immobilized Notch ligand DLL4 (a member of the Delta‐like family) while inhibition by treatment with the γ‐secretase inhibitor LY411575, which prevents Notch cleavage (Figure 3d). As expected, Notch activation efficiently increased the mRNA level of Notch target genes HEYL and HES1 as well as NOTCH3 itself at P1. The stimulatory effects were completely inhibited by the presence of LY411575. Similar effects were observed in P3 and P6 progenitors, clearly showing that the senescent state did not alter Notch responsiveness (Figure 3e). Interestingly, Notch activation by DLL4 did not affect the expression of GREM1 nor the one of LIF. SFRP4 and INHBA, on the contrary, were upregulated regardless of the cellular state of senescence and in a γ‐secretase‐dependent manner (Figure 3f), indicating that Notch acted upstream of the SASP factors SFRP4 and INHBA. Such an effect was not related to a pro‐senescent impact of Notch activation since it did not increase but even rather slightly decreased the expression of both CDKN2A and CCND1 at P3 and CCND1 at P6 without affecting the expression level of CDKN1A (Figure 3g).
**FIGURE 3:** *Replicative senescence upregulates the expression of Notch3 and does not alter the responses to Notch activation. (a) NOTCH3 and NOTCH1 transcript levels in progenitor cells at P1, at P6, after H2O2 or etoposide (eto) or doxorubicin (dox) treatments, determined by RTqPCR and RNAseq (H2O2 treatment) and normalized to respective P1 control cells (values are means ± SEM of n = 3–8 donors, two‐way ANOVA, Dunnett's post‐test, ****p < 0.0001). (b) NOTCH3 protein level in progenitor cells at P1, P3, and P6 determined by western blot and quantification of the ratio between the shorter and the higher cleaved form normalized to the β‐tubulin level. Results are expressed as fold change over P1, means ± SEM of n = 7–8 donors (one‐way ANOVA, Dunn's post‐test, **p < 0.01). (c) mRNA levels of HEYL and HES1 in progenitor cells at P1, P3, and P6 determined by RTqPCR (values are means ± SEM of n = 8 distinct donors, one‐way ANOVA, Dunn's post‐test). (d) Immuno‐selected ScAT P1, P3, or P6 progenitor cells were seeded on immobilized IGG control or NOTCH ligand DLL4 and cultured in basal medium supplemented or not with gamma‐secretase inhibitor LY411575 (LY) for 4 days. mRNA levels of (e) NOTCH3 and NOTCH target genes HEYL and HES1, (f) SASP factors INHBA, SFRP4, GREM1, and LIF determined at day 4 by RTqPCR. Results are expressed as log2 fold change over IGG, means ± SEM of n = 7 donors (two‐way ANOVA, Tukey's post‐test, *p < 0.05, **p < 0.01, ***p < 0.001 compared with IGG, $p < 0.05, $$p < 0.01, $$$p < 0.001 compared with DLL4). (g) mRNA levels of senescence markers CDKN1A, CDKN2A, and CCND1 in P3 and P6 progenitor cells determined at day 4 by RTqPCR. Results are expressed as log2 fold change over IGG P3, means ± sem of n = 3–4 donors (two‐way ANOVA, Dunnett's post‐test)*
## NOTCH activation shifts progenitor cell fate balance toward myofibrogenesis over adipogenesis
To investigate the impact of Notch signaling on progenitor differentiation, immuno‐selected progenitor cells (P1) were seeded on immobilized NOTCH ligands JAG1 (a member of the Jagged family) or DLL4 and maintained under adipogenic or myofibrogenic culture conditions.
Under adipogenic conditions (Figure 4a), DLL4 and JAG1 ligands had different effects on NOTCH3 activation. Stimulation with DLL4, and not with JAG1, led to a rapid and transient increase in the Notch target gene HES1 at day 1. By contrast, DLL4 led to a marked induction of NOTCH3 mRNA at day 1, which remained elevated until day 10, while the expression of NOTCH1 was weakly increased only at day 1 (Figure 4b). Notch activation by DLL4 led to a mild, albeit significant, decrease in adipogenesis, as shown by the decrease in lipid‐laden cells (stained with Bodipy), without changing the number of cells (Figure 4c,d). It also reduced the expression of adipogenic genes (ADIPOQ, GPDH, and PPARG2) and brite adipogenic genes (UCP1 and ELOVL3; Figure 4e). Adipogenic differentiation per se decreased the expression of NOTCH3 and tended to increase the one of NOTCH1 (Figure 4b). Likewise, isolated mature adipocytes expressed lower levels of NOTCH3 and higher levels of NOTCH1 compared with progenitor cells (Figure S3). Under myofibrogenic culture conditions in the presence of TGFβ (Figure 4f), DLL4 but not JAG1 induced an increase of HES1 and NOTCH3 mRNA levels, which remained elevated until day 4 (Figure 4g). NOTCH1 expression was not significantly modulated under the same conditions (Figure 4g). Notch activation by DLL4 further enhanced myofibrogenesis, as shown by the increase in αSMA‐positive cells and the expression of myofibroblast genes (ACTA2, COL1A1, and INHBA; Figure 4h,j) with no impact on total cell number (Figure 4I). Analyses of RNAseq data retrieved from whole ScAT and VsAT from the GTEx database (https://www.gtexportal.org) showed that NOTCH3 transcript levels were positively correlated with the ones of myofibrogenic‐ and SASP‐related genes such as ACTA2, COL1A1, COL3A1, INHBA, GREM1, and SFRP4 and with TGFβ and EMT‐related transcription factor SNAI2 but not SNAI1. Conversely, NOTCH3 levels were negatively correlated with the expression of the adipogenic‐related markers ADIPOQ, GPDD1, PLIN1, and PPARG. Similar trends were observed for both ScAT and VsAT, but correlations were weaker in VsAT. These observations were specific for NOTCH3 as the correlation with NOTCH1 transcript levels exhibited the opposite pattern (Figure S4a,b).
**FIGURE 4:** *NOTCH activation inhibits adipogenesis and promotes myofibrogenesis. (a) Immuno‐selected ScAT P1 progenitor cells were seeded on immobilized IGG control (white) or NOTCH ligands JAG1 (cyan) or DLL4 (pink) and cultured in adipogenic medium for 10 days. (b) mRNA levels of HES1, NOTCH3, and NOTCH1 determined at day 1 and day 10 by RTqPCR. Results are expressed as fold change over day 1 IGG control, means ± SEM of n = 6 donors (two‐way ANOVA, Dunnett's, and Sidak's post‐test, $$p < 0.01, $$$p < 0.001 compared with respective treatment at day 1). (c) Representative photomicrographs of Bodipy (green) and DAPI (blue) stainings at day 10 (scale bar = 100 μm). (d) Quantification of Bodipy area per cell (left panel) and cell number (right panel) at day 10, results are expressed as fold change over day 10 IGG control, means ± SEM of n = 3 donors (one‐way ANOVA, Dunnett's post‐test). (e) mRNA levels of adipocyte markers (ADIPOQ, GPDH, PLIN1, and PPARG2) and brite adipocyte markers (UCP1, PGC1A, DIO2, CITED1, ELOVL3, PGC1B, TFAM, and CYCS) determined at day 10 by RTqPCR. Results are expressed as log2 fold change over IGG control, means ± SEM of n = 6 donors (two‐way ANOVA, Dunnett's post‐test). (f) Immuno‐selected ScAT P1 progenitor cells were seeded on immobilized IGG control or NOTCH ligands JAG1 or DLL4 (colored as in a) and cultured in the presence of TGFβ1 during 4 days. (g) mRNA levels of HES1, NOTCH3, and NOTCH1 determined at day 1 and day 4 by RTqPCR. Results are expressed as fold change over day 1 IGG control, means ± SEM of n = 4 donors (two‐way ANOVA, Tukey's post‐test). (h) Representative photomicrographs of αSMA (green) and DAPI (blue) staining at day 4 (scale bar = 50 μm). (i) Quantification of cell number at day 4. Results are expressed as fold change over IGG, means ± SEM of n = 5 independent experiments (one‐way ANOVA, Dunnett's post‐test). (j) mRNA levels of myofibroblast markers (ACTA2, COL1A1, COL3A1, ELN, and INHBA) determined at day 4 by RTqPCR. Results are expressed as log2 fold change over IGG, means ± sem of n = 4 donors (two‐way ANOVA, Dunnett's post‐test). *p < 0.05, **p < 0.01, ***p < 0.001 compared with IGG*
## NOTCH activation induces myofibrogenesis without exogenous addition of TGFβ and remains active in senescent cells
To further characterize the cell‐intrinsic impact of Notch activation in myofibrogenesis, immuno‐selected progenitor cells (P1) were cultured in the presence or absence of DLL4 and LY411575, without TGFβ1, during 4 days. Since JAG1 had at best a slight effect on myofibrogenesis, only the DLL4 ligand was used. Notch activation was sufficient to induce myofibroblast differentiation, as demonstrated by an increase in the number of αSMA‐positive cells and the induction of myofibroblast genes (ACTA2, COL1A1, and COL3A1) by day 4 (Figure 5a,b). In parallel, the expression of NGFR, a marker for the premyofibroblast subset (Esteve et al., 2019), was increased within 4 days (Figure 5c). Notch‐induced myofibrogenesis was completely abolished in the presence of the γ‐secretase inhibitor LY411575 (Figure 5a–c), indicating a requirement for NOTCH receptor cleavage. Neither DLL4 nor LY411575 affected the cell number (Figure 5b). In addition, wound closure assays clearly demonstrated that the stimulation of Notch signaling by DLL4 increased the migratory capacity of progenitor cells compared with the IGG control, but to a lesser extent than TGFβ treatment (Figure 5d). Notch activation increased EMT‐inducing transcription factors SNAI1 and SNAI2 (Figure 5e) in an LY411575 sensitive manner. Immunostainings revealed a cytoplasmic fiber localization of NOTCH3, which coincided with αSMA labeling, at day 4 only in DLL4‐differentiated progenitor cells (Figure 5f). Treatment with TGFβ receptor inhibitor SB431542 did not impact the DLL4‐induced upregulation of myofibroblast mRNA markers nor the DLL4‐mediated cell migration although it partially inhibited, as expected, the one stimulated by TGFβ (Figure 5g,h).C3H/HeOuJ mice, a strain known to develop AT fibrosis in diet‐induced obesity (Vila et al., 2014), were fed a high‐fat diet for up to 6 weeks, and the visceral AT was used in RTqPCR for myofibrogenic‐related genes Acta2, Col1a1, Col3a1, and Eln. While positive correlations were observed between Notch3 expression and the panel of fibrosis markers, no correlations were found with Notch1 mRNA levels (Figure S5a,b). In addition, a weak correlation was observed between Notch3 and Cdkn2a levels but not with Cdkn1a or Ccnd1. Immuno‐selected ScAT progenitor cells were submitted to serial passaging and cultured at P3 and P6 for 4 days on DLL4 under basal conditions or in the presence of LY411575. Notch‐induced myofibrogenesis was maintained at P3 and P6, as shown by an increased expression in myofibroblast genes (COL1A1, COL3A1, and INHBA), EMT‐inducing transcription factors SNAI1 and SNAI2 and premyofibroblast marker NGFR (Figure 5I). All effects of DLL4 were abrogated by the γ‐secretase inhibitor LY41575. Treatments of P3 and P6 progenitors with senolytics (dasatinib and quercetin) downregulated the expression of both CDKN2A and CCND1, as expected, with a stronger impact in P3 compared with P6 progenitors in the presence or not of DLL4 (Figure 5j). In parallel, senolytics repressed the basal expression of myofibrogenic‐related genes including COL1A1 and COL3A1 at P3 and P6 and the ones of INHBA and ACTA2 at P6, as well as the upregulated expression of COL1A and COL3A1 promoted by Notch signaling at P3 (Figure 5k).
**FIGURE 5:** *NOTCH‐mediated promotion of myofibrogenesis is not dependent of the TGFβ pathway. (a) Representative photomicrographs of αSMA (green) and DAPI (blue) stainings in immuno‐selected ScAT P1 progenitor cells seeded on immobilized IGG control (white) or NOTCH ligand DLL4 (pink) and cultured in basal medium (without TGFβ1) supplemented or not with gamma‐secretase inhibitor LY411575 (LY: IGG gray; DLL4 light pink) during 4 days. (scale bar = 100 μm). (b) Quantification of cell number at day 4. Results are expressed as fold change over IGG, means ± SEM of n = 3 donors (one‐way ANOVA, Tukey's post‐test). (c) mRNA levels of myofibroblast markers (ACTA2, COL1A1, and COL3A1) determined at day 4 by RTqPCR. (d) Results are expressed as log2 fold change over IGG, means ± SEM of n = 7–11 donors two‐way ANOVA, Dunnett's post‐test, *p < 0.05, **p < 0.01, ***p < 0.001 compared with IGG, $p < 0.05, $$p < 0.01, $$$p < 0.001 compared with DLL4). (d) Relative wound closure expressed as percentage of area over time of progenitor cells seeded on immobilized IGG control or DLL4, treated or not with gamma‐secretase inhibitor LY411575, or in the presence of TGFβ1 (black), and subjected to scratch assay. Images were captured every 3 h during 69 h. The scratch area at time point 0 was set to 0. Values are means ± SEM of n = 3 donors, two‐way ANOVA, Dunnett's post‐test, *p < 0.05, **p < 0.01, ***p < 0.001 (from time points 45–69) compared with IGG. (e) mRNA levels of epithelial‐mesenchymal transition genes SNAI1 and SNAI2 determined at day 4 by RTqPCR. Results are expressed as log2 fold change over IGG, means ± SEM of n = 7 donors (two‐way ANOVA, Dunnett's post‐test, ***p < 0.001 compared with IGG, $p < 0.05, $$p < 0.01 compared with DLL4). (f) Representative photomicrographs of NOTCH3 (red), DAPI (blue) and αSMA (green) stainings in progenitor cells seeded on IGG or DLL4 ligand with and without LY411575 at day 4 (scale bar = 20 μm). (g) mRNA levels of myofibroblast markers (COL1A1, COL3A1, and INHBA) in immuno‐selected P1 progenitor cells seeded on IGG (white) or NOTCH ligand DLL4 (pink) and cultured in basal medium supplemented or not with TGFβ receptor inhibitor SB431542 (IGG + SB hatched white, DLL4 + SB hatched pink) during 4 days, determined by RTqPCR. Results are expressed as log2 fold change over IGG, means ± sem of n = 4 donors (two‐way ANOVA, Tukey's post‐test, *p < 0.05, **p < 0.01 compared with IGG, $$p < 0.01 compared with DLL4). (h) Relative wound closure (expressed as percentage of area) 72 h after scratch wound of progenitor cells seeded on DLL4 or treated with TGFβ1 (black) in the presence or not of SB431542 (TGFβ + SB hatched black). Values are mean ± SEM of n = 4 donors (one‐way ANOVA and Sidak's post‐test). (i) mRNA levels of myofibroblast markers (ACTA2, COL1A1, and COL3A1), epithelial‐mesenchymal transition (SNAI1 and SNAI2) and premyofibroblast marker (NGFR) in immuno‐selected ScAT P3 and P6 progenitor cells seeded on IGG control (white) or DLL4 (pink) and cultured in basal medium supplemented or not with gamma‐secretase inhibitor LY411575 (LY: IGG gray; DLL4 light pink) during 4 days determined by RTqPCR. Results are expressed as log2 fold change over IGG, means ± SEM of n = 7 donors (two‐way ANOVA, Tukey's post‐test, *p < 0.05, **p < 0.01, ***p < 0.001 compared with IGG, $p < 0.05, $$p < 0.01, $$$p < 0.001 compared with DLL4). (j) mRNA levels of senescence markers (CDKN2A and CCND1) in immuno‐selected ScAT P3 and P6 progenitor cells seeded on IGG control (white) or DLL4 (pink) and cultured in basal medium supplemented or not with dasatinib and quercetin (DQ; IGG + DQ light blue; DLL4 + DQ dark blue) during 4 days, determined by RTqPCR. Results are expressed as log2 fold change over P3 IGG, means ± SEM of n = 4 donors (two‐way ANOVA, Tukey's post‐test, *p < 0.05, **p < 0.01 compared with IGG, $p < 0.05 compared with DLL4). (k) mRNA levels of myofibroblast markers (ACTA2, COL1A1, COL3A1, and INHBA) determined at day 4 by RTqPCR. Results are expressed as log2 fold change over IGG, means ± SEM of n = 4 donors (two‐way ANOVA, Tukey's post‐test, *p 0.05, **p < 0.01, ***p < 0.001 compared with IGG, $p < 0.05, $$p < 0.01, $$$p < 0.001 compared with DLL4).*
## Native premyofibroblasts accumulate in human VsAT with high levels of senescence and are the target of the NOTCH pathway
Obese patients were grouped in tertiles of low, intermediate, and high VsAT progenitor senescence. The group of patients with the highest percentage of SA‐β‐gal+ progenitors in VsAT had also a higher percentage of SA‐β‐gal+ progenitors in ScAT (Figure 6a). Among the clinical and anthropometrical characteristics and in agreement with the multivariate analysis (Figure 1e), the history of obesity reflected by the patient's BMI at the age of 20 was determinant of the degree of senescence in VsAT (Table 2). Age was also statistically significant between low and high senescence groups but with younger AT donors in the high senescence group (Table 2). Considering the two groups of patients with low and high percentage of VsAT senescent progenitors, we analyzed the progenitor subsets: MSCA1‐/CD271‐ (−/−) progenitors, MSCA1−/CD271+ (−/CD271+) premyofibroblasts, and MSCA1+ preadipocytes by flow cytometry. While the number of progenitor subsets was not different in ScAT between the two groups, there was a marked accumulation of −/CD271+ premyofibroblasts in VsAT from the high senescence group (Figure 6b). The adipocyte diameter distribution exhibited no differences in ScAT regardless of the degree of senescence; however, we found a reduction in the percentage of 20 μm small adipocytes in VsAT from the high senescence group (Figure S6). Finally, the three progenitor subsets were immuno‐selected from ScAT. RTqPCR analyses revealed higher expression levels of both NOTCH3 and the NOTCH target gene HEYL in the −/CD271+ subset compared with −/− and MSCA1+ progenitor subsets (Figure 6c). Immuno‐selected −/CD271+ premyofibroblasts and −/− progenitors were seeded on DLL4 under basal conditions for 4 days (Figure 6e). NOTCH3 activation induced myofibroblast differentiation of −/CD271+ premyofibroblasts only, as shown by an increase in αSMA‐positive cells and myofibroblast gene expression (COL1A1, ELN, and INHBA), together with increased expression of the EMT‐inducing transcription factor SNAI1 (Figure 6d,e). No effects were observed with the −/− progenitor subset.
**FIGURE 6:** *Native −/CD271+ premyofibroblasts accumulate in VsAT with senescence and are the target of the NOTCH3 pathway. (a) Percentage of SA‐β‐gal+ progenitors in ScAT and VsAT when donors were partitioned into tertiles by low, intermediate (int), or high SA‐β‐gal+ percentage in VsAT. Values represent means ± SEM of $$n = 38$$ donors in low, $$n = 39$$ in int and $$n = 39$$ in high VsAT senescence groups (two‐way ANOVA and Tukey's post‐test). (b) Number of progenitor cell subset per gram of ScAT and VsAT from patients in the low (white) and high (black) VsAT senescent tertiles from Figure 1e. Results are expressed as means ± SEM of $$n = 38$$ low and $$n = 39$$ high donors per group (two‐way ANOVA, Tukey's post‐test). (c) mRNA levels of NOTCH3 and HEYL in freshly isolated progenitor cell subsets (MSCA1−/CD271− (−/−, blue), MSCA1‐/CD271+ (−/CD271+, NGFR+, light gray) premyofibroblasts and MSCA1+ (ALPL+, dark gray) preadipocytes) immuno‐selected from ScAT. Results are expressed as means ± SEM of $$n = 7$$ donors (one‐way ANOVA, Dunn's post‐test). (d) Representative photomicrographs of NOTCH3 (red), αSMA (green) and DAPI (blue) stainings P1 in −/CD271+ and −/− progenitor subsets immuno‐selected from ScAT, seeded on IGG control or NOTCH ligand DLL4, and cultured in basal medium during 4 days (scale bar = 50 μm). (e) mRNA levels of myofibroblast markers (ACTA2, COL1A1, COL3A1, ELN, and INHBA) and epithelial‐mesenchymal transition markers (SNAI1 and SNAI2) in −/CD271+ and −/− progenitor subsets seeded on IGG control (−/CD271+ black, −/− white) or NOTCH ligand DLL4 (−/CD271+ pink, −/− pink outline) determined at day 4 by RTqPCR. Results are expressed as log2 fold change over IGG −/CD271+ cells, means ± SEM of $$n = 5$$ independent experiments (two‐way ANOVA, Dunnett's post‐test, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ compared with IGG* TABLE_PLACEHOLDER:TABLE 2
## DISCUSSION
There has been an increasing amount of evidence, implicating that adipose tissue (AT) senescence plays a role in both obesity‐ and age‐induced processes responsible for metabolic abnormalities. We show that SA‐β‐Gal‐positive cells accumulate in both ScAT and VsAT of obese subjects by colorimetric imaging approaches, as described previously (Conley et al., 2020; Gustafson et al., 2019; Rouault et al., 2021). Differences in whole tissue SA‐β‐Gal staining have been reported between ScAT and VsAT of obese patients (Rouault et al., 2021). Unsupervised analysis of flow cytometry datasets allows the identification of SA‐β‐Gal+ cells in the SVC, revealing that macrophages represent the largest proportion, followed by progenitors and endothelial cells. SA‐β‐Gal+ macrophages have been previously reported in AT, but their senescent phenotype remains debated (Hall et al., 2017). Additional experiments are needed to further characterize the SA‐β‐Gal+ macrophage population in human AT. They are probably located within the SA‐β‐Gal+ crown‐like structures we observed surrounding human adipocytes in this study, also described in mouse AT (Rabhi et al., 2022). While in ScAT, the majority of SA‐β‐Gal+ stromal cells are macrophages, progenitors and macrophages are equally represented in VsAT. No difference in the absolute number of SA‐β‐Gal+ macrophages is found between fat depots. The endothelial cell cluster, which does contain SA‐β‐Gal+ cells, consists of a limited number of cells, due to the small biopsy size, and was not analyzed further. Endothelial senescence has been shown to be sufficient to induce adipocyte dysfunction and metabolic disorders in a mouse model (Barinda et al., 2020). We previously reported higher endothelial senescence in VsAT from obese individuals compared with ScAT and with age in ScAT (Briot et al., 2018; Villaret et al., 2010). Finally, SA‐β‐Gal+ progenitors are both modulated by AT location in terms of their proportion and number, with higher amounts found in VsAT. Progenitors are key cellular components for fat depot repair, physiological turnover, and expansion under energy excess. Although adipocyte turnover is considered to be a slow process in adults (Spalding et al., 2008), chronic renewal pressure exerted on progenitor cells is higher in obese versus lean individuals due to the greater number of adipocytes. In the present study, multivariable unbiased analysis highlighted the association between senescence parameters and obesity, more marked in VsAT compared with ScAT. Moreover, the group of patients with the highest proportion of senescent progenitors in VsAT is characterized by an early obesity trajectory reflected by an increased BMI at the age of 20 years old. It is therefore tempting to speculate that high VsAT progenitor senescence is a consequence of the chronic replicative pressure exerted by a longer history of obesity. The proliferative rate of AT progenitor cells is distinct according to fat depot location, with less proliferation in visceral compared with subcutaneous AT (Tchkonia et al., 2006). The present data suggest that the depot‐specific differences in senescence may be an additional determinant of the progenitor proliferation rate. Combined transcriptomics and proteomics datasets, obtained from both native and in vitro replicative senescent immuno‐selected progenitors and comparison with other available SASP datasets, highlight common core SASP factors in both ScAT and VsAT including INHBA as well as fat depot‐ and replicative senescence‐specific SASP factors. In particular, GREM1 and SFRP4, endogenous antagonists of the BMP and WNT pathways, respectively, are markers of the VsAT secretome and elevated with replicative senescence. SFRP4 and GREM1 are not only emerging adipokines with higher expression in VsAT (Hedjazifar et al., 2020) but also contained within published senescent gene sets (Gustafson et al., 2019) (https://senequest.net/). In addition to finding BMP and WNT antagonists in the SASP, our study highlights the importance of the Notch developmental pathway in the senescence‐associated intrinsic phenotype. Increased NOTCH3 expression has already been reported during replicative senescence induced by telomere shortening in human cell lines (Cui et al., 2013). Our present data demonstrate that NOTCH3 expression also increased with senescence in human AT progenitors. This increase is specific for NOTCH3 since the mRNA levels of NOTCH1 are not impacted and specific to replicative senescence compared with oxidative or DNA damage stress. The canonical Notch target genes HES1 and HEYL were not modulated with cell passaging, suggesting that replicative senescence is not associated with a sustained activation of Notch‐dependent pathway without precluding a pulsatile activation (Nandagopal et al., 2018) and/or oscillatory changes in the levels of these genes with short mRNA half‐lives (Kobayashi & Kageyama, 2014). Developmental pathways modulate progenitor fate during self‐renewal and differentiation. While BMP and WNT‐dependent pathways are potent pro‐adipogenic stimuli in AT, the net impact of NOTCH signaling on adipogenesis, in particular NOTCH1, is controversial (Shan et al., 2017). As an antidevelopmental process, senescence is more often associated with impaired stem cell/progenitor differentiation. Senescent AT stromal cells from aged/obese mice and humans exhibit a decreased adipogenic potential (Le Pelletier et al., 2021; Xu, Palmer, et al., 2015), while a targeted reduction in senescence enhances adipogenesis (Xu, Palmer, et al., 2015). Depending on tissue context and cell type, senescence may also promote the myofibrogenic differentiation, such as in wounded skin (Demaria et al., 2014) or inhibit it through a modulation of Notch/TGFβ axis (Lopez‐Antona et al., 2022). Our data demonstrate that Notch activation has a major impact in the determination of AT progenitor fate since it impairs white and brite adipogenesis while promoting myofibrogenesis. Both NOTCH ligands, DLL4 and JAG1, did not trigger similar effects, with weak to no impact of JAG1. Although additional experiments will be needed to clearly define the underlying molecular mechanism, distinct ligands can activate, through the same Notch receptor, different target genes and cell fate by defining distinct Notch receptor activation dynamics, that is, sustained or pulsatile (Nandagopal et al., 2018). Interestingly, the dynamic fluctuation of NOTCH1 activity associated with oncogene‐induced senescence has been involved in the balance between distinct SASP promoting a TGFβ rich secretome (Hoare et al., 2016). The present study clearly showed that the promotion of myofibrogenesis by Notch activity was observed even in the presence of exogenously added TGFβ, strongly suggesting additive distinct mechanisms. In agreement, the pharmacological inhibition of the TGFβ pathway did not alter the Notch‐dependent activation of progenitor migration and myofibrogenesis. Notch activity in nonsenescent and in replicative senescent progenitors promoted the expression of SASP‐related factors including SFRP4 and INHBA. In oncogene‐induced senescence, the SASP induces a secondary senescence in surrounding cells that is mediated by Notch signaling (Teo et al., 2019). Whether a similar impact is at play in the AT progenitors remains to be studied but is unlikely since Notch activation was associated with an inhibition rather than a stimulation of the senescent markers CDKN2A and CCDN1. NOTCH3 has already been involved in fibrosis (Ramachandran et al., 2019). Since positive correlations between NOTCH3 (but not with NOTCH1) and myofibrogenic‐related gene expression were observed in human and mouse AT, it is strongly suggested that NOTCH3 is also the active Notch receptor in AT myofibrogenesis although gene editing approaches will permit to clearly state about the molecular identity of the Notch receptor. We show that NOTCH3 protein, upon activation, localizes on fibers, a staining pattern that can be found within the Human Protein Atlas. While further investigation is required to conclude that the change in protein subcellular localization from membranes to fibers is responsible for cytoskeletal remodeling of myofibroblasts, it is associated with increased migratory capacity and expression of EMT transcription factors, SNAI1 and SNAI2. Our data show that the NOTCH3‐dependent pathway is enriched in the −/CD271+ premyofibroblasts compared with the other cell subsets and sufficient to induce their differentiation. Additionally, the group of patients with the highest progenitor senescence in VsAT is characterized by higher −/CD271+ premyofibroblast accumulation, supporting the hypothesis that NOTCH activity contributes to the depot‐specific enrichment of −/CD271+ subsets that we previously reported (Esteve et al., 2019). Our findings also suggest that it will be worthwhile to investigate the role of NOTCH3 in wound healing, especially in light of a recent study describing that insufficient induction of AT senescence after injury is a pathological mechanism of diabetic wound healing (Kita et al., 2022).
As a consequence of the defect in adipocyte renewal, the accumulation of SA‐β‐Gal+ cells in mouse and human ScAT has been associated with adipocyte hypertrophy (Gustafson et al., 2019; Xu, Palmer, et al., 2015) and has a negative impact on systemic metabolism. In agreement with a recent study (Ishaq et al., 2022), we did not observe differences in adipocyte hypertrophy nor in systemic metabolic parameters in the patients grouped according to VsAT progenitor senescence. One cannot exclude that other cell types, including adipocytes, endothelial cells, and/or macrophages may provide a stronger contribution than progenitors alone in the link between SA‐β‐Gal+ cell accumulation and adipocyte hypertrophy and/or metabolic impairment associated with obesity. Whether such a mechanism is also involved in aging remains to be established, especially since uncontrolled fibrosis is a common hallmark of aged tissues; referred to as “fibroageing” (Selman & Pardo, 2021). We did not observe positive association between the accumulation of senescent progenitors and age but rather the inverse association; however, our cohort was not designed for the study of chronological aging but rather for the study of obesity‐accelerated aging. Finally, since in vitro cell expansion is required for mesenchymal stromal cell‐based regenerative therapy, NOTCH3 may represent an interesting molecular actor to monitor and target replicative senescence.
## AUTHOR CONTRIBUTIONS
N.B., A.Br., J‐C.G., and A.Bo. designed the experiments. N.B., V.J., D.E., A.R., C.B., P.V., J.F., L.M., P.D., E.M., C.C., and J.G. performed experiments and collected the data. A.Z.G. isolated the progenitor subsets with cell sorter for in vitro studies. C.D., M.G., C.P., Y.V., M.D., and J.S.I. were involved in generating and analysis of mass spectrometry and transcriptomic data. S.L. collected AT and patient information from the SENADIP cohort. J‐L.G. collected AT from dermolipectomies. N.B., A.Br., J.G., J‐C.G., and A.Bo. were involved in the guidance and concept developments of the study. N.B. and A.Bo. wrote the paper. All authors carefully read and reviewed the final version of the paper.
## CONFLICT OF INTEREST
The authors declare no competing interests.
## DATA AVAILABILITY STATEMENT
All RNAseq datasets generated and used in this study will be deposited and available from the NCBI Gene Expression Omnibus (GEO) portal after the acceptance of the manuscript.
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|
---
title: Protein phosphatase 2A activators reverse age‐related behavioral changes by
targeting neural cell senescence
authors:
- Jun Xing
- Kehua Chen
- Shuaiyun Gao
- Mélanie Pousse
- Yilin Ying
- Bo Wang
- Lianxiang Chen
- Cuicui Wang
- Lei Wang
- Weiguo Hu
- Yiming Lu
- Eric Gilson
- Jing Ye
journal: Aging Cell
year: 2023
pmcid: PMC10014060
doi: 10.1111/acel.13780
license: CC BY 4.0
---
# Protein phosphatase 2A activators reverse age‐related behavioral changes by targeting neural cell senescence
## Abstract
The contribution of cellular senescence to the behavioral changes observed in the elderly remains elusive. Here, we observed that aging is associated with a decline in protein phosphatase 2A (PP2A) activity in the brains of zebrafish and mice. Moreover, drugs activating PP2A reversed age‐related behavioral changes. We developed a transgenic zebrafish model to decrease PP2A activity in the brain through knockout of the ppp2r2c gene encoding a regulatory subunit of PP2A. Mutant fish exhibited the behavioral phenotype observed in old animals and premature accumulation of neural cells positive for markers of cellular senescence, including senescence‐associated β‐galactosidase, elevated levels cdkn2a/b, cdkn1a, senescence‐associated secretory phenotype gene expression, and an increased level of DNA damage signaling. The behavioral and cell senescence phenotypes were reversed in mutant fish through treatment with the senolytic ABT263 or diverse PP2A activators as well as through cdkn1a or tp53 gene ablation. Senomorphic function of PP2A activators was demonstrated in mouse primary neural cells with downregulated Ppp2r2c. We conclude that PP2A reduction leads to neural cell senescence thereby contributing to age‐related behavioral changes and that PP2A activators have senotherapeutic properties against deleterious behavioral effects of brain aging.
During aging, the activity of the Protein Phosphatase 2A (PP2A) declines in the brain of mice and zebrafish, leading to neural cell senescence and aged‐related behavioral changes. This process can be reversed by pharmacological activators of PP2A and senolytics.
## INTRODUCTION
A wealth of recent research indicates that cellular senescence is a basic aging process that greatly contributes to health deterioration and thereby critically impedes healthy aging (Baker et al., 2016; Childs et al., 2017; Song et al., 2020; Tchkonia et al., 2021). Cellular senescence is an essentially permanent arrest of the cell cycle that occurs in response to numerous stressors. This process is accompanied by a permanent activation of the DNA damage response (DDR) and widespread changes in chromatin structure, metabolism, and gene expression, including a senescence‐associated secretory phenotype (SASP) involving the expression and secretion of inflammatory cytokines, growth factors, proteases, and other molecules that can alter tissue microenvironments and cell–cell interactions (Acosta et al., 2013; Correia‐Melo et al., 2016; Wiley et al., 2016). The most prominent senescence‐inducing stimuli are telomere changes, intrinsic and extrinsic sources of genomic and epigenomic damage, activated oncogenes, reactive oxygen species (ROS), and various toxins. An emerging paradigm suggests that senescent cells are major contributors to age‐related illnesses. Indeed, senotherapeutic interventions that counteract cellular senescence either through removal of senescent cells (senolytics) or modification of their phenotype (senomorphic) can restore organ function and slow the aging process (Chang et al., 2016; Di Micco et al., 2021; Gutierrez‐Martinez et al., 2018; Xu et al., 2018; Zhang et al., 2022).
Recent studies have indicated that genes associated with neurological diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, major depressive disorder, bipolar disorder, and schizophrenia, are highly overrepresented among age‐related genes in people with these conditions (Ding et al., 2015; Glorioso et al., 2011). Moreover, evidence indicates that senescent glial cells affect mouse cognition (Ogrodnik et al., 2019) and Alzheimer's disease (Zhang et al., 2019). These observations, together with reports that various types of neuropsychiatric disorders are associated with increased risks of age‐related diseases (Goldstein et al., 2009), mortality (Diniz et al., 2014), and shortened telomeres (Kiecolt‐Glaser & Wilson, 2016), suggest that cellular senescence and premature aging may be important etiologies of brain disorders. In addition to age‐related brain disorders, increasing age is associated with impaired of cognitive abilities (Liu et al., 2015), which affect the quality of life, even for healthy people (Salinas‐Rodríguez et al., 2022). Very little is known about the mechanisms leading to neural cell senescence, age‐related cognitive disabilities, and neurodegenerative diseases.
A candidate protein to connect senescence and age‐related cognitive disabilities is the Ser/Thr protein phosphatase 2A (PP2A), which reverses the phosphorylation of key actors in the DDR, such as γH2AX (Chowdhury et al., 2005; Ferrari et al., 2017). The brain‐isoform PPP2R2C is downregulated in aging brains of wild‐type (WT) and Alzheimer's transgenic mice (Leong et al., 2020). Moreover, it is associated with various mental disorders in humans (Backx et al., 2010; Jacob et al., 2012; Kimura et al., 2019; Xu et al., 2014) and is upregulated by TRF2, which is a telomere capping protein that prevents replicative senescence (Karlseder et al., 2002; Mendez‐Bermudez et al., 2022). Here, we demonstrate in zebrafish and mice that the level of PP2A is reduced in the aged brain. In zebrafish, this reduction led to a neural cell senescence phenotype responsible for a behavioral phenotype characterized by anxiety and hyperactivity. These results pave the way for the use of PP2A activators and senescence modulators to prevent age‐related cognitive disabilities.
## PP2A activity declines in the aging brains of zebrafish and mice
To assess the role of PP2A activity in age‐related brain changes, we first assayed for PP2A phosphatase activity through immunoprecipitation of the PP2A complex (Frohner et al., 2020) from the brains of young and old zebrafish and mice. We observed decreased PP2A activity in both old zebrafish (22 months) and mice (14 months), as compared to young animals (Figures 1a and S1a). Then, we investigated whether this decline can be reversed using three types of pharmacological activators of PP2A: DT‐061 (Leonard et al., 2020), FTY720 (Vicente et al., 2020), and methylphenidate (MPH), a psychostimulant drug with PP2A activator properties (Schmitz et al., 2018). Treatment of aged fish for 3 days with DT‐061(5 mg/kg), FTY720 (5 mg/kg), or MPH (MPH 1.08 mg/kg), respectively, and of aged mice with MPH for 10 days at a dose of 12.3 mg/kg is sufficient to increase the level of PP2A activity as measured via immunoprecipitation with antibodies directly against the PP2A catalytic subunit (Figures 1a and S1a). This PP2A activity augmentation is not accompanied by an increase in the level of the PP2A catalytic subunit (Frohner et al., 2020; Figure S1b), in agreement with a mode of action of the drugs based on the stabilization of the holoenzyme in an active state (Leonard et al., 2020).
**FIGURE 1:** *PP2A activators reverse the behavioral changes of old fish. (a) PP2A phosphatase assay after the treatment of PP2A activators in old fish (n = 3 independent biological samples, each group contained two brains; one‐way ANOVA). The IgG assays correspond to controls of the PP2A immunoprecipitation. (b) Experimental design of behavioral tests with 6‐month‐old (adult) and 22‐month‐old (old) WT with or without PP2A activators treatment for 3 days. (c) Experimental scheme for the light–dark transition assay. Parameters recorded and analyzed in light–dark transition assay. (d) Representative diagram of locomotion activity during the 30‐s lights‐on period and 30 s before lights‐on. (e) Quantification of the highly activation state (see Section 4) duration during the 30‐s lights‐on period (n = 8 for 6 m, n = 15 for 22 m, n = 11 for 22m‐MPH, n = 9 for 22m‐DT‐061 and 22m‐FTY720; one‐way ANOVA). (f) Representative movement tracks (gray lines) in the mirror attack assay of adult fish during the 5‐min trial. Blue boxes show the position of the mirror. (g) Mirror attacking latency at indicated time point in 5‐min trail. (Two‐way ANOVA, statistic difference is shown as WT‐6m vs. WT‐22m (black *), WT‐22m vs. WT‐22m‐MPH (red *), WT‐22m vs. WT‐22m‐DT‐061 (blue *), WT‐22m vs. WT‐22m‐FTY720 (orange *)). (h) Quantification of the number of mirror attacks in 5‐min intervals (n = 14 for 6 m, n = 15 for 22 m, n = 12 for 22m‐MPH, n = 9 for 22m‐DT‐061 and 22m‐FTY720; one‐way ANOVA). (i) Representative movement tracks (gray lines) during the 30‐min trial in the open field test. The red box shows the central area of the tank. (j) Central area latency at indicated time point in 30‐min trail. (Two‐way ANOVA, statistic difference are shown as WT‐6m vs. WT‐22m (black *), WT‐22m vs. WT‐22m‐MPH (red *), WT‐22m vs. WT‐22m‐DT‐061 (blue *), WT‐22m vs. WT‐22m‐FTY720 (orange *)). (k) Cumulative time that the adult fish stayed within the central area (n = 16 for 6 m, n = 8 for 22 m, n = 10 for 22m‐MPH, n = 9 for 22m‐DT‐061 and 22m‐FTY720; one‐way ANOVA). Data are means ± SEM. *p < 0.05, **p < 0.01*
## Pharmacological activation of PP2A in old zebrafish and mice reverses age‐related behavioral changes
Next, we explored whether the age‐related decline of PP2A is responsible for the behavioral phenotypes observed in aged animals. Compared to young zebrafish, old animals (22 months) exhibited a behavioral phenotype involving increased locomotor activity upon the transition between light and dark (Figure 1c–e), reflecting an abnormal hyperactivity or startling in response to an environmental change, impulsive and aggressive behavior as revealed by continued attacks on a mirror (Huang et al., 2015; Liu & Liu, 2020; Figure 1f–h) and reduced exploratory behavior in an open field test (Roybal et al., 2007; Figure 1i–k), which can be interpreted as an anxiogenic behavior (Egan et al., 2009). Swimming speed of 22‐month‐old fish is not differ from that of young fish (Figure S1c). Notably, in addition to the total time spent in the center of the tank being lower, the longest duration in the center (measured every 6 min) was greatly reduced (Figure 1j). Treatment of the aged fish with DT‐061 or MPH or FTY720 for 3 days was sufficient to reverse their behavioral phenotypes (Figure 1d–k), indicating that the age‐related decline in PP2A activity is responsible for the behavior changes.
Mice at 14 months of age exhibited greater anxiety (Figure S1e), as well as cognitive and learning impairment (Figure S1f,g), relative to WT mice at 3 months, in agreement with previous research (Belblidia et al., 2018; Shoji et al., 2016). Similar to aged zebrafish, treating aged mice with MPH ameliorated the age‐related anxiety phenotype, as well as cognitive and learning deficits, relative to young mice in the light/dark transition and Morris tests (Figure S1d–g).
## ppp2r2c mutant zebrafish exhibit a behavioral deficit phenotype similar to aged fish
The results presented above encouraged us to develop a transgenic fish model with reduced PP2A activity in the brain. We first confirmed that the ppp2r2c gene encoding a brain‐specific isoform of the regulatory subunits of PP2A (Fagerberg et al., 2014) is highly expressed in the zebrafish brain (Figure S2a). Then, we generated two ppp2r2c mutant zebrafish lines (ppp2r2c m1/m1 and ppp2r2c m2/m2) through the introduction of two different frameshift mutations within exon 9. These mutations led to a reduction of the mutant mRNA level, likely driven by nonsense‐mediated mRNA decay due to the presence of a premature stop codon (Figure S2b,c). The predicted truncated ppp2r2c proteins expressed by the two mutant genes lack the three terminal WD40 repeats. Such a truncation prevents the folding of the protein into a typical circular solenoid WD40 domain (Smith et al., 1999) and is therefore expected to produce a nonfunctional protein. In accordance with the loss of functional ppp2r2c and the brain‐specific expression of ppp2r2c, the PP2A activity was reduced by roughly $30\%$ in the brain but not in other organs (Figure S2d). Notably, the expression of the catalytic subunit in the phosphatase assay was similar between the WT and mutant brain extracts (Figure S2e), indicating that the reduced level of the regulatory subunit ppp2r2c affects the specific activity of the complexes containing the PP2A catalytic subunit.
Homozygous ppp2r2c m1/m1 and ppp2r2c m2/m2 fish had a reduced lifespan (Figure S2f). Remarkably, the adult ppp2r2c m1/m1 and ppp2r2c m2/m2 fish exhibited a behavioral deficit phenotype similar to old WT fish, including hyperactivity or a startle response, reflected in an increase in locomotor activity in the light–dark transition assay (Figures 2a–c and S3a,b, see Section 4), which cannot be explained by a change in swimming speed (Figure S3c), continuation of impulsive or aggressive behavior as indicated by a mirror attack assay (Figures 2a,d–f and S3d–f), and anxiety since ppp2r2c m1/m1 and ppp2r2c m2/m2 fish were markedly less exploratory than WT fish in an open field test (Figures 2a,g‐i and S3g–i), although the total swimming distance was the same (Figure S3j). These behavioral changes were not accompanied by sleep problems or social withdrawal in ppp2r2c m1/m1 fish (Figure S3k–m). As the two mutant fish lines exhibited similar behavioral phenotypes, we used ppp2r2c m1/m1 for further experimentation.
**FIGURE 2:** *ppp2r2c‐compromised fish exhibit abnormal behaviors that are reversed by MPH. (a) Experimental design. Behavioral tests in WT and ppp2r2c
m1/m1
(6 months old) with or without MPH treatment for 3 days. Parameters recorded and analyzed in light–dark transition assay. (b) Representative diagram of locomotion activity of adult fish during the 30‐s lights‐on period and 30 s before lights‐on. (c) Quantification of the highly active state duration during the 30‐s lights‐on period (see Section 4; n = 9 for every group). (d) Representative movement tracks (gray lines) in the mirror attack assay of WT and ppp2r2c
m1/m1
(6 months old), with or without MPH treatment for 3 days, during the 5‐min trial. Blue boxes show the position of the mirror. (e) Mirror attacking latency at indicated time point in 5‐min trail. (Statistic difference are shown as WT vs. ppp2r2c
m1/m1
(black *), ppp2r2c
m1/m1
vs. ppp2r2c
m1/m1
‐MPH (red *)). (f) Quantification of the number of mirror attacks in the 5‐min intervals (n = 9 for WT‐vehicle, WT‐MPH, and ppp2r2c
m1/m1
‐vehicle; n = 8 for ppp2r2c
m1/m1
‐MPH). (g) Representative movement tracks (gray lines) of WT and ppp2r2c
m1/m1
(6 months old), with or without MPH treatment for 3 days, during the 30‐min trial in the open field test. The red square shows the central area of the tank. (h) Central area latency at indicated time point in 30‐min trail. (Statistic difference is shown as WT vs. ppp2r2c
m1/m1
(black *), ppp2r2c
m1/m1
vs. ppp2r2c
m1/m1
‐MPH (red *)). (i) Cumulative time that adult fish stayed within the central area (n = 7 for ppp2r2c
m1/m1
‐vehicle; n = 8 for WT‐vehicle and WT‐MPH; n = 9 for ppp2r2c
m1/m1
‐MPH). Data are means ± SEM. *p < 0.05, **p < 0.01; two‐way ANOVA*
Administration of three types of PP2A pharmacological activators (MPH, DT‐061, and FTY720) to ppp2r2c m1/m1 fish over 3 days restored PP2A activity (Figure S4a) and reversed their behavioral phenotype (Figures 2a‐i and S4b–g). That these PP2A activators can enhance PP2A enzyme activity in zebrafish lacking the regulatory subunit ppp2r2c might be explained by the stabilization of holoenzymes containing other regulatory subunits than the one encoded by the ppp2r2c gene (Leonard et al., 2020). Treatment with both MPH and DT‐061 did not show an additive effect in the behavioral tests (Figure S4b–g), further supporting the conclusion that MPH and DT‐061 can reverse behavioral changes through their shared capacity to activate PP2A.
In summary, two different loss‐of‐function mutations in ppp2r2c in zebrafish led to the phenotype of aged WT zebrafish, which could be reversed by PP2A activators.
## ppp2r2c mutations exhibit transcriptional signatures of oxidative stress and uncontrolled replication
To study the mechanisms through which ppp2r2c mutation leads to the observed behavioral deficit phenotype, we analyzed the quantities of 23 classical neurotransmitters in the brain of 6‐month‐old ppp2r2c m1/m1 and WT fish using liquid chromatograph mass spectrometer. Among the detected peaks, the only significant change was an increase in the level of L‐tyrosine in mutant fish and no alterations in the levels of catecholamines, including dopamine, were observed (Figure S5).
We next investigated the transcriptomic changes induced by the ppp2r2c mutation using RNA sequencing (RNA‐seq) analysis. Comparison of the ppp2r2c m1/m1 and WT brain transcriptomes revealed differential expression of 473 genes (Table S1; Figure S6a). The differential expression of eight genes was checked using reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR; Figure S6b). Analysis of Gene Ontology terms and Ingenuity pathways indicated marked deregulation of age‐related processes such as cell cycle control, DNA repair, L‐tyrosine catabolism, and antioxidant pathways (Figures 3a and S6c). Transcriptional downregulation of two genes in the L‐tyrosine catabolism pathway might contribute to the increased L‐tyrosine level (Figures 3a and S5). Augmentation of oxidative defense was correlated with increased levels of ROS in the brains of the mutant fish (Figure S6d). The brains of the ppp2r2c m1/m1 fish also overexpressed genes involved in chromosomal DNA replication, such as subunits of the replicative helicase subunits (MCM), Top2A and ligase 1 (Figures 3a and S6b).
**FIGURE 3:** *ppp2r2c‐compromised fish exhibit an increased rate of neural cells with senescence markers. (a) Ingenuity pathway analysis of ppp2r2c
m1/m1
versus WT brains. Inner circles show selected pathways enriched among the DEGs. Outer circles show upregulated (red) and downregulated (blue) DEGs in ppp2r2c
m1/m1
compared with WT brains (n = 3 for every group) (Fisher's exact test, p value <0.0001 for DNA replication; p value <0.01 for the rest pathway). (b) Representative images of confocal section of immunofluorescence showing NeuN (green) co‐staining with γH2AX (magenta) in the OT of WT and ppp2r2c
m1/m1
at 6 months old (scale bars, 5 μm). Quantification of γH2AX‐positive (positive values indicate at least five γH2AX foci) neuronal (NeuN+) and non‐neuronal (NeuN−) cells (n = 6 for each group and over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group,
#
represents statistical difference in NeuN− group). (c) Representative images of confocal section of NeuN (green) co‐staining with cdkn2a/b (magenta) and cdkn1a (gray) mRNA by RNA‐Scope in the OT of WT and ppp2r2c
m1/m1
at 6 months old (scale bars, 5 μm). Arrows and triangles point to the cdkn2a/b (magenta) and cdkn1a (grey) mRNA signal, respectively. Quantification of the percentage of cdkn2a/b, cdkn1a positive cells, respectively (positive values indicate at least one mRNA signal), and percentage of double positive cells in neuronal (NeuN+) and non‐neuronal (NeuN−) cells (n = 5 for each group and over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group,
#
represents statistical difference in NeuN− group). (d) Representative images of confocal section of NeuN (green) co‐staining with tnfa (magenta) and il‐8 (gray) mRNA by RNA‐Scope in the OT of WT and ppp2r2c
m1/m1
at 6 months old (scale bars, 5 μm). Arrows and triangles point to the tnfa and il‐8 mRNA signals, respectively. Quantification of the percentage of tnfa, il‐8‐positive cells, respectively (positive values indicate at least one mRNA signal) and percentage of double positive cells in neuronal (NeuN+) and non‐neuronal (NeuN−) cells (n = 5 for each group and over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group, # represents statistical difference in NeuN− group). Data are means ± SEM. *p < 0.05, **p < 0.01, #
p < 0.05, ##
p < 0.01; unpaired two‐sided t test.*
## Tectal cells of ppp2r2c mutant fish exhibit senescence markers, apoptosis, and signs of replication stress
The transcriptional signatures described above suggested that the ppp2r2c leads to genomic instability and aberrant cell cycle control in the brain. Indeed, adult ppp2r2c m1/m1 (6‐month‐old) brain cells showed increased DNA damage signaling, as revealed by a higher proportion of nuclei harboring at least five γH2AX foci specifically in the optic tectum (OT; Figures 3b and S7a–c). An increase in apoptotic cells was also detected in the OT of 6‐month‐old ppp2r2c m1/m1 fish (Figure S7d). The specificity in OT localization of these cellular defects is likely to stem from the higher level of ppp2r2c mRNA expression in the OT compared to other parts of the brain (Figure S7e,f). These results indicate that ppp2r2c plays an important role in protecting tectal cells against unwanted DDR activation and death.
We then explored whether the increased level of γH2X in the OT is accompanied by other markers of cellular senescence. In 6‐month‐old ppp2r2c m1/m1 fish, we observed an increased number of cells exhibiting senescence‐associated β‐galactosidase (SA‐β‐gal) specifically in the OT (Figure S7g–i) and increased expression of checkpoint and SASP genes as revealed using RNAscope at the single cell level in the OT (Figure 3c,d), as well as by RT‐qPCR and (Figure S7j). In accordance with the brain‐specific expression of ppp2r2c, the senescence markers were absent from the heart or kidney cells of age‐matched fish (Figure S7k–n).
In summary, ppp2r2c m1/m1 fish exhibited several outcomes associated with DDR activation, including apoptosis (based on the terminal deoxynucleotidyl transferase dUTP nick end labeling assay), DNA damage signaling (γH2X, cell‐cycle checkpoint gene expression), and senescence (SA‐β‐gal and SASP gene expression). This was seen both in tectal cells stained for NeuN (NeuN (+)), which marks developing, immature and mature neurons, and in cells that are NeuN (−). These results indicate that multiple types of neural tectal cells are altered upon ppp2r2c inhibition.
## Pharmacological PP2A activators reduce the senescence markers in ppp2r2c mutant brains
We investigated the effects of PP2A activators on the transcriptional and cellular alterations caused by ppp2r2c loss. To this end, we used RNAseq to identify genes with dysregulated expression in ppp2r2c m1/m1 compared to WT fish rescued by MPH treatment. We found 101 differentially expressed genes in the comparisons of ppp2r2c m1/m1 with WT and ppp2r2c m1/m1 without and with treatment (Table S2; Figure S8a–d). Notably, in these two situations, the genes were differentially expressed in opposite directions, demonstrating that they are aberrantly expressed due to PPP2R2C loss but rescued by MPH treatment. This opposite pattern direction of differential gene expression was confirmed using RT‐qPCR (Figure S8e).
The first two representative pathways enriched in these genes, which are identical to the pathways altered in mutant fish in comparison with WT controls, were “cell cycle control of chromosomal replication” and “senescence pathway” (Figure S8d). The level of γH2X‐positive cells was reduced in both the neuronal (NeuN (+)) and non‐neuronal (NeuN (−)) tectal cells of 6‐month‐old ppp2r2c m1/m1 fish treated for 3 days with MPH (Figures 4a,b and S8f). Moreover, MPH mainly decreased the amount of SA‐β‐gal‐positive NeuN (+) cells (Figure S8g) and the expressed levels of checkpoint and SASP genes, as revealed by monitoring with RNAscope (Figure 4c,d) and RT‐qPCR (Figure 4e). *No* genes whose expression levels were restored by MPH are known targets of MPH in the dopamine pathway. In particular, the dat1/slc6a3 gene encoding the dopamine transporter was not among the 101 MPH‐restored genes (Table S2). This finding supports the uncoupling of the MPH activity in ppp2r2c mutant fish and the dopamine pathway, as suggested by the absence of catecholamine modulation in the mutant fish (Figure S5).
**FIGURE 4:** *MPH treatment decreases the rate of neural cells with senescence markers in the ppp2r2c‐compromised fish. (a) Immunofluorescence detection of NeuN (green) and γH2AX (magenta) in the OT of WT and ppp2r2c
m1/m1
(6‐month‐old) treated with or without MPH for 3 days (scale bars, 5 μm). (b) Quantification shows the percentage of γH2AX positive (positive values indicate at least five γH2AX foci in the nucleus) neuronal (NeuN+) and non‐neuronal (NeuN−) nuclei in (a) (n = 6 brains per group and over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group,
#
represents statistical difference in NeuN− group; one‐way ANOVA). (c) Representative images of confocal section of NeuN (green) co‐staining with cdkn2a/b (magenta) and cdkn1a (gray) mRNA by RNA‐Scope in the OT of WT and ppp2r2c
m1/m1
treated with or without MPH for 3 days at 6 months old (scale bars, 5 μm). Arrows and triangles point to the cdkn2a/b and cdkn1a mRNA signals, respectively. Quantification of the percentage of cdkn2a/b, cdkn1a positive cells, respectively (positive values indicate at least one mRNA signal) and the percentage of double positive cells in neuronal (NeuN+) and non‐neuronal (NeuN−) cells (n = 5 for each group and over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group,
#
represents statistical difference in NeuN− group; one‐way ANOVA). (d) Representative images of confocal section of NeuN (green) co‐staining with tnfa (magenta) and il‐8 (gray) mRNA by RNA‐Scope in the OT of WT and ppp2r2c
m1/m1
treated with or without MPH for 3 days at 6 months old (scale bars, 5 μm). Arrows and triangles point to the tnfa and il‐8 mRNA signals, respectively. Quantification of the percentage of tnfa, il‐8‐positive cells, respectively (positive values indicate at least one mRNA signal) and percentage of double positive cells in neuronal (NeuN+) and non‐neuronal (NeuN−) cells (n = 5 for each group and over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group, # represents statistical difference in NeuN− group; one‐way ANOVA). (e) Relative mRNA expression levels of cdkn1a, cdkn2a/b, and key SASP components determined by RT‐qPCR in WT and ppp2r2c
m1/m1
brains with or without MPH treatment (n = 3 independent biological samples, every sample pool two brains; two‐way ANOVA). Data are means ± SEM. **p < 0.01, #
p < 0.05, ##
p < 0.01*
Next, we investigated whether the other PP2A activators also rescued the senescence‐like abnormalities of tectal cells in mutant fish. Indeed, DT‐061 and FTY720 preferentially decreased the DNA damage and SA‐β‐gal index in tectal NeuN (+) cells (Figure S8f,g). Moreover, ppp2r2c mutant fish treated with both DT‐061 and MPH had no additional effect on the level of neuronal senescence (Figure S8f,g). These data support the notion that the mode of action of MPH in ppp2r2c mutant fish is better explained by its PP2A activator properties than its activity in the dopamine pathway. Based on these results, we conclude that ppp2r2c inhibition leads to a senescence‐like phenotype in the OT that can be alleviated by treatment with PP2A activators.
## Targeting senescence alleviates behavioral abnormalities in ppp2r2c
m1/m1
fish
The results presented above, namely that PP2A activators alleviate the behavioral abnormalities while also reversing senescence markers suggest that tectal cell senescence is involved in the behavioral deficit phenotype of ppp2r2c m1/m1 fish. Therefore, we tested whether preventing or reversing senescence is sufficient to restore the behavioral deficits of mutant fish.
Inactivation of two key checkpoint genes (tp53 and cdkn1a) that trigger senescence or treatment with the senolytic drug ABT263, which triggers apoptosis of senescent cells (Wu et al., 2016), relieved the behavioral abnormalities of ppp2r2c m1/m1 fish (Figures 5a–f and S9a,b). As expected, ABT263 treatment and tp53 inactivation decreased the number of senescent tectal cells in the ppp2r2c m1/m1 fish brains (Figure S9c–e). In accordance with tp53 functioning downstream of γH2AX activation, its loss only barely affected the number of γH2X‐positive cells (Figure S9e). The pro‐apoptotic targets of tp53 are likely not responsible for the behavioral phenotype of the ppp2r2c m1/m1 fish, as the behavioral phenotype is rescued by the ablation of cdkn1a (Figure 5a–f), a target of tp53 that does not trigger apoptosis.
**FIGURE 5:** *Inhibiting senescence decreases the behavioral disabilities of ppp2r2c‐compromised fish. Behavioral tests of WT and ppp2r2c
m1/m1
with or without ABT263 treatment for 3 days, ppp2r2c
m1/m1
p53
−/−
and ppp2r2c
m1/m1
cdkn1a
−/−
at 6 months old. (a) Representative result from locomotion activity upon light–dark transition assay during the 30‐s lights‐on period and 30 s before lights‐on. (b) Quantification of the highly active state duration during the 30‐s lights‐on period (n = 11 for ppp2r2c
m1/m1
p53
−/−
; n = 10 for WT‐vehicle, ppp2r2c
m1/m1
‐vehicle and ppp2r2c
m1/m1
cdkn1a
−/−
groups; n = 9 for WT‐ABT263; n = 8 for ppp2r2c
m1/m1
‐ABT263). (c) Representative movement tracks (gray lines) of WT and ppp2r2c
m1/m1
with or without ABT263 treatment for 3 days, ppp2r2c
m1/m1
p53
−/−
and ppp2r2c
m1/m1
cdkn1a
−/−
in the mirror image attack assay. Blue boxes show the position of the mirror. (d) Quantification of the number of mirror attacks in 5‐min intervals (n = 11 for ppp2r2c
m1/m1
p53
−/−
; n = 10 for WT‐vehicle, ppp2r2c
m1/m1
‐vehiclem and ppp2r2c
m1/m1
cdkn1a
−/−
groups; n = 9 for WT‐ABT263 and ppp2r2c
m1/m1
‐ABT263). (e) Representative movement tracks (gray lines) of WT and ppp2r2c
m1/m1
with or without ABT263 treatment for 3 days, ppp2r2c
m1/m1
p53
−/−
and ppp2r2c
m1/m1
cdkn1a
−/−
during a 30‐min trial in the open field test. The red box shows the central area of the tank. (f) Quantification of the cumulative time that fish stayed within the central area (n = 10 for WT‐vehicle, ppp2r2c
m1/m1
‐vehicle, and ppp2r2c
m1/m1
cdkn1a
−/−
groups; n = 9 for WT‐ABT263, ppp2r2c
m1/m1
‐ABT263, and ppp2r2c
m1/m1
p53
−/−
). Data are means ± SEM. **p < 0.01; one‐way ANOVA*
## PP2A activators reverse senescence markers in the brains of aged fish
Next, we asked whether, similar to ppp2r2c mutant fish, PP2A activators could reverse neural cell senescence in old animals. We found that 22‐month‐old WT fish exhibit increased levels of γH2X and SA‐β‐gal‐positive cells mostly in the OT (Figures 6a,b and S10a,b), along with elevated level of SASP gene expression (Figure 6c–e) as compared to 6‐month‐old WT fish. When aged WT fish were treated with MPH, DT‐061, and FTY720 for 3 days, the number of γH2X‐positive cells in the OT was markedly reduced (Figure 6a,b). In addition, MPH and DT‐061 treatment of aged fish reduced SASP gene expression (Figure 6c) and SA‐β‐gal (Figure S10a,b). The single cell levels of cdkn2a/b p16, cdkn1a, and cytokine gene transcription were reduced in NeuN (+) cells of aged fish after MPH treatment (Figure 6d,e).
**FIGURE 6:** *PP2A activators decrease the rate of neural cells with senescence markers in old fish. (a) Representative confocal image of NeuN (green) and γH2AX (magenta) co‐staining in the brain of 22‐month‐old fish treated with or without PP2A activators for 3 days (scale bars, 5 μm). (b) Quantification of γH2AX‐positive nuclei (positive values indicate at least five γH2AX foci) in (a) (n = 4 for 6 and 22 m; n = 7 for 22m‐MPH, n = 3 for 22m‐DT‐061 and 22m‐FTY720, over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group,
#
represents statistical difference in NeuN− group; one‐way ANOVA). (c) Relative mRNA expression levels of cdkn1a, cdkn2a/b, and key SASP components determined by RT‐qPCR in 6 and 22 m WT brains with or without MPH or DT‐061 treatment (n = 3 independent biological samples, every sample pool two brains; one‐way ANOVA). (d) Representative image of confocal section of NeuN (green) co‐staining with cdkn2a/b (magenta) and cdkn1a (gray) mRNA by RNA‐Scope in the OT of 6 and 22 m WT treated with or without MPH for 3 days (scale bars, 5 μm). Arrows and triangles point to the cdkn2a/b and cdkn1a mRNA signals, respectively. Quantification of the percentage of cdkn2a/b, cdkn1a positive cells, respectively (positive values indicate at least one mRNA signal) and percentage of double positive cells in neuronal (NeuN+) and non‐neuronal (NeuN−) cells (n = 5 for each group and over 100 nuclei were analyzed per fish, * represents statistical difference in NeuN+ group, # represents statistical difference in NeuN− group; one‐way ANOVA). (e) Representative images of confocal section of NeuN (green) co‐staining with tnfa (magenta) and il‐8 (gray) mRNA by RNA‐Scope in the OT of 6 and 22 m WT treated with or without MPH for 3 days (scale bars, 5 μm). Arrows and triangles point to the tnfa and il‐8 mRNA signals, respectively. Quantification of the percentage of tnfa, il‐8‐positive cells, respectively (positive values indicate at least one mRNA signal) and percentage of double positive cells in neuronal (NeuN+) and non‐neuronal (NeuN−) cells (n = 5 for each group and over 100 nuclei were analyzed per fish; * represents statistical difference in NeuN+ group, # represents statistical difference in NeuN− group; one‐way ANOVA). Data are shown in means ± SEM. *p < 0.05, **p < 0.01, #
p < 0.05, ##
p < 0.01*
## Ppp2r2c loss triggers aberrant replication, DDR, and senescence in mouse neural cells
The results described above suggest that reduction of PP2A activity in the brain, resulting from either ppp2r2c mutation or natural aging, leads to increased levels of senescence markers in brain cells. Therefore, we investigated the capacity of ppp2r2c loss to trigger senescence in well‐defined mouse neural cell models.
We investigated the alterations induced by Ppp2r2c inhibition in mouse primary neural cell through analysis of glial cells, neural progenitor cells (NPCs), and neurons isolated from embryonic mouse brains (Figure 7a). Inhibition of Ppp2r2c in these three types of primary cells induced a potent DDR signaling as indicated by increased levels of cells positive for γH2AX foci (Figure 7b–d). Except in glial cells, Ppp2r2c downregulation also triggered cellular senescence, as demonstrated through a SA‐β‐gal assay (Figure 7e–g). Mirroring the transcriptomic changes observed in the adult brains of ppp2r2c m1/m1 zebrafish (Figure 3a), the Ppp2r2c inhibition in mouse glial cells and progenitor cells triggered the overexpression of genes involved in replication (Figure 7h). In contrast, Ppp2r2c loss led to the downregulation of replication‐related genes in cultured primary mouse neurons (Figure 7h). Overall, these results reveal that Ppp2r2c connects the processes of DNA damage, replication control, and senescence in mouse neural cells.
**FIGURE 7:** *MPH attenuates H2AX phosphorylation induced by ppp2r2c knockdown in mice primary neural cells. (a) Experimental design for MPH treatment of ppp2r2c knockdown in isolated primary neural cells from newborn mice. (b–d) Representative confocal images and relevant quantification of γH2AX foci (magenta) in primary neurons (stained with β‐tubulin, green), neural progenitor cells (NPCs, stained with SOX2, green), and glial cells (stained with GFAP, green) that were dissected from new born mice and cultured for 1 week and then were transfected with siRNA and treated with or without MPH for 3 days (scale bars, 15 μm). White boxes indicate the enlarged regions (n = 3 independent experiments for neurons, n = 2 independent experiments for NPCs and glial cells, above five fields containing at least 100 cells were analyzed per condition; two‐way ANOVA). (e–g) Representative microscopy images and its quantification of SA‐β‐gal signals in primary neurons (stained with β‐tubulin, green), NPCs (stained with SOX2, green), and glial (stained with GFAP, green) cells (scale bars, 25 μm for neuron, 50 μm for NPCs and glial cells). Quantification analysis of percentage of SA‐β‐gal‐positive cells (n = 3 independent experiments for neurons, n = 2 independent experiments for NPCs and glial cells, more than 100 cells were analyzed per condition; two‐way ANOVA). (h) Relative mRNA expression levels of Ppp2r2c, Mcm2, Mcm5, Mcm6, and Top2a in primary mouse neurons, NPCs, and glial cells determined by RT‐qPCR (n = 5 independent experiments for neurons and NPCs; n = 3 independent experiments for glial cells; unpaired two‐sided t test). Data are means ± SEM. *p < 0.05, **p < 0.01*
## PP2A activators have senomorphic properties
Finally, we investigated the capacity of a PP2A activator to act as a general geroprotective drug by testing whether MPH could attenuate the effects of DDR in cultured ppp2r2c‐compromised neural cells. DDR activation in glial cells, NPCs, and neurons was reduced with MPH treatment (Figure 7b–d), while the appearance of SA‐β‐gal‐positive cell was prevented by MPH treatment only in neurons (Figure 7e–g). Moreover, the upregulation of mcm genes in glial cells and NPCs was prevented by MPH treatment (Figure 7h). The capacity of MPH to reverse the increased level of γH2X, senescence, and mcm gene dysregulation in mouse primary neural cells after ppp2r2c downregulation mirrored the effects of the drug in the ppp2r2c m1/m1 zebrafish brain.
## DISCUSSION
This study provides evidence that natural aging is accompanied by a decreased level of PP2A activity in the brain and certain behavioral phenotypes in zebrafish (22 months) and mouse (14 months). The association between the reduction of PP2A activity and age‐related changes was demonstrated by the similar behavioral phenotype observed in young fish with a mutated brain‐specific isoform of the PP2A regulatory subunit (ppp2r2c), as well as by the reversal of the age‐related behavioral changes with PP2A activator treatment. Whether the behavioral effects of ppp2r2c loss described here are mechanistically related to the various human mental illnesses associated with ppp2r2c gene polymorphism(Backx et al., 2010; Jacob et al., 2012; Kimura et al., 2019; Xu et al., 2014) is an interesting question for future research.
In both ppp2r2c‐compromised adult and WT aged zebrafish brains, more neuronal (NeuN (+)) and non‐neuronal (NeuN (−)) cells exhibit senescence markers (γH2X, cell cycle checkpoints, SA‐β‐gal and SASP) as compared to WT adult and young zebrafish, respectively. This senescence phenotype is absent in fish treated with various PP2A activators, indicating that decreased PP2A in neural cells, whether due to the loss of ppp2r2c or natural aging, can lead to cell senescence. We also provide evidence that this senescence phenotype plays a role in the behavioral abnormalities observed in zebrafish. Indeed, they disappear with inactivation of the tp53 and cdkn1a genes or treatment with ABT263, which is a classical senolytic agent that provokes the apoptosis of senescent cells. Notably, in both types of zebrafish, the senescence phenotype triggered by PP2A activity impairment specifically affects the OT region of the brain. This localization likely stems from the higher level of ppp2r2c mRNA expression in neural cells of the OT relative to other parts of the brain (Figure S7e,f). The behavioral consequences of this OT‐specific alteration might be explained by an impact of the visuomotor dysfunction leading to anxiety and hyperactivity, as the OT mediates the detection and the integration of visual information to generate behavior (Duchemin et al., 2022). As the neural tectal cells of ppp2r2c‐compromised zebrafish exhibit higher levels of pro‐inflammatory gene expression than WT zebrafish (Figure 3d), neuroinflammation effect could play a role in behavioral abnormalities related to PP2A decline.
An important finding of this study is that that PP2A activation has anti‐senescence properties. Since the DDR marker that we used (γH2AX) can be a substrate of PP2A (Chowdhury et al., 2005; Ferrari et al., 2017), the decreased γH2AX level upon PP2A activation cannot simply be interpreted as a decreased level of physical DNA damages but also according to the role of γH2AX in cell‐cycle checkpoint activation and senescence. That PP2A activation can lead to γH2AX dephosphorylation does not exclude other effects of PP2A in modulating DDR and senescence (Ramos et al., 2019).
Our results link PP2A, brain aging, neural senescence, and age‐related behavioral changes. We propose that the age‐related decrease in PP2A activity increases the level of DDR leading to neural senescence, neuroinflammation, and behavioral changes. An important finding of this study is that drugs with PP2A activation activity can prevent age‐related cognitive decline. In particular, we revealed that the classical psychostimulant MPH (Ritalin R) activates PP2A activity in aged brains, attenuates the DDR, and reduces the abundance of SA‐β‐gal‐positive neurons. This mechanism can explain the findings of recent clinical studies showing that treatment with MPH improves the outcomes of age‐related dementia (Padala et al., 2018; Scherer et al., 2018). As the long‐term use of MPH may cause side effects, including cardiac attack (Shin et al., 2016), the finding that MPH is effective even with a very short period of application, in terms of increasing PP2A activity, reducing the DDR and neural cell senescence, and countering age‐related behavioral changes, suggests that it has promise as an anti‐aging intervention when used intermittently during short periods.
## METHODS AND MATERIALS
Experimental details can be found in the Methods and Materials S1. Details on chemical regents, antibody, and PCR primers can be found in the Methods and Materials S1 and Tables S3 and S4.
## AUTHOR CONTRIBUTIONS
JY and EG designed the experiments. JX, KHC, SYG, YLY, BW, CCW, and LXC performed the experiments. JY, YML, and EG analyzed the data. EG and JY wrote the paper.
## CONFLICT OF INTEREST
The authors have declared that no conflict of interest exists.
## DATA AVAILABILITY STATEMENTS
Source data are available for Figures 1, 2, 3, 4, 5, 6, 7 and S1–S10. All data and information relevant to this study are available from the corresponding author upon reasonable request. RNA‐seq data had been deposited in the Gene Expression Omnibus (GEO) with accession number GSE151307.
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|
---
title: Reversine ameliorates hallmarks of cellular senescence in human skeletal myoblasts
via reactivation of autophagy
authors:
- Nika Rajabian
- Debanik Choudhury
- Izuagie Ikhapoh
- Shilpashree Saha
- Aishwarya S. Kalyankar
- Pihu Mehrotra
- Aref Shahini
- Kendall Breed
- Stelios T. Andreadis
journal: Aging Cell
year: 2023
pmcid: PMC10014065
doi: 10.1111/acel.13764
license: CC BY 4.0
---
# Reversine ameliorates hallmarks of cellular senescence in human skeletal myoblasts via reactivation of autophagy
## Abstract
Cellular senescence leads to the depletion of myogenic progenitors and decreased regenerative capacity. We show that the small molecule 2,6‐disubstituted purine, reversine, can improve some well‐known hallmarks of cellular aging in senescent myoblast cells. Reversine reactivated autophagy and insulin signaling pathway via upregulation of Adenosine Monophosphate‐activated protein kinase (AMPK) and Akt2, restoring insulin sensitivity and glucose uptake in senescent cells. Reversine also restored the loss of connectivity of glycolysis to the TCA cycle, thus restoring dysfunctional mitochondria and the impaired myogenic differentiation potential of senescent myoblasts. Altogether, our data suggest that cellular senescence can be reversed by treatment with a single small molecule without employing genetic reprogramming technologies.
Our results suggest that short‐term treatment of senescent myoblasts with reversine could restore insulin resistance, enhance glucose metabolism and oxidative phosphorylation, likely via reactivation of autophagy. Restoring DNA damage and the state of heterochromatin preceded restoration of proliferation, SA‐β‐Gal expression, and cell size in reversine‐treated cells, ultimately restoring the differentiation ability of myoblasts to form myofibers. Therefore, reversine may have the potential to be used as a novel, anti‐aging treatment, without the tumorigenic complications of genetic reprogramming technologies.
## INTRODUCTION
Aging results in gradual loss of muscle function, which is associated with reduction in muscle mass and strength (Etienne et al., 2020). Age‐related loss of muscle mass may decrease mobility and increase the risk of morbidities and mortality. As of 2010, 524 million people around the world were 65 years of age or older and by 2050, experts expect this number to grow to 1.5 billion (Morgan et al., 2016). This is a major health issue given that aging is accompanied by increased need for healthcare, long‐term care and social services to support older adults.
Skeletal muscle has a remarkable capacity to regenerate by activation of myogenic progenitor cells; however, both the number of these progenitors and their regenerative capacity decline with aging and cellular senescence (Muñoz‐Cánoves et al., 2020; Yamakawa et al., 2020). Recent studies showed that DNA damage and epigenetic alterations are primary hallmarks of aging leading to dysregulated nutrient sensing, mitochondrial dysfunction, and ultimately loss of muscle function (López‐Otín et al., 2013). Metabolic changes such as impaired glycolysis, insulin sensitivity, and mitochondrial respiration are affected by senescence contributing to loss of the myoblast capacity to differentiate (Baraibar et al., 2016; Pääsuke et al., 2016; Ravera et al., 2019; Shou et al., 2020; Trifunovic & Larsson, 2008). Aging is also associated with impaired autophagy, which is essential to maintain satellite cell stemness and mitochondrial turn over (García‐Prat et al., 2016; Tang & Rando, 2014). Cellular reprogramming using the four Yamanaka factors (Oct4, Sox2, Klf4, and c‐Myc) has been shown to ameliorate the aging hallmarks in somatic cells (Ravaioli et al., 2018; Strässler et al., 2018; Yener Ilce et al., 2018), including skeletal muscle (Wang et al., 2021) and mesenchymal stem cells supporting wound regeneration (Kurita et al., 2018). However, genetic reprogramming can lead to teratoma formation (de Lázaro et al., 2017; Ohnishi et al., 2014; Tamanini et al., 2018; Wang et al., 2021), thereby posing significant concerns as a rejuvenation strategy.
Several studies found that the small molecule 2,6‐disubstituted purine, reversine, increased cellular plasticity as demonstrated by increased differentiation potential of progenitor cells toward the neuroectodermal lineage (Lee et al., 2009); dedifferentiation of C2C12 myoblasts to a progenitor‐like state (Chen et al., 2004, 2007); as well as dedifferentiation of sheep fibroblasts into multipotent progenitor cells, possibly via expression of the pluripotent factor, Oct4 (Guo et al., 2021). These progenitors could be induced to differentiate into adipocytes, osteoblasts, hepatocytes, and neurocytes in vitro (Guo et al., 2021). Other studies reported that reversine may have anticancer properties (D'Alise et al., 2008; Piccoli et al., 2020). Indeed, reversine is an Aurora B protein kinase inhibitor, causing failure in mitotic chromosome segregation, cytokinesis, and cell proliferation (Amabile et al., 2009; D'Alise et al., 2008). Besides the inhibition of Aurora B kinase, reversine was found to induce autophagic cell death of cancer cells (Fang et al., 2018; Lu et al., 2012; Prajumwongs et al., 2021).
Based on these results, we hypothesized that reversine might ameliorate the hallmarks of cellular senescence in human myoblasts. We discovered that short‐term treatment of fully senescent myoblasts with reversine could restore insulin resistance, enhance glucose metabolism and oxidative phosphorylation, likely via reactivation of autophagy, ultimately restoring the differentiation ability of myoblasts to form myofibers. Our results suggest that reversine may have the potential to be used as a novel, antiaging treatment, without the tumorigenic complications of genetic reprogramming technologies.
## Reversine improves hallmarks of cellular senescence in human myoblasts
Recently, our laboratory showed that human myoblasts that were cultured for 20 population doublings (equivalent to >50 days of culture, replicative senescence) exhibited all major hallmarks of senescence including expression of SA‐β‐gal, increased size, flattened morphology, reduced proliferation, DNA damage, histone modifications, and inability to form myotubes (Rajabian et al., 2020; Shahini et al., 2021). Immunostaining for desmin and MyoD (a myoblast marker) was performed in young and senescent myoblasts from all three donors (Figure S1a–c). The vast majority of young and senescent myoblasts expressed desmin ($94.8\%$–$97.5\%$). The percentage of cells positive for MyoD was high in young myoblasts ($73.5\%$–$79.8\%$), and decreased significantly in senescent myoblasts ($9.9\%$–$14.9\%$). The decline in the % of MyoD+ in senescent cells is in agreement with the impaired proliferation and myogenic differentiation capacity.
The doubling times of young myoblasts from all donors (18yrM: 42.1 ± 6, 25yrF: 52.3 ± 6, 75yrF: 46.3 ± 1 h) were significantly shorter than those of senescent myoblasts (18yrM: 89.7 ± 9, 25yrF: 89.2 ± 5, 75yrF:84.3 ± 11 h) (Figure S1d). In addition, senescent myoblasts from all three donors exhibited significant loss of myogenic differentiation capacity, as evidenced by the measures of myotube diameter and fusion index (Figure S1e,f) (Myotube diameter: 18yrM, YM: 95 ± 7 μm, SM: 25 ± 1 μm; 25yrF, YM: 92 ± 6 μm, SM: 22 ± 2 μm; 75yrF, YM: 101 ± 6 μm, SM: 23 ± 2 μm; Fusion Index: 18yrM, YM: 63 ± $2\%$, SM: 15 ± $1\%$; 25yrF, YM: 61 ± $2\%$, SM: 13 ± $2\%$; 75yrF, YM: 63 ± $5\%$, SM: 14 ± $2\%$).
Furthermore, we evaluated some hallmarks of senescence such as expression of senescence‐associated‐β‐galactosidase, DNA damage (γH2AX), and histone modifications (H3K9me3 and H3K27me3). Each dot in the bar graph (Figure S1g–j) indicates one donor. The percentage of young myoblasts that stained positive for SA‐β‐Gal varied between different donors (18yrM: $7.3\%$, 25yrF: $11.5\%$, 75yrF: $15.1\%$) but was significantly lower than that of senescent myoblasts (18yrM: $61.8\%$, 25yrF: $78.4\%$, 75yrF: $72.8\%$) (Figure S1g). The level of γH2AX was significantly higher in senescent myoblasts (18yrM: ~1.5 fold change, 25yrF: ~3.2 fold change, 75yrF: ~1.74 fold change) as compared to young myoblasts cells (Figure S1h). We also observed decreased levels of H3K9me3 and H3K27me3 in senescent myoblasts as compared to young ones (H3K9me3; 18yrM: ~2.3 fold change, 25yrF: ~1.57 fold change, 75yrF: ~2.1 fold change, H3K27me3; 18yrM: ~3.1 fold change, 25yrF: ~1.81 fold change, 75yrF: ~1.8 fold change) (Figure S1i,j).
In this study, we examined whether the small molecule 2,6‐disubstituted purine or reversine could reverse the hallmarks of replicative senescence in human myoblasts in vitro. To this end, senescent human myoblast (SM) cells were treated with reversine or DMSO (control) for 4 days, and cellular morphology was determined at different times after treatment using F‐actin staining (Figure 1a). The area of senescent myoblasts was much larger, and their morphology appeared flattened as compared to young myoblasts. While there was no significant effect on the shape or size immediately after treatment ($t = 0$ days), by 12 days after reversine withdrawal, cell size decreased significantly as compared to senescent myoblasts (Figure 1b,c). By Day 8, many smaller cells appeared in culture and by 12 days after withdrawal, the majority of cells were smaller than the starting senescent cells (Figure S2a). Immunostaining showed that small cells stained positive for the myoblast marker desmin, indicating that they retained their myoblast phenotype (Figure S2a,b). We also examined whether longer treatment with reversine would be even more effective. Our results showed that there was no significant difference between 4 days and 12 days of reversine treatment with regard to the levels of desmin, γH2AX, and H3K9me3 expressions (Figure S1k–n). Therefore, we chose the 4‐day treatment for the rest of our experiments.
**FIGURE 1:** *Improvements of senescence hallmarks in human myoblasts upon reversine treatment. (a) Schematic illustration of the experimental design: Senescent myoblast cells were treated with reversine for 4 days, reversine was removed and various measurements were performed at 0, 4, 8 or 12 days post‐treatment. (b and c) Immunostaining for F‐Actin to show morphology and cells size in young, senescent and reversine‐treated myoblasts at 0 or 12 days post‐treatment (scale bar = 100 μm, n = 150 cells). (d and e) Staining for SA‐β‐gal in young, senescent, 0 and 12 days myoblast cells and quantification of the percentage of SA‐β‐gal‐positive cells (n = 250 cells) (scale bar = 100 μm). (f and g) Immunostaining for Ki67 and quantification of the percentage of Ki67+ cells in young, senescent, 0 and 12 days myoblast cells (scale bar = 50 μm, n = 150 cells). (h and i) Immunostaining and quantification for γH2AX, H3K9me3 and H3K27me3 on 0 and 12 days cells (scale bar = 50 μm, n = 150 cells). Data in bar graphs are presented as mean ± SD and data in dot plots are presented as mean ± 95% confidence interval. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001, **** denotes p < 0.0001 and ns, not significant.*
As senescence‐associated β‐galactosidase (SA‐β‐Gal) is a well‐known marker of cellular senescence (Debacq‐Chainiaux et al., 2009), we measured the percentage of SA‐β‐Gal+ cells. The % of SA‐β‐Gal+ cells in senescent myoblast cultures was higher as compared to young myoblasts (Figure 1d,e; YM: 7.3 ± $1.7\%$, SM: 61.8 ± $6.7\%$). The % SA‐β‐Gal+ cells did not change significantly immediately after treatment ($t = 0$ days) but decreased dramatically 12 days after reversine withdrawal (Figure 1d,e; 0 days: 60.6 ± $5.2\%$, 12 days: 23.9 ± $3.8\%$).
As expected, the cumulative cell number of senescent myoblast cells decreased as compared to young myoblasts. Similar to cell size and SA‐β‐gal expression, the cell proliferation started to increase after 8 days of reversine withdrawal and continued for 40 days after reversine withdrawal, when the cumulative cell number was significantly higher (19.1 ± 1.8 fold change) than that of senescence myoblasts (Figure S2c). As expected, immunostaining for the proliferation marker, Ki67, showed that the % Ki67+ senescent myoblasts was significantly lower than in young ones (Figure 1f,g; YM: 76.5 ± $7.8\%$, SM: 22.8 ± $8.6\%$). However, 12 days after reversine treatment, the %Ki67+ cells increased significantly (Figure 1f,g; 0 days: 20.7 ± $8.4\%$, 12 days: 47.8 ± $11.2\%$), in agreement with the increased cell number.
Finally, we examined the effects of reversine on senescence‐associated DNA damage and heterochromatin modifications (López‐Otín et al., 2013). Interestingly, reversine treatment for 4 days reduced the expression level of γH2AX, a marker of DNA damage, in senescent myoblast cells and the low γH2AX levels persisted 12 ‐day post‐treatment (Figure 1h,i). In agreement with previous reports (Ocampo et al., 2016; Rajabian et al., 2020), we also observed decreased heterochromatin in senescent myoblasts as shown by immunostaining for heterochromatin marks H3K9me3 and H3K27me3. The level of H3K9me3 increased significantly with 4 days of reversine treatment and remained high after 12 days. On the contrary, the level of H3K27me3 did not change immediately after treatment but increased significantly 12 days later (Figure 1h,i). These results suggested that reversine may be used to ameliorate hallmarks of senescence in aged SM and prompted us to examine its potential effects on cellular metabolism.
As reversine was dissolved in DMSO, we examined whether treatment with DMSO alone for 4 days had any effects on senescent myoblasts. Although no effects were seen immediately after treatment (0 days), on 12 days after DMSO withdrawal, we observed small but significant increase in %SA‐b‐gal+ cells (Figure S3a,c; SM: 60.5 ± $9.4\%$, 0 days: 63.2 ± $8.5\%$, 12 days: 79.3 ± $8.5\%$), increased level of γH2AX (Figure S3a,e), decreased %Ki67+ cells (Figure S3a,d; SM: 18.9 ± $3.9\%$, 0 days: 18.5 ± $4.7\%$, 12 days: 14.3 ± $4.8\%$) and decreased levels of H3K9me3 and H3K27me3 (Figure S3a,f,g), all of them in opposite direction to reversine treatment. Therefore, DMSO appears to slightly counteract the effects of reversine, suggesting that the effects of reversine might be underestimated.
## Impaired glycolytic capacity is restored in reversine‐treated cells
Previous studies demonstrated that aging is associated with impairment of glycolysis (Baraibar et al., 2016; Pääsuke et al., 2016; Ravera et al., 2019), prompting us to examine the effect of reversine treatment on glucose metabolism. To this end, we employed *Seahorse analysis* to measure the ECAR (ExtraCellular Acidification Rate), a measure of lactate secretion. Interestingly, increasing dosage of glucose gradually increased ECAR in young myoblast and reversine‐treated cells (immediately after treatment, 0 and 12 days after reversine withdrawal) but not in senescent myoblasts (Figure 2a,b).
**FIGURE 2:** *Impaired glycolytic capacity is restored by reversine. (a) Measurements of extracellular acidification rate with different dosage of glucose. (b) Acute response indicating glucose sensitivity. (c–e) Measurements of extracellular acidification rate and calculation of glycolysis and glycolytic capacity from ECAR. Data in ECAR plot is presented as mean ± SEM. *** denotes p < 0.001, **** denotes p < 0.0001.*
Furthermore, we investigated the effect of reversine on glycolysis and glycolytic capacity using the *Seahorse glycolysis* stress test. In agreement with the previous result, glucose injection induced greater ECAR in young and reversine‐treated myoblasts (0 and 12 days) as compared to senescent myoblasts. In addition, young and reversine‐treated senescent myoblasts (0 and 12 days) were more responsive to oligomycin (ATP synthase inhibitor, IC50 = 1 μM) than senescent myoblasts. As a result, glycolysis and glycolytic capacity were significantly lower in senescent myoblast cells but were partially restored by reversine (Figure 2c–e). In agreement with restoring glycolysis, young and reversine‐treated senescent cells (0 and 12 days) demonstrated a dose‐dependent decrease in ATP in response to 2‐DG (hexokinase inhibitor, IC50 = 5 mM), which was not observed in senescent myoblasts (Figure S4a). These results suggested that reversine could restore glycolysis, prompting us to investigate this pathway further.
## Reversine treatment improves insulin resistance in senescent myoblasts
Since age‐impaired glucose homeostasis is associated with insulin resistance and defects in insulin signaling in skeletal muscle (Refaie et al., 2006; Shou et al., 2020), we investigated the effect of reversine treatment on insulin sensitivity, by measuring ECAR after treating the cells with insulin. Insulin induced greater ECAR in young and interestingly, also in reversine‐treated cells (0 and 12 days), but had no effect on senescent myoblasts (Figure 3a–d). This result prompted us to investigate the serine/threonine kinase Akt (protein kinase B) pathway, which has been implicated in insulin‐stimulated glucose uptake (Cho et al., 2001; Jaiswal et al., 2019; McCurdy & Cartee, 2005). To this end, we examined the expression of total (t)‐Akt2 and the capacity of insulin to induce phosphorylation, (p)‐Akt$\frac{1}{2.}$ All four groups expressed t‐Akt2, which was phosphorylated by insulin (20 nM) in young myoblast and reversine‐treated cells (0 and 12 days) but not senescent myoblast cells (Figure 3e,f). At the same time, all four groups expressed t‐Akt1; the level of phosphorylation of Akt1 was not affected immediately after reversine treatment (0 days) but increased significantly 12 days after reversine withdrawal (Figure S5a).
**FIGURE 3:** *Reversine treatment improves insulin resistance in senescent myoblasts. Measurements of extracellular acidification rate in young, senescent control and reversine‐‐treated myoblasts at 0 or 12 days post‐treatment. Each condition was done with or without 20 nM insulin for 30 min using Seahorse extracellular flux analyzer. (e) Western blots of total (t) and phosphorylated (p)Akt2; GAPDH served as a loading control. (f) Quantification of Western blot showing the ratio of p‐Akt2 to t‐Akt2 in young, senescent, 0 and 12 days myoblast cells. (g) Measurement of glucose uptake in response to insulin in the presence or absence of Akt2 inhibition. Data in ECAR plot are presented as mean ± SEM and data in bar graphs are presented as mean ± SD. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001, and ns. not significant.*
As Akt2 is essential in insulin resistance (Cho et al., 2001), we further tested whether the inhibition of Akt2 signaling could suppress insulin‐dependent glucose uptake. To this end, we measured glucose uptake using 1 mM of a glucose analog (GA) that fluoresces upon phosphorylation of the achiral carbon. Glucose uptake increased upon insulin stimulation of young and reversine‐treated myoblasts (0 and 12 days) and decreased significantly in the presence of the Akt2 selective inhibitor (CCT128930, IC50 = 5 μM) (Figure 3g). By contrast, glucose uptake was low and was not affected by insulin treatment or Akt2 inhibition in senescent myoblasts, demonstrating that senescent cells developed insulin resistance, which was restored by reversine. Furthermore, Akt2 inhibition decreased the stimulatory effect of insulin on ATP in young and reversine‐treated myoblasts (0 and 12 days) but not in senescent cells (Figure S5b).
## Reversine induces autophagy and decreases the size of senescent myoblasts
As shown above, reversine significantly decreased senescence‐associated cell size (Figure 4a,b), suggesting that autophagy might be a contributing factor (Hosokawa et al., 2006; Miettinen & Björklund, 2015). To address this hypothesis, we measured the phosphorylation of adenosine monophosphate‐activated protein kinase α (AMPKα) at Thr172, which activates AMPKα and positively regulates autophagy (Jeon, 2016). Immunoblots revealed that phosphorylation of AMPKα (pAMPKα) was significantly decreased in senescent myoblasts but was restored in reversine‐treated cells (0 and 12 days) (Figure 4c,d).
**FIGURE 4:** *Reversine induces autophagy and decreases the size of senescent myoblasts. (a) Phase images of the myoblasts detached from the surface. (b) Quantification of cell radius; data shown as means ±95% CI (n = 250 cells) (scale bar = 30 μm). (c and d) Western blotting analysis for quantifying the total protein and phosphorylation of AMPKα (Thr172). GAPDH served as a loading control. (e and f) Western blotting analysis of LC3 protein upon starvation+chloroquine treatment for 6 h and quantification of the autophagy flux using the formula: Autophagy flux = (LC3‐II/LC3‐I)CQ − (LC3‐II/LC3‐I)Control. Data in ECAR plot are presented as mean ± SEM. Data in bar graphs are presented as mean ± SD. * denotes p < 0.05, ** denotes p < 0.01, **** denotes p < 0.0001 and ns, not significant.*
To evaluate the effect of reversine on autophagy, we measured autophagosome formation by starving the cells for 6 h, while inhibiting autophagosome‐lysosome fusion by chloroquine (CQ, (starvation+chloroquine) condition). Western blot for the autophagosome marker, microtubule‐associated protein (MAP) light chain 3 (LC3) showed that the autophagy flux decreased in senescent myoblasts but was restored by reversine to the level of young myoblasts (0 and 12 days) (Figure 4e,f).
## Reversine improves mitochondrial function in senescent myoblast cells
Senescence is associated with mitochondrial DNA (mtDNA) damage, mitochondrial dysfunction and decline in respiratory chain function (Trifunovic & Larsson, 2008). To examine the effects of reversine on mitochondrial function, first we measured the ratio of mitochondrial over nuclear DNA (mtDNA/nDNA) using qPCR. As expected, the level of mtDNA/nDNA was higher in senescent as compared to young myoblast cells, but was restored by reversine treatment (Figure 5a). Furthermore, senescent myoblasts had lower levels of Parkin and PINK1, both of which were restored by reversine (Figure 5b–d). PINK1 senses mitochondria damage and accumulates on the outer membrane of damaged mitochondrial where it recruits the ubiquitin ligase Parkin to induce mitophagy (Bingol & Sheng, 2016; Truban et al., 2017). Therefore, the increased level of PINK1 and Parkin suggest restoration of mitophagy that was diminished in senescent myoblasts. Similarly, immunostaining for TMRM and MitoTracker Red CMXRos fluorescent probes showed that the mitochondrial membrane potential was significantly lower in senescent myoblasts but was also restored by reversine (Figure 5e–g), suggesting that reversine might restore the impaired mitochondrial function of senescent myoblasts.
**FIGURE 5:** *Reversine improves mitochondrial function in senescent myoblasts. (a) Quantitative real‐time PCR quantification of mtDNA relative to nDNA (mtDNA/nDNA) as measured by primers specific to mitochondrial gene MT‐TL1 and nuclear gene B2M in young, senescent, 0 and 12 days myoblast cells. (b–d) Western blotting and quantification of parkin and PINK1 proteins. (e) Representative images of tetramethylrhodamine methyl ester (TMRM) and MitoTracker live stains corresponding to mitochondrial membrane potential (scale bar = 50 μm). (f and g) Quantification of TMRM and MitoTracker intensity per cell; data shown as means ±95% CI (n = 150 cells). (h) Western blots of total (t) and phosphorylated (p)PDH; GAPDH served as a loading control. (i) Quantification of western blot showing ratio of p‐PDH to PDH in young, senescent, 0 and 12 days myoblast cells. (j) Measurements of oxygen consumption rate (OCR) using Seahorse extracellular flux analyzer. (k‐n) Calculations of the basal, maximal, reserved and ATP‐linked respiration rates. Data in OCR plot is presented as mean ± SEM and data in bar graphs are presented as mean ± SD. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001 and **** denotes p < 0.0001.*
Since reversine restored glycolysis and mitochondrial function, we examined whether it also affected the pyruvate dehydrogenase complex, which catalyzes the formation of acetyl‐CoA from pyruvate, linking glycolysis to oxidative phosphorylation (Patel et al., 2014). As expected, we found higher levels of phosphorylated (p)‐PDH in SM cells (Figure 5h,k), suggesting loss of connectivity of glycolysis to the TCA cycle. Interestingly, pPDH decreased by reversine (0 and 12 days) to even lower levels than in young myoblasts (Figure 5h,i). This result suggested that mitochondrial respiration and ATP production could also be fully restored by reversine. Indeed, measurements of OCR showed that the basal, maximal, reserved, and ATP‐linked respiration capacity were all restored in reversine‐treated cells (0 and 12 days) (Figure 5j‐n), suggesting that reversine increased oxidative phosphorylation and mitochondrial respiration.
## Reversine improves metabolic changes in senescent myoblasts by reactivating autophagy
The reversal of pAMPKα/AMPKα levels prompted us to hypothesize that reversine might improve mitochondrial function and glucose metabolism by restoring autophagy. To address this hypothesis, we inhibited AMPK in reversine‐treated cells by the addition of compound C (CC; 1 μg/ml during the 4 days of reversine treatment), which inhibits AMPK by occupying a pocket that partially overlaps with the ATP active site in the AMPKα subunit (Handa et al., 2011). Western blots for AMPKα and pAMPKα showed that CC decreased pAMPKα/AMPKα to a similar level as that of senescent myoblasts (Figure 6a,b). As a result, the autophagy flux decreased significantly with inhibition of AMPKα phosphorylation in reversine‐treated cells (Figure 6c,d). AMP‐activated protein kinase inhibition also abrogated the effects of reversine on mitochondrial membrane potential as measured by the intensity of MitoTracker and tetramethylrhodamine methyl ester (TMRM) (Figure 6e–g) and affected cellular metabolism significantly. Specifically, CC reduced the oxygen consumption rate (OCR) and reversed the effects of reversine on basal, ATP‐linked, maximal respiration and spare respiratory capacity; as well as extra cellular acidification rate (Decary et al., 1997), reversing the effects of reversine on glycolysis and glycolytic capacity (Figure 6h–o). These results suggest that reversine may be reversing the loss of metabolic function of senescent cells by activating autophagy.
**FIGURE 6:** *Reversine improves the metabolism of senescent myoblast cells by reactivating autophagy. (a, and b) Western blotting analysis for total and phosphorylated AMPKα (Thr172). GAPDH served as a loading control. (c and d) Western blotting analysis of LC3 protein upon starvation+chloroquine treatment for 6 h and quantification of the autophagy flux using the formula: Autophagy flux = (LC3‐II/LC3‐I)CQ – (LC3‐II/LC3‐I)control. (e–g) Representative images and quantification of tetramethylrhodamine methyl ester (TMRM) and MitoTracker live staining (scale bar = 50 μm); data shown as means ±95% CI (n = 150 cells). (h) Measurements of oxygen consumption rate (OCR) using seahorse extracellular flux analyzer. (i–l) Calculations of the basal, maximal, reserved and ATP‐linked respiration rates. (m) Measurements of extracellular acidification rate and (n and o) calculation of glycolysis and glycolytic capacity from ECAR. Data in OCR and ECAR plot are presented as mean ± SEM and data in bar graphs are presented as mean ± SD. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001 and **** denotes p < 0.0001.*
## Reversine restores myogenic differentiation potential in senescent myoblast cells
Since reversine improved some well‐known hallmarks of cellular senescence as well as glycolysis and mitochondrial function, we examined whether it could also restore the impaired myogenic differentiation potential of senescent myoblasts. In line with previous studies (Bigot et al., 2008; Rajabian et al., 2020), we observed that replicative senescence impaired the ability of myoblasts to form myotubes (Figure 7a). The myotube diameter and the fusion index (FI, defined as the percentage of nuclei in myotubes) decreased significantly in senescent as compared to young myoblasts (Figure 7a–c; myotube diameter, YM: 106 ± 34 μm, SM: 24 ± 8 μm; FI, YM: 60 ± $2\%$, SM: 23 ± $5\%$, $$n = 3$$, $p \leq 0.0001$ as compared to SM). Interestingly, reversine treatment for 4 days decreased MyoD and Mef2c, known as key regulators of skeletal muscle myogenesis, in senescent myoblasts (Figure S6a–c), in agreement with decreased myotube formation after 4 days of reversine treatment. However, upon reversine withdrawal, both myotube diameter and FI increased over time and were fully restored to the level of young myoblasts after 12 days (Figure 7a–c; myotube diameter, 4 days: 61 ± 21 μm, 8 days: 88 ± 32 μm, 12 days: 97 ± 30 μm; FI, 4 days: 40 ± $3\%$, 8 days: 53 ± $5\%$, 12 days: 60 ± $7\%$, $$n = 3$$, $p \leq 0.001$ as compared to SM).
**FIGURE 7:** *Reversine restores myogenic differentiation potential in senescent myoblasts. (a) Immunostaining for MHC (red) and Actinin of myotubes formed by young, senescent control and reversine‐treated myoblasts. Nuclei were stained with DAPI (blue). Differentiation started on 0, 4, 8, or 12 days after reversine withdrawal (scale bar = 400 μm). (b) Quantification of the fusion index (number of myonuclei/total number of nuclei × 100%) and (c) the average diameter of myotubes generated by young, senescent control, and reversine‐treated myoblasts, where differentiation started on 0, 4, 8 or 12 days after reversine withdrawal. (d–f) Quantitative real‐time PCR quantification of gene expression of MyoG, Mef2a and Mef2c during myogenic differentiation. Data in bar graphs are presented as mean ± SD and data in dot plots are presented as mean ± 95% confidence interval. *** denotes p < 0.001 and **** denotes p < 0.0001.*
We also investigated the effect of reversine on the dynamics of gene expression of key myogenic differentiation genes, including MyoG, Mef2a, and Mef2c, which are critical for myoblast fusion during skeletal muscle development (Bryantsev et al., 2012; Ganassi et al., 2018). To this end, myoblasts were coaxed to differentiate when they reached confluence (Day 0), and gene expression was measured over time using qRT‐PCR. For reversine‐treated cells, differentiation started 12 days after reversine withdrawal (Day 0). Our results showed that the expression of MyoG, Mef2a, and Mef2c was significantly increased in young cells from Day 1 of differentiation. Interestingly, in reversine‐treated cells, the expression of all three genes was delayed by 2 days but increased sharply thereafter, reaching similar (MyoG, Mef2a) or even higher (Mef2c) levels that those of young cells. Although MyoG and Mef2a remained at low levels in senescent myoblasts throughout the differentiation, Mef2c increased after two days but even in senescent myoblasts, it reached significantly higher levels in reversine‐treated cells (Figure 7d–f). These results suggested that reversine can improve myogenic differentiation capacity through increasing level of early differentiation markers, which are identified as essential regulators of skeletal muscle‐specific transcription.
## Reversine improves heterochromatin modification and cellular metabolism of extreme DNA‐damage‐induced senescence
In addition to replicative senescence, we also induced senescence by acute and severe DNA damage using the chemical etoposide (Bang et al., 2019; Tamamori‐Adachi et al., 2018; Teng et al., 2021). In order to reach close to $100\%$ senescent cells, young human myoblasts were treated with 50 μM etoposide for 24 h, followed by 48 h in fresh culture medium. This treatment resulted in almost $100\%$ SA‐β‐gal+ cells (Figure 8a,c). In this acute model of senescence, treatment with reversine for 4 days had no effect on cell size, % SA‐β‐gal+ or γH2AX+ cells (Figure 8a–d) cells or proliferation (Figure 8a,e; Figure S6d). Nevertheless, it did increase the expression of heterochromatin marks H3K9me3 and H3K27me3, as well as glycolysis, mitochondrial function, and oxidative phosphorylation (Figure 8a,f–t). These results suggest that reversine could enhance heterochromatin modification and improve cellular metabolism of terminally senescent cells, experiencing severe DNA damage.
**FIGURE 8:** *Reversine improves heterochromatin modification and cellular metabolism of DNA‐damage‐induced senescent myoblasts. (a) Immunostaining for F‐actin, SA‐β‐Gal, Ki67, γH2AX, H3K9me3, and H3K27me3 in young, etoposide‐treated myoblast (eSM), and eSM treated with reversine (scale bar = 100 μm for F‐actin, SA‐β‐Gal staining; scale bar = 50 μm for γH2AX, Ki67, H3K9me3 and H3K27me3, n = 150 cells) (b–g) Quantification of F‐actin, SA‐β‐Gal, γH2AX, Ki67, H3K9me3 and H3K27me3 positive myoblasts. (h) Measurements of extracellular acidification rate with different dosages of glucose. (i) Acute response indicating glucose sensitivity. (j–l) Measurements of extracellular acidification rate and calculation of glycolysis and glycolytic capacity from ECAR. (m and n) Representative images and quantification of tetramethylrhodamine methyl ester (TMRM) (scale bar = 50 μm, n = 150 cells). (o) Quantitative real‐time PCR quantification of mtDNA relative to nDNA (mtDNA/nDNA) as measured by primers specific to mitochondrial gene MT‐TL1 and nuclear gene Beta‐2‐Microglobulin (B2M). (p) Measurements of oxygen consumption rate (OCR) using Seahorse extracellular flux analyzer. (q–t) Calculations of the basal, maximal, reserved and ATP‐linked respiration rates. Data in OCR and ECAR plot is presented as mean ± SEM. Data in bar graphs are presented as mean ± SD and data in dot plots are presented as mean ± 95% confidence interval. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001, **** denotes p < 0.0001 and ns, not significant.*
## DISCUSSION
Recent work from our laboratory showed that the expression of a single pluripotency factor, NANOG, ameliorated many hallmarks associated with age‐related deterioration of myoblasts, including autophagy, energy homeostasis, genomic stability, nuclear integrity, and mitochondrial function (Shahini et al., 2021). NANOG expression also increased the number of muscle stem cells in a mouse model with no signs of tumorigenesis, demonstrating the feasibility of reversing cellular senescence in vitro and in vivo (Shahini et al., 2021). In this study, we report that the small molecule, reversine, can reverse several of the hallmarks of cellular senescence in human skeletal muscle cells. While several studies have shown that reversine increased cell plasticity and induced dedifferentiation to progenitor‐like state (Anastasia et al., 2006; Chen et al., 2004, 2007; Li et al., 2016), the effect of reversine on cellular senescence has not been reported yet. As previous work showed that cellular senescence in myoblasts does not depend on donor age but is strongly dependent on time in culture (Alsharidah et al., 2013; Decary et al., 1997; Rajabian et al., 2020), we used cells that were cultured for >30 population doublings as a model of cellular senescence. These cells exhibited all well‐known senescence hallmarks, including reduced proliferation, enlarged size and flattened morphology, DNA damage, mitochondrial dysfunction, impaired autophagy, and insulin resistance.
Treatment with reversine affected various cellular functions in a temporal sequence. For example, reversine quickly restored tri‐methylation of histone 3 at Lys9 and Lys27 (H3K9me3 and H3K27me3), which play crucial role in maintaining nuclear architecture and have been shown to decrease in senescent cells (Dillinger et al., 2017; Ito et al., 2018; Ocampo et al., 2016). Also, reversine decreased DNA damage, a hallmark of cellular senescence (López‐Otín et al., 2013), after only 4 days of treatment. On the contrary, SA‐β‐Gal expression, cell size and shape were restored 8–12 days after reversine treatment, indicating major reorganization of the actin cytoskeleton in agreement with previous work (Anastasia et al., 2006; Guo et al., 2021; Li et al., 2016). In agreement with previous reports where reversine was shown to inhibit cell proliferation (Huang et al., 2016; Park et al., 2019; Xia et al., 2021), cells did not proliferate during reversine treatment, likely due to inhibition of Aurora B kinase, but they started to proliferate after reversine was removed. Indeed, groups of small cells became apparent by Day 8 after reversine removal, and their number kept on increasing for up to 40 days. These results showed that restoring DNA damage and the state of heterochromatin preceded restoration of proliferation, SA‐β‐Gal expression, and cell size, prompting us to probe whether cellular functions such as metabolism or autophagy might be mediating the effects of reversine on revamping the function of senescent myoblasts.
In line with the existing literature (Baraibar et al., 2016; Pääsuke et al., 2016; Ravera et al., 2019; Refaie et al., 2006), senescent myoblasts showed reduced glycolysis and insulin resistance, stemming from impaired Akt2 signaling. Interestingly, reversine treatment for 4 days restored Akt2 signaling, glucose uptake, and insulin sensitivity. It also increased phosphorylation of AMPKα, a well‐known sensor of cellular energy that maintains cellular homeostasis through autophagy and modulates glycolysis and insulin sensitivity in the muscle (Herzig & Shaw, 2018). In addition to glucose metabolism, AMPK signaling is known to activate autophagy and improve mitochondrial health (Jeon, 2016). In agreement, we found that reversine induced autophagy in senescent cells as evidenced by increased LC3‐II and autophagy flux. Interestingly, reversine increased autophagy but it also decreased Akt phosphorylation in thyroid cancer cells (Lu et al., 2012). Inhibition of AMPK diminished the effect of reversine on mitochondrial function and oxidative phosphorylation, as well as glycolysis and glucose uptake, suggesting that autophagy activation might be necessary for improving mitochondrial health and restoring glycolysis and oxidative phosphorylation.
In addition, increased levels of Parkin and PINK1 suggested that reversine also enhanced mitophagy that was impaired in senescent cells. Indeed, reversine decreased mitochondrial content and restored mitochondrial membrane potential. It also improved the connectivity between glycolysis and oxidative phosphorylation, increasing the overall metabolic activity. Ultimately, the activation of autophagy and mitophagy in senescent cells might be necessary for the decreased cell size that was observed several days after treatment.
Our results showed that reversine could restore heterochromatin modifications, glycolysis, oxidative phosphorylation, and mitochondrial function with only a short 4‐day treatment, but the effects on proliferation were seen only several days after removal of reversine. This result suggested that the population of senescent cells might be heterogenous, with only a subset of cells being able to proliferate in response to reversine. To address this question, we employed etoposide to induce senescence by acute and extreme DNA damage, yielding $100\%$ of SA‐β‐gal+ cells. Surprisingly, even under these conditions, reversine could restore the expression of heterochromatin marks H3K9me3 and H3K27me3, as well as glycolysis, mitochondrial function, and oxidative phosphorylation but did not restore cell proliferation. This may be the result of extreme DNA damage making it impossible for cells to re‐enter the cell cycle. It may also suggest that reversine could restore proliferation only in cells that were not terminally senescent. It would be very interesting to discover markers that distinguish between various stages of cellular senescence to identify the stage where proliferation can be restored by reversine.
Glycolysis, mitochondrial function, and the flow from glycolysis to the TCA cycle mediated by PDH play a crucial role in the myogenic differentiation capacity of myoblast cells (Hori et al., 2019; Tixier et al., 2013; Wagatsuma & Sakuma, 2013). Our results showed that senescent myoblasts lost their ability to form myotubes, but reversine treatment restored the myotube formation capacity. We demonstrated that reversine increased the level of early differentiation genes, such as MyoG and MEF2, which are essential regulators of skeletal muscle‐specific transcription (Bryantsev et al., 2012; Ganassi et al., 2018). As reversine treatment restored glycolysis, insulin sensitivity, mitochondrial membrane potential, the connectivity between glycolysis and TCA cycle, and expression of early myogenic differentiation markers, reversine‐treated cells restored myotube formation capacity several days after treatment.
Based on the temporal sequence that cellular functions were affected by reversine, it appears that reversine may first activate autophagy and mitophagy, leading to restored metabolic functions such as glycolysis and mitochondrial respiration within 4 days of treatment. These changes enhance the overall cellular bioenergetics, setting the stage for further changes, including actin reorganization, reduction in cell size, increased proliferation and restoration of myofiber formation capacity, ultimately restoring the function of senescent muscle cells. Cellular reprogramming using the four Yamanaka factors (OSKM) has also been employed as a strategy for cellular rejuvenation in vitro and in vivo (Takahashi et al., 2007). Although a lot can be learned about the biology of aging using this approach, translating OSKM reprogramming into therapy remains a major challenge (de Magalhães & Ocampo, 2022), mostly due the high likelihood of teratoma formation, even when used intermittently (Abad et al., 2013). Alternatively, senolytics have been shown to selectively eliminate senescent cells restoring the function of aging tissues (Kirkland & Tchkonia, 2020; Zhu et al., 2015), but they do so in a cell and tissue‐specific manner (Niedernhofer & Robbins, 2018), suggesting that although some drug combinations may enhance the function of some tissues, they may impart unwanted side effects on others. Here, we show that treating senescent myoblasts with a single small molecule, reversine, for 4 days ameliorated many aspects of cellular senescence, restoring metabolism, and myofiber formation capacity by activating autophagy. Since reversine is already being investigated as an anticancer drug (Lu et al., 2012; Park et al., 2019; Piccoli et al., 2019), our results suggest that it may have the potential for clinical translation to enhance the function of aging tissues and improve health span.
## Cell culture
Human myoblasts were purchased from Cook MyoSite (Pittsburgh, PA). Cells were isolated from quadriceps muscle of three donors (18‐year‐old male, 25‐year‐old female, and 75‐year‐old female). The cells were cultured on Matrigel (0.1 mg/ml; CORNING, Corning, NY)‐coated T175 flasks and expanded in skeletal muscle cell growth medium (GM) as described previously (Rajabian et al., 2020). The cells cultured for <5 passages (<7 population doubling, termed young myoblast or YM) and $p \leq 10$ passages (> 20 population doubling, termed senescent myoblast or SM). The cells were cultured in a humidified incubator at 37°C and $10\%$ CO2, and the medium was replenished every other day. Cells were passaged every 4–5 days before they reached $80\%$ confluence.
The human myoblasts were cultured for more than 10 passages (equivalent to >50 days of culture) to show well‐known hallmarks of cellular senescence as described previously (Rajabian et al., 2020; Shahini et al., 2021). These senescent cells were treated with reversine (Sigma‐Aldrich Chemical Company) at final concentration of 5 μM in GM. As reversine is dissolved in dimethylsulfoxide (DMSO), the control cells were cultured in the same volume of DMSO without reversine. To block AMPK activity, Compound C (CC; Cayman Chemical, Ann Arbor, MI, catalog no. 11967) was added to the GM for at least 18 h prior to experiments. To differentiate myoblasts into multinucleated myotubes, myoblasts were seeded on Matrigel (0.1 mg/ml)‐coated dishes and allowed to reach >$90\%$ confluence in GM. Then, the cells were switched to differentiation medium composed of high‐glucose DMEM supplemented with insulin (10 μg/ml), epidermal growth factor (10 ng/ml), BSA (500 μg/ml), and gentamicin (50 μg/ml) for a period of 7 days. For reversine‐treated cells, reversine was removed for 0, 4, 8, and 12 days prior to differentiation.
## Etoposide treatment to induce cellular senescence
Young human myoblasts were treated with 50 μM etoposide (E1383, Sigma‐Aldrich, St. Louis, MO) for 24 h, followed by 48 h in fresh culture medium. This concentration of etoposide induced almost $100\%$ positive for SA‐β‐Gal.
## Measurement of cell radius
To measure the cellular radius, the cells were detached from the surface using $0.25\%$ Trypsin–EDTA and centrifuged at 300g for 5 min. The cell pellet was resuspended in PBS, and 10 μl was inserted in hemocytometer. Phase images were acquired using EVOS FL inverted digital microscope (ThermoFisher Scientific), and the cellular area was measured using the ImageJ software. Cell radius was calculated using the following equation: area = π × R 2 (Morgan et al., 2016).
## Senescence‐associated‐β‐galactosidase
The SA‐β‐Gal activity was detected using the Senescence Detection Kit (ab65351, Abcam) according to the manufacturer's instructions. Cells were photographed using the Zeiss Axio Observer Z1a microscope and a 10 × objective (Plan‐APOCHROMAT). The number of SA‐β‐Gal‐positive and total cells were counted in five randomly selected fields of view (total of >250 cells were counted per sample).
## mtDNA content quantification
DNA was isolated using the QIAmp DNA Mini Kit (QIAGEN, Germantown, MD catalog no. 51304) according to the manufacturer's instructions. Quantitative real‐time PCR was performed using the SYBR Green Kit (Bio‐Rad, Hercules, CA, catalog no. 172–5120) with 25 ng of DNA used per reaction. mtDNA was quantified using the following human primers for mitochondrially encoded tRNALeu (UUR) gene (MT‐TL1, forward primer: 5′‐CACCCAAGAACAGGGTTTGT‐3′ and reverse primer: 5′‐TGGCCATGGGTATGTTGTTA‐3′), and nDNA was quantified using the following human primers for Beta‐2‐Microglobulin (B2M) gene [forward primer: 5′‐GAGGCTATCCAGCGTACTCCA‐3′ and reverse primer: 5′‐CGGCAGGCATACTCATCTTTT‐3′], respectively. Both mtDNA and nDNA threshold cycle average values were obtained, and the mtDNA content was calculated relative to nDNA, mtDNA/nDNA = 2(CTnDNA−CTmtDNA).
## Seahorse assay
We used Seahorse extracellular flux (XFe96) analyzer (Agilent technologies, Santa Clara, CA) to measure the extracellular acidification rate, which is a measure of glycolysis. The myoblast cells were seeded at a density of 3000 cell/cm2 on matrigel‐coated XFe96 seahorse culture plates for 12 h. At the time of the assay, the cells were washed and culture medium was changed to seahorse assay medium for 45 min (XF DMEM medium, Cat No. 103575‐100, Agilent technologies, Santa Clara, CA), and glycolysis and glycolytic capacity were measured after sequential injection of 10 mM glucose, 1 μM oligomycin, and 10 mM 2‐DG. All calculations were based on Agilent Seahorse XF Technology white paper document or manufacturer protocol. Acute response (glucose sensitivity) was measured after different dosage of glucose (0.1, 0.5 and 1 mM) and is equal to [Max ECAR after glucose injection‐ Min ECAR before injection].
We also measured the oxygen consumption rate (OCR) and performed mito stress test. The cells were seeded at a density of 10,000 cell/cm2 on matrigel‐coated XFe96 seahorse culture plates for 12 h. At 1.5 h prior to the assay, the cells were incubated in XF base medium (Agilent) supplemented with 10 mM glucose, 1 mM pyruvate, and 2 mM glutamine. Subsequently, OCR was measured after the addition of 1 μM oligomycin, 1.5 μM FCCP, and a mixture containing 0.5 μM each of antimycin A and rotenone. After the seahorse measurements were completed, total cellular content was measured using a CyQUANT Cell Proliferation Assay kit (ThermoFisher Scientific, catalog no. C7026), and the OCR values were normalized to (number of cells × mtDNA/nDNA).
## Adenosine triphosphate measurement assay
Myoblasts were seeded on Matrigel (0.1 mg/ml)‐coated 48‐well plates (5 × 103 cell/cm2). ATP concentration was measured per manufacturer's instructions. In brief, 50 μl of detergent was added to each sample for 5 min to lyse the cells under continuous mixing in an orbital shaker at 600–700 rpm. Then, 50 μl of substrate solution was added to cell lysate and incubated for 5 min in an orbital shaker at 600–700 rpm. Hundred microliters of each lysate was transferred to a well in a 96‐well plate, and luminescence was recorded using a Biotek Synergy 4 plate reader as directed by the manufacturer (background luminescence of empty wells was subtracted from each value).
## Western blots
Myoblast cells were lysed in buffer containing 62.5 mM Tris–HCl (pH 6.8 at 25 °C), $2\%$ (w/v) SDS, $10\%$ (v/v) glycerol, $0.1\%$ (w/v) bromophenol blue, 41.67 mM dithiothreitol (DTT) (Cell Signaling, Danver, MA), and protease inhibitor cocktail (Sigma‐Aldrich, St. Louis, MO). The protein concentration was determined using the Bradford assay (Bradford, 1976). Lysates were denatured by incubation at 95°C for 5 min, and proteins were loaded at 45 μg per lane and were separated in $12\%$ acrylamide gels (Waltham, MA) by SDS‐polyacrylamide gel electrophoresis based on their molecular weight. After transferring proteins to nitrocellulose membranes (Bio‐Rad) using the Trans‐Blot Turbo Transfer System (Bio‐Rad, Hercules, California), the membranes were blocked $5\%$ (w/v) nonfat dry milk in Tris‐buffered saline with $0.1\%$ Tween® 20 detergent (TBST) buffer (20 mM tris, 150 mM NaCl, and $0.1\%$ Tween 20) for 1 h at room temperature. Subsequently, membranes were incubated overnight at 4°C with antibodies including, Akt1 (Clone No. 9Q7, 1:1000 dilution in blocking buffer, Invitrogen), pAkt1‐ Ser473 (Clone No. 14–6, 1:1000 dilution in blocking buffer, Invitrogen), Akt2 (Clone No. 4H7, 1:1000 dilution in blocking buffer, Invitrogen), pAkt2‐Ser474 (Cat No. PA5‐104870, 1:1000 dilution in blocking buffer, Invitrogen), LC3A/B (Clone No. D3U4C, Cat No. 12741, Cell Signaling Technology), AMPKα (Cat No. 2532, 1:1000 dilution in blocking buffer, Cell Signaling Technology), pAMPKα (Thr172, Clone No. 40H9, Cat No. 2535, 1:1000 dilution in blocking buffer, Cell Signaling Technology), Parkin (Cat No. 2132, 1:1000 dilution in blocking buffer, Cell Signaling Technology), PINK1 (Clone No. D8G3, Cat No. 6946, 1:1000 dilution in blocking buffer, Cell Signaling Technology), PDH (Cat No. ab126203, 1:1000 dilution in blocking buffer; Abcam, Cambridge, MA), pPDH‐E1α (pSer232) (Cat No. AP1063, 1:1000 dilution in blocking buffer; Millipore, Billerica, MA), and glyceraldehyde 3‐phosphate dehydrogenase (GAPDH, Clone No. 14C10, Cat No. 2118, 1:10,000 dilution in blocking buffer; Cell Signaling). Finally, the protein bands were visualized using horseradish peroxidase conjugated secondary antibodies and a chemiluminescence kit (Cell Signalling, Danvers, MA) according to the manufacturer's instructions. Luminescent blots were imaged using ChemiDoc™ Touch Imaging System (Bio‐Rad, Hercules, CA).
## Immunocytochemistry
Myoblasts were fixed in $10\%$ formalin for 10 min at RT. The fixed cells were permeabilized with $0.1\%$ (v/v) Triton X‐100/PBS for 10 min at RT and blocked with blocking buffer [$5\%$ (v/v) goat serum in $0.01\%$ (w/v) Triton X‐100/PBS] at RT for 1 h. Next, samples were immunostained with antibodies against myosin heavy chain (MYHC; Clone No. A4.1025, 1:500 dilution in blocking buffer; Millipore, Billerica, MA), sarcomeric alpha actinin (SAA; Clone No. EP2529Y, 1:200 dilution in blocking buffer; Abcam), γH2AX (Clone No. S139, 1:200 in blocking buffer; Cell Signaling Technology), H3K9me3 (Cat No. ab8898, 1:500 in blocking buffer; Abcam), H3K27me3 (Clone No. C36B11, 1:200 in blocking buffer; Cell Signaling Technology), desmin (Clone No. D93F5, 1:100 in blocking buffer; Cell Signaling Technology), Ki67 (Cat No. ab15580, 1:200 in blocking buffer; Abcam), F‐Actin (Alexa Fluor 488 Phalloidin, Thermo Fisher Scientific; 1:100 dilution in PBS with $1\%$ (w/v) Bovine Serum Albumin for 2 h). Subsequently, the cells were stained with Alexa Fluor 568 or 488 conjugated goat antimouse or goat antirabbit antibodies (1:250 dilution in blocking buffer; ThermoFisher Scientific) and counterstained with Hoechst 33342 nuclear dye for 5 min (1:1000 dilution in PBS, Thermo Fisher Scientific).
TMRM (ThermoFisher Scientific) and MitoTracker Red CMXRos (ThermoFisher Scientific) were used to monitor the mitochondrial membrane potential. The cells were stained by the addition of these dyes to the culture medium at 100 nM for 30 min at 37°C. The cells were stained with the Hoechst 33342 nuclear dye for 5 min (1:1000 dilution in PBS; ThermoFisher Scientific). Cells were imaged using Zeiss Axio Observer Z1 (LSM 510; Zeiss, Oberkochen, Germany) equipped with digital camera (ORCA‐ER C4742‐80; Hamamatsu, Bridgewater, NJ).
## Statistical analysis
Statistical analysis was performed using one‐way or two‐way analysis of variance (ANOVA) analysis followed by Tukey's multiple comparisons test using GraphPad Prism version 8 software. Each experiment was repeated three times with at least triplicate samples in each experiment. Data were reported as mean ± standard deviation (SD). Statistical significance was denoted as *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001$, and ns, not significant.
## AUTHOR CONTRIBUTIONS
Experiments were planned and designed by Nika Rajabian and Stelios T. Andreadis. Experimental data were generated and collected by Nika Rajabian, Izuagie Ikhapoh, Debanik Choudhury, Shilpashree Saha, Aishwarya Surendra Kalyankar, Aref Shahini, Kendall Breed, and Pihu Mehrotra. Data analysis and interpretation were performed by Nika Rajabian and Stelios T. Andreadis. Writing and critical revisions of the manuscript were performed by Nika Rajabian and Stelios T. Andreadis.
## CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available in Mendeley Data at https://data.mendeley.com/datasets/wbymrhr5g$\frac{7}{1}$, DOI:10.17632/wbymrhr5g7.1.
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|
---
title: 'Difficulty Regulating Social Media Content of Age-Restricted Products: Comparing
JUUL’s Official Twitter Timeline and Social Media Content About JUUL'
journal: JMIR Infodemiology
year: 2021
pmcid: PMC10014088
doi: 10.2196/29011
license: CC BY 4.0
---
# Difficulty Regulating Social Media Content of Age-Restricted Products: Comparing JUUL’s Official Twitter Timeline and Social Media Content About JUUL
## Abstract
### Background
In 2018, JUUL Labs Inc, a popular e-cigarette manufacturer, announced it would substantially limit its social media presence in compliance with the Food and Drug Administration’s (FDA) call to curb underage e-cigarette use. However, shortly after the announcement, a series of JUUL-related hashtags emerged on various social media platforms, calling the effectiveness of the FDA’s regulations into question.
### Objective
The purpose of this study is to determine whether hashtags remain a common venue to market age-restricted products on social media.
### Methods
We used Twitter’s standard application programming interface to download the 3200 most-recent tweets originating from JUUL Labs Inc’s official Twitter Account (@JUULVapor), and a series of tweets ($$n = 28$$,989) from other Twitter users containing either #JUUL or mentioned JUUL in the tweet text. We ran exploratory (10×10) and iterative Latent Dirichlet Allocation (LDA) topic models to compare @JUULVapor’s content versus our hashtag corpus. We qualitatively deliberated topic meanings and substantiated our interpretations with tweets from either corpus.
### Results
The topic models generated for @JUULVapor’s timeline seemingly alluded to compliance with the FDA’s call to prohibit marketing of age-restricted products on social media. However, the topic models generated for the hashtag corpus of tweets from other Twitter users contained several references to flavors, vaping paraphernalia, and illicit drugs, which may be appealing to younger audiences.
### Conclusions
Our findings underscore the complicated nature of social media regulation. Although JUUL Labs Inc seemingly complied with the FDA to limit its social media presence, JUUL and other e-cigarette manufacturers are still discussed openly in social media spaces. Much discourse about JUUL and e-cigarettes is spread via hashtags, which allow messages to reach a wide audience quickly. This suggests that social media regulations on manufacturers cannot prevent e-cigarette users, influencers, or marketers from spreading information about e-cigarette attributes that appeal to the youth, such as flavors. Stricter protocols are needed to regulate discourse about age-restricted products on social media.
## Introduction
Following the Food and Drug Administration’s (FDA’s) call to curb underage e-cigarette use and increasing criticism of JUUL’s youth-oriented “Vaporized” campaign [1], JUUL Labs Inc announced it would limit its social media presence. As part of the FDA agreement, JUUL deleted its official Facebook and Instagram accounts, reduced its Twitter activity, and removed older Twitter posts that could be attractive to youth or interpreted as marketing to youth [1].
FDA can regulate what JUUL and other e-cigarette manufacturers can post on official social media platforms [2]. However, the FDA cannot regulate posts about JUUL by customers or influencers, who can identify their posts as JUUL-related by using hashtags—short words or phrases preceded by the ‘#’ symbol that label the content of a social media post and cause the post to appear in users’ keyword searches. Hashtags spread social media content rapidly [3] and are therefore used for branding and marketing of certain products for mainstream appeal [4]. Any social media user (including paid or unpaid social media influencers, retailers, or enthusiastic consumers) can use hashtags to spread content about any topic, including age-restricted products subject to federal regulations. Alcohol, for example, is heavily marketed through hashtags [5-7], though much social media content about alcohol does not originate from official corporate accounts.
Marketing research further suggests that hashtags are used as branding or marketing ploys to promote age-restricted products including alcohol [7] and tobacco [8] on social networking websites. Using hashtags for age-restricted products may help circumvent age-gates, which are already proven to be ineffective at deterring underage engagement with age-restricted products [9]. Thus, any effort by JUUL Labs Inc to curb marketing to underage users may be stunted by the presence (and popularity) of vaping-related hashtags not subject to regulations imposed on manufacturers.
Indeed, the prevalence of JUUL-related hashtags on Instagram increased after JUUL reduced its own social media presence [1]. This suggests a limitation to FDA regulations wherein age-restricted products can still be marketed separately from official company platforms. By consequence, age-restricted items that are popular among youth including alcohol, tobacco, and e-cigarettes remain overtly visible and marketable to this audience, despite official corporate positions that denounce such use.
Regulation of harmful social media content is a critical public health issue [10]. To our knowledge, however, no study has compared verified corporate accounts versus similarly related hashtags from noncorporate posters to examine the effectiveness of social media regulation efforts. This study uses an inductive approach and natural language processing (NLP) modeling to examine differences in JUUL’s official, regulated Twitter account @JUULVapor, JUUL-related content posted on social media. Our study is guided by three research questions: Collectively, findings from our study will contribute to discourse about information diffusion via social media. We hypothesize that despite JUUL’s efforts to scrub their social media platforms of youth-oriented content, hashtags about JUUL remain pervasive and highly visible to youth.
## Data
Data for this study were procured by leveraging Twitter’s application programming interface (API). From the API, we collected two corpora unique to this study: [1] @JUULVapor’s Twitter timeline ($$n = 3200$$ tweets, the maximum number of most recent tweets posted by a single user allotted for download through the standard API), hereafter referred to as the @JUULVapor corpus (January 1 to May 31, 2021) and [2] a 1-month collection of tweets containing #JUUL or “JUUL” ($$n = 29$$,989 tweets), referred to as the #JUUL corpus (May 1 to June 1, 2021). For the #JUUL corpus, specifically, we performed a *Bot analysis* [11] to remove tweets that originated from nonhuman accounts ($$n = 135$$). No *Bot analysis* was required for the @JUULVapor corpus considering those tweets were pulled from JUUL Lab Inc’s official Twitter account. We performed this procedure to ensure that discourse captured in subsequent analyses originated from humans and not an automated program. Upon removing bot accounts, 2 raters independently reviewed the text of the #JUUL tweets and removed any from the corpus that were not expressly about e-cigarettes or vaping ($$n = 23$$). An author of this study also cross-checked tweet IDs in either corpus to ensure there was no accidental overlap in tweets (ie, the same tweet appearing in both the @JUUL and #JUUL corpora). Note that our total sample inclusive of both corpora ($$n = 33$$,189 tweets) exceeds the mean observed sample size of collected tweets in a meta-analysis of public health social media studies ($$n = 10$$,000) [12].
## Analysis
Our research questions are exploratory. Thus, we chose to use LDA topic models, a Bayes-driven, unsupervised NLP method, to examine differences in themes for the @JUULVapor and #JUUL corpora. LDA and related topic modeling analyses have been similarly leveraged in other health contexts, including studying discourse about the COVID-19 pandemic [13] and map themes among corpora of age-restricted products [14].
While previous studies generate topic models for differing corpora, qualitatively review differences between corpora, and discuss the meaning of those differences, our study takes a 2-step approach. The first step broadly examines themes for a fixed set of topics or words per topic (ie, 10 topics and 10 words per topic). Valdez et al [15] have provided examples of exploratory topic models in practice. The second step uses an iterative topic model analysis that meta-analytically generates models with an increasing number of topics per corpus (ie, 1 topic, 2 topics…20 topics) [16]. This analysis generates a coherence score for each iteration, such that higher scores are equated with better model fit and interpretability. We used this second analysis to identify the optimal number of topics per corpus and further refine the models (ie, eliminate redundancy and noise) for maximum interpretability. To ensure the validity of our coherence scores, we selected a random sample of 50 tweets per corpus and matched each tweet’s content to a respective theme identified by the topic model. We successfully placed each tweet within a topic, suggesting our topic models were both coherent and precise.
## Procedure
Our workflow is detailed in Figure 1. Upon downloading and cleaning the @JUULVapor and #JUUL corpora, we performed the following. First, we calculated standard descriptive statistics for each corpus, including the average number of likes, retweets, and number of tweets that originated from Verified accounts, or accounts reviewed to ensure they are owned and operated by a specific person (research question 1). Second, we performed an exploratory 10×10 topic model for the #JUUL and @JUULVapor corpora and qualitatively compared differences between them. Lastly, we performed an iterative analysis to identify the optimal number of topics and again qualitatively reviewed the topic model for each corpus for differences (research questions 2 and 3).
**Figure 1:** *Conceptual framework guiding our study. LDA: Latent Dirichlet Allocation.*
## Ethical Use of Data
All procedures and analyses undertaken in this study conform to the Twitter’s terms for data use agreement. Our study was exempt from institutional review board review, given the secondary nature of this data collection and analysis.
## Descriptive Differences
We identified differences in total retweets and favorites per corpus. On average, content in the @JUULVapor corpus, JUUL Lab Inc’s official Twitter handle, was retweeted 1.29 times (SD 16.77 times) and favorited 0.25 times (SD 4.49 times). For the #JUUL corpus, tweets were on average favorited 0.41 times (SD 4.74 times) and retweeted 4.53 times (SD 52.07 times). Exactly 237 tweets in the #JUUL corpus originated from verified twitter accounts. Given that such a marginal number of tweets originated from a verified account, we did not perform statistical tests to determine whether scope and reach were significantly different between verified and nonverified accounts.
## Exploratory Topic Models
Table 1 outlines an exploratory topic model for the @JUULVapor corpus. This topic model represents a condensed version of JUUL Lab Inc’s 3200 most recent tweets delineated by 10 topics and 10 words per topic. The themes in the @JUULVapor’s topic model were generally interpretable. Five of the topics in the @JUULVapor topic model contained references related to customer support or product warrant-related queries—which we interpreted as responses to complaints about JUUL associated products. Words recurrent among this body of topics include please, dm [direct message], sorry, thank, contact, and customer for support-oriented topics; and JUUL, device, limited, warranty, information for warranty-related topics. This model also referenced adult product use (ie, legal, adults, and age) and acknowledgement of underage use and underage use prevention (ie, underage, prevention, minors, market, and seriously)—which we interpreted as JUUL Lab Inc’s forthrightly attempt to address controversies long associated with its brand. Notably, there were very few references to controversial topics such as flavors, addiction, and other drugs such as cannabis—which we interpreted as JUUL Lab Inc’s seeming attempt to distance itself from controversial aspects also associated with its brand. Other topics that emerged in this model including “Recycling” and “Warranty.” Recycling-associated tweets generally referenced the importance of recycling used JUUL cartridges (which are disposable). We interpreted warranty as a topic related to customer support—namely ways in which customers can secure refunds if products are defective.
Table 2 outlines an exploratory topic model for the #JUUL corpus, which is a random collection of tweets discussing JUUL but not originating from JUUL’s official Twitter timeline. This topic model represents a condensed version of a months’ worth of tweets about JUUL, which were identified by either using #JUUL or containing the word “JUUL” in each tweet’s text. The themes in the #JUUL topic model were somewhat interpretable, though less so than the @JUULVapor corpus reflecting greater content diversity. For example, topics that were somewhat vague, yet still referenced vaping, were labeled as a “General Vape” topic, which comprised seemingly unrelated words related to various aspects of vaping but not necessarily related to JUUL as a brand. Words recurrent among these topics include vape, JUUL, hit, smoke, smoking, take, and others. Beyond vague references to vaping, several clearer topics also emerged; these include a topic about marijuana and cannabis, the intersection of vaping and cigarettes, and a topic about nicotine, which we collectively interpreted as youth-appealing narratives about vaping. Note, topics consisting of “nicotine” or “flavors” are entirely absent from the @JUUL 10×10 topic model, which may be indicative of JUUL Lab Inc’s attempt to distance itself from web-based controversies and present a cleaner image.
## Iterative Topic Model
Iterative analysis revealed the optimal number of topics given the total number of words in each corpus. Figure 2 plots the coherence score, which measures the semantic similarity of words in each topic, per corpus [17]. Peaks in the graphs denote the optimal number of topics for each corpus.
For the @JUULVapor corpus, there were 2 optimal topics (coherence score=0.36) (Textbox 1). Both topics were interpreted as referring to either responses to customer’s concerns or complaints about JUUL products. Topics from the general topic model that centered on underage use, purchasing, and recycling were absent. This may suggest, at least partially, that the renewed purpose of JUUL Lab Inc’s official Twitter account is to field customer complaints and comments.
For the #JUUL corpus, there were 4 optimal topics (Coherence score=0.50) (Textbox 2). These topics were more diverse than the @JUULVapor corpus; containing topics related to marijuana, vape/smoking, general vaping, and vaping-related damage. Here, there is more emphasis on the elicit side of vaping/smoking, and youth appealing narratives. These topics stand in sharp contrast with the #JUUL Optimal Topic Model (Textbox 1), which only revealed customer support–related topics.
**Figure 2:** *Coherence score plot by corpus. The X axis represents the total number of topics; the Y axis represents coherence score per iteration.*
## Principal Findings
This study examined the use of hashtags to indirectly market age-restricted products on social media. We leveraged the Twitter API to archive and compare 2 corpora specific to e-cigarette use with LDA topic models. The first corpus (ie, @JUULVapor) contained 3200 tweets derived from JUUL Lab Inc’s official Twitter account. The second corpus (ie, #JUUL) contained a month’s worth of tweets (May 1 to June 1, 2021) that contained #JUUL or mentioned JUUL within the tweet text ($$n = 28$$,989). When the corpora were compared, we identified several telling observations within each corpus, which showcase disparate uses in @JUULVapor vs #JUUL. These partially include the @JUULVapor corpus seeming compliance to prevent underage marketing versus an array of random, youth-appealing content in the #JUUL corpus. Below we discuss these differences within the scope of their current literature delineated by each research question.
## RQ1: Evidence of Greater Reach in the #JUUL Corpus
Our first research question asked whether content about JUUL contained evidence of greater reach and visibility than content posted on JUUL’s official Twitter account. Reach and visibility, here, was measured by the average number of likes and retweets per tweet in each corpus. Our findings suggest that, overall, content about JUUL, and e-cigarettes more broadly, are clearly visible on social media spaces via hashtags. This finding corroborates a large body of work that suggests hashtags are often used to quickly distribute branding and product marketing information [4,18,19].
Regarding content, tweets in the #JUUL corpus were, on average, retweeted and liked with greater frequency than content posted by @JUULVapor. That content in the #JUUL corpus was retweeted more often than @JUUL is perhaps not entirely surprising. As mentioned previously (and throughout the remainder of the discussion), content in the #JUUL corpus contained topics of discussion that are inherently appealing to youth, versus content in the @JUUL corpus that seemed to unilaterally focus on customer complaints. For example, in the #JUUL corpus, we observed mentions of flavors (ie, mint, mango, and cucumber), cannabis vaping (ie, vape cartridges), and meme/joke-sharing, all of which are inherently conducive appealing to youth and higher post engagement. Additionally, as hashtags are used for rapid content organization of content, it is likely any social media user (agnostic of age differences) can see content posted by #JUUL, including those who did not expressly seek this information themselves.
## RQ2: LDA Topic Models as Tools to Contrast Corporate Corpora With an Assortment of Related Tweets
Beyond the reach and scope of tweets, we also investigated whether LDA could be leveraged to identify content differences in corporate versus lay user social media accounts. LDA topic models have been historically leveraged in an exploratory capacity to consolidate an overwhelming amount of text data into manageable chunks (ie, themes) that represent the most salient components of that text data [20,21]. For example, prior studies have used topic models to explore the underlying thematic structures across a broad range of corpora, including studying discourse about societal events [13], identifying alcohol branding strategies [14], and mapping publication histories of leading Health journals [22]. As topic models become increasingly used in the social and medical sciences, it remains debatable how these models can be used to test applied, rather than exploratory, hypotheses [22]. This includes ample discussion how topic models could theoretically be used to inform possible digital e-health interventions [23,24] and to construct bots from topic modeling data that meaningfully identify mental health distress [25].
To our knowledge, LDA topic models have not been used in either exploratory or applied capacities to compare social media content originating from a specific corporation and a collection of tweets about that product (though not necessarily originating from the corporate account). Our findings show that such models can be leveraged for this purpose, evidenced by our findings that identified qualitative differences in content between the @JUULVaporVapor and #JUUL corpora. We used exploratory models as a standardized metric to generate the same number of topics and words per topic for each corpus. We then ran iterative models to identify the optimal number of topics within each corpus (ie, improve granularity and precision of the models). Gethers and Poshyvanyk [26] provide more insight into granularity and relational topic models.
We contend the combined use of exploratory and iterative model may provide a conceptual framework for future topic modeling studies. For example, exploratory models may uncover broad themes in a corpus. Iterative models will then only identify highly salient (or themes of highest priority) given a corpus. The range of topics uncovered by the iterative models may highlight how broad or narrow the corpus is in scope—more themes equate to broad content in a corpus, few themes indicate narrow scope or focus. For our study @JUULVapor’s two optimal topics, contrasted with four in the #JUUL corpus, suggests the content in the @JUULVapor corpus was much narrower and more defined; for @JUULVApor, that is customer support. More topics in the #JUUL corpus suggest the content was more diverse, containing a wider array of underlying themes; that is, more youth-appealing narrative. More research is needed to identify optimal use of exploratory and precision topic models in a research context. However, we encourage the use of both exploratory and iterative models when comparing corpora of vastly different sizes.
## RQ3: Implications for Content differences between @JUULVapor and #JUUL
Our final research question posited whether content differences identified between corpora were meaningful. Across each analysis, we identified differences that clearly distinguished each corpus, including vastly different ways in which e-cigarettes were mentioned and discussed between @JUULVapor and #JUUL. This includes, as mentioned, a narrow scope of content in the @JUULVapor corpus, versus more diverse, often youth-appealing content in the #JUUL corpus. This finding, coupled with increased engagement in the #JUUL corpus supports extant research that hashtags are effective means of disseminating age-inappropriate content rapidly [3,27]. Nonetheless, deeper insights into topic nuance are needed.
First, cursory insights into JUUL Lab Inc’s corporate Twitter account show a seeming attempt to comply with FDA regulations barring youth marketing. In 2018, JUUL Labs Inc had been accused of using corporate social media accounts to market to youth and, in compliance with court orders and regulations, scrubbed their social media histories of youth-appealing content. Our inability to collect any deleted tweets suggests a natural limitation to social media research; namely that deleted content is truly removed from archives and cannot be accessed. However, remaining tweets posted by @JUULVapor—that is, those analyzed in this study—showcase a semiactive Twitter account almost entirely devoid of marketing content. Indeed, both exploratory and iterative topic models, the majority of topics and words per topic for @JUULVapor were customer support oriented. A review of individual tweets further revealed that the majority of posts were corporate response to complaints about JUUL products (eg, TWEET Hi there, we’re sorry to hear that. You can access troubleshooting tips for your JUUL device at…). This shift in content may indicate that JUUL is trying to position itself as a responsible company, similar to the corporate responsibility advertising campaigns used by Big Tobacco companies to present a respectable image while selling a dangerous product (eg, TWEET Minors should not use any nicotine product and we take the prevention of underage use of JUUL very seriously) [28].
By contrast, themes in the #JUUL exploratory and iterative models were more diverse and contained several references that may be appealing to youth. For example, the #JUUL corpus contained references to cartridge flavors, which have been banned in the United States because they are attractive to youth but are still legal in disposable JUUL-like products [29]. Although the JUUL company is no longer actively promoting flavors, it appears users continue to associate the JUUL product with flavors, including mango, cucumber, mint, and others. Beyond flavors, we also observed a high co-occurrence of flavors with “marijuana.” Marijuana was prominent in the exploratory #JUUL model (ie, topic 2) and retained its prominence during the iterative model. This suggests a significant portion of the #JUUL corpus contained references to cannabis. Interestingly, few tweets or topics directly mentioned JUUL (the company). JUUL not being expressly mentioned topics indicates few tweets expressly mentioned JUUL and marijuana together in the same post. However, despite not mentioning the brand directly, the web-based conversation regularly discussed the use of vape products for marijuana, which may be at least partially explained by JUUL’s evolution in mainstream vernacular form a noun (ie, JUUL products) to a verb (ie, JUUL’ing, a specific and colloquial term for “vaping”). We also observed profanity in the #JUUL topic models, which was entirely absent in @JUULVapor (eg, TWEET Bro, we are all [expletive] high on this vape). Profanity may indicate the presence of younger social media users [30].
Although the #JUUL corpus contained youth-appealing content (ie, profanity, high mentions of marijuana, and flavors among other indicators), we also observed topics in both @JUULVapor and #JUUL corpora detailing antivaping-related advocacy. For the @JUULVapor corpus, perhaps unsurprisingly, this may provide evidence that JUUL Labs Inc complied with court orders to stop marketing to underage users (eg, TWEET today we’re implementing a series of new measures that build upon or existing efforts to reduce underage use). For the #JUUL corpus, this may also suggest a substantive body of antivaping-related advocacy that adopted a hashtag strategy to spread their messages more effectively (eg, #vanishvaping). However, evidence of antivape advocacy in either corpus does not suggest that the messaging effectively deters youth use or substantively changes the wider web-based conversation. Rather, in some cases, there seem to be additional sarcastic comments that offset antivape messaging (eg, TWEET All these anti-vape adds make me want to snort meth). Thus, such policies and court orders are ineffective in regulating the totality of messages received by underage users, particularly given that nonofficial tweets were more likely to be shared/favorited than official @JUULVapor messages. Antimessaging campaigns of other age-restricting products have also shown to have wide reach but inconclusive results [31], which may suggest that despite the best efforts of antivape advocates, their behavior change attempts may fail.
## Implications for Social Media Messaging About Vaping
Together, these findings further demonstrate the overall lack of control the official @JUULVaporVapor account has in directing web-based conversations about vape products. Despite JUUL presenting a “clean image,” their brand remains associated with a dangerous and addictive product that is naturally appealing to youth. However, it is also clear that more research on who is tweeting about JUUL and vaping, and how hashtags facilitate marketing illicit behavior, is needed. Future research should consider adding deep learning models to partition tweets about vaping by demographic variables to, among other matters, predict the likelihood an account posted about JUUL was that of an underage user.
From a public health/medical/interventionist perspective, our findings also compel us to ponder how mining communication patterns (ie, tracking discourse about JUUL) can be further leveraged to identify intervention targets promoting antivape messaging. In this study, the sharp divide in content between @JUUL and #JUUL suggest that provape messaging did not end after JUUL Lab Inc’s court order to curb marketing efforts. Rather, marketing manifested through shared user content about products that remain popular while not necessarily referencing the elicit product. It is indeed possible that some influencers are paid by manufacturers such as JUUL to surreptitiously market products without seeming association with the brand. This was a strategy used by Big Tobacco to continue marketing indirectly while seemingly complying with antimarking efforts. Additionally, on Twitter, it is difficult to determine which posts from celebrity and other verified accounts are paid advertisements. Other platforms, including Instagram and Facebook, expressly designate ads with a “#ad” notice; however, this is less common with Twitter. Policy efforts should also center on clearer guidelines for designating paid or sponsored posts versus regular posts on Twitter.
## Limitations
Our study is subject to limitations we hope to address in future work. First, we acknowledge a likely demographic bias inherent to social media studies. This includes a sample that likely skews younger, male, wealthier, and whiter than the general population [32]. Given that our study was exploratory, we also did not control spatial and geographic patterns in social media data, which affect how and what users post within a given time (ie, rural vs urban settings, or older users posting earlier in the morning than younger users) [33,34]. Regarding data analysis, we acknowledge that we did not perform a formal qualitative analysis with these data. Topic models were used to, instead, consolidate each corpus and allow us to draw inferences about the data from those topics. Because of sample size constraints, we were also unable to draw meaningful comparisons between verified and nonverified users in the #JUUL corpus. However, despite these limitations, we believe gaps in our study present opportunities for future research related to social media discourse on age restricted products. This includes performing other NLP methodologies with similar data (ie, sentiment analysis) to understand polarity in discourse or applying classifiers to accounts tweeting about age-restricted products to predict age and gender among other demographic traits. Dai et al [35] have provided further information about the M3 classifier in a health context. Such studies may provide a deeper and more nuanced landscape of social media discourse related to age-restricted products.
## Conclusions
@JUUVapor may be compliant with web-based marketing restrictions and promoting antivaping messaging. However, JUUL Labs *Inc is* powerless to control the larger narrative about vaping on social media. Indeed, hashtags about vaping and JUUL contain much of the youth-directed content that led to the initial impositions placed on JUUL by the FDA. Our results underscore the difficulty of regulating social media content despite federal impositions that ban marketing of age-restricted products in web-based spaces. Given the limited ability of social media to restrict what underage users see on their websites, companies can bypass marketing regulations by allowing users to freely share related hashtags or by paying social media influencers to disseminate these hashtags [36]. Although it is impossible to regulate free speech on the internet, perhaps public health advocates could harness the power of hashtags to deliver antivaping messages, though the effectiveness of such campaigns is not guaranteed.
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|
---
title: 'Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment
Analysis of YouTube Users'' Comments'
journal: JMIR Infodemiology
year: 2022
pmcid: PMC10014090
doi: 10.2196/38749
license: CC BY 4.0
---
# Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments
## Abstract
### Background
With direct-to-consumer (DTC) genetic testing enabling self-responsible access to novel information on ancestry, traits, or health, consumers often turn to social media for assistance and discussion. YouTube, the largest social media platform for videos, offers an abundance of DTC genetic testing–related videos. Nevertheless, user discourse in the comments sections of these videos is largely unexplored.
### Objective
This study aims to address the lack of knowledge concerning user discourse in the comments sections of DTC genetic testing–related videos on YouTube by exploring topics discussed and users' attitudes toward these videos.
### Methods
We employed a 3-step research approach. First, we collected metadata and comments of the 248 most viewed DTC genetic testing–related videos on YouTube. Second, we conducted topic modeling using word frequency analysis, bigram analysis, and structural topic modeling to identify topics discussed in the comments sections of those videos. Finally, we employed Bing (binary), National Research Council Canada (NRC) emotion, and 9-level sentiment analysis to identify users' attitudes toward these DTC genetic testing–related videos, as expressed in their comments.
### Results
We collected 84,082 comments from the 248 most viewed DTC genetic testing–related YouTube videos. With topic modeling, we identified 6 prevailing topics on [1] general genetic testing, [2] ancestry testing, [3] relationship testing, [4] health and trait testing, [5] ethical concerns, and [6] YouTube video reaction. Further, our sentiment analysis indicates strong positive emotions (anticipation, joy, surprise, and trust) and a neutral-to-positive attitude toward DTC genetic testing–related videos.
### Conclusions
With this study, we demonstrate how to identify users' attitudes on DTC genetic testing by examining topics and opinions based on YouTube video comments. Shedding light on user discourse on social media, our findings suggest that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market constantly evolving, service providers, content providers, or regulatory authorities may still need to adapt their services to users' interests and desires.
## Background and Objectives
Since the completion of the human genome project in 2003, dwindling genome sequencing costs and a rising interest in genomics among the general public have paved the way for direct-to-consumer (DTC) genetic testing [1]. Today, users can purchase DTC genetic tests via the internet for less than US $100 to gain genetic insights into their health, traits, heritage, and more without the involvement of health care professionals [2]. By providing users with such interesting and novel insights, DTC genetic testing markets are growing continuously. For example, North America's DTC genetic testing market alone accounted for $39\%$ of an estimated global market value of US $1.5 billion in 2021. Moreover, with a projected annual growth rate of $15.3\%$, the DTC genetic testing market value is expected to triple in the next 8 years [3].
The uprise of DTC genetic testing and self-responsible genetics has also sparked countless ethical, social, technical, and legal issues [1]. For example, critics argue that DTC genetic testing lacks clinical validity and meaningful interpretation of test results, whereas service providers can make unregulated advertising and marketing claims, especially for health-related tests [1,2,4-7]. Indeed, consumers taking multiple DTC genetic tests found themselves receiving different results depending on the service provider [8]. Another concern often discussed by researchers and consumers is the potential sharing and reselling of genetic data (eg, to pharmaceutical companies) and the resulting implications on genetic privacy, including genetic data access to insurance companies, employers, law enforcement agencies, or malicious entities like hackers [9-14]. Although many consumers perceive these practices as unfair, low prices and potential genetic insights often outweigh the aforementioned concerns [15]. However, due to genetic similarity, these consequences may also apply to blood relatives who were not involved or did not consent to genetic testing [13,16]. This also ties in with media and research reporting that consumers in the United States use DTC genetic ancestry tests to prove their “genetic purity,” leading to instances of racism and genetic discrimination on social media [17,18].
With the increasing spread and availability of DTC genetic testing [2] and a general tendency in society to retrieve as well as discuss health information and health-related topics on the internet [19], it is by no means surprising that DTC genetic testing is a frequent and recent topic on many social media platforms [18,20,21]. In particular, YouTube, one of the largest social media platforms and the most comprehensive web-based video platform [22], serves as the first port of call for many internet users to discuss health information and DTC genetic testing in particular [23]. While YouTube can serve to share health information and experiences with a big audience for content creators (eg, consumers, service providers, health care professionals, or journalists), it also enables user discourse through textual comments below individual videos [24].
Understanding the topics, opinions, and attitudes discussed by the users can prove crucial for many stakeholders, as comments are the main form of user reaction and feedback on social media [23]. Service providers may gain, for instance, insights into consumer demands, whereas content creators may improve their videos by adjusting their content to meet user preferences. Moreover, with the ongoing debate on ethical and legal concerns toward DTC genetic testing [1,7], user opinions are of utmost importance to regulation authorities, politicians, and the industry in general. However, many stakeholders lack the means to extract the core themes discussed and attitudes expressed in the comments sections effectively and efficiently, given the sheer number of comments and manifold writing styles of users.
Extant research regarding DTC genetic testing on social media confirms this lack of understanding. Prior research focuses on microblogging services such as Twitter [25,26], Reddit [27], or 4chan [18] to investigate user discourse on DTC genetic testing and shows that we are still puzzled about users' interests and opinions toward DTC genetic testing. Inconsistent findings regarding which topics users discuss on different platforms (eg, ancestry testing on Twitter [25] and health testing on Reddit [27]) suggest that the DTC genetic testing discourse varies from platform to platform and must thus be investigated separately. Moreover, research has already shown the value of analyzing users' opinions and attitudes through user comments from select platforms for DTC genetic testing–related content. For instance, Mittos et al [18] have uncovered extensive use of hate speech on Twitter, whereas Basch et al [20] have identified the need for educational content about genetic testing on TikTok. Few studies have investigated information about DTC genetic testing on YouTube while primarily analyzing the multimedia information (ie, the content of the videos) [28-31] and overlooking the textual information provided by users' comments (see Multimedia Appendix 1 for a complete overview of research on DTC genetic testing on social media). Because most users do not actively produce YouTube videos but only consume them, we believe that analyzing the topics that users discuss in the YouTube comments sections provides a new perspective on the ongoing discussion regarding DTC genetic testing–related videos on social media platforms. Consequently, we ask the following research questions (RQs): RQ1: What topics do YouTube users discuss in the comments sections of DTC genetic testing–related videos?
RQ2: What are users' attitudes toward DTC genetic testing–related videos, as expressed in their comments on YouTube?
To answer our RQs, we analyzed the 248 most viewed videos dealing with DTC genetics in a 3-step exploratory approach. First, we analyzed the selected videos regarding media type, genetic test purpose, and related health information. Second, we employed topic modeling to investigate user discourse in the comments sections of those videos. Third, we conducted a sentiment analysis unveiling users' attitudes toward the discussed topics and DTC genetic testing videos in general.
Through our study, we contribute to research and practice in several ways. As for research, we add to the literature on user attitudes toward DTC genetic testing by delineating topics and opinions discussed about these genetic tests. Further, we contribute to the research stream regarding health information on social media by showing that YouTube comments provide valuable insights on user discourse on social media and demonstrate that DTC genetic testing and health information topics may generally vary from platform to platform. As for practice, our research may help providers of DTC genetic testing services and regulatory authorities gain further insights into user attitudes and consequently adapt or improve genetic testing services and regulations. As most videos are user-generated, our analysis of user discourse can provide valuable insights on the topics discussed in the comments sections of these videos, providing content creators with valuable information for improving their future DTC genetic testing–themed videos.
## Health Information on Social Media Platforms
During the past decade, social media platforms have become increasingly attractive in the digital health sector as a means of communicating medical information [32]. In addition to accessing professional and nonprofessional medical information, users can also share their experiences and get in touch with each other [33]. Users already discuss various health topics like diabetes, medication and medication information, physical health, mental health, cancer, or more recently, COVID-19 on social media [19,34-38].
Consequently, information dissemination platforms (see Multimedia Appendix 1 for a detailed description of social media platform types), such as YouTube, have garnered interest from researchers to study various health care–related topics. For example, studies have investigated users' attitudes toward the effect of sleep-aiding music [24], users' preferences regarding treatment and symptoms of diabetes as well as the social culture pertaining to diabetes-related video clips [39], or public opinions and concerns about daily coverage of the COVID-19 crisis in Canada [23].
## DTC Genetic Testing
DTC genetic testing differs from traditional clinical genetic testing in that it is initiated by the consumers and does not require the direct interaction of consumers with health care professionals [2]. With the internet being the leading advertising and distribution channel, the DTC genetic testing service provider usually sends a DNA sample collection kit (eg, buccal swab or blood spot collection) to the consumers' homes for self-collection [5] or arranges for sample collection at a local laboratory [7]. Afterward, the service provider may perform various genetic analyses and then return the results directly to the consumers via the internet or mail [5]. Regarding DTC genetic testing, the consumers can choose the interpreter (ie, service provider) and the type and objective of the analysis of their genetic information (as opposed to a health care professional interpreting the genetic data). The most common types of testing services offered include ancestry tests (eg, AncestryDNA), nonmedical lifestyle tests (eg, FitnessGenes), relationship tests (eg, EasyDNA), and health tests (eg, 23andMe) [2]. Although DTC genetic testing provides consumers with novel and valuable information, it also has its downsides, such as consumers being responsible for managing and ensuring the security of their personal genetic information [1].
## Research Approach
We employed a 3-step exploratory research approach to answer our RQs (see Figure 1). First, we performed comprehensive data collection by gathering DTC genetic testing–related videos on YouTube, including their comments, and coding the contents of these videos. Second, we performed topic modeling for the user discourse in the comments sections to reveal topics discussed in those comments (answering RQ1). Third, we analyzed users' attitudes toward DTC genetic testing videos using sentiment analysis (answering RQ2).
**Figure 1:** *Overview of the 3-step research approach. NRC: National Research Council Canada.*
## Data Collection
We used the official YouTube application programming interface (API) to create a list of the most relevant DTC genetic testing–related videos on YouTube. With the region set to the United States (ie, the largest DTC genetic testing market), we queried the 300 most viewed video results for each of 6 different DTC genetic testing–related search terms (ie, direct to consumer genetic testing, home genetic testing, ancestry testing, DNA testing, genetic testing, and 23andMe). Thereafter, we combined the 1800 results from the 6 queries, removed duplicates, and sorted them by video views in descending order. We further excluded all videos with less than 50,000 views because they had very few comments per video (average of 61.2), with many having no comments ($$n = 336$$).
Next, the remaining 468 videos were reviewed for relevance through iterative manual inspection by 2 researchers, with a third researcher breaking ties in case of differences. For this, our predefined exclusion criteria were as follows: [1] videos not focusing on DTC genetic testing, [2] videos focusing on genetic testing of animals, [3] videos focusing on clinical prenatal genetic testing, [4] videos not in English, [5] live stream videos, [6] duplicate videos (ie, reuploads from different users), [7] videos commenting/reacting on videos (ie, showing the original video and adding commentary), or [8] videos with disabled ratings and comments sections (see Multimedia Appendix 2 for a detailed overview of the data collection process, including a rationale for each exclusion criterion). This resulted in a total of 250 relevant videos.
To gain insights on what topics the videos entailed, particularly the goal of the genetic test presented and the presentation type of the video, we coded the included videos according to their genetic test purpose and media type. For the genetic test purpose, we selected the most common test types suggested in the literature (ie, ancestry, traits, genetic predisposition, relationship, and other [2,7]). As for the media type, we adapted the categories used by Zhang et al [39] to our set of videos. Therefore, the categories were advertising, documentary, interview, news, user-generated video, and other. After the initial coding and comparison of 20 videos, 2 researchers conducted deductive coding of the remaining videos in parallel. *In* general, the agreement between both researchers was high, with the genetic test purpose and media type having Cohen κ values of 0.581 and 0.613, respectively. Differences in coding were discussed with a third author to break ties. This coding information allowed us to further analyze the comments regarding the contents of the videos and served as a base to evaluate the discussions in the comments.
With the final coded set of 250 videos in place, we again used the YouTube API to download each video's 500 most recent comments. This number was chosen due to the YouTube API download limitations while still allowing meaningful analysis. Among these, 80 videos had less than 500 comments, and 2 videos were no longer available, leaving us with 84,082 comments from 248 videos, which is a sufficient number for topic modeling and sentiment analysis [eg, 28,31,40,41].
## Topic Modeling of Comments
To answer our first RQ, we employed topic modeling to identify common topics discussed by users in the comments sections of DTC genetic testing–related YouTube videos. Topic modeling is frequently used in medical informatics and related disciplines for text mining large data sets (such as comments or tweets) and deducing meaningful topics [23,37,38,40,41]. For our study, we used several topic modeling approaches, including word frequency, bigram correlations, and structural topic modeling, as described and recommended by Silge and Robinson [42]. Because they are some of the most common topic modeling methods and include different approaches [42-44], they are well suited for our exploratory study design. All analyses and visualizations were conducted using R (version 4.1.0, R Foundation for Statistical Computing) in RStudio (version 1.4.1106) and the tidytext package (version 0.3.2).
Before conducting any topic modeling, we first separated the comments into 1-word tokens (ie, comments were split into single words) and performed 2 essential data cleaning tasks. First, we used the SnowballC package to perform word stemming. This step was necessary to ensure that words with identical meanings (eg, plural or verb) were grouped together to allow for meaningful topic modeling. For each word stem, the most frequent word was used to represent its stem (eg, test represents test, tests, test's, and testing). Second, we removed common stop words with the stop word list included in the tidytext package. This list comprises 1149 common stop words such as the, of, or to. As these do not hold any topical information, removing stop words reduces the data set size and benefits topic accuracy [42].
With the cleansed word list in place, we first conducted a word frequency analysis by grouping, counting, and listing the words in descending order. This provides an overview of the most used words and can give a first insight into topics discussed most prominently (eg, “DNA” occurs 15,702 times and “test” 10,902 times).
Second, we created word bigrams. We created a frequency list of 2-word tokens, which are found by pairing every 2 consecutive words in each comment (eg, “DTC genetic testing” results in the bigrams “DTC genetic” and “genetic testing”). In contrast to the single word list, bigrams can be used to span a network with the number of occurrences indicating the weight of each bigram edge [42]. To allow for meaningful interpretation, we found that setting a minimum of 70 occurrences resulted in a comprehensible network. Lower values led to the inclusion of less interpretable and impactful bigrams while cluttering the network (eg, “grocery store,” “hey kelsey,” or “omg lol”).
Finally, we conducted structural topic modeling with the help of the stm package [43]. Structural topic modeling aims to group words from different documents (ie, comments) into topics based on their co-occurrences [43]. The stm package uses document-level covariate information to estimate topic models for a given number of topics. We estimated models ranging from 15 to 100 topics in increments of 5. We then compared these models in terms of best-practice metrics, such as held-out likelihood, lower bound, residuals, and semantic coherence [42,45].
Although there is no definite answer for the correct number of topics [43], after a manual review of these metrics and discussion among 3 researchers, we selected 50 as the appropriate number of topics. A more detailed description of the structural topic modeling process and metrics, as well as a comparison with the 45- and 55-topic model, can be found in Multimedia Appendix 3.
With the 50-topic model chosen, we sorted topics according to prevalence and within each topic, the words contributing to it in descending order. We then manually inspected the 50 most prevalent topics and their 10 most contributing words to deduce meaningful topics and categorized them according to their content. For this, we relied on our prior knowledge of DTC genetic testing as well as knowledge on the content of the videos that we gained during the video coding phase of the data collection step. All topic assignments were discussed among 3 researchers.
## Sentiment Analysis of Comments
Because topic modeling can only help us identify topics discussed in the comments but not users' attitudes toward the videos, we next conducted word- and comment-level sentiment analyses to answer our second RQ. Sentiment analysis is a common tool to elicit people's opinions, sentiments, emotions, and attitudes from written language [46]. Although sentiment and attitude are near equivalents and often used synonymously, they do differ in the sense that sentiment is a more permanent disposition to react emotionally, cognitively, and conatively, whereas attitude is a disposition to react with belief, thought, feeling, and overt behavior as part of a larger sentiment [47]. In this sense, we can only deduce users' attitudes from a single YouTube comment and not their whole sentiment toward a certain topic.
Therefore, we decided to conduct 2 word-level sentiment analyses and 1 comment-level sentiment analysis to deduce users' attitudes. For the word-level sentiment, we again used the tidytext package, which entails typical word-level approaches that are well suited for a first exploratory overview [42]. We then followed an approach similar to that used by Mittos et al [18] for the comment-level analysis, who also performed sentiment analysis in the DTC genetic testing context.
Consequently, we first conducted a positive and negative sentiment analysis using the Bing lexicon, which consists of approximately 6800 words that are predefined and classified as either positive or negative [48]. Subsequently, we aggregated the sentiments by word and overall sentiment. Even though this method provides a good sentiment overview, the lexicon's limited number of words omits most topic-specific words.
We also used the National Research Council Canada (NRC) emotion lexicon to get a more detailed overview of users' sentiments toward DTC genetic testing [49]. This lexicon attributes 1 or multiple emotions to approximately 14,000 words (ie, a word may have more than 1 emotion), whereby the classification is also predefined. The emotions covered are anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. Similar to the Bing lexicon, we classified and aggregated all words by NRC sentiment. However, initial inspection revealed that the terms “black” and “white” were strongly associated with negative and positive emotions, respectively. Because it was likely that the overproportional use of these words in our data set was due to ancestry testing–related topics, and to avoid a strong association of ethnicity with emotions, we reran the analysis without them.
For the comment-level sentiment analysis, we used SentiStrength [50], a Java-based sentiment tool optimized for short social web text in English such as Twitter tweets or YouTube comments. The tool reports 2 predefined and experience-based sentiments for each document (ie, comment). First, a negative sentiment ranging from –1 (not negative) to –5 (extremely negative) and a second, positive sentiment ranging from 1 (not positive) to 5 (extremely positive). When combining both, we obtained a total sentiment score between –4 and +4. After calculating the sentiment score for each comment, we performed several analyses regarding sentiment as well as media type and test purpose.
## Ethical Considerations
Ethics approval was not necessary for this study, as it did not directly involve human participants. All data used in this study (ie, videos and video comments) were publicly available on YouTube and accessible through the YouTube API at the time of retrieval. All results are only published in aggregated form, and single references are presented anonymously and without context to protect the privacy of the comments’ authors.
## Overview of Video Contents and Comments
We examined a total of 248 videos related to DTC genetic testing, collected on September 14, 2020, with a total of 30 videos from official company accounts (21 videos from 23andMe, 8 videos from Ancestry.com, and 1 video from MyHeritage). Based on the media type, these included 27 advertising-related videos, 14 documentaries, 16 interviews, 12 news, 174 user-generated videos, and 5 with other media types (mainly recordings of television shows such as The Late Show with Stephen Colbert or The Jim Jefferies Show/Comedy Central). Among the 248 videos, 194 videos address ancestry as a test purpose, 15 address trait testing, 9 address genetic predispositions, 19 address relationship testing, and 11 address other purposes (such as how to use a test kit or comparison/presentation of multiple genetic test purposes). In total, the videos had 724,574 comments on the day of video data aggregation. We collected the comments of the videos on January 3, 2021, focusing on the 500 most recent comments of each video (total number of comments=84,082). An overview of the video metadata, content, and comments is provided in Table 1.
**Table 1**
| Video characteristic | Video characteristic.1 | Value |
| --- | --- | --- |
| Number (N) | Number (N) | 248 |
| Date of collection | Date of collection | September 14, 2020 |
| Media type (n) | Media type (n) | Media type (n) |
| | Advertising | 27 |
| | Documentary | 14 |
| | Interview | 16 |
| | News | 12 |
| | User-generated videos | 174 |
| | Other | 5 |
| Test purpose addressed (n) | Test purpose addressed (n) | Test purpose addressed (n) |
| | Ancestry | 194 |
| | Traits/characteristics | 15 |
| | Genetic predisposition | 9 |
| | Relationship | 19 |
| | Other | 11 |
| Upload date | Upload date | Upload date |
| | Oldest | January 15, 2015 |
| | Newest | July 7, 2020 |
| View count | View count | View count |
| | Minimum | 52802 |
| | Maximum | 20453890 |
| | Average | 1158064 |
| Likes | Likes | Likes |
| | Minimum | 0 |
| | Maximum | 368294 |
| | Average | 22114 |
| Dislikes | Dislikes | Dislikes |
| | Minimum | 0 |
| | Maximum | 10277 |
| | Average | 813 |
| Duration (minutes) | Duration (minutes) | Duration (minutes) |
| | Minimum | 00:31 |
| | Maximum | 34:23 |
| | Average | 09:30 |
| Comments | Comments | Comments |
| | Minimum | 2 |
| | Maximum | 24523 |
| | Average | 2922 |
| Comment publication date | Comment publication date | Comment publication date |
| | Oldest | March 29, 2017 |
| | Newest | January 2, 2021 |
## Topics of the DTC Genetic Testing Video Comments
Word frequency analysis using the comments on DTC genetic testing–related videos provides valuable insights into the topics discussed by users. DNA ($$n = 15$$,702), test ($$n = 10$$,902), and people ($$n = 9259$$) are by far the most frequent terms, thus indicating that users indeed primarily discuss DTC genetic testing in their comments. Additionally, we identified many words referring to ancestry testing such as ancestry ($$n = 5015$$), african ($$n = 6268$$), or american ($$n = 6139$$). Moreover, words such as family ($$n = 5252$$), dad ($$n = 2932$$), or parents ($$n = 2228$$) can be attributed to relationship tests. Overall, the 100 most frequent words resemble the test purposes identified from the videos themselves as well as a general excitement for DTC genetic testing videos (eg, video, $$n = 4794$$; love, $$n = 4751$$). Table 2 provides an overview of the 20 most frequent words. Additionally, Multimedia Appendix 4 provides a word cloud and overview of the 100 most frequent words.
The bigram network of the comments provides a more fine-grained picture of the words used together often. Unlike the single word cloud, it allows us to see how multiple words are connected. Additionally, the arrows indicate in which order the words appear, whereas the shade of the edge represents the frequency of the word pair. Therefore, we can deduce possible topics discussed by users from the network.
As shown in Figure 2, we identified 5 main topics within the network. The largest topic we identified revolves around ancestry testing (blue cluster). Although the most indicative bigram is “ancestry DNA” ($$n = 679$$), most bigrams in this topic describe a specific heritage such as “native american” ($$n = 3255$$), “north african” ($$n = 831$$), or “middle eastern” ($$n = 756$$), further substantiating that users largely discuss ancestry results of genetic testing in the comments. The second-largest topic deals with trait testing (green cluster) and holds bigrams such as “blonde/brown/red hair” ($$n = 203$$/$$n = 72$$/$$n = 41$$), “skin color” ($$n = 131$$), or “blue eyes” ($$n = 285$$). The third topic entails bigrams related to health testing (yellow cluster). Typical bigrams include “insurance companies” ($$n = 121$$), “genetic makeup” ($$n = 76$$), and “23andme test” ($$n = 72$$). The last topic related to genetic testing indicates relationship testing (red cluster). It includes bigrams such as “identical twins” ($$n = 231$$), “half sister” ($$n = 124$$), or “biological parents” ($$n = 74$$). We also identified 1 topic not specific to DTC genetic testing but YouTube as a platform in general (gray cluster). The bigrams found in this topic are parts of video URLs, for example, “https youtu.be” ($$n = 246$$) or “www.youtube.com watch” ($$n = 201$$). This indicates that users often share videos in the comments sections of videos, possibly on related topics.
Finally, we trained structural topic models, of which we selected the 50-topic model. Figure 3 shows the 20 most prevalent topics, including the 10 most important words for each topic of this model. The complete list of all 50 topics can be found in Multimedia Appendix 3. For a better overview of the topics discussed in the comments sections, we grouped these 20 topics into 6 categories, briefly described in the following:
## General Genetic Testing
This topic group indicates a general interest in DTC genetic testing (eg, topics 16, 31, 49), entailing company names such as MyHeritage, AncestryDNA, or Ancestry.com and words of interest (eg, excited or expect). Moreover, topic 16 touches on the home collection (spit, tube) and financial (money) aspects of DTC genetic testing.
## Ancestry Testing
In line with our previous findings, most topics are about the results of genetic ancestry testing. Topic 8 shows a general interest in ancestry testing by users. Topics 17, 26, 37, and 47 describe findings on heritage from a specific region, whereas topic 41 is about paternal and maternal ancestry. Additionally, topic 19 might indicate that users hope to find lost relatives through ancestry testing.
## Relationship Testing
We also identified 3 topics about genetic relationship testing. Topics 34 and 48 deal with relationships between children such as identical twins, whereas topic 36 entails the aspects of adoption and genealogy (ie, searching for one's biological family).
## Health and Trait Testing
Although less prevalent, health genetic testing and trait testing are also covered in the top 20 topics. Topic 44 focuses on health information and data, whereas topic 28 entails words on traits such as hair or eye color.
## Ethical Concerns
The 50-topic model also reveals some topics not contained in our previous findings. Topic 32 touches on instances of racism signified through words such as black, racist, or mad. Given the ongoing and complex debate toward instances of racism in the United States and the majority of DTC genetic testing revolving around ancestry and heritage, this could explain why this topic was found in the comments of these videos. Moreover, topic 22 deals with users' concerns regarding genetic testing and the government, with words such as lie, ad, or crime.
## YouTube Video Reaction
In contrast to the previous findings, topics 18, 27, and 43 do not directly relate to genetic testing but rather entail reactions to the videos on YouTube (eg, love, awesome, watching, video, or channel). Further, users seem interested in personal stories (eg, amazing, story, or reaction).
## Comparison of Topic Modeling Approaches and Identified Topics
Although the bigram network and structural topic modeling use different approaches, the majority of the identified topics are present in both methods. Both approaches show strong indications of ancestry testing, relationship testing, trait testing, and health testing topics. Moreover, both methods led to the deduction of a YouTube or YouTube video–related topic. Table 3 compares the topics covered by the bigram network and structural topic modeling and lists some of the most indicative bigrams and words for each method, respectively.
**Table 3**
| Topic | Bigram network | Structural topic modeling |
| --- | --- | --- |
| General genetic testing | N/Aa | Myheritage; ancestrydna; ancestrycom; excited; expect; spit; tube; money; genes; dna; genetic |
| Ancestry testing | Ancestry dna; native american; north african: middle eastern | Ancestry; african; american; native; irish; german; french; father; parents; race; mexican |
| Relationship testing | Identical twins; half sister; biological parents | Kids; cry; family; adopted; genealogy; lies |
| Trait testing | Blonde/brown/red hair; skin color; blue eyes | Hair; eyes; blonde; blue; red |
| Health testing | Insurance companies; genetic makeup; 23andme test | Companies; information; health; pay |
| Ethical concerns | | Black; racist; claim; government; clone; crime; evidence |
| YouTube-related | https youtu.be; www.youtube.com watch | |
| YouTube video reaction | | Love; awesome; watching; video; channel; amazing; story; reaction |
## Sentiments of DTC Genetic Testing Video Comments
Even though topic modeling can help unveil what users discuss in the comments sections, it does not provide insights into users' attitudes toward these topics. Therefore, conducting a Bing sentiment analysis can provide a first overview of the sentiment regarding words used in the comments sections. Figure 4 shows the 20 most used words with negative and positive sentiments. The results show that the most used positive words are used significantly more often. In fact, the first negative word, funny ($$n = 864$$), is only the seventh most used word overall in the sentiment list. Moreover, the positive word love ($$n = 4751$$) is used overproportionally, having more than twice as many occurrences as the second most used word, beautiful ($$n = 1953$$). However, when observing all positively and negatively classified occurrences, we can identify more negative word uses ($$n = 38$$,734) than positive ones ($$n = 35$$,897).
Another type of sentiment analysis is the identification of emotions with the NRC lexicon. Our results show that the most frequent words representing positive emotions, namely anticipation, joy, surprise, and trust, have higher occurrences than the words expressing negative emotions, namely anger, fear, disgust, and sadness (see Figure 5). This finding is also supported by overall occurrences of positive word emotions ($$n = 148$$,791) and negative word emotions ($$n = 76$$,761). Love, the single most used word ($$n = 4751$$), is associated with the emotion of joy, and the most frequent emotion is trust ($$n = 54$$,814). In contrast, disgust ($$n = 15$$,541) has the least word occurrences.
The comment-level sentiment analysis provides insights into user attitudes as well as attitudes toward DTC genetic testing videos and their respective content (ie, test purpose and media type). Although the SentiStrength sentiment can vary on a scale of –4 to 4, the average sentiment score of all comments is 0.32, meaning slightly positive. This is also reflected by almost half of all the comments ($$n = 36$$,804) having a neutral sentiment (ie, 0). Grouping comment sentiment by video shows that the lowest sentiment score per video comments section is –0.62, whereas the highest is 1.33. Overall, only 30 of the 248 inspected videos have a negative sentiment, indicating an overall positive attitude toward DTC genetic testing videos.
When comparing comment sentiment regarding the test purpose of the videos, our results show that from the comments with a sentiment score of 4, $91.6\%$ ($\frac{230}{251}$) are in the comments sections of videos about ancestry testing (most frequent test purpose), whereas for comments with a sentiment score of –4, ancestry testing videos only account for $67.9\%$ ($\frac{76}{112}$). In contrast, only $1.6\%$ ($\frac{4}{251}$) of the comments with a sentiment score of 4 are in the responses to a video dealing with relationship testing. However, this increases to $17\%$ ($\frac{19}{112}$) for comments with a sentiment score of –4. As shown in Figure 6 (left), videos with an ancestry test purpose seem to evoke more positive user comments, whereas this is the opposite for relationship test videos.
The analysis of comment sentiment regarding media type unveils that user-generated videos account for the most significant number of positive comments with $91.6\%$ ($\frac{230}{251}$) for a sentiment score of 4. On the contrary, for a sentiment score of –4, user-generated videos only account for $60.7\%$ ($\frac{68}{112}$) of the comments. Consequently, as shown in Figure 6 (right), user-generated videos tend to evoke the most positive attitude toward their video content. This is in contrast to the media types advertising, documentary, and interview; all of these show an increase in the number of comments with decreasing sentiment values. For example, the number of comments for the media type documentary increases from $2\%$ ($\frac{5}{251}$) with a sentiment score of 4 to $15.2\%$ ($\frac{17}{112}$) with a sentiment score of –4. Therefore, advertisements, documentaries, and interviews may evoke more negative responses than user-generated videos.
**Figure 4:** *Bing sentiment by most frequent words for negative and positive sentiments.* **Figure 5:** *National Research Council Canada (NRC) sentiment by most frequent words for the emotions anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.* **Figure 6:** *Spreads for test purpose (left) and media type (right) by sentiment.*
## Principal Findings
Our analysis of user comments on DTC genetic testing–related YouTube videos yields several valuable findings. The test purposes found in the videos largely resemble the most common genetic test purposes, with most videos talking about ancestry or relationship testing and fewer about trait and health testing. This finding is in line with previous research on YouTube videos related to DTC genetic testing [28,31] and social media in general [20,21,25]. Nonetheless, in contrast to our study, Yin et al [27] found in their collected *Reddit data* set that relationship and health testing were more often mentioned than ancestry testing. Although Mittos et al [18] do not report the same finding for their *Reddit data* set, this may indicate that users of different social media platforms have other interests regarding DTC genetic testing. Another possible explanation for this could be that platform suggestion algorithms differ and may hence propose distinct content to users depending on the platform. Thus, discourses on the respective platforms should be investigated individually before assuming DTC genetic testing–related findings to be true across multiple platforms.
Moreover, most topics found with the bigram network and structural topic modeling can be attributed to common DTC genetic testing purposes. This indicates that user discourse revolves around the contents of the videos and DTC genetic testing. In line with previous research, we also identified topics dealing with general genetic testing and users' interest in and excitement for DTC genetic testing [18,51].
Besides, research has also shown instances of racism regarding ancestry testing on Twitter [18], which we also identified as a topic in the video comments. Even though it is unclear whether these comments relate directly to the content of the respective video or are in the replies to other comments, the identified topics largely revolve around racism and discrimination against African Americans and Native Americans. However, our results did not show any specific topics on the educational content of DTC genetic testing. Considering that consumers in the United States continue to use DTC ancestry tests to prove their “genetic purity” and discriminate against marginalized ethnic groups such as the aforementioned ones, especially on social media [17,18], research has called for more educational content and scientific explanations about DTC genetic testing [20,21]. Despite finding some videos expressing concerns toward DTC genetic testing (eg, documentaries), the majority of the videos seem to fail to highlight the advantages as well as the disadvantages and risks of DTC genetic testing. Hence, the discussions in the comments section may also largely neglect these aspects.
Sentiment analysis revealed that users have more negative attitudes toward the content of advertisements, news, or documentary videos compared to user-generated videos on DTC genetic testing. Although this finding could be explained through some media types being more thought-provoking (eg, documentaries covering disadvantages and risks of DTC genetic testing or news covering stories of genetic discrimination), another explanation might be that user-generated videos are often produced by single creators often trying to engage more with their YouTube community (eg, through specific content or active discussion in the comments sections) than, for example, a news broadcaster or DTC genetic testing service provider. Hence, this may result in a more positive user attitude. This assumption is further supported by our findings on YouTube-related and YouTube video reaction topics. On the one hand, these findings once again indicate that users discuss and respond to the content discussed in the respective videos, and on the other hand, they suggest a more complex discussion between content creators and their community (eg, through expressing enjoyment of content or including links to further YouTube videos). It should be noted that the revealed user attitudes on DTC genetic testing videos do not necessarily reflect user attitudes toward DTC genetic testing in general. However, as our topic modeling results suggest that user comments largely revolve around DTC genetic testing, it is likely that users’ attitudes toward DTC genetic testing videos also reflect their attitudes toward DTC genetic testing to some degree. This notion is further supported by the finding that videos discussing the disadvantages and risks of DTC genetic testing tend to have more negative user attitudes. Comparable results on user attitudes toward DTC genetic testing were also found for Twitter and related textual platforms [18,25,51], thereby strengthening this assumption.
Similar to DTC genetic testing–related Reddit posts [41], we found that user emotions toward DTC genetic testing videos expressed through the comments are mainly positive. The NRC sentiment and comment-level sentiment analyses also indicate a clear tendency toward a positive user attitude. This may be explained by the majority of videos being user-generated ones and aforementioned higher community engagement of content creators. Previous research on user sentiment toward Twitter tweets also shows a positive sentiment toward DTC genetic testing [51]. However, Mittos et al [18] found that most tweets only have a sentiment score of 0 or 1. In line with previous research [21,51], these less positive emotions and attitudes could indicate that although users are generally interested in DTC genetic testing, they still have reservations regarding this new technology. These reservations are mirrored in the results of the NRC sentiment analysis that highlighted fear as the most prominent negative attitude toward DTC genetic testing, whereas trust was the most prominent positive attitude. These reservations toward DTC genetic testing were also highlighted in prior research [7].
## Implications for Research and Practice
This study conveys several implications for research and practice. As for research, we contribute to the literature on user attitudes toward DTC genetic testing by investigating topics and opinions discussed about these genetic tests. We examined the 248 most viewed DTC genetic testing videos on YouTube in terms of their content (ie, test purpose, media type) and analyzed users' attitudes in the form of their comments. Further, we contribute to research regarding health information on social media by showing that YouTube comments provide valuable insights into user discourse on social media. This study suggests that video content and user comments are co-dependent and should therefore be investigated together. To this end, we provide new insights into the discourse on genetic testing on YouTube by showing that the discourse in the comments primarily revolves around the content of the videos. Our research indicates that the discourse on YouTube may differ from that on other social media platforms, and hence, a detailed and differentiated consideration of the different platforms may be necessary. We further contribute to knowledge regarding user behavior on social media by examining users' attitudes and emotions toward DTC genetic testing videos on YouTube.
As for practice, our research offers important implications for DTC genetic testing service providers, content creators, and regulatory authorities regarding user attitudes, which may help adapt or improve genetic testing services, multimedia content, or regulations. Similar to the study of Lee et al [21] involving Twitter, our identified topics indicate a lack of educational information about DTC genetic testing in YouTube videos. Further, sentiment analysis shows that users have more negative attitudes toward advertisements, news, or documentary videos and prefer user-generated content on DTC genetic testing. Hence, authorities could consider working with content creators to promote user education on DTC genetic testing. Finally, our topic modeling indicates instances of racism, especially regarding ancestry testing. Service providers and authorities should be aware of this and ensure genetic testing is not used for discrimination. Therefore, we suggest that it may be helpful to flag videos with high numbers of negative comments, including racism or anxiety, and provide further information regarding DTC genetic testing via banners or other visual cues, similar to those used on many platforms for content related to COVID-19 [52].
## Limitations and Future Research
The limitations of this study are as follows. First, we only considered a limited number of videos and comments. Even though we attempted to include an appropriate sample by saturating the videos and comments using metrics such as views and number of comments, examining all the initially identified videos ($$n = 1325$$) and comments could provide further insight, particularly concerning topic modeling and sentiment analysis. Second, we limited our YouTube API queries to the United States because the related DTC genetic testing market is the most evolved there. However, other regions with striving markets, such as Asia [30], could offer further insights into user discourse and should therefore be investigated in future research. Third, because there is no way to determine the optimal number of topics [42], we concentrated on models in increments of 5, selecting the 50-topic model. Although adjacent models tend to have many similar topics, it is possible that we did not identify a vital topic covered in a different solution. Future research could also attempt using different topic modeling methods and larger sample sizes to unveil a more fine-grained view of the topics discussed. Fourth, despite covering several sentiment lexicons, they may have been limited with respect to words associated with a sentiment (eg, Bing sentiment), and research should further investigate YouTube comment sentiment to gain deeper insight into user attitudes. It should also be pointed out that the generic association of words with sentiment values and emotions could omit or alter some findings in specific contexts such as DTC genetic testing. However, we tried to minimize this effect by using different approaches and content-specific modifications such as removing the words “white” and “black” from the NRC sentiment analysis, as these were used overproportionally. Finally, although this study investigated videos spanning from 2015 to 2020, we did not specifically focus on whether or how user discourse and attitudes might have changed over time. Because we only collected the 500 most recent comments, the majority of these can be dated to 2021. However, the DTC genetic testing market has and continues to evolve and change rapidly [1,2,7,14]. Future research should thus consider a temporal analysis of DTC genetic testing videos and comments to investigate if the market changes also affected user discourse and attitudes.
## Conclusions
This study examined 248 DTC genetic testing videos and 84,082 comments on YouTube to investigate user discourse. To this end, we employed topic modeling and identified 6 prevailing topics discussed among users, which largely revolve around the test purposes mentioned within those videos, such as ancestry or relationship testing. Further, we conducted sentiment analysis, showing that users have positive emotions, as indicated by the NRC sentiments of anticipation, joy, surprise, and trust, and a generally neutral-to-positive attitude toward DTC genetic testing expressed through words such as love, beautiful, pretty, and cool as well as a positive attitude toward DTC genetic testing–related videos on YouTube in general. Through this study, we show how users' attitudes toward DTC genetic testing can be determined by analyzing topics and opinions based on YouTube video comments. Our findings show that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market still evolving, service providers, content providers, or regulatory authorities may need to adapt their services to users' interests and desires.
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|
---
title: Single-cell characterization of anti–LAG-3 and anti–PD-1 combination treatment
in patients with melanoma
authors:
- Jani Huuhtanen
- Henna Kasanen
- Katriina Peltola
- Tapio Lönnberg
- Virpi Glumoff
- Oscar Brück
- Olli Dufva
- Karita Peltonen
- Johanna Vikkula
- Emmi Jokinen
- Mette Ilander
- Moon Hee Lee
- Siru Mäkelä
- Marta Nyakas
- Bin Li
- Micaela Hernberg
- Petri Bono
- Harri Lähdesmäki
- Anna Kreutzman
- Satu Mustjoki
journal: The Journal of Clinical Investigation
year: 2023
pmcid: PMC10014104
doi: 10.1172/JCI164809
license: CC BY 4.0
---
# Single-cell characterization of anti–LAG-3 and anti–PD-1 combination treatment in patients with melanoma
## Abstract
### Background
Relatlimab plus nivolumab (anti–lymphocyte-activation gene 3 plus anti–programmed death 1 [anti–LAG-3+anti–PD-1]) has been approved by the FDA as a first-line therapy for stage III/IV melanoma, but its detailed effect on the immune system is unknown.
### Methods
We evaluated blood samples from 40 immunotherapy-naive or prior immunotherapy–refractory patients with metastatic melanoma treated with anti–LAG-3+anti–PD-1 in a phase I trial using single-cell RNA and T cell receptor sequencing (scRNA+TCRαβ-Seq) combined with other multiomics profiling.
### Results
The highest LAG3 expression was noted in NK cells, Tregs, and CD8+ T cells, and these cell populations underwent the most significant changes during the treatment. Adaptive NK cells were enriched in responders and underwent profound transcriptomic changes during the therapy, resulting in an active phenotype. LAG3+ Tregs expanded, but based on the transcriptome profile, became metabolically silent during the treatment. Last, higher baseline TCR clonality was observed in responding patients, and their expanding CD8+ T cell clones gained a more cytotoxic and NK-like phenotype.
### Conclusion
Anti–LAG-3+anti–PD-1 therapy has profound effects on NK cells and Tregs in addition to CD8+ T cells.
### Trial registration
ClinicalTrials.gov (NCT01968109)
### Funding
Cancer Foundation Finland, Sigrid Juselius Foundation, Signe and Ane Gyllenberg Foundation, Relander Foundation, State funding for university-level health research in Finland, a Helsinki Institute of Life Sciences Fellow grant, Academy of Finland (grant numbers 314442, 311081, 335432, and 335436), and an investigator-initiated research grant from BMS.
## Introduction
Even though immune checkpoint inhibitor therapies have revolutionized the treatment of metastatic melanoma, a majority of patients fail to achieve sustainable responses. As currently available immune checkpoint inhibitor therapies (anti–cytotoxic T lymphocyte–associated protein 4 [anti–CTLA-4], anti–programmed death 1 [anti–PD-1], and anti–programmed death ligand 1 [anti–PD-L1] therapies) primarily target effector CD8+ T cells, novel combination treatments that could also invigorate other immune cell types could increase the response rates in patients. Lymphocyte-activation gene 3 (LAG-3) is an inhibitory receptor expressed widely on different activated and exhausted immune cell subtypes (1–7), rendering it one of the most interesting novel immune checkpoint targets. Coinhibition of anti–LAG-3+anti–PD-1 is more attractive than blocking either LAG-3 or PD-1 alone [8], with encouraging efficacy even in patients with anti–PD-1/anti–PD-L1–refractory melanoma [9, 10]. Relatlimab plus nivolumab (anti–LAG-3+anti–PD-1) combination therapy has shown a progression-free survival benefit over anti–PD-1 monotherapy as a first-line treatment for patients with metastatic melanoma [11] and has now been approved by the FDA. Although it is known that LAG-3 attenuates T cell activation, viability, and proliferation by binding to MHC class II molecules, knowledge of its effects on other immune cells is lagging (8, 12–15).
In this study, we used single-cell RNA and T cell receptor (TCR) sequencing (scRNA+TCRαβ-Seq), flow cytometry, TCRβ-Seq, and serum protein profiling together with ex vivo functional validations to analyze immune cell responses to anti–LAG-3+anti–PD-1 treatment (relatlimab+nivolumab, phase I, ClinicalTrials.gov NCT01968109) in pretreatment blood samples and blood samples taken 1 and 3 months after therapy from 40 patients with metastatic melanoma (Figure 1). The patients were either immunotherapy naive (IO naive) or prior immunotherapy refractory (IO refractory) (patient details are provided in Table 1 and Supplemental Table 1; supplemental material available online with this article; https://doi.org/10.1172/JCI164809DS1), and during the therapy more changes were observed in the immune cell repertoire of IO-naive patients. Anti–LAG-3+anti–PD-1 shifted LAG-3+CD8+ antigen-experienced T cells from an exhausted to a more cytotoxic phenotype. However, we observed the greatest effect in CMV seropositivity–associated cell populations, such as in adaptive NK cells, resulting in an activated phenotype. This was mainly observed in the responding patients, and they had higher numbers of adaptive NK cells, CMV seropositivity, and a costimulatory cytokine environment before initiation of the treatment. Our results provide an understanding of the effects of anti–LAG-3+anti–PD-1 combination treatment in vivo in patients and highlight previously overlooked subpopulations of cells as targets of immune checkpoint therapies.
## Adaptive NK cells and CD8+ T cells have the highest LAG3 expression and are more numerous in responders.
In total, we had 40 patients, 11 of whom were IO naive (7 of 11 [$63.6\%$] were complete responders [CRs] or partial responders [PRs] and 4 [$36.4\%$] had progressive disease [PD]) and 29 of whom were IO refractory (15 of 29 [$51.7\%$] were CR/PR or had stable disease [SD] and 14 of 29 [$48.3\%$] had PD). All the patients in the IO-naive cohort received 80+240 mg doses of relatlimab+nivolumab, while in the IO-refractory cohort 20 of 29 ($68.9\%$) received 80+240 mg doses and 9 of 29 ($31.0\%$) received 160+480 mg doses. The prior IO-refractory patients received previously anti–PD-1 therapy (22 of 29 [$75.9\%$]) or anti–CTLA-4 and then anti–PD-1 (7 of 29 [$24.1\%$]) (Table 1 and Supplemental Table 1).
With scRNA+TCRαβ-Seq, we profiled 18 peripheral blood (PB) samples from 5 IO-naive and 1 IO-refractory patients with metastatic melanoma treated with anti–LAG-3+anti–PD-1 combination therapy (CRs $$n = 2$$, PRs $$n = 1$$, PD $$n = 3$$; patient details are provided in Supplemental Table 1). We identified 24 cell clusters in the scRNA-*Seq data* (Figure 2, A and B), all of which were present in every sample, but their abundances varied between patients and time points (Supplemental Figure 1, A–E).
Prior to anti–LAG-3+anti–PD-1 treatment, we found that LAG3 was highly expressed in CD8+ T cells, CD4+ Tregs, and B cells, but the highest expression of LAG3 was surprisingly detected in adaptive NK cells in the scRNA-*Seq data* (adjusted P value [Padj] < 0.0001, Bonferroni-corrected t test, Figure 2C), which was validated by flow cytometry ($$n = 8$$, Supplemental Figure 2, A and B). The largest difference in cell population abundances between patients with a response (CR/PR) and without a response (PD) was seen in adaptive NK cells in the scRNA-*Seq data* ($$n = 3$$ vs. $$n = 3$$, $P \leq 0.0001$, Fisher’s 2-sided exact test, Figure 2, B and D). This analysis was extended with flow cytometric data, in which we saw a similar, albeit not statistically significant, trend (CD56dimNKG2C+, IO naive $$n = 7$$ vs. $$n = 4$$ Padj > 0.05, prior IO refractory $$n = 3$$ vs. $$n = 26$$, Benjamini-Hochberg–corrected Mann-Whitney 2-sided U test, Figure 2E and Supplemental Table 1). Also, patients with a response had at least a 2-fold increase in the abundance of 3 non-naive CD8+ T cell clusters in the scRNA-*Seq data* ($P \leq 0.0001$, Fisher’s 2-sided exact test, Figure 2, B and D).
Adaptive NK cells, which were defined by the expression of FCGR3A (CD16a), KLRC2 (NKG2C), and ZEB2 as in the previous scRNA-Seq publications (16–18) and a lack of TCRs (Figure 2F and Supplemental Figure 2, C an D), share hallmarks of adaptive immunity with CD8+ T cells [19], including LAG-3–induced dysfunction [20]. Adaptive NK cells are terminally mature NK cells (CD56bright NK → CD56dim NK → adaptive NK), and their maturation is accelerated by CMV infection [21]. Accordingly, adaptive NK cells were found almost exclusively in CMV+ patients in the scRNA-*Seq data* (CMV+ $$n = 4$$, CMV– $$n = 2$$, Supplemental Figure 2E) and in the more extensive flow cytometric data as well (CD56dimNKG2C+ [ref. 22], CMV+ $$n = 26$$, CMV– $$n = 13$$, $P \leq 0.01$; Supplemental Figure 2F). CMV seropositivity was also associated with T cells with a NK-like phenotype in the flow cytometric data (CD4+CX3CR1+ and CD8+CX3CR1+, both $P \leq 0.01$, Supplemental Figure 2F) and increased T cell clonality in the TCRβ-*Seq data* ($P \leq 0.01$, Supplemental Figure 2G), both of which have previously been linked to immune checkpoint blockade responses (23–27).
## Immune cells in patients with melanoma have the highest expression of LAG3 in a pan-cancer analysis.
To validate LAG3 expression levels in immune subsets in melanoma and to compare the levels with those in other human cancers, we pooled 131 tumor biopsy or bone marrow aspirate samples from 9 different cancers (acute myeloid leukemia [AML], breast cancer [BC], basal cell carcinoma [BCC], colorectal carcinoma [CRC], endometrial cancer [EC], non–small cell lung carcinoma [NSCLC], renal cell carcinoma [RCC], squamous cell carcinoma [SCC], skin cutaneous melanoma [SKCM], and uveal melanoma [UM]) profiled by similar scRNA-Seq methods (3, 28–33), together with deep generative modeling [34] and annotated tumor-infiltrating lymphocytes (TILs) with a cluster-agnostic approach [35] (Figure 2G and Supplemental Figure 3, A–F, cohorts in Supplemental Table 1). LAG3 was confirmed to be highly expressed in tumor-infiltrating NK cells, Tregs, and B cell subsets in addition to CD8+ T cells (Supplemental Figure 3D), reflecting our data from PB. Across human cancers, melanoma samples exhibited the highest number of LAG3+ TILs, including LAG3+ NK cells, LAG3+ Tregs, and LAG3+CD8+ effector memory T cells (Figure 2H and Supplemental Figure 4, A and B). Importantly, LAG3 expression was more abundant than PDCD1 expression in all major TIL subsets in melanoma, unlike in other cancers (Supplemental Figure 4, C and D). These findings highlight the potential benefit of adding anti–LAG-3 to anti–PD-1 treatment, especially for patients with melanoma.
## Anti–LAG-3+anti–PD-1 treatment expands LAG3+ NK cells, CD8+ T cells, and CD4+ T cells in responding patients.
Following anti–LAG-3+anti–PD-1 treatment, we noted statistically significant NK and T cell expansions in the flow cytometric data (Padj < 0.05, Benjamini-Hochberg–corrected, 2-sided Mann-Whitney U test) only in patients with a response (CR/PR in IO-naive patients $$n = 7$$, CR/PR/SD in prior IO–refractory patients $$n = 10$$), and no expansion in patients without a response (SD/PD in IO-naive patients $$n = 6$$, PD in prior IO–refractory patients $$n = 13$$, Figure 3A and Supplemental Figure 5, A–C; patient details and full results are provided in Supplemental Table 1). In the responders, LAG3+ lymphocyte expansion was noted in both the scRNA-Seq and flow cytometric data, including LAG3+CD4+ T cells in both IO-naive and prior IO–refractory patients, whereas expansion of LAG3+CD56dim NK cells and LAG3+CD8+ T cells was only seen in the IO-naive cohort (Figure 3A and Supplemental Figure 5C). We found that cell populations coexpressing LAG-3 and PD-1 also expanded in the flow cytometric data, but unlike LAG-3+PD-1– cells, the LAG-3–PD-1+ cells did not expand. These results hint that the therapy had a more noted effect on responding patients’ LAG3+ immune repertoire, especially in IO-naive patients. The different treatment doses of relatlimab+nivolumab (120+480 mg vs. 60+240 mg) did not result in differentially abundant cell populations (Supplemental Figure 5D).
## The phenotype of NK cells becomes active during anti–LAG-3+anti–PD-1 treatment in responding patients.
As therapies can have effects without causing population expansions, we calculated differentially expressed genes (DEGs) within subsets from the scRNA-*Seq data* (DEGs are listed in Supplemental Table 2). Responding patients (CR/PR, $$n = 3$$) had more notable transcriptomic changes (DEGs) already after 1 month of therapy in comparison with nonresponders (PD, $$n = 3$$) ($P \leq 0.05$, 2-sided Mann-Whitney U test, Figure 3B). At the 1-month point, the clusters with significantly more DEGs in responders in comparison with nonresponders included adaptive NK cells, CD8+ central memory T (Tcm) cells, and CD8+ effector T (Teff) cells ($P \leq 0.001$, Fisher’s exact 2-sided test, Figure 3C).
Although adaptive NK cells can effectively kill tumor cells [36], they have a limited proliferative capacity [37] and, hence unsurprisingly, did not expand following anti–LAG-3+anti–PD-1 therapy. Regardless, NK cells had the second highest number of DEGs, and in responders, the upregulated genes were associated with enhanced adaptive NK cell function (FCGR3A [CD16], CD52, HLA-E, KLRC2), cytotoxicity (GZMA/B/H/K, GNLY, FGFBP2, and CST7), and cytoskeletal remodeling to enable lytic granule secretion (ACTB, ARPC$\frac{3}{5}$, CORO1A, CFL1), as well as immediate early genes (JUNB, NR4A2) and antiapoptosis genes (BAX, DUSP2) in the scRNA-Seq data, and we did not observe these effects in nonresponders (Figure 3, D and E). The overall effect was that of upregulated pathways associated with the response to IFN-γ, which, together with the upregulated genes, indicate an active phenotype of these cells (Supplemental Figure 5E) [38]. We tested this hypothesis in the scRNA-*Seq data* by studying the RNA turnover rate with RNA velocity [39], which showed that the previously quiescent adaptive NK cells initiated a strong directional flow after anti–LAG-3+anti–PD-1 therapy, suggesting elevated RNA transcription production previously not observed in these cells (Figure 3F and Supplemental Figure 6A).
## NK cells degranulate and secrete cytokines, and CD8+ T cells proliferate following anti–PD-1+anti–LAG-3+ therapy.
To further validate the activated cell type, we performed ex vivo studies using the K562 cell line as a target for primary NK cells (Supplemental Figure 7A). In comparison with untreated samples, 3 of 4 post-therapy samples exhibited increased degranulation responses (CD107a/b) and elevated production of IFN-γ and TNF-α (Figure 4A), although the findings did not reach statistical significance due to the small number of samples. However, TNF was also one of the DEGs in the scRNA-*Seq data* for NK cells. Furthermore, LAG-3 expression correlated significantly with degranulation responses in NK cells ($P \leq 0.0001$, R2 = 0.79, Spearman’s rank correlation), thus serving as a marker for elevated NK cell cytotoxicity (Figure 4B).
We observed upregulated expression of activation markers (GZMA/H/K/M, GNLY, PRF1) and downregulated expression of exhaustion markers (CTLA4) in responding patients’ CD8+ T cells in the scRNA-Seq data, and the most upregulated pathway was related to the NF-κB pathway and not the IFN-γ pathway (Supplemental Figure 5E). When stimulated with CD3/CD28 beads, we detected a trend toward higher T cell proliferation (including both CD4+ and CD8+ T cells) in ex vivo samples ($$n = 3$$) after anti–LAG-3+anti–PD-1 therapy compared with pre-therapy samples (Figure 4C). These findings were congruent with the flow cytometric data showing the expansion of LAG-3+CD4+ and LAG-3+CD8+ T cells in follow-up samples (Figure 3A and Supplemental Figure 5, A–C).
## Tregs expand in the periphery following anti–LAG-3+anti–PD-1 therapy.
According to the flow cytometric data, LAG-3+CD4+ T cells were the most notably expanded cell population, in both IO-naive ($$n = 11$$) and prior IO–refractory patients ($$n = 29$$), especially in responding patients (Figure 3A, Supplemental Figure 5, A–D, and Supplemental Table 1). This population was already more abundant in the nonresponding IO-naive patients at baseline (Figure 2E). In the scRNA-Seq data, Tregs among the CD4+ T cell population showed the highest LAG3 expression (Figure 2C). This CD4+LAG-3+ T cell population also expanded significantly following treatment in patients in the IO-naive cohort according to the flow cytometric data (Figure 5A). After therapy, scRNA-*Seq data* revealed upregulated expression in Tregs of LAG3, among other genes that are known to inhibit the proliferation and function of these cells [6] (Figure 5B and Supplemental Table 2). The top pathways lost in Tregs following anti–LAG-3+anti–PD-1 treatment in HALLMARK, Gene Ontology (GO), Reactome Pathway Database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) categories included oxidative phosphorylation, the citric acid cycle, and ATP formation, suggesting decreased metabolic function [40] (Figure 5C, full pathways in Supplemental Table 2). This was also observed in a cell velocity analysis, in which Tregs appeared to adopt a more metabolically silent phenotype (Figure 3F).
As Tregs can inhibit immune cells via cell-cell contacts, we next sought to determine whether the observed changes resulted in alterations in predicted ligand-receptor interactions with CellPhoneDB [41] in the scRNA-*Seq data* (full ligand-receptor interactions are detailed in Supplemental Table 3). At baseline, the responders had more interactions than did nonresponders, most notably in different CD8+ T cell and NK cell populations (Figure 5D and Supplemental Figure 8A). The responders’ Tregs interacted more with adaptive NK cells, cycling NK cells, and exhausted T cells than did Tregs of nonresponders (Supplemental Figure 9A). The interactions seen with Tregs were mostly shared between both response groups, including different HLA interactions with NK cells, but the interactions between LGALS9 (galectin 9) and its receptors (HAVCR2/TIM-3, CD44, and CD47) were exclusive to nonresponders (Figure 5E), and LGALS9 was found to be upregulated in nonresponders’ Tregs (Figure 5F).
The cells that gained the most interactions in the scRNA-*Seq data* during the anti–LAG-3+anti–PD-1 therapy included adaptive NK cells, exhausted T cells, and different B cells (Supplemental Figure 9A). In contrast, Tregs were among the subsets that lost the most interactions, especially in responders (Supplemental Figure 9A). In the nonresponding patients, the interactions between Tregs and adaptive NK cells and CD8+ T effector cells increased (Supplemental Figure 9B). The interactions lost in responders included inhibitor interactions, such as MIF-TNFRSF14, CTLA4-CD80/CD86, and KLRB1-CLEC2D, which was not seen in the nonresponders (Supplemental Table 3).
To translate our findings to Treg phenotypes ex vivo, we performed coculture assays with primary Tregs and primary effector CD8+ or CD4+ T cells from the limited number of patient samples available ($$n = 3$$ at different time points). After 24 hours of coculturing, Tregs inhibited CD8+ and CD4+ Teff cell proliferation compared with controls without Tregs. Three months after the initiation of anti–LAG-3+anti–PD-1 therapy, the inhibitory effect of Tregs on CD8+ and CD4+ proliferation was slightly lower in 2 of the 3 pre-therapy samples, suggesting a decrease in Treg-suppressive function (Figure 5G and Supplemental Figure 9C).
## Anti–LAG-3+anti–PD-1 therapy increases chemotaxis and chemoattraction.
As anti–LAG-3+anti–PD-1 treatment increased the number of cell-cell interactions in the scRNA-*Seq data* for most samples, we explored the cellular interactions further and profiled the levels of 78 different extracellular serum proteins ($$n = 35$$, 79 samples, Supplemental Table 1). Based on unsupervised principal component analysis (PCA) of the serum protein data, the largest variation (PC1, $12.11\%$) was between the pre– and post–anti–LAG-3+anti–PD-1 treatment samples ($P \leq 0.05$, Kruskal-Wallis test, Figure 6A). Following therapy, a greater number of cytokines were increased than decreased, especially in the IO-naive patients ($$n = 11$$, Figure 6B and Supplemental Figure 10, A–C); this was congruent with the increase in ligand-receptor pair predictions following therapy. The differences in cytokine environment were less prominent in the prior IO–refractory patients ($$n = 29$$, Supplemental Figure 10, A–C). The cytokines upregulated by the therapy hinted toward increased chemotaxis and chemoattraction for different leukocytes (CXCL9/-10/-11/-12, CCL3/-20), costimulating-enhancing molecules (CD27, TNFRSF4/OX40), IFN-γ production–enhancing molecules (IL-12/-18), and also antiinflammatory molecules (IL-10, PD-L1) (Figure 6B).
Before treatment initiation, the proteins that correlated with a favorable response included cytokines associated with a costimulatory environment, with upregulated CXCL9, TNFRSF9 (4-1BB), and KLRD1 ($P \leq 0.05$, 2-sided Mann-Whitney U test, Figure 6C), all implicated in favorable NK cell responses. The protein upregulated in nonresponding patients included only MCP2 (CCL8) ($P \leq 0.05$, Figure 6C), a known chemoattractant for myeloid cells.
## Higher T cell clonality in patients responding to anti–LAG-3+anti–PD-1 therapy.
We analyzed TCRβ-Seq (anti–LAG-3+anti–PD-1–treated melanoma $$n = 34$$, 86 samples; healthy donors $$n = 783$$) and scRNA+TCRαβ-Seq ($$n = 6$$, 18 samples) data to understand the antigen restriction of expanded T cells. The baseline clonality was significantly higher in responding patients in the IO-naive cohort ($P \leq 0.05$, 2-sided Mann-Whitney U test) (Figure 7A and Supplemental Table 4). We also observed a similar trend in the prior IO–refractory cohort in responding patients at baseline, but it was not statistically significant ($P \leq 0.05$) (Figure 7A and Supplemental Table 4). In the prior IO–refractory cohort, the anti–LAG-3+anti–PD-1 treatment appeared to decrease overall blood T cell clonality in responding patients in the TCRβ-Seq data, although statistical significance was not reached.
## LAG3+CD8+ T cell clones expand following therapy and gain more cytotoxic and NK-like profiles in responding patients.
We linked the TCR information to T cell phenotype and noticed that the proportion of CD4+ and naive CD8+ T cells in the flow cytometric data correlated negatively with clonality in the TCRβ-Seq data, whereas the proportion of cytotoxic CD8+CD57+ T cells and CD56dimLAG3+PD-1+ NK cells were positively correlated (Supplemental Figure 11A). We also reclustered scRNA+ TCRαβ-Seq profiles of cells with detected TCRs (Figure 7B and Supplemental Figure 11, B and C). In the CMV+ patients ($$n = 4$$), we observed larger clones than in the CMV– patients ($$n = 2$$), and the large clones frequently persisted following therapy, although novel clones also expanded (Figure 7C). The large clones (explaining at least >0.5 % of the repertoire) were of CD8+LAG3+ effector (cluster 4) and CD8+LAG3+ effector memory phenotypes (cluster 2) (Figure 7D). The clonotypes that were from these LAG3+ clusters 2 and 4 were the ones that were most likely to expand following therapy, both at the 1- and 3-month time points (Figure 7E).
We next analyzed the individual clonotypes in the scRNA+TCRαβ-*Seq data* and noticed that clones from responding patients had more transcriptomic alterations than did those from nonresponding patients at early and late response time points (1 month vs. baseline $P \leq 0.0001$, 3 month vs. baseline $P \leq 0.05$, 2-sided Mann-Whitney U test, Figure 8A). The most recurrently upregulated genes in the clones from responding patients included genes associated with cytotoxicity (GZMA/H, PRF1, PNF1, S100A4, KLRG1, CST7), cytokines (IL32, IL2RG), cell structure remodeling (ADGRG1, ARPC1B, ANXA6, TMSB4X, FLNA), class I HLA (HLA-F, B2M), and calcium signaling (AHNAK1, S100A4) (Figure 8B). When studying the clones that expanded over 2-fold, involuted over 2-fold, or persisted, we noticed major upregulation in cytotoxicity in the expanded clones but also to a great extent in the persisting clonotypes (Figure 8C). The increased expression of genes associated with the NK-like phenotype was more clearly observed in the expanded clones than in the persisting clones or involuted clones (Figure 8C).
## Conserved antigen targets for LAG-3+CD8+ T cell clones.
Next, we sought to find the targets of the LAG-3+CD8+ T cell clones. We performed additional TCRβ-Seq on CD4+LAG-3+– or CD8+LAG-3+–sorted cells ($$n = 6$$) and noted their diversification following therapy (Supplemental Figure 11D), although it was insignificant. We performed clustering of TCRs to putative antigen-specific clusters with GLIPH2 [42] using CD8+LAG-3+–sorted cells and gained 38 antigen-specific groups of CD8+LAG-3+ T cells (Figure 9A). To determine whether these motifs are recurrent, we wanted to validate these motifs with orthogonal data and thus matched them back to our scRNA+TCRαβ-Seq data, which were profiled from different donors. We found 20 of 38 motifs in CD8+ T cells, with the most common motif being “SQDS” (Figure 9B). The phenotype of T cells with the same “SQDS” motif showed a bias toward the CD8+ exhausted phenotype, marked by expression of, e.g., LAG3 and PDCD1 (Figure 9C), meaning that the “SQDS” motif showed a similar phenotype in both the bulk TCRβ-*Seq data* from 1 set of donors and in the scRNA-TCRαβ-*Seq data* from another set of donors. After anti–LAG-3+anti–PD-1 therapy, the proportion of these exhausted cells was reduced, and the proportion of LAG-3+CD8+ Teff (cluster 4) and CD8+ T cells with stem-like properties (cluster 3), which have previously been associated with therapy response [43], was increased (Figure 9D).
## Immune checkpoint inhibitor therapies reverse the exhaustion of clonotypes recognizing melanoma-associated antigens in responders.
Finally, as we could link the found motifs to any antigen-specificities, we predicted the antigen specificities of scRNA+TCRαβ-Seq cells with TCRGP [44], our machine-learning method that predicts the probability for T cells to recognize epitopes with known TCR-epitope pairs. We predicted T cells targeting melanoma-associated antigens (MAAs) (e.g., MART1AAGIGILTV, MART1ELAGIGILTV) [45] and compared these with clones targeting antiviral epitopes (e.g., CMV, EBV, and influenza A).
We chose to focus on clonotypes predicted to target MART1AAGIGILTV (16 clonotypes) and EBV BMLF1GLGTLVAML (5 clonotypes) epitopes, as they were the most abundantly predicted targets in the cohort. In CR/PR patients, the anti-MAA T cells had higher expression of cytotoxicity-related genes throughout the treatment than did patients with PD (Figure 9E). Especially following therapy, IFNG production of the anti-MAA T cells was elevated in CR/PR patients, which was not seen in patients with PD. Also, in the CR/PR patients the antiviral T cells were more cytotoxic than those in patients with PD but less toxic than the anti-MAA T cells in the same patients (Figure 9E). Although none of the patients had known active viremia, the level of exhaustion was higher in the antiviral T cells than in the anti-MAA T cells, but this did not alter during the therapy.
## Discussion
Currently used immune checkpoint inhibitor therapies are primarily directed at invigorating cytotoxic T cells, but dual-checkpoint inhibition may allow the simultaneous activation of other important immune cell subpopulations to improve response rates. Our comprehensive multiomics data set demonstrates that LAG3 is not only expressed in CD8+ T cells but that it is also highly expressed in NK cells and Tregs of patients with melanoma. Furthermore, our results illuminate how successful anti–LAG-3+anti–PD-1 therapy can have an impact on these cells by increasing the cytotoxic phenotype of NK cells, activating antigen-restricted T cells, and changing the expression profile of Tregs.
As NK cells have a high potential to kill tumor cells, enhancing their function could augment responses in tumors in which T cell responses are deficient, e.g., in tumors with a low number of neoantigens and low or no expression of class I HLA (46–48). In addition to classical inhibitory NK cell receptors (killer cell immunoglobulin-like receptors [KIRs], leukocyte immunoglobin-like receptors [LIRs], and NKG2A), recent studies have found that immune checkpoint receptors previously associated with T cells, such as PD1, HAVCR2, and LAG3, can drive NK cell dysfunction [49]. Although the balance between immune cell activation and exhaustion is delicate, our results demonstrate how anti–LAG-3+anti–PD-1 therapy can stimulate NK cells, and especially in adaptive NK cells, we observed an increase in the IFN-γ response, upregulation of cytotoxicity-associated genes, and elevated degranulation and cytokine production. Importantly, in the patients who had an objective response (CR/PR), the adaptive NK cells were increased already at baseline, and they underwent significant transcriptional changes during the therapy. These results suggest that (adaptive) NK cells may participate in antitumor activities by directly killing tumor cells and/or modifying the cytokine environment to elicit T cell responses, but further ex vivo and in vivo studies are needed to understand the putative mechanisms in detail. Tregs can lead to the failure of immune checkpoint inhibitor therapy by reducing cytotoxic cell proliferation, suppressing immune cell–mediated lysis, and providing an unfavorable cytokine environment [50]. *In* general, the expression of LAG3 in Tregs has been linked to the suppression of Tregs [6]. Hence, it was interesting to observe in the scRNA-*Seq data* that, following the anti–LAG-3+anti–PD-1 therapy, Tregs (and CD4+LAG-3+ cells, putative Tregs, in the flow cytometric analysis) expanded in both responding and nonresponding patients. Previous studies with anti–PD-1 have also detected a similar rise in Tregs in patients responding to anti–PD-1 treatment [43, 51, 52], which could be thought to prevent a prolonged, nonspecific immune reaction once the antitumor immune response has been activated. In the responding patients, anti–LAG-3+anti–PD-1 combination therapy decreased the number of predicted interactions between Tregs and other immune cells, which may affect their function and suppressive capacity. This is in accordance with the noted decrease in the suppressive capacity of CD4+ and CD8+ T cells by Tregs in the post-therapy samples, although the small number of samples available prevents strong conclusions. Interestingly, nonresponding patients had multiple Gal9-TIM3 interactions between Tregs and effector cells, offering a hypothesis for a resistance mechanism for anti–LAG-3+anti–PD-1 therapy.
Prior studies have noted that patients responding to anti–PD-1 (with or without anti–CTLA-4) therapy have higher baseline TCR clonality in the tumor microenvironment [24] and harbor large clones that gain more DEGs during therapy than do nonresponding patients [23]. Similarly, in our cohort, the patients responding to anti–LAG-3+anti–PD-1 therapy had higher baseline clonalities and larger clones that also had increased numbers of DEGs. However, our sample size was not sufficient large enough to determine whether the therapy significantly alters the clonality or diversity of the PB TCR repertoire. Anti–LAG-3+anti–PD-1 therapy also expanded CD8+LAG-3+ clones and shifted the phenotype of these clones to a more cytotoxic one with NK-like properties. We observed the highest increase in cytotoxicity in the clones that expanded, and rarely in involuting clones. Interestingly, we found the same TCR motifs in the CD8+LAG-3+ clones in different data sets, which could denote that cells with this phenotype have similar targets across patients, and anti–LAG-3+anti–PD-1 treatment may reduce the exhaustion of these antigen-specific T cells or prevent the precursor exhausted cells to become fully exhausted. Further studies with modern methods combining simultaneously scRNA+TCRαβ+peptide-major histocompatibility complex (pMHC) are needed to identify the specificity of these expanded T cell clones [53]. However, we were able to obtain some evidence that anti–LAG-3+anti–PD-1 treatment can affect the phenotype of tumor-targeting T cell clones, as our supervised analysis with predicted anti-MART1AAGIGILTV clones showed that during treatment, the phenotype of these cells changed from LAG3+ effector cells to cells with increased IFNG expression.
Our study has several potential limitations. As no tumor biopsies were available, the analysis was performed on immune cell subsets from PB samples and not from tumor samples, which could have been more informative for some aspects of the tumor reactivity. However, we used publicly available data on previously profiled baseline tumor samples to validate and extrapolate our results from PB samples. Also, the scRNA-Seq cohort was limited to 6 patients, the functional validations were done with a small number of patient samples, and no control arm with nivolumab alone was available in this phase I trial. Further studies are warranted to associate our findings in peripheral immune cell subsets with tumor samples from larger patient cohorts with comparison with other types of therapies in randomized phase II/III trials studying anti–LAG-3 combination therapies to reveal the detailed mechanism of action of this drug.
In summary, our study provides insights into anti–LAG-3+anti–PD-1 therapy in the human immune system and demonstrates the impact of the combination treatment on both NK cells and T cells.
## Patients and samples.
This translational substudy includes 40 patients with metastatic melanoma, who were enrolled in the multicenter phase I trial (NCT01968109) [9] and treated with relatlimab (anti–LAG-3) in combination with nivolumab (anti–PD-1) according to the inclusion and exclusion criteria stated in the clinical trial protocol (protocol no. CA224020, BMS) at the Helsinki University Hospital Comprehensive Cancer Center in Finland or Oslo University Hospital in Norway. Given the phase I nature of the trial, no patients or investigators were blinded to the treatment. Of the 40 patients, 11 were IO naive, and 29 patients had been previously treated with either anti–PD-1 monotherapy or with anti–PD-1 in combination with anti-CTLA-4. One patient had also been treated with macrophage colony-stimulating factor 1 in combination with anti–PD-1. Clinical response data are presented as the best overall confirmed response per immune-related response criteria, with confirmation per Response Evaluation Criteria in Solid Tumors 1.1, according to the clinical study protocol. The database was locked in February in 2021. For detailed patient characteristics, see Supplemental Table 1. PB samples were obtained from the patients before initiation of the treatment at cycle 1, day 1 (C1D1), and follow-up samples were obtained during cycle 1, day 29 (C1D29) and cycle 2, day 1 (C2D1). PBMCs were separated using Ficoll-Paque density-gradient centrifugation (GE Healthcare) and were live-frozen at –150°C in $10\%$ DMSO-FBS solution for further assays. Plasma was separated by centrifugation and then stored at –70°C.
## scRNA-Seq and analysis of the anti–LAG-3+anti–PD-1–treated cohort.
Single cells were partitioned using a Chromium Controller (10X Genomics), and scRNA-Seq and TCRαβ libraries were prepared using the Chromium Single Cell 5′ Library & Gel Bead Kit (10X Genomics), as per the manufacturer’s instructions (CG000086). In brief, approximately 17,000 cells from each sample, suspended in $0.04\%$ BSA in PBS, were loaded onto the Chromium Single Cell A Chip. During the run, single-cell barcoded cDNA was generated in nanodroplet partitions. The droplets were subsequently reversed, and the remaining steps were performed in bulk. Full-length cDNA was amplified using 14 cycles of PCR (Veriti, Applied Biosystems). TCR cDNA was further amplified in a heminested PCR reaction using the Chromium Single Cell Human T Cell V(D)J Enrichment Kit (10X Genomics). Finally, total cDNA and TCR-enriched cDNA were subjected to fragmentation, end-repair and A-tailing, adaptor ligation, and sample index PCR (14 and 9 cycles, respectively). *The* gene expression libraries were sequenced using an Illumina NovaSeq, S1 flowcell with the following read length configuration: read 1 = 26, i7 = 8, i5 = 0, read 2 = 91. The TCR-enriched libraries were sequenced using an Illumina HiSeq 2500 in Rapid Run mode with the following read length configuration: read 1 = 150, i7 = 8, i5 = 0, read 2 = 150. The raw data were processed using Cell Ranger 3.0.0 with GRCh38 as the reference genome with default parameters.
All cells were subjected to quality control. Cells with high amounts of mitochondrial transcripts (>$15\%$ of all UMI counts) or ribosomal transcripts (>$50\%$); cells with fewer than 100 genes or more than 4,500 genes expressed; cells expressing low or high (<$25\%$ or >$60\%$) amounts of housekeeping genes; or cells with low or high read depths (<500 or >30,000 UMI counts) were excluded from the analyses.
To overcome the batch effect, we used a probabilistic framework to account for different nuisance factors of variation in an unsupervised manner with a deep generative modeling method scVI. Briefly, the transcriptome of each cell is encoded through a nonlinear transformation into a low-dimensional, batch-corrected, latent embedding. The scVI (0.5.0) algorithm [34] was ran with default parameters (n_hidden = 128, n_latent=30, n_layers =2, dispersion = ‘gene’) on all cells passing the quality control, with each sample treated as a separate batch. The latent embedding was then used for graph-based clustering implemented in Seurat (3.0.0) [54] and uniform manifold approximation and projection (UMAP) dimensionality reduction. For each different clustering, the genes related to V(D)J-recombination were removed, and the resolution values in FindClusters function were inspected visually within the range of 0.1–3 with intervals of 0.1, where the chosen values were within 0.2–0.5 to prevent overclustering. Clusters were named in descending order (cluster 0 contained the most cells) and annotated by analysis of canonical markers, DEGs, and relationships to other clusters in dimensionality-reduced plots, calculating different scores with predefined pathways used in previous publications [16, 17, 55, 56] and with the automated, reference-based cell annotation tool SingleR (1.2.4) [35], where Blueprint was used as a reference. For UMAP dimensionality reductions, the default parameters in RunUMAP function were used throughout. Pseudotime analyses were performed with Slingshot (version 1.1.4) [57] in unsupervised mode on precalculated UMAP coordinates with default parameters.
Different scores were calculated with Seurat’s AddModuleScore function, which is an implementation of the method suggested by Tirosh et al. [ 58]. Differential expression analyses were performed on the basis of the t test, as suggested by Soneson and Robinson [59]. Pathway analyses were conducted with the hypergeometric test on HALLMARK, GO, Reactome, or the KEGG categories with R package clusterProfiler (3.16.0) [60]. The results are shown in Supplemental Table 2.
Heatmaps were generated with the ComplexHeatmap package (version 2.4.2), in which different clustering analyses were performed with Ward’s linkage with default parameters and the seed set at 123.
The abundances of spliced and unspliced reads (RNA velocity) were analyzed with dropEst (0.8.5) [61] and Velocyto (0.17.17) [39] with default parameters. After normalization, the data were smoothed using kNN-smoothed pooling ($k = 500$) on the PCA reduced space, while the high-dimensional velocity vectors were projected on the predefined UMAP embeddings. Receptor-ligand interactions were calculated with CellPhoneDB (version 2.0.0) [41] with default parameters on subsampled cells from each cell type to have an identical number of cells for each subtype (at least 50) with 1,000 iterations for the permutation testing. The costimulatory and coinhibitory receptor-ligand pairs were gathered from Dufva and Pölönen et al. [ 29]. Pseudotime analyses were done with Slingshot (version 1.1.4) [57] in the unsupervised mode on precalculated UMAP coordinates with default parameters.
For scTCRαβ-Seq, only TCR productive full-length sequence information was considered, and all ambiguous cells with multiple TCRα and/or TCRβ chains were removed. Clones were defined as having the same CDR3 amino acid sequence in both TCRαβ chains, if available, or just in the TCRβ chain.
## Immunophenotyping with flow cytometry and analysis.
Different immune cell subpopulations were immunophenotyped with flow cytometry from fresh PB samples with 6 panels of different cell-surface markers, including the following immune checkpoint receptors and cytotoxicity and migration markers: CD3-PerCP-Cy5.5 (BD, catalog 332771), CD4-PE-Cy7 (BD, catalog 560649), CD45-APC-H7 (BD, catalog 560178), CD8-BV510 (BD, 563919), CD56-BV421 (BD, 562751), CXCR1-FITC (BioLegend, catalog 341606), CD16-PE (BD, 561313), TCR γδ-APC (BD, catalog 555718), PD1-FITC (BD, catalog 557860), LAG3-PE (BD, catalog 125209), ICOS-PE-Cy7 (eBioscience, catalog 25-9948-42), CTLA-4–APC (BD, catalog 560938), HLA-DR-BB515 (BD, catalog 560938), CD27-PE (BD, catalog 555441), CD25-PE-Cy7 (BD, catalog 561405), CD11b-APC (BD, catalog 550019), NKG2C-AF488 (R&D Systems, catalog FAB138G), CD161-PE (BD, catalog 556081), NKG2D-PE-Cy7 (BD, catalog 562365), NKG2A-APC (R&D Systems, catalog FAB1059A), DNAM-BB515 (BD, catalog 565152), CD57-PE (BD, catalog 560844), NKp46-PE-Cy7 (BD, catalog 562101), NKp30-AF647 (BD, 558408), CXCR3-AF488 (BD, catalog 561730), CCR7-PE (R&D Systems, catalog FAB197P), CD45RO-PE-Cy7 (BD, catalog 560608), and CXCR4-APC (BD, catalog 560936). CD45+ lymphocytes were acquired with the BD FACS Verse, and the data were analyzed with FlowJo, version 10.4. The results are shown in Supplemental Table 1.
## Serum protein analysis with a multiplex immunoassay.
Serum samples separated from fresh PB using centrifugation were analyzed with a proximity extension assay (Proseek Multiplex Inflammation panel, Olink Bioscience). The samples were run on 2 separate plates, and duplicate samples were used to normalize the differences between the 2 runs. Protein levels were expressed as normalized protein expression (NPX) values, an arbitrary log2 scale unit. The results are shown in Supplemental Table 1.
## NK cytokine secretion and CD107a/b degranulation assay.
To study cytokine secretion and CD107a/b degranulation of LAG-3–expressing NK cells, previously frozen PBMCs from 4 patients at 0-, 1-, and 3-month time points and from 5 healthy controls were thawed in warm RPMI) media and allowed to rest overnight at 37°C $5\%$ CO2 before stimulation with K562 cells for 6 hours at 37°C in $5\%$ CO2. Anti–CD107a-FITC (BD, catalog 555800) and anti–CD107b-FITC (BD, catalog 555804) were used to measure degranulation. Calcium ionophore (MilliporeSigma, catalog C9275-1MG) and PMA (Cell Signaling Technology, catalog 4174S) stimulation was used as a positive control, and no stimulation was used for the negative control.
After the stimulation, the cells were washed once with PBS and stained with the following membrane-antibody mixes: CD3-PerCP-Cy5.5 (BD, catalog 332771), CD8-PE-Cy7 (BD, catalog 335822), CD45-APC-H7 (BD, catalog 560178), CD56-PE (BD, catalog 345812), and LAG3-APC (BioLegend, catalog 369212). After staining, the cells fixed and permeabilized with the Cytofix/Cytoperm kit (BD, catalog 554714) according to the manufacturer’s instructions and stained with an intracellular antibody mix of IFN-γ–BV450 (BD, catalog 560371), TNF-α–BV450 (BD, catalog 561311), and GZMB-BV510 (BD, catalog 563388) cytokines. A total of 150,000 cells were acquired with FACS Verse (BD), and the data were analyzed with FlowJo, version 10.4. The gating of cell populations is shown in Supplemental Figure 7A.
## Treg proliferation and suppression test.
CD3 cells were isolated from 10 million previously frozen PBMCs using the EasySep Human T Cell Isolation Kit (STEMCELL Technologies). The isolated CD3 cells were stained with antibodies CD3-AF488 (BD, catalog 557694), CD4-PerCP (BD, catalog 345770), CD25-APC (BD, catalog 555434), and CD127-PE (BD, catalog 557938) and sorted with the BD FACSAria III cell sorter to isolate CD8 (CD3+CD4–) and CD4 (CD4+CD127+) effector cells and Tregs (CD4+CD127–CD25hi).
The sorted CD4+ or CD8+ T cells were stained with the CellTrace Violet Cell Proliferation Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. Labeled effector cells (10,000 cells/well) were seeded in a 96-well, U-bottomed plate. To induce proliferation, the Gibco Dynabeads Human T Activator CD3/CD28 kit (Thermo Fisher Scientific) was used at a 1:20 Teff cell ratio. The proliferation of Teff cells was carried out for 72 hours before 10,000 freshly isolated Tregs were added either for 24 or 48 hours. To measure the proliferation after 96 or 120 hours of incubation, the cells were stained with a CD4-PE (BD, catalog 561841) and CD8-APC (BD, catalog 561953) antibody mix. The suppressive capacity of Tregs was calculated by comparing the division of Teff cells in the presence of Tregs with the control wells without Tregs.
## TCRβ-Seq and analysis.
TCRβ-Seq was conducted as previously described with the ImmunoSEQ assay by Adaptive Biotechnologies. Genomic DNA was used in all cases.
Analyses started with the TCRβ matrices provided by Adaptive Biotechnologies preprocessing pipeline. All data were transformed to the VDJtools [62] format to reduce the complexity of the data. Nonproductive clonotypes were removed from the analysis. To assess the saturation of the sequencing results between the cohorts, the dependencies between sample diversity and sample size were determined with rarefaction plots inspired by Colwell et al. [ 63] and implemented in VDJtools. We used a minimum sampling depth of 20,000 reads per sample and subsampled all samples with more reads to 20,000 reads to normalize the samples and remove biases for depth-dependent statistics. Multiple different diversity metrics, including Shannon-Wiener, Simpson, and clonality indexes, were calculated with the CalcDiversityStats function on both unsampled and subsampled repertoire data (see Supplemental Table 3).
To identify T cells with the same epitope specificities, we used the online server for GLIPH 2 [42], which groups TCRs that potentially recognize the same target by calculating global and local amino acid similarities and compares these clusters with clusters found from a reference set of TCRs from naive, singleton T cells to determine the statistical significance with default parameters.
Epitope specificity predictions were performed with TCRGP (version 1.0.0), and anti-MAA models (MART1AAGIGILTV, MART1ELAGIGILTV, MELOE1TLNDECWPA, TKTAMFWSVPTV, and SEC24AFLYNLLTRV) were gathered from the Huuhtanen et al. report [45]. The antiviral models (influenza A M1 GILGFVFTL, EBV BMLF1GLCTLVAML, CMV pp65IPSINVHHY, CMV pp65NLVPMVATV, EBV BZLF1RAKFKQLL, B7RPRGEVRFL, CMV pp65TPRVTGGGAM, and EBV BRLF1YVLDHLIVV epitope) were gathered from the TCRGP [44] package’s GitHub page (https://github.com/emmijokinen/TCRGP; commit ID e3c8e5c). For the predictions used in all analyses, a threshold corresponding to a false-positive rate (FPR) of $5\%$ was determined for each epitope separately from the ROC curves obtained from the cross-validation experiments in the original publications.
## Data and code availability.
Patient characteristics, flow cytometry–profiled cell population abundances, and serum cytokine assay results are provided in Supplemental Table 1. The processed and raw single-cell data can be downloaded from the European Genome-Phenome Archive (EGA) (data set ID: EGAS00001005580). The preprocessed scRNA-Seq counts, scTCRαβ-Seq clonotypes, TCRβ-Seq clonotypes, and Seurat objects are available at Zenodo (https://doi.org/10.5281/zenodo.5747250). The processed scRNA+TCR-*Seq data* are available in ArrayExpress (E-MTAB-12733) and TCR-*Seq data* are in immuneAccess (https://clients.adaptivebiotech.com/pub/huuhtanen-2023-jci) The TCRβ-Seq results are also shown in Supplemental Table 3. All the custom scripts to reproduce the key findings can be found in github.com/janihuuh/lag3_manuscript (commit ID a60e65a).
## Statistics.
P values were calculated with nonparametric tests, including the Mann-Whitney U test (2 groups), the Kruskal-Wallis test (more than 2 groups), and Fisher’s exact test where the alternative hypotheses are reported. Adjustments for multiple testing were performed when the number of tests exceeded 20 and were either done with Benjamini-Hochberg correction or Bonferroni correction in the DEG analyses. Nominal P values and adjusted P values of less than 0.05 were considered significant. All calculations were done with R (version 4.0.2) or Python (version 3.7.4). In the box plots, the center line corresponds to the median, the box corresponds to the IQR, and the whiskers are 1.5 × the IQR, while outlier points are plotted individually where present.
## Study approval.
All patients and healthy controls gave their written informed consent. The study was approved by the Helsinki University Central Hospital (HUCH) ethics committee (Dnro $\frac{115}{13}$/$\frac{03}{02}$/15) and was conducted in accordance with the Declaration of Helsinki.
## Author contributions
The study was conceived by JH, HK, AK, and S Mustjoki. The patients were recruited by MH, K Peltola, S Mäkelä, MN, HK, and PB. The clinical data were collected by OB. The clinical trial–associated data were provided by BL. scRNA-Seq was carried out by TL and HK with the help of JH. Flow cytometry was performed by AK and HK with help from MI, K Peltonen, and MHL. TCRβ-Seq and serum protein profiling were carried out by HK. Ex vivo functional validations were done by HK and VG. All the data analyses were designed and performed by JH with the help of OD, who assisted with interpreting the single-cell clusters, EJ, who helped with the TCR analyses, and JV, who assisted with the RNA velocity analyses. The comparison data to support the findings were acquired by JH. JH wrote the manuscript and produced the figures with the help of S Mustjoki and with contributions from all authors. The project was supervised by S Mustjoki with the help of AK and HL. The order of the co–first authors was determined on the basis of their contributions to the manuscript. All authors read and approved the final manuscript.
## 01/31/2023
In-Press Preview
## 03/15/2023
Electronic publication
## 03/29/2023
Updated Acknowledgments
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|
---
title: HMGA1 induces FGF19 to drive pancreatic carcinogenesis and stroma formation
authors:
- Lionel Chia
- Bowen Wang
- Jung-Hyun Kim
- Li Z. Luo
- Shuai Shuai
- Iliana Herrera
- Sophia Y. Chen
- Liping Li
- Lingling Xian
- Tait Huso
- Mohammad Heydarian
- Karen Reddy
- Woo Jung Sung
- Shun Ishiyama
- Gongbo Guo
- Elizabeth Jaffee
- Lei Zheng
- Leslie M. Cope
- Kathy Gabrielson
- Laura Wood
- Linda Resar
journal: The Journal of Clinical Investigation
year: 2023
pmcid: PMC10014113
doi: 10.1172/JCI151601
license: CC BY 4.0
---
# HMGA1 induces FGF19 to drive pancreatic carcinogenesis and stroma formation
## Abstract
High mobility group A1 (HMGA1) chromatin regulators are upregulated in diverse tumors where they portend adverse outcomes, although how they function in cancer remains unclear. Pancreatic ductal adenocarcinomas (PDACs) are highly lethal tumors characterized by dense desmoplastic stroma composed predominantly of cancer-associated fibroblasts and fibrotic tissue. Here, we uncover an epigenetic program whereby HMGA1 upregulates FGF19 during tumor progression and stroma formation. HMGA1 deficiency disrupts oncogenic properties in vitro while impairing tumor inception and progression in KPC mice and subcutaneous or orthotopic models of PDAC. RNA sequencing revealed HMGA1 transcriptional networks governing proliferation and tumor-stroma interactions, including the FGF19 gene. HMGA1 directly induces FGF19 expression and increases its protein secretion by recruiting active histone marks (H3K4me3, H3K27Ac). Surprisingly, disrupting FGF19 via gene silencing or the FGFR4 inhibitor BLU9931 recapitulates most phenotypes observed with HMGA1 deficiency, decreasing tumor growth and formation of a desmoplastic stroma in mouse models of PDAC. In human PDAC, overexpression of HMGA1 and FGF19 defines a subset of tumors with extremely poor outcomes. Our results reveal what we believe is a new paradigm whereby HMGA1 and FGF19 drive tumor progression and stroma formation, thus illuminating FGF19 as a rational therapeutic target for a molecularly defined PDAC subtype.
## Introduction
Pancreatic ductal adenocarcinoma (PDAC) has emerged as a major public health problem in industrialized countries, and its incidence is rising (1–3). PDAC is predicted to become the second leading cause of cancer death in the United States by 2030, overtaking breast, prostate, and colorectal cancer [3]. Most patients present with locally advanced or widely metastatic disease, rendering these tumors surgically unresectable (1–3). Even patients with localized tumors amenable to surgical resection will succumb to metastatic disease in almost all cases, suggesting that metastases occur prior to clinical presentation [1]. Although previous studies identified mutant KRAS and molecular alterations inactivating CDKN2A, TP53, and TGF-β pathway components, these findings have not translated into improved therapies, nor have they led to effective screening strategies [3, 4, 5]. Thus, there is a dire need to discover actionable mechanisms and new therapeutic targets for this exceptionally refractory tumor.
In contrast to many solid tumors, PDACs are characterized by a dense desmoplastic stroma composed of cancer-associated fibroblasts (CAFs) and fibrous scar tissue, although the role of the stroma in tumor progression remains controversial (6–11). While immune cells are found within the stroma, PDACs tend to be “cold” tumors, lacking an antitumor immune response [12]. In vitro studies show that CAFs secrete factors that provide inflammatory signals and stimulate tumor growth and progression (9–11). Similarly, biomechanical analyses suggest that a “stiff” tumor microenvironment alters tumor cells to enhance motility and facilitate metastases (13–15). Further, PDAC cells grow faster when implanted with CAFs in mouse xenografts [16]. The dense fibroblastic stroma also provides a barrier that prevents cytotoxic therapy from reaching tumor cells [9]. Conversely, studies in transgenic mouse models of PDAC found that the stroma restrains tumor growth and progression [7, 8]. More recent studies employing single-cell sequencing revealed that stromal cells, like cancer cells, are heterogeneous and impart tumor heterogeneity by creating various interfaces for tumor cells within their microenvironment (9, 17–23). These studies reveal a complex and nuanced role for the PDAC stroma, underscoring the need to better understand its role in disease progression.
Epigenetic alterations have emerged as a fundamental hallmark of cancer that drive tumorigenesis by altering cell fate decisions and differentiation [24]. For example, genetic lesions involving the switch/sucrose nonfermentable (SWI/SNF) nucleosome remodeling complex occur in up to $15\%$ of PDAC [25]. Mutations affecting histone methyltransferase genes (mixed-lineage leukemia 2 and 3) and the gene encoding the histone demethylase lysine demethylase 6A (KDM6A), also arise in PDAC [25]. Accordingly, aberrant methylation patterns are characteristic of PDAC (26–28). Genetic alterations that decrease sirtuin 6 (SIRT6) protein levels, a nutrient sensor and histone deacetylase that removes acetyl groups from histone 3 lysine 9 (H3K9) and histone 3 lysine 56 (H3K56), drive pancreatic tumorigenesis in murine models and predict a subclass of human PDAC with decreased survival [29]. Although these discoveries shed light on epigenetic abnormalities in PDAC, they have not led to better therapies.
Overexpression of the gene encoding the chromatin regulator HMGA1 occurs in most aggressive tumors, including PDAC, where high levels portend poor differentiation and adverse outcomes (30–50). The HMGA1 gene is normally expressed during embryogenesis [30, 39, 51] and in adult stem cells [46, 49, 52], but silenced postnatally in most differentiated cells. Through alternatively spliced mRNA, HMGA1 encodes HMGA1a and HMGA1b isoforms, which bind to AT-rich sequences, bend chromatin, and recruit transcriptional complexes to modulate gene expression (31–35, 37, 39, 42, 45–47, 49, 53). When overexpressed in lymphoid cells of transgenic mice, Hmga1 induces aggressive leukemia by upregulating transcriptional networks active in proliferating stem cells, poorly differentiated cancer cells, and inflammation [32, 35, 43, 47, 53]. While mechanisms driving HMGA1 expression in cancer are incompletely understood, growth factors [54, 55], cancer-associated mutations, including Kras [56] or mutant Apc [57], and oncogenic transcription factors, such as cMYC (58–60), upregulate HMGA1, suggesting that diverse oncogenic pathways converge on HMGA1 to induce its expression. HMGA1 also cooperates with KRAS in immortalized pancreatic ductal epithelial cells to foster clonogenicity [61], whereas silencing HMGA1 in PDAC cell lines disrupts metastatic progression following orthotopic implantation in immunodeficient mice [62]. In intestinal stem cells, HMGA1 amplifies Wnt signals from the stroma and epithelial niches by inducing the expression of genes encoding Wnt agonist receptors (Fzd$\frac{5}{7}$, Lrp$\frac{5}{6}$, and Lgr5) and Wnt effectors, such as cMyc and Sox9 [46]. Together, these findings suggest that HMGA1 fosters tumor progression through both cell-intrinsic and stromal interactions, though little is known about transcriptional networks and tumor-stroma crosstalk governed by HMGA1 in PDAC.
Here, we uncover what we believe is a previously unknown epigenetic program whereby HMGA1 upregulates transcriptional networks involved in proliferation and tumor-stroma interactions during tumor progression and development of a fibroblastic stroma in PDAC. HMGA1 binds directly to the fibroblast growth factor 19 (FGF19) promoter and recruits active histone marks to induce FGF19 expression and secretion from PDAC cells. Silencing either HMGA1 or FGF19 disrupts phenotypes required for tumor progression. Surprisingly, loss of just a single Hmga1 allele within the pancreatic ductal epithelium significantly prolongs survival in Kras+/LSL-G12D; Trp53+LSL-R172H; Pdx1-Cre (KPC) [63] mice compared with those with both Hmga1 alleles intact. In mice with human PDAC xenografts, silencing HMGA1 or FGF19 depletes tumor-initiating cells while disrupting tumor growth and stroma formation. Moreover, treatment with an FGF receptor 4 (FGFR4) inhibitor, BLU9931, to block FGF19 function [64] recapitulates the effects of HMGA1 or FGF19 silencing, decreasing tumor growth and stroma formation in orthotopic models. Importantly, high expression of both HMGA1 and FGF19 defines a subclass of human PDAC with exceptionally poor outcomes. Together, our findings reveal a unique role for HMGA1 in tumor progression and “building” a stromal wall through FGF19 and highlight a new therapeutic target for a subset of highly recalcitrant tumors.
## Silencing HMGA1 disrupts oncogenic properties and depletes tumor-initiating cells.
Because HMGA1 is upregulated in PDACs where high levels associate with decreased survival [36, 38, 61, 62], we sought to elucidate HMGA1 function in pancreatic carcinogenesis. First, we found that HMGA1 expression (mRNA and protein) is higher in PDAC cell lines derived from metastatic tumors compared with those from primary tumors [65] (Supplemental Figure 1, A–E; supplemental material available online with this article; https://doi.org/10.1172/JCI151601DS1). Next, we silenced HMGA1 via lentiviral delivery of short hairpin RNAs (shRNAs) targeting 2 different sequences [49] in cell lines from primary and metastatic tumors harboring common PDAC mutations: (a) E3LZ10.7 [66], from a liver metastasis with KRASG12D and homozygous SMAD4 deletion; (b) MIA PaCa-2 [67], from a primary PDAC with homozygous CDKN2A/p16INK4A deletion, mutant KRASG12C, and TP53; and (c) AsPC-1 [67], from PDAC ascites fluid with homozygous mutations in KRASG12D, TP53C135fs*35, and CDKN2AL78fs*41. Strikingly, HMGA1 deficiency disrupted proliferation, clonogenicity, migration, invasion, and 3-dimensional (3D) sphere formation in all cell lines tested (Figure 1), indicating that HMGA1 is required for these oncogenic properties.
To define HMGA1 function in vivo, we assessed xenograft tumorigenesis from PDAC cell lines (E3LZ10.7 and AsPC-1), which showed that HMGA1 deficiency decreases tumor volumes (Figure 2, A and B). Intriguingly, tumors that formed from the pool of cells with HMGA1 silencing (E3LZ10.7 and AsPC-1 cells) express higher HMGA1 than the injected cells, suggesting that escape from gene silencing and a specific level of HMGA1 is required for tumor formation (Supplemental Figure 1, F and G). HMGA1 deficiency also depletes tumor-initiating cells in both cell lines (E3LZ10.7 and AsPC-1), demonstrating that HMGA1 is required for tumor initiation and growth in xenograft models (Figure 2C and Supplemental Figure 1, H and I).
## HMGA1 regulates transcriptional networks involved in proliferation and signaling.
To identify HMGA1 transcriptional networks, we performed RNA sequencing (GSE222890) in E3LZ10.7 cells (Figure 3, A and B) with or without HMGA1 silencing. Unsupervised hierarchical clustering separated cells with high HMGA1 (controls) from those with HMGA1 silencing (Supplemental Figure 2A). Differentially expressed genes ($P \leq 0.05$, log2[fold change] > 1.5) [68] included 660 up- and 565 downregulated genes (Figure 3B). Gene set enrichment analysis (GSEA, MSigDb *Hallmark* gene sets) revealed an HMGA1 signature of genes involved in cell cycle progression (E2F targets, G2/M checkpoint, mitotic spindle genes) (Figure 3C), while curated gene sets showed enrichment for cell cycle progression, cell signaling, metastatic progression, cancer stem cells, and embryonic stem cells (Supplemental Table 1) [69, 70]. Unexpectedly, we identified gene sets associated with bile acid metabolism, a pathway regulated, in part, by FGF19. Intriguingly, FGF19 (Figure 3B) was among the genes most robustly upregulated by HMGA1, with greater than 20-fold differential expression. Given this robust upregulation and because growth factors can function in cell-autonomous and tumor-stroma interactions, we focused on FGF19 first. In other contexts, FGF19 promotes proliferation, and Fgf15, the murine homolog, induces hepatocellular carcinogenesis and fibrosis in mice (71–74). Further, clinical inhibitors are available to target FGF19 or its receptor, FGFR4 (64, 75–77), although the role of FGF19 in pancreatic carcinogenesis is unknown.
## HMGA1 induces FGF19 expression and secretion.
HMGA1-dependent expression of FGF19 (mRNA, protein) was validated in PDAC cell lines (E3LZ10.7, MIA PaCa-2, and AsPC-1; Figure 3, D and E). Intriguingly, FGF19 levels were much higher in the metastatic cell lines (E3LZ10.7 and AsPC-1) compared with MIA PaCa-2 cells derived from a localized tumor (Supplemental Figure 2B). Because FGF19 protein was barely detectable in MIA PaCa-2 cells, we validated its HMGA1 dependence by immunoprecipitation (IP) (Supplemental Figure 2C). Because FGF19 is secreted from cells and could function in an autocrine and/or paracrine fashion, we assessed secretion from E3LZ10.7 cells by cytokine arrays, which show a marked decrease with HMGA1 silencing (Figure 3, F and G); these results were validated by immunoblotting and ELISA of media (Figure 3, Hand I). Six additional secreted factors were repressed with HMGA1 silencing, 7 were increased, and 9 were unchanged (Supplemental Figure 2, D–F). Similar to the gene expression results, secreted FGF19 was among the most robustly repressed factors with HMGA1 deficiency. FGF19 secretion from AsPC-1 or MIA-PaCa-2 cells also decreased with HMGA1 silencing, as detected by ELISA of media (Figure 3I and Supplemental Figure 2G). Together, these results demonstrate that FGF19 gene expression, protein levels within PDAC cells, and secretion depend upon HMGA1 in E3LZ10.7, MIA PaCa-2, and AsPC-1 cell lines.
## Silencing FGF19 recapitulates effects of silencing HMGA1.
To determine whether FGF19 is required for HMGA1 function in PDAC, we silenced FGF19 in PDAC cell lines (E3LZ10.7, MIA PaCa-2, and AsPC-1) via lentiviral delivery of shRNAs targeting 2 different sequences (Figure 4, A and B, and Supplemental Figure 3A). Surprisingly, silencing FGF19 faithfully recapitulated phenotypes observed with HMGA1 deficiency, disrupting proliferation, colony formation, migration, invasion, and 3D sphere formation (Figure 4, C–I). As an alternative approach to inhibit FGF19, we tested BLU9931, an inhibitor that specifically blocks the canonical FGF19 receptor (FGFR4) [64], demonstrating that BLU9931 impairs the proliferation, migration, and invasiveness of PDAC cell lines (E3LZ10.7 and MIA PaCa-2; Supplemental Figure 3, B–D). In xenograft tumorigenesis with E3LZ10.7 and AsPC-1 cells, both of which express higher levels of FGF19, the knockdown of FGF19 decreased tumor volumes and tumor-initiating cells (Figure 5, A–C, and Supplemental Figure 3, E and F). Intriguingly, in FGF19-silenced tumors, one E3LZ10.7 tumor at each dilution and one AsPC-1 tumor at the lowest dilution grew to proportions equal to or greater than controls. We therefore reassessed FGF19 levels in these tumors and noted a marked increase in FGF19 relative to the injected pool, suggesting that escape from FGF19 silencing allowed enhanced tumor growth (Supplemental Figure 3, G and H).
To determine whether exogenous FGF19 could rescue the effects of HMGA1 silencing, we exposed PDAC cells with HMGA1 silencing (E3LZ10.7) to recombinant human FGF19 (hFGF19). Proliferation (via 5-ethynyl-2′-deoxyuridine [EdU] incorporation) increased upon treatment with hFGF19, but not to levels of the control cells (Supplemental Figure 3I), indicating that FGF19 is required, but not sufficient, for proliferation mediated by HMGA1. Together, our results indicate that FGF19 is a partial mediator of HMGA1 oncogenic function in these PDAC models.
## HMGA1 binds directly to the FGF19 promoter and recruits activating histone marks.
Using an in silico prediction algorithm (MatInspector) [78], we identified putative HMGA1 DNA binding sites within the FGF19 promoter at –1092, –832, and –810 base pairs (designated sites A, B, and C, respectively) upstream of the transcription start site (TSS) (Figure 6A). HMGA1 occupancy by chromatin IP–PCR (ChIP-PCR) demonstrated that regions (~200 base pairs) surrounding site A (region 1, R1) or the region encompassing sites B and C (R2) show enrichment for HMGA in cell lines (E3LZ10.7, MIA PaCa-2, and AsPC-1), which was depleted with HMGA1 knockdown (Figure 6, B–D). The positive control, histone H3, was unchanged with HMGA1 deficiency. By contrast, there was no significant occupancy, nor were there changes with HMGA1 deficiency using a negative control IgG antibody (Figure 6E).
Because our gene expression data show that HMGA1 induces FGF19, we assessed occupancy of active histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 acetylation (H3K27Ac), both of which mark promoter and enhancer regions. In 3 cell lines (E3LZ10.7, MIA PaCa-2, and AsPC-1), H3K4me3 was abundant at R1 and R2 and decreased with HMGA1 silencing (Figure 6, E–G). HMGA1 deficiency also depleted H3K4me3 at R1 in AsPC-1 cells (Figure 6G). These data indicate that HMGA1 binds directly to the FGF19 promoter at R1 and R2 and recruits H3K4me3 in all 3 PDAC cell lines. In the metastatic E3LZ10.7 and AsPC-1 cell lines, HMGA1 also recruited H3K27Ac to R2. Of note, H3K27Ac histone marks associate with poised chromatin, stretch, or “super-enhancers,” and regulation of developmental or stem cell–like genes during normal development and in cancer [79]. Poised enhancers at developmental promoters are also implicated in poorly differentiated cancers and cancer stem cells [80]. Although there are differences in the specific histone marks between cell lines, HMGA1 was consistently associated with occupancy of active histone marks at the FGF19 promoter in all 3 cell lines.
To functionally validate these chromatin marks, we determined whether HMGA1 transactivates the FGF19 promoter linked to a luciferase reporter gene. We tested a promoter construct (–1144) including regions R1, R2, and downstream sequences up to the TSS compared with constructs with 5′ deletions: (a) –1046, lacking R1 and site A; (b) –816, lacking R1, site A, 5′ sequences of R2, and site B; and (c) –756, lacking R1, R2, and sites A, B, and C. As expected, the –1144 construct showed the greatest reporter activation, with decreases in constructs –816 and –756, and the lowest activity in the construct lacking both R1 and R2 (Figure 7A). Promoter activity of the full-length construct also decreases to levels of the deletion constructs in the presence of either a dominant-negative HMGA1 that no longer binds to DNA [81] or with HMGA1 silencing (Figure 7, B and C). These findings indicate that HMGA1 directly transactivates FGF19 expression by binding to R1 and R2 and recruiting active histone marks.
## HMGA1 signals through FGF19/FGFR4.
To determine whether HMGA1 and FGF19 signal through FGFR4, we assessed phosphorylation of FGFR4 and downstream signals (ERK and AKT) by flow cytometry and Western blotting in PDAC cells (E3LZ10.7 and AsPC-1). Silencing either HMGA1 or FGF19 decreased phosphorylation of FGFR4 (p-FGFR4; by flow cytometry) and downstream signaling molecules (ERK and AKT) without affecting unphosphorylated protein levels, indicating that both HMGA1 and FGF19 transduce signals through canonical FGF19/FGFR4 pathways (Figure 8, A–F). After rendering cells (E3LZ10.7, MIA-PaCa-2, and AsPC-1) quiescent by serum deprivation, FGFR4 phosphorylation and proliferation increased with exposure to recombinant hFGF19 (Supplemental Figure 4, A–F). Together, these results suggest that HMGA1 induces FGF19 expression and protein secretion, resulting in the phosphorylation of FGFR4 and downstream signaling molecules to enhance proliferation in PDAC cells.
## HMGA1 and FGF19 associate with fibrotic stroma formation.
Because secreted FGF19 could interact with stroma, we determined whether HMGA1 or FGF19 modulates fibrosis (via trichrome staining) and CAF composition within the stroma. Fibrosis scores were assigned based on area staining with trichrome: 0 (<$5\%$), 1 ($5\%$–$30\%$), 2 ($30\%$–$60\%$), and 3 (>$60\%$). In control PDAC xenografts, extensive fibrosis comprised over $30\%$–$60\%$ of tumor volumes (fibrosis scores 2–3) and included both stromal cells with a characteristic fibroblast appearance (Figure 9, A and B, and Supplemental Figure 5, A and B) and tumor cells with extensive intranuclear HMGA1 staining and cytoplasmic FGF19 staining by immunohistochemistry (IHC) (Figure 9A and Supplemental Figure 5A). In contrast, xenografts from PDAC cells with HMGA1 or FGF19 silencing had less fibrosis (<$30\%$ area; Figure 9, A and B, and Supplemental Figure 5, A and B). Both HMGA1 and FGF19 staining also decreased in tumors from PDAC cells with HMGA1 silencing and FGF19 silencing also decreased FGF19 staining (Figure 9A and Supplemental Figure 5A). Of note, tumors arising from cells with HMGA1 knockdown included a subset of tumor cells with HMGA1 intranuclear staining resembling controls, consistent with our gene expression data suggesting that escape from HMGA1 silencing allows tumor cells to grow as xenografts (Supplemental Figure 1, F and G). Further, the proliferation marker Ki-67 decreased with HMGA1 or FGF19 silencing in PDAC xenografts (Figure 9, A and C, and Supplemental Figure 5, A and C). These findings indicate that HMGA1 and FGF19 promote tumor proliferation and stroma formation in xenografted tumors.
To elucidate HMGA1-dependent changes in CAF composition within the stroma of xenografted tumors, we performed immunofluorescence (IF) to classify CAFs into 3 major subtypes previously defined in KPC mice and human tumors (19–23) based on positive staining for podoplanin (PDPN; a pan-CAF marker) and (a) α-smooth muscle actin (α-SMA); (b) CD74, a transmembrane molecule involved in formation and transport of major histocompatibility (MCH) class II peptides; and (c) IL-6, an inflammatory cytokine. In PDAC xenografts from all 3 cell lines, α-SMA+ CAFs comprised the majority, with less contribution from CD74+ and IL-6+ CAFs. Silencing HMGA1 or FGF19 reduced the proportion of all 3 CAF subtypes (Figure 9, D and E, and Supplemental Figure 5, D and E). Together, these findings indicate that HMGA1 and FGF19 modulate CAF composition to induce the formation of a desmoplastic stroma in xenografted tumors.
## Hmga1 deficiency in KPC mice impairs tumor and stroma formation.
To investigate Hmga1 in tumorigenesis, CAF composition, and stroma formation in mice with a competent immune system, we crossed KPC mice, in which PDAC develops more gradually [63], with mice with global deficiency of one or both Hmga1 alleles (all on C57BL/6 backgrounds) and followed offspring for evidence of PDAC (abdominal distension, rectal prolapse, palpable tumors) or ill appearance (hunching, or decreased activity, oral intake, or weight; Table 1). Similar to prior reports [63], KPC mice (24 of 24 evaluable mice) developed pancreatic tumors by 14.1 weeks [median survival time]) (Supplemental Figure 6A). Tissue autolysis precluded further analyses in 2 mice that died at 8 and 16 weeks. A subset of KPC mice developed rectal prolapse (5 of 24) and/or ascites (3 of 24) (Table 1). In all cases, invasive pancreatic tumors developed (24 of 24) with pathology consistent with PDAC in most ($92\%$; 22 of 24); 2 developed an undifferentiated sarcomatoid pancreatic tumor. By contrast, KPC mice with Hmga1 heterozygosity had delayed tumorigenesis and prolonged survival ($$n = 9$$; median survival 17.0 weeks). One KPC/Hmga1 heterozygous mouse developed a large salivary gland tumor at 7.4 weeks of age; the pancreas showed only rare foci of acinar ductal metaplasia. Of note, Hmga1 heterozygous mice have normal life expectancy with no evidence of abnormal growth or development [48, 49]. We also generated 1 KPC mouse null for Hmga1, which had a normal pancreas size and only rare foci of acinar ductal metaplasia at 22 weeks; it was sacrificed prior to any evidence of illness. Hmga1-knockout mice have decreased embryonic viability, whereas those that survive development are slightly small but appear grossly normal up to 30 weeks of age when they develop signs of premature aging (graying, osteopenia, decreased gait velocity) [49]. We used ultrasound to confirm the presence of pancreatic tumors in a subset of mice (Supplemental Figure 6B).
To ascertain whether Hmga1 deficiency alters pancreatic stroma development and fibrosis in KPC mice, we validated HMGA1 deficiency (via IHC), which showed robust HMGA1 intranuclear staining in KPC mice, less staining in KPC mice with Hmga1 heterozygosity, and complete absence of HMGA1 in KPC/Hmga1-knockout mice (Supplemental Figure 6C). FGF19 staining paralleled results observed with HMGA1, with robust FGF15 staining in KPC mice, moderate staining in KPC/Hmga1 heterozygous mice, and low levels in the KPC pancreas with Hmga1 knockout (Supplemental Figure 6C). Similarly, fibrosis scores decreased in KPC mice with a deficiency of one Hmga1 allele (Supplemental Figure 6, C and D, and Table 1), while the Hmga1-knockout mouse did not develop PDAC or fibrosis by 22 weeks. These data demonstrate that HMGA1 is required for pancreatic tumorigenesis and stromal formation in KPC mice.
## Hmga1 haploinsufficiency within pancreatic ductal epithelium is sufficient to mitigate tumor and stroma formation in KPC mice.
To determine whether Hmga1 deficiency within the pancreatic ductal epithelium is sufficient to mitigate tumorigenesis and stroma formation, we generated KPC mice crossed with mice with one or both Hmga1 alleles floxed, resulting in deletion of floxed alleles within pancreatic epithelium, including KPC mice with pancreas-specific heterozygous (KPC/Hmga1fl/+) or homozygous (KPC/Hmga1fl/fl) deletions. Surprisingly, loss of just a single Hmga1 allele within the pancreas (KPC/Hmga1fl/+) was sufficient to delay tumorigenesis and prolong survival ($$n = 5$$; 22.3 weeks) in KPC mice, and survival was prolonged even more than what we observed for KPC mice with global Hmga1 heterozygous deficiency (Figure 10A). Survival was also prolonged in mice with pancreas-specific deletion of both Hmga1 alleles ($$n = 7$$; KPC/Hmga1fl/fl; 22.0 weeks) similar to the KPC/Hmga1fl/+, suggesting that loss of just a single Hmga1 allele is sufficient to mitigate tumorigenesis in KPC mice. Accordingly, HMGA1 IHC in KPC/Hmga1fl/+ or KPC/Hmga1fl/fl mice showed a decrease or absence of HMGA1 in tumors cells, respectively, and FGF15 staining also decreased in parallel with HMGA1 (Figure 10B). Moreover, fibrosis scores and Ki-67 decreased in KPC mice with Hmga1 deficiency within pancreatic epithelium (Figure 10, B–D). Further, all 3 major CAF subtypes (by IF) decreased with pancreatic epithelial Hmga1 deficiency (Figure 10, E and F). Together, these striking results demonstrate that the loss of just a single Hmga1 allele within the pancreatic ductal epithelium is sufficient to mitigate tumorigenesis, stroma formation, and modulate CAF composition, thereby prolonging survival in KPC mice.
## HMGA1 and FGF19 are upregulated in human PDAC with exceptionally poor outcomes.
To determine whether HMGA1 and FGF19 are relevant in human PDAC, we queried published data sets (GSE15471; $$n = 36$$ nonmalignant tissue, $$n = 36$$ tumor samples) [82]. As expected, HMGA1 was robustly upregulated in most human PDACs, consistent with prior studies (Figure 11A) [36, 38]. By contrast, FGF19 was variable, with tumors demonstrating low, moderate, or high expression (Figure 11A). However, HMGA1 and FGF19 correlated positively in all tumors, albeit weakly (Figure 11B). In another independent data set (GSE16515) [83], we validated similar patterns with consistent HMGA1 overexpression and a broader range of FGF19 expression (Supplemental Figure 7D). Since HMGA1 is overexpressed in most tumors, whereas FGF19 is upregulated in only a subset (~$25\%$), we determined whether high expression of both HMGA1 and FGF19 predicts outcomes. In a PDAC database with survival data (GSE21501; $$n = 102$$ PDAC tumors) [84], we categorized PDAC tumors ($$n = 102$$) by quartiles based on relative expression of both genes, with the upper quartile representing tumors with highest expression of HMGA1 and FGF19 (red line; $$n = 26$$) and the lower quartile representing tumors with lowest expression of HMGA1 and FGF19 (black line; $$n = 26$$). We included a quartile with high HMGA1 and low FGF19 (green line; $$n = 25$$) and relatively low HMGA1 with high FGF19 (blue line; $$n = 25$$) (Figure 11C). Strikingly, tumors with high levels of both HMGA1 and FGF19 had worse overall survival ($$P \leq 0.005$$), indicating that this pathway is relevant to human PDAC and further underscoring FGF19 as a plausible therapeutic target for this highly recalcitrant molecular subtype (Figure 11C).
## BLU9931 decreases tumorigenesis and stroma formation in orthotopic PDAC models.
Because our primary goal is to identify actionable mechanisms in PDAC, we determined whether targeting the HMGA1/FGF19 pathway with BLU9931 mitigates tumor and stroma formation. We tested BLU9931 at doses established to reach pharmacologic levels in mice [64] in human PDAC xenografts from E2LZ10.7 cells (1 × 106) injected into the midpancreas of immunosuppressed mice (NOD Scid γ, NSG). Once tumors reached a volume of 100–200 mm3 by ultrasound, mice were given BLU9931 twice daily by oral gavage (300 mg/kg or vehicle control) approximately 1 week following implantation. Mice underwent necropsy after 4 weeks of therapy when controls began to appear ill. Strikingly, there was a marked decrease in tumor volumes in mice treated with BLU9931, along with decreased staining for HMGA1, FGF19, Ki-67, and fibrosis (trichrome) (Figure 12, A–D). The 3 CAF subtypes also decreased with BLU9931 (Figure 12, E and F), suggesting that targeting FGFR4 with BLU9931 is a promising approach for human PDAC overexpressing HMGA1 and FGF19.
Next, we tested BLU9931 in syngeneic mice with an intact immune system and KPC orthotopic implants. *After* generating subcutaneous xenografts from KPC and KPC/Hmga1fl/+ heterozygous cell lines with tumor volumes of approximately 100–200 mm3, tumor fragments were implanted surgically into the pancreas of mice. One week after implantation, we confirmed tumor formation (volumes of 100–200 mm3) by ultrasound, after which mice were divided into treatment arms with similar tumor volume distributions ($$n = 8$$–10/group): (a) KPC implants, BLU9931 treatment (twice daily oral gavage); b) KPC implants, vehicle control (twice daily oral gavage); c) KPC-Hmga1 heterozygous implants, BLU9931 treatment; and (d) KPC-Hmga1 heterozygous implants, vehicle control. Mice were followed by weekly ultrasounds and necropsies performed when recipients of KPC implants treated with vehicle control appeared ill (after 4 weeks). We discovered a marked decrease in tumor volume in recipients of KPC implants treated with BLU9931 compared with vehicle control (Figure 13A). Further, KPC implant recipients treated with BLU9931 had decreased levels of HMGA1, FGF19, fibrosis, and Ki-67 (Figure 13, B–D). Similar to KPC mice with Hmga1 deficiency, the frequency of all CAF subtypes decreased (Figure 13, E and F). Recipients of KPC implants with Hmga1 heterozygous deficiency had slightly smaller tumors than KPC mice treated with BLU9931. Although BLU9931 resulted in slightly lower mean tumor volumes in KPC/Hmga1 heterozygous implants in addition to decreased HMGA1, FGF15, and Ki-67 staining, and 2 of 3 CAF subtypes, the changes were modest, as tumor growth was markedly diminished by Hmga1 haploinsufficiency alone (Supplemental Figure 8, A–E). Taken together, these results indicate that HMGA1 drives PDAC tumor initiation, progression, and stroma formation, at least in part, by inducing FGF19 expression and secretion. Moreover, this pathway can be disrupted with an FGFR4 inhibitor, BLU9931. Most importantly, overexpression of HMGA1 and FGF19 defines a subset of human PDAC with exceptionally poor outcomes, underscoring the need for further studies to assess targeting FGF19 in PDAC.
## Discussion
Alterations in chromatin regulators frequently occur in cancer, although most epigenetic modulators have eluded therapeutic targeting (85–87). For example, genes encoding chromatin regulators involved in pluripotency, OCT4, SOX2, KLF4, NANOG, and LIN28, are rarely mutated, but frequently overexpressed in cancer, thus rendering pharmacologic interventions challenging [87]. Such factors are believed to reprogram the epigenome to a more plastic, stem-like state, thereby endowing tumor cells with the capacity to proliferate in a deregulated fashion, circumvent differentiation cues, evade therapy, and metastasize. HMGA1 chromatin regulators are oncofetal proteins that enhance cellular reprogramming by upregulating pluripotency networks [47, 88, 89]. Similar to pluripotency factors, HMGA1 is rarely mutated, but almost universally overexpressed in aggressive cancers, consistent with a fundamental role in tumorigenesis [47, 88]. Indeed, HMGA1 is among the most abundant, nonhistone chromatin-binding proteins within nuclei of cancer cells where it induces genes expressed in stem cells and tumor progression [30, 38, 46, 48, 88, 89].
While many studies show HMGA1 upregulation in PDAC [36, 38, 50, 61], transcriptional networks governed by HMGA1 that could be targeted in therapy remained elusive until now. We identified a single growth factor, FGF19, that fosters not only oncogenic properties, but also signals within the microenvironment to induce fibrotic desmoplasia. This mechanism is potentially unique because it involves both tumor cell–intrinsic and microenvironmental interactions that collaborate during tumor progression. Intriguingly, we recently found that HMGA1 causes bone marrow fibrosis during progression in mouse models of chronic myeloid malignancies (JAK2V617F myeloproliferative neoplasms), suggesting that fibrosis mediated by HMGA1 is relevant to diverse tumors [49]. Importantly, HMGA1 also regulates transcriptional networks involved in cell cycle progression (E2F targets, G2/M checkpoint, mitotic spindle) in myeloid malignancies, although FGF19 and bile acid metabolic genes are unique to PDAC cells. Surprisingly, silencing FGF19 recapitulates most, but not all, phenotypes associated with HMGA1 silencing, suggesting that it is an important transcriptional target, although other HMGA1 transcriptional networks clearly contribute to PDAC carcinogenesis in our models.
Prior studies revealed mutations and epigenetic alterations that arise early in pancreatic carcinogenesis, although this has not impacted therapies [3]. Less is known about later mechanisms driving progression. Clonal evolution studies suggest that PDACs evolve over many years, or even decades, which could foster clonal diversity and facilitate tumor progression [90]. Another vexing characteristic of PDAC is the desmoplastic stroma composed of fibrotic scar tissue and CAFs, which also exhibit heterogeneity [9, 10]. Although studies of CAF signaling and biophysical properties of stroma suggest that desmoplasia fuels tumor progression, the stroma restrains tumor growth in KPC models [7, 8]. These studies, together with our results, suggest that the stroma has multiple functions, which may depend on tumor stage and properties of tumor cells, and stromal composition. The stroma could provide an initial barrier that is circumvented as tumor cells become more plastic [18]. While we could not dissect the contribution of the stroma in isolation, our models suggest that HMGA1 and FGF19 collaborate to promote tumor progression and stroma formation. Because HMGA1 proteins are detectable only in late-stage precursor lesions (pancreatic intraepithelial neoplasia [PanIN] 3) or invasive tumors, this mechanism may be relevant later in carcinogenesis when tumor cells invade and metastasize [38]. Of note, we found lower frequencies of IL-6+ CAFs in KPC mice compared with other studies [19]. Although the reason for this is unknown, inflammatory signals may vary in different mouse colonies from factors such as the microbiome. Despite these differences, however, tumors formed within a time frame similar to those of published studies with KPC mice. Together, our work reveals a therapeutic target relevant to a newly defined molecular subclass of human PDAC characterized by high expression of HMGA1 and FGF19. Indeed, gene expression and survival data indicate that such tumors are among the most rapidly lethal PDACs.
FGF19 is a pleiotropic, hormone-like protein that regulates lipid, carbohydrate, and bile acid metabolism through the receptor FGFR4 [72]. Released from the ilium into enterohepatic circulation after exposure to bile salts in postprandial states, FGF19 dampens further bile acid release [72]. FGF19 is also expressed in embryonic stem cells [91]. In mice, FGF15 is required for embryogenesis and liver regeneration [92], and FGF15 induces hepatocellular carcinoma (HCC) when overexpressed in skeletal muscle, presumably through paracrine effects [71]. FGF19 is also overexpressed in human HCC harboring amplifications involving the FGF19 locus (chromosome 11q13) [93], which led to the development of clinical inhibitors (64, 75–77). A recent study in HCC, however, showed only modest responses to an FGFR4 inhibitor [75], although chemically induced HCC in mice with Fgf15 deficiency show less fibrosis [74], suggesting that FGF15 fosters fibrosis in HCC. HMGA1 is also upregulated in human HCC [94, 95], and FGF19 is overexpressed or amplified in other tumors with HMGA1 overexpression [77]. In a PDAC cell line, GLI/ERK signaling upregulates FGF19 and xenograft tumorigenesis [96], and our GSEA analyses link HMGA1 to ERK networks (Supplemental Table 1), consistent with HMGA1 as a central hub through which multiple oncogenic pathways converge. In PDAC models, FGF19 promotes tumor growth and stroma formation. Moreover, KPC mice with loss of a single Hmga1 allele within pancreatic ductal epithelium exhibit increased tumor latency, less fibrosis, and decreased FGF15 immunoreactivity, further supporting a collaborative role for HMGA1 and FGF15 in tumorigenesis and fibrotic desmoplasia (Figure 10, B and C).
In human PDAC, FGF19 expression is more variable than HMGA1, the latter of which is upregulated in most tumors [36, 38]. Why FGF19 is induced in only a fraction of tumors remains unclear. Pancreatic carcinogenesis may proceed through stepwise accumulation of mutations, or chromothripsis, whereby thousands of clustered chromosomal rearrangements occur simultaneously [3, 97]. The complex genome likely contributes to PDAC heterogeneity, and some genetic alterations may affect FGF19 expression. Notably, FGF19 only partially restores proliferation in cells with HMGA1 silencing, indicating that other HMGA1 pathways foster tumorigenesis. Our transcriptomes reveal multiple HMGA1 pathways and further investigation could reveal other actionable mechanisms. However, FGF19 deficiency recapitulates most effects of HMGA1 silencing and our KPC studies are consistent with FGF15 as a downstream HMGA1 effector. Despite the circumscribed population of human tumors with both HMGA1 and FGF19 overexpression, these data delineate a molecular subclass with worse outcomes that could be targeted in therapy [98].
KRAS-driven tumors, and PDAC in particular, have proven formidable therapeutic challenges. Therapies that target KRAS are emerging, although their efficacy in PDAC is unknown [99, 100]. While inhibitors of chromatin regulators, such as bromodomain proteins, show efficacy, successes in PDAC are lacking [101]. Growth factors provide attractive targets because they can be neutralized by antibodies or receptor blockade. Our work illuminates HMGA1 and FGF19 as key players in PDAC tumorigenesis and stroma formation. Most importantly, this pathway is conserved in a subset of human tumors with exceptionally poor outcomes. Together, we discovered what we believe is a previously undescribed paradigm whereby tumor cells collaborate via HMGA1 and FGF19 to drive progression, thus illuminating FGF19 as a rational therapeutic target for a molecular subclass composed of the most aggressive human PDACs.
## Methods
Detailed methods, statistical analyses, and reagents are provided in the supplemental material, including culture medium, primers, and antibodies (Supplemental Tables 2–4). RNA sequencing data were deposited into the NBCI Gene Expression Omnibus (GEO GSE222890). See complete unedited blots in the supplemental material.
## Author contributions
LR conceptualized the project. LC and BW drafted parts of the manuscript, and LR wrote the final draft, which was reviewed by all authors prior to submission. LC, BW, JHK, LZL, SS, IH, SYC, LL, LX, TH, MH, WJS, SI, GG, LMC, KG, LW, and LR performed experiments and analyzed data. EJ, LZ, KR, KG, and LMC provided reagents and guidance with experiments. LW, BW, LC, LZL, and WJS interpreted histology.
## 03/15/2023
Electronic publication
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|
---
title: Terpenoids and Bio-Functions of Essential Oils Hydrodistilled Differently from
Freshly Immature and Mature Blumea balsamifera Leaves
authors:
- Sirinapha Jirakitticharoen
- Wudtichai Wisuitiprot
- Pongphen Jitareerat
- Chalermchai Wongs-Aree
journal: Journal of Tropical Medicine
year: 2023
pmcid: PMC10014153
doi: 10.1155/2023/5152506
license: CC BY 4.0
---
# Terpenoids and Bio-Functions of Essential Oils Hydrodistilled Differently from Freshly Immature and Mature Blumea balsamifera Leaves
## Abstract
The volatiles and antioxidant capacity of essential oils (EOs) extracted from freshly immature and mature leaves of *Blumea balsamifera* at various hydrodistillation times were investigated. Seven major terpenoids were identified: two monoterpenes, camphor and L-borneol, and five sesquiterpenes, silphiperfol-5-ene, 7-epi-silphiperfol-5-ene, ß-caryophyllene, ɤ-eudesmol, and α-eudesmol. The quantity and terpenoid composition of the EOs were impressed by leaf maturity and hydrodistillation times. The yield of EOs from the immature leaves was 1.4 times that of mature leaves, with $73\%$ of the yield acquired within the first 6 hours (hrs) of hydrodistillation. Approximately $97\%$ of camphor and L-borneol, $80\%$ of ß-caryophyllene, silphiperfolene, and 7-epi-silphiperfolene, $32\%$ of ɤ-eudesmol, and $54\%$ α-eudesmol were collected in the first 6 hrs of hydrodistillation. More ß-caryophyllene, ɤ-eudesmol, and α-eudesmol were found in the mature leaf EOs. The antioxidant capacity of the EOs was proportionally related to their terpenoid contents. The EOs extracted from immature leaves at 0–6 hrs of hydrodistillation demonstrated distinctive antibacterial activity against Staphylococcus aureus, with minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values of 0.5 mg/mL and 1 mg/mL, respectively.
## 1. Introduction
Blumea balsamifera (Linn.), a traditional herb locally grown in Thailand, belongs to the family Asteraceae. Based on medical concerns such as cost-effectiveness and widespread use in the local community, its leaves have been utilized in traditional medicine to treat bruising, beriberi, eczema, dermatitis, lumbago, menorrhagia, and rheumatism [1]. In Thailand, the leaves of B. balsamifera have long been employed as a flavoring agent in traditional foods and beverages. Furthermore, the leaf extract exhibits free-radical scavenging [2], antiobesity [3], anticancer [4], anti-insomnia activities [5], insecticidal action against the maize weevil (Sitophilus zeamais) [6], plasmin inhibition [7], and inhibition of the sympathetic nervous system [5].
B. balsamifera leaves have been shown to contain a number of volatile compounds [1], among which are L-borneol, camphor, ß-caryophyllene, 10-epi-eudesmol, ß-eudesmol, and α-eudesmol [8], among other essential oils. Effects of growing conditions and plant maturity on the chemical composition and quantity of EOs from B. balsamifera have been explored [9]. B. balsamifera growing in the Chinese provinces of Hongshuihe, Luodian, and Guizhou was found to have high quantities of L-borneol. The chemical composition, yield, and antioxidant activity also vary with the plant organs [10]. Camphor, L-borneol, and ß-caryophyllene are three primary bioactive chemicals found in B. balsamifera leaves, promoting the percutaneous absorption of salbutamol sulfate [11] and having antimutagenic properties [8, 12]. Additionally, research has been conducted to improve extraction efficiency and optimize the preparation and separation of high-purity borneol by modifying the hydrodistillation and sublimation processes [13]. The plant production of B. balsamifera has progressively increased in South Asia as a healthy dietary component. Different stages of leaf maturation may accumulate varying levels of bioactive chemicals that may be suited for specific human health purposes [14, 15]. To date, there is no official report, but a bioRxiv preprint [16] on the characteristics of EOs extracted from fresh leaves of B. balsamifera at various maturity stages and the extraction efficiency of certain volatiles. Therefore, we explored the volatile compounds in immature and mature leaves of B. balsamifera using a hydrodistillation approach at different extraction times and analyzed the EOs for antioxidant and antibacterial activities.
## 2.1. Chemicals
Absolute ethanol was purchased from Daejung (South Korea). Trolox (6-hydroxy-2,5,7,8-tetramethyl-chromane-2-carboxylic acid), DPPH (2,2′-diphenyl-1-hydrazyl), ABTS (2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt, and potassium persulfate were supplied by Sigma-Aldrich (USA). ß-Caryophyllene was purchased from Tokyo Chemical Industry (Japan). Camphor and endo-borneol were provided by Alfa Aesar (USA). Thiophene was the internal standard for EO analysis (Sigma, USA). All chemicals and reagents were of analytical grade.
## 2.2. Plant Materials
Fresh, bright green immature leaves (the 2nd–4th leaves from the shoot, containing small soft trichomes and a soft surface on dorsal epidermis) (Figure 1(a)) and dark green mature leaves (containing small stiff trichomes and a matted surface on dorsal epidermis) (Figure 1(b)) of B. balsamifera were collected in 2019 from 2-year-old plants (Figures 1(c) and 1(d)) cultivated with sufficient water supply in KMUTT Bangkhuntien (N 13.57631; E 100.44295), Bangkok, Thailand. The plant was certified (Voucher Specimen No. ttm-0003856; Crude Drug No. ttm-1000500) by the Thai Traditional Medicine Research Institute, Department of Thai Traditional and Alternative Medicine.
## 2.3. Extraction of Essential Oils
Freshly immature and mature leaves (500 g) were blended and then put in a 10-liter round bottom flask with deionized water (5 L). Next, the flask was subjected to hydrodistillation using a clevenger-type apparatus. Sampling was carried out every 6 hrs until 24 hrs. The recovered EOs were dried over anhydrous sodium sulfate (Figure S1) and stored in sealed vials at −20°C [17] until analysis.
## 2.4. Determination of Volatile Components by GC-MS
Volatile compounds were analyzed using gas chromatography-mass spectroscopy (GC-MS) [18] on an Agilent 6890N (Agilent Technologies, USA) gas chromatograph equipped with an HP-5MS ($5\%$ phenyl dimethylpolysiloxane) fused silica capillary column (30 m length × 0.25 mm internal diameter × 0.25 μm film thickness) and an Agilent 5973 Network mass selective detector (Agilent, USA). One μL of the EOs was injected, and helium was used as the carrier gas. The column temperature was initially held at 120°C for 5 mins and then increased to 250°C at 10°C/min. The temperatures of the injector, detector, manifold, and transfer line were 250°C, 200°C, 70°C, and 240°C, respectively, and the ionization energy was 70 eV. The mass spectra ranged from 30–500 amu. The obtained spectra matched to a mass spectral library (the NIST V.14L library values, Palisade Corp., USA) were compared with an internal standard (100 μg/mL thiophene). Camphor, L-borneol, and ß-caryophyllene authentic chemicals were subsequently used for quantitative analysis of the compounds. All experiments were analyzed in triplicates.
## 2.5.1. DPPH Radical Scavenging Assay
The free radical scavenging capacity of the EOs was estimated following the DPPH free radical method [19] with slight modifications. The DPPH solution was prepared by dissolving 2.4 mg of DPPH in 100 mL of absolute ethanol. The test solution (50 μL) was added to 1.950 mL of the ethanolic DPPH. The mixture was shaken vigorously and kept at room temperature for 30 min in the dark. The absorbance of the mixture was measured at 517 nm using a UV spectrophotometer (UV-1800, Shimadzu, Japan).
## 2.5.2. ABTS Radical Scavenging Assay
Free radical scavenging activity of the EOs was also determined using the ABTS radical cation decolorization assay [20]. The ABTS solution was obtained by mixing 7 mM ABTS stock solution with 2.45 mM potassium persulfate at a ratio of 1: 0.5 and was stored in the dark at room temperature for 12–16 hrs. The ABTS solution was then diluted with absolute ethanol to an absorbance of 0.700 at 734 nm. The B. balsamifera extract (50 μL) was mixed with 1.950 mL of the diluted ABTS solution. The absorbance of the mixture was spectrophotometrically measured at 734 nm.
## 2.6. Determination of Antibacterial Activity
The antibacterial activities of EOs extracted from immature leaves at 0–6 hrs and 12–18 hrs of hydrodistillation (Figure S2) were tested in vitro. The broth dilution approach described in modified CLSI M7-A7 [21], which is briefly detailed in the next paragraph, was used to determine the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC). The MIC and MBC of EOs obtained against three different species, namely, *Staphylococcus aureus* (ATCC 6538), *Escherichia coli* (ATCC 8739), and *Pseudomonas aeruginosa* (ATCC 9027), were tested and certified by the Expert Centre of Innovative Herbal Products (Inno Herb), Thailand Institute of Scientific and Technological Research (TISTR).
The inoculums were prepared by growing each of the three bacteria in Mueller-Hinton broth (MHB) at 35 ± 2°C for 18–24 hrs. The bacterial suspensions were adjusted to a turbidity of approximately 1 × 108 CFU/mL, and then 50 μL of each inoculum was put in a tube containing 5 mL of MHB to achieve the final concentrations of each EO in the media. The final concentrations of each EO in MHB were 0, 0.5, 1, and 5 mg/mL. The cultures were incubated at 35 ± 2°C for 18–24 hrs before being assessed for the MIC (Figure S3). Subsequently, the cultures in diluted-EOs broth with zero turbidity (effective inhibition of bacterial growth) were streaked on sterilized MHA plates containing the same concentration of EOs, incubated at 35 ± 2°C for 18–24 hrs, and MBC was determined by observing the presence or absence of bacterial growth (Figure S4). The MBC value was defined as the concentration of EOs in the absence of bacterial colony development.
## 2.7. Statistical Analysis
All experiments were carried out in three replications (3 trees for a replication), and the data were expressed as mean values ± standard deviation. The data were statistically analyzed by variance analysis (ANOVA) at $P \leq 0.05$–$P \leq 0.01$ using SPSS software version 18. Mean comparisons were performed using Duncan's multiple range test (DMRT).
## 3.1. Volatile Compounds of Immature and Mature B. balsamifera Leaf Extract
The EOs of B. balsamifera's freshly immature, and mature leaves were yellowish in hue (Figures S1 and S5). When compared to mature leaves (352.4 mg/100 g FW), immature leaves produced more EOs (501.9 mg/100 g). The EO yield from immature leaves was $29.8\%$ higher than the amount from mature leaves. According to the research on Syzygium aromaticum, a considerable proportion of essential oil is collected in the juvenile stages of leaves, with the largest output being in young leaves ($5.1\%$), followed by expanded leaves ($4.5\%$), expanded leaves ($4.1\%$), and the mature leaves ($3.8\%$) [22]. Previous research has found a link between canopy structure, sunshine exposure, and the resulting plant bioactive compounds [23]. Immature leaves receive more sunlight and acquire more secondary phytochemical compounds due to their outer position, as previously reported in Zingiber officinale [24], Cistus ladanifer [25], and *Moringa oleifera* [26]. Plants are protected from abiotic and biotic stress by secondary metabolites such as phenolics and flavonoids [27]. Most of the EOs ($73\%$) were extracted during the first 6 hrs of hydrodistillation (Table 1), with the latter stage (18–24 hrs) accounting for only $6.5\%$ of the total yield. The EOs comprised Seven essential volatile compounds, namely, two monoterpenoids, camphor and L-borneol, and five sesquiterpenoids, silphiperfol-5-ene, 7-epi-silphiperfol-5-ene, ß-caryophyllene, ɤ-eudesmol, and α-eudesmol (Table 2, Figure 2, Figures S6–S7). The contents of camphor, borneol, and 7-epi-silphiperfol-5-ene in the EOs from immature and mature leaves were not significantly different (Table 3), even though monoterpenes are biosynthesized in chloroplasts and mature leaves generally contain a higher chlorophyll content. Light has been shown to greatly stimulate monoterpene synthesis in plants while having little effect on sesquiterpene hydrocarbon concentrations [28]. This is probably due to different precursors of exogenous origin in photosynthesis [29]. Moreover, over $90\%$ of the camphor, borneol, sesquiterpenoids of silphiperfolene, 7-epi-silphiperfolene, and ß-caryophyllene were eluted from the leaves within the first 6 hrs, whereas only $32.4\%$ of ɤ-eudesmol and $54.6\%$ of α-eudesmol extracted in the same period.
Camphor, borneol, and ß-caryophyllene contents were subsequently determined using authentic standards that corresponded to the compounds (Table 4). Camphor and borneol are both important ingredients used in traditional Thai medicine [30], while ß-caryophyllene is an important sesquiterpene in the pharmaceutical industry for nervous system-related disorders such as depression, pain, anxiety, and Alzheimer's disease [12]. Despite the decline in yield during the later stages of the hydrodistillation, yields of sesquiterpenoids like ɤ-eudesmol and α-eudesmol remained considerably high at 12–18 hrs (Figure3), in both immature and mature leaves. All eudesmol isomers (α-, ß-, and ɤ-eudesmol) are cytotoxic to cancer cells [31]. Moreover, sesquiterpenoids have a unique tricyclopentane ring structure and a variety of intriguing biomedical and pharmaceutical properties [32]. At 6 hrs of hydrodistillation, the immature leaves' EOs contained more ß-caryophyllene and borneol than mature leaves. Camphor was the most prevalent constituent of the EOs, followed by borneol and ß-caryophyllene (Figure 4). Other studies have identified L-borneol as the primary component, followed by camphor, ß-caryophyllene, eudesmol, isoborneol, and 1,8-cineole [8, 30]. L-borneol, camphor, and ß-caryophyllene contents of 42.06, 1.07, and $12.24\%$ were reported for EOs from the leaves of B. balsamifera grown in China [10], indicating a possible role of environmental conditions on the chemical composition and yield.
## 3.2. Antioxidant Activity of Extracts from Immature and Mature Leaves
Terpenes and their OH and NH2 functional groups of plant volatile compounds have been associated with antioxidant capacity (DPPH and ABTS) [33]. L-borneol, ɤ-eudesmol, and α-eudesmol containing some OH hydroxyl groups are all greater in immature leaves (5.73, 2.60, and 1.68 g thiophene/100 g FW, respectively) than in mature leaves (4.00, 2.03, and 1.35 g thiophene/100 g FW, respectively). The bioactive compounds have a favorable link with antioxidant capacity, with DPPH capacities of 67.89 g Trolox/100 g FW in immature leaves and 60.39 g Trolox/100 g FW in mature leaves, and ABTS capacities of 224.86 g Trolox/100 g FW in immature leaves and 190.12 g Trolox/100 g FW in mature leaves. The EOs from immature leaves contained higher amounts of these compounds (Table 3) and showed higher DPPH and ABTS activities than EOs from mature leaves, indicating that they were more active (Table 5). Extracts obtained at 0–6 hrs had the highest DPPH and ABTS activities, and the antioxidant activity decreased with extended hydrodistillation periods, mirroring the EOs yield pattern. DPPH is only soluble in organic solvents [34], while ABTS can determine both the hydrophilic and lipophilic antioxidant capacities of samples [35]. Hence, the DPPH value was generally lower, whereas the ABTS assay revealed greater treatment-related differences in antioxidant activity, both in terms of hydrodistillation time and leaf maturity. This implies that the extracted volatile compounds are predominantly lipophilic rather than hydrophilic in nature.
## 3.3. Antibacterial Activity of Extract from Immature Leaves
The antibacterial activity of EOs extracted from immature B. balsamifera leaves at 0–6 hrs and 12–18 hrs was determined in vitro using three pathogenic bacteria, Gram-positiveS. aureus (ATCC 6538), Gram-negative E. coli (ATCC 8739), and P. aeruginosa (ATCC 9027). By 0–6 hrs, the EOs included larger concentrations of camphor, L-borneol, and ß-caryophyllene, whereas at 12–18 hrs, ɤ-eudesmol and α-eudesmol were prevalent (Figure 3). S. aureus causes skin and soft tissue infections, bone and joint infections, bacteremia, and endocarditis [36], E. coli causes diarrheal disease, sepsis, and urinary tract infections [37], and P. aeruginosa causes gastrointestinal infection, keratitis, otitis media, and pneumonia [38]. Both hydrodistillation fractions (0–6 hrs and 12–18 hrs) were effective against S. aureus, with MIC values of 0.5 mg/mL and 1 mg/mL, respectively. The broth inoculated with S. aureus showed zero turbidity of culture starting from 0.5 to 5 mg/mL of the first 6 hrs fraction, while it was from 1 to 5 mg/mL of 12–18 hrs fraction. All of the concentrations of EOs (0.5–5 mg/mL) were ineffective against E. coli and P. aeruginosa (Table 6, Figure S3). Sakee et al. [ 39] reported that leaf-derived EOs of B. balsamifera in Thailand showed effective antimicrobial activity against S. aureus (1.2 mg/mL MIC), *Bacillus cereus* (150 μg/mL MIC), and Candida albicans (1.2 mg/mL MIC), but there was no effect against Salmonella enterica, Enterobacter cloacae, Klebsiella pneumoniae, E. coli, and P. aeruginosa [39]. EOs of B. balsamifera leaves from Luodian, China, had antibacterial activity against S. aureus (9.6 mg/mL MIC) and E. coli (4.8 mg/mL MIC), whereas EOs from Hainan, China, inhibited S. aureus (2 mg/mL MIC), C. albicans (1.2 mg/mL MIC), P. aeruginosa (1 mg/mL MIC), and good antifungal activity (62.5–250 μg/mL MIC) [9]. Variation in antimicrobial activity is probably due to differences in the chemical and extraction methods [9]. Furthermore, EOs obtained within the first 6 hrs had an MBC value of 1 mg/mL against S. aureus (Table 6, Figure S4). Camphor is a major component of EOs and exhibits the highest aqueous solubility of other terpenoids in EOs [40]. This property probably enables it to penetrate through the outer membrane of bacteria [41]. Gram-negative bacteria are generally more resistant to EOs than Gram-positive ones, which is mainly because the outer layer of Gram-negative bacteria comprises lipopolysaccharides, which restrict the diffusion of hydrophobic compounds through the lipopolysaccharide covering [42].
## 4. Conclusions
From our study, camphor, L-borneol, silphiperfol-5-ene, 7-epi-silphiperfol-5-ene, ß-caryophyllene, ɤ-eudesmol, and α-eudesmol were the primary terpenoids discovered in the essential oils isolated from B. balsamifera leaves. In Eos, extracted from immature leaves, ß-caryophyllene, ɤ-eudesmol, and α-eudesmol were found to be more prevalent, but silphiperfol-5-ene was abundant in mature leaves. Camphor, L-borneol, and 7-epi-silphiperfol-5-ene concentrations in essential oils isolated from immature and mature leaves were not significantly different. The majority of the compounds (>$90\%$) were extracted during the first 6 hrs of hydrodistillation, including camphor, borneol, sesquiterpenoids of silphiperfolene, 7-epi-silphiperfolene, and ß-caryophyllene, while only $32.4\%$ of ɤ-eudesmol and $54.6\%$ of α-eudesmol were extracted during this period. At 6 hrs hydrodistillation, the essential oils isolated from immature leaves had the highest antioxidant activity (DPPH and ABTS). The antioxidant capacity was related to the L-borneol, ɤ-eudesmol, and α-eudesmol contents. EOs extracted from immature leaves after 0–6 hrs of hydrodistillation also showed antibacterial action against S. aureus, with MIC and MBC values of 0.5 mg/mL and 1 mg/mL. This study demonstrates that it is advantageous to both alternative and traditional therapies Young leaves of B. balsamifera with high bioactive components, antioxidant activity, and antibacterial capabilities are suitable to be chosen for local food or therapy. In addition, 6–hour hydrodistillation of immature leaves can give a significant yield of Eos, and the bioactive chemicals required for further relevant product improvement for modern pharmacists or the cosmetic and food industries.
## Data Availability
The authors confirm that the data supporting the findings of this study are available within the article and its supplementary material. Raw data that support the findings of this study are available from the corresponding authors upon reasonable request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Sirinapha Jirakitticharoen was primarily responsible for conceiving the study, the experiments' design, and implementation. Technical analysis and experimental progress were advised by Wudtichai Wisuitiprot and Pongphen Jitareerat. Chalermchai Wongs-Aree provided conceptual and experimental work support, project management, and statistical interpretation assistance. Sirinapha Jirakitticharoen evaluated the data and wrote, managed, and revised the paper draft. As the correspondent, Chalermchai Wongs-Aree advised, corrected, and approved the manuscript.
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---
title: A Water-Soluble Hydrogen Sulfide Donor Suppresses the Growth of Hepatocellular
Carcinoma via Inhibiting the AKT/GSK-3β/β-Catenin and TGF-β/Smad2/3 Signaling Pathways
authors:
- Shao-Feng Duan
- Meng-Meng Zhang
- Qian Dong
- Bo Yang
- Wei Liu
- Xin Zhang
- Hai-Lan Yu
- Shi-Hui Zhang
- Nazeer Hussain Khan
- Dong-Dong Wu
- Xiao-Ju Zhang
- Juan Cen
journal: Journal of Oncology
year: 2023
pmcid: PMC10014162
doi: 10.1155/2023/8456852
license: CC BY 4.0
---
# A Water-Soluble Hydrogen Sulfide Donor Suppresses the Growth of Hepatocellular Carcinoma via Inhibiting the AKT/GSK-3β/β-Catenin and TGF-β/Smad2/3 Signaling Pathways
## Abstract
Hepatocellular carcinoma (HCC) is a disease with high morbidity, high mortality, and low cure rate. Hyaluronic acid (HA) is widely adopted in tissue engineering and drug delivery. 5-(4-Hydroxyphenyl)-3H-1, 2-dithiol-3-thione (ADT-OH) is one of commonly used H2S donors. In our previous study, HA-ADT was designed and synthesized via coupling of HA and ADT-OH. In this study, compared with sodium hydrosulfide (NaHS, a fast H2S-releasing donor) and morpholin-4-ium (4-methoxyphenyl)-morpholin-4-ylsulfanylidenesulfido-λ5-phosphane (GYY4137, a slow H2S-releasing donor), HA-ADT showed stronger inhibitory effect on the proliferation, migration, invasion, and cell cycle of human HCC cells. HA-ADT promoted apoptosis by suppressing the expressions of phospho (p)-protein kinase B (PKB/AKT), p‐glycogen synthase kinase‐3β (GSK-3β), p‐β‐catenin, and also inhibited autophagy via the downregulation of the protein levels of p‐Smad2, p‐Smad3, and transforming growth factor‐β (TGF‐β) in human HCC cells. Moreover, HA-ADT inhibited HCC xenograft tumor growth more effectively than both NaHS and GYY4137. Therefore, HA-ADT can suppress the growth of HCC cells by blocking the AKT/GSK-3β/β-catenin and TGF‐β/Smad$\frac{2}{3}$ signaling pathways. HA-ADT and its derivatives may be developed as promising antitumor drugs.
## 1. Introduction
Liver cancer is one of the leading causes of cancer death worldwide [1–4]. It is a heterogeneous, invasive, and drug-resistant malignant disease with poor prognosis [5–7], which is also the most common malignant tumor in the digestive system, with high mortality and low survival rate [3, 8]. Infection with hepatitis viruses and dietary exposure to aflatoxin are the main causes of liver cancer [9]. Hepatocellular carcinoma (HCC) is a highly invasive and most common liver cancer [10–12]. Many factors can contribute to the development of liver cancer, including hepatitis B virus, hepatitis C virus, cirrhosis, alcoholism, obesity, poor diet and inactivity, and mycotoxins [13–18]. Recent studies have shown undifferentiated liver cancer stem cells to be the main cause for the occurrence, metastasis, recurrence, and chemotherapy resistance of liver cancer [11, 19]. Liver cancer patients often die due to difficulties in early diagnosis, missed opportunity for surgical resection, and delayed treatment [3, 6]. Further research is urgent to advance the prevention, diagnosis, and treatment of liver cancer [13, 20].
Hydrogen sulfide (H2S) is a water-soluble and colorless gas with the rotten egg odor [21, 22]. The physiological function of H2S has been firstly demonstrated in the mammalian brain [23]. H2S has been identified as a gaseous signaling molecule, and together with carbon monoxide (CO) and nitric oxide (NO), forms a bioactive gas transmitter group [24–28]. H2S plays an important role in signal transduction in a variety of physiological and pathological processes [29–35]. Cystathionine γ-lyase (CSE), cystathionine β-synthetase (CBS), and 3-mercaptopyruvate sulfurtransferase (3-MST) are three main enzymes involved in endogenous H2S biosynthesis under physiological conditions [21, 32, 36, 37]. H2S breakdown is accomplished by a mitochondrial pathway that couples H2S oxidation into sulfate and thiosulfate to adenosine triphosphate synthesis [32]. In addition to the absorption of H2S through diffusion, a small amount of endogenous H2S can also be produced by the dietary L-homocysteine through the sulfur transfer pathway. This process is mainly completed by two enzymes, CBS and CSE, which depend on pyridoxal-5′-phosphate. CBS and CSE use cystathionine to convert homocysteine into cysteine, and H2S is a byproduct. In addition, 3-MST can also generate H2S. It uses mercaptopyruvate to form persulphide intermediately through α-ketoglutarate and cysteine transaminase, and then release H2S and pyruvate through reduction reaction [26, 29, 36, 38].
Hyaluronic acid (HA) is a glycosaminoglycan widely existing in human body. HA, a natural polysaccharide, is composed of N-acetylglucosamine and glucuronic acid, which are alternately linked by β-1, 3 and β-1, 4 glycoside bonds [39, 40]. Because HA and its derivatives have the characteristics of high viscoelasticity, plasticity, nonimmunogenicity, good biocompatibility, degradation, and binding with specific receptors on the cell surface, they are often used as slow-release carriers of drugs or as active target ligands of modified nano carriers to achieve the thickening, slow-release, transdermal absorption of drugs and improve the targeting and bioavailability of drugs [39–41]. HA and drug conjugates have been shown to have the dual advantages of tumor site aggregation and receptor-mediated endocytosis [40]. 5-(4-hydroxyphenyl)-3H-1, 2-dithiol-3-thione (ADT-OH) is the most widely used agent in the synthesis of slow H2S-releasing donors. ADT is a methyl derivative of ADT-OH, which can be metabolized by mitochondrial enzymes to produce H2S [42, 43].
A number of studies have shown that the protein kinase B (PKB/AKT)/glycogen synthase kinase‐3β (GSK-3β)/β-catenin pathway plays an important role in the progression of liver cancer [44–46]. Furthermore, the transforming growth factor‐β (TGF‐β)/Smad$\frac{2}{3}$ pathway is involved in the proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of liver cancer [47–49]. Diallyl trisulfide, an H2S donor, regulates cell invasion and apoptosis via the phosphatidylinositol 3-kinase/AKT/GSK-3β signaling pathway in human osteosarcoma U2OS cells [50]. Another study indicates that H2S can attenuate paraquat-induced EMT of human alveolar epithelial cells by regulating the TGF-β1/Smad$\frac{2}{3}$ pathway [51]. Our previous study has demonstrated that H2S plays a double-edged sword role in human HCC cells, suggesting that novel H2S donors can be designed and applied for the treatment of cancer [52].
In this study, a new coupling compound HA-ADT was designed and synthesized as previous described [53]. HA-ADT could generate more H2S than both sodium hydrosulfide (NaHS, a fast H2S-releasing donor) and morpholin-4-ium (4-methoxyphenyl)-morpholin-4-ylsulfanylidenesulfido-λ5-phosphane (GYY4137, a slow H2S-releasing donor) [53]. The roles of HA-ADT in the proliferation, migration, and invasion of human HCC cells were investigated. Then we conducted in vivo experiments to further determine the effect of HA-ADT on the growth of human HCC xenografts.
## 2.1. Cell Culture
Human HCC cell lines SMMC-7721 and Huh-7 were obtained from Kebai Biological Technology Co., Ltd. (Nanjing, Jiangsu, China). SMMC-7721 cells were grown in RPMI1640 medium supplemented with $10\%$ fetal bovine serum (FBS), penicillin (100 U/ml), and streptomycin (100 μg/mL). Huh-7 cells were grown in DMEM supplemented with $10\%$ FBS, penicillin (100 U/ml), and streptomycin (100 μg/mL). Cells were cultured in an incubator at 37°C with $95\%$ air and $5\%$ CO2. Cells were treated with NaHS (200 μM), GYY4137 (200 μM), and HA-ADT (200 μM) (provided by Prof. Shao-Feng Duan), respectively [53]. The control group was treated with phosphate-buffered saline (PBS). After treatment for 24 h, the cells were adopted for following experiments.
## 2.2. Cell Growth Assay
The Cell-Light 5-ethynyl-2-deoxyuridine (EdU) Apollo 567 Kits (RiboBio, Guangzhou, China) were used to detect cell proliferation. Cell proliferation rate = (EdU-positive cells)/(total cells) × $100\%$ [54]. The 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT) (Sigma, St. Louis, MO, USA) and CCK-8 detection kits (Beyotime, Shanghai, China) were adopted to determine cell viability [55–57].
## 2.3. Colony Formation Assay
The cells (1 × 103 per well) were cultivated in a culture medium for 2 weeks at 37°C. After washing with PBS, the colonies were fixed with methanol. Crystal violet was then added and incubated at room temperature for 30 min. The plates were washed, air-dried, and scanned. Then, the colony number was counted.
## 2.4. Wound Healing Assay
The cultured monolayer-confluent cells were scratched. The migration distance was photographed under an inverted microscope (Olympus CKX41, Tokyo, Japan). The cell migration rate (MR) was calculated as MR (%) = [(A − B)/A] × 100, where A and B are the widths at 0 h and 24 h, respectively [58].
## 2.5. Migration and Invasion Assays
The cells (1 × 105) were seeded into the matrigel coated/uncoated upper chamber. The medium supplemented with $20\%$ FBS was added into the lower chamber. After 24 h of treatment, the remaining cells on the upper side were scrubbed off, and the cells on the bottom side were fixed with $4\%$ paraformaldehyde, and then stained with $0.1\%$ crystal violet for 20 min. The cells were counted using an Axioskop 2 plus microscope (Zeiss, Thornwood, NY, USA).
## 2.6. Flow Cytometry Analysis
1 × 106 cells were trypsinized and then fixed in ice-cold $75\%$ ethanol overnight. After washing with PBS, the cells were incubated in propidium iodide (PI)/RNase A mixture at room temperature for 30 min. The cell cycle distribution was analyzed with a FACSVerse flow cytometer (CytoFLEX S, Beckmann, CA, USA). The apoptotic level was detected using the Annexin V and PI apoptosis kits (UE, Suzhou, Jiangsu, China) and analyzed using a FACSVerse flow cytometer.
## 2.7. TdT-Mediated dUTP-Biotin Nick End Labeling (TUNEL) Assay
TUNEL assay was carried out using the in situ cell death detection kits (Beyotime). The cells were examined with a fluorescence microscope (Nikon Eclipse Ti, Melville, NY, USA). The percentage of TUNEL positive cells was further quantified.
## 2.8. Western Blotting
Total protein was extracted from human HCC cells. Western blotting was used to determine the expression levels of relevant proteins. The primary antibodies include anti-Cyclin E1, anti-Cyclin D1, anti-cyclin-dependent kinase (CDK) 2, anti-CDK4, anti-p27, anti-p21, anti-AKT, anti-phospho (p)-AKT (Ser473), anti-glycogen synthase kinase-3 beta (Gsk‐3β), anti-p-Gsk-3β (Ser9), anti-β-catenin, anti-p-β-catenin (Ser552), anti-beclin-1, anti-p62, anti-LC3A/B, anti-Smad2, anti-p-Smad2 (Ser$\frac{465}{467}$), anti-Smad3, anti-p-Smad3 (Ser$\frac{423}{425}$), and anti-transforming growth factor-beta (TGF‐β) antibodies, as well as the horseradish peroxidase-conjugated secondary antibody obtained from Cell Signaling Technology (CST, Danvers, MA, USA). Anti-B-cell lymphoma-2 (Bcl-2), anti-B-cell lymphoma-extra large (Bcl-xl), anti-Bcl-2-associated X protein (Bax), anti-Bcl-xl/Bcl-2-associated death promoter (Bad), anti-cleaved caspase (cas)-3, anti-cleaved cas-9, anti-cleaved poly adenosine diphosphate‐ribose polymerase (PARP), and anti-β-actin antibodies were obtained from ProteinTech (Chicago, IL, USA). The protein bands were detected with the enhanced chemiluminescence system (Thermo, Rockford, IL, USA) and semiquantified by ImageJ software.
## 2.9. Animal Study
Animal experiments were approved by the Committee of Medical Ethics and Welfare for Experimental Animals of Henan University School of Medicine (HUSOM-2017-218). Animal study was carried out as previously described [53]. BALB/c nude mice (male, 4-week-old) were obtained from Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). 5 × 106 SMMC-7721/Huh-7 cells in PBS (200 μL) were injected subcutaneously into the right flank of each mouse. The mice were divided randomly into 4 groups ($$n = 6$$ per group). NaHS (200 μM), GYY4137 (200 μM), HA-ADT (200 μM), and PBS were administered subcutaneously once-a-day for 21 days. During the animal experiment, the mice were daily weighed. The tumor volume was calculated as follows: Volume (V) = $\frac{1}{2}$ × W2 × L, where L and W are the longest and widest dimension, respectively [59]. The tumor volume doubling time (TVDT) was calculated as follows: TVDT = (T − T0) × log2/log (V2/V1), where V2 and V1 are the tumor volume at two measurement times and (T − T0) is the time interval [60, 61]. At the end of the experiment, all mice were sacrificed. Then the tumors were excised, weighted, and photographed. Inhibition rate (IR) = [(A − B)/A] × $100\%$, where A and B are the average tumor weight of control group and the treatment group, respectively [53].
## 2.10. Hematoxylin and Eosin (HE) Staining
Tumor tissues were fixed in $10\%$ neutral-buffered formalin, embedded in paraffin, sectioned at 5 μm thickness, and then stained with HE. The tissues were observed using a Zeiss Axioskop 2 plus microscope.
## 2.11. Immunohistochemistry (IHC)
Microvessel density (MVD) has been widely used as an index for the angiogenic activity [62]. The proliferation index (PI) was calculated as the percentage of Ki67-positive cells to total cells [63]. Apoptotic index was determined as cleaved cas-3 -positive cells to total cells [64]. Tumor tissues were stained with anti-CD31 (CST), anti-Ki67 (CST), anti-p21, anti-cleaved cas-3, and anti-beclin-1 antibodies, respectively. The proliferation index (PI), apoptosis index, MVD, p21-positive cells, and autophagy index were counted according to the ratio of positive cells to total cells.
## 2.12. Statistical Analysis
All results are expressed as the mean ± standard error of the mean (SEM). Differences among groups were determined using one-way analysis of variance with SPSS 19.0 software, followed by Tukey's test. A P value of less than 0.05 was considered statistically significant.
## 3.1. HA-ADT Inhibits the Growth, Migration, and Invasion of Human HCC Cells
Compared with the control, NaHS, and GYY4137 group, HA-ADT significantly suppressed the viability and proliferation of Huh-7 and SMMC-7721 cells (Figures 1(a)–1(e)). HA-ADT also more effectively reduced the colony formation of SMMC-7721 and Huh-7 cells (Figures 1(f) and 1(g)). In addition, HA-ADT showed more inhibitory effects on the migration and invasion of Huh-7 and SMMC-7721 cells than the control, NaHS, and GYY4137 group (Figure 2). In summary, the results show that HA-ADT could inhibit the growth, migration, and invasion of HCC cells.
## 3.2. HA-ADT Blocks Cell Cycle of Human HCC Cells
As shown in Figures 3(a) and 3(b), HA-ADT significantly upregulated the percentage of cells in S phase but downregulated the percentage of cells in G2 phase, suggesting that HA-ADT blocked the cell cycle in S phase. Western blot was further conducted to detect the protein levels of cyclin D1/E1, CDK$\frac{2}{4}$, p21, and p27. The results indicated that HA-ADT decreased the levels of cyclin D1/E1 and CDK$\frac{2}{4}$, but increased the levels of both p21 and p27 (Figures 3(c) and 3(d)). These results indicate that HA-ADT can block the cell cycle of human HCC cells at S phase by regulating the expression levels of cell cycle-related proteins.
## 3.3. HA-ADT Promotes Apoptosis by Suppressing the AKT/GSK-3β/β-Catenin Signaling Pathway in Human HCC Cells
Apoptosis was detected by TUNEL and flow cytometry assays. As shown in Figures 4(a)–4(d), the apoptotic level in the HA-ADT group was significantly increased when compared with the control, NaHS, and GYY4137 group. The ratios of Bax/Bcl-2 and Bad/Bcl-xl are important indicators of apoptosis. Increased Bax/Bcl-2 and Bad/Bcl-xl ratios have been found in mitochondria-mediated apoptosis [65, 66]. As shown in Figure S1, Bax/Bcl-2 and Bad/Bcl-xl ratios in the HA-ADT group were obviously increased. In addition, the expression levels of cleaved cas-3, 9, and cleaved PARP exhibited similar trends. The AKT/GSK-3β/β-catenin cascade is a key pathway involved in the survival, growth, and metabolic stability of tumor cells [67, 68]. As shown in Figures 4(e) and 4(f), the phosphorylation levels of AKT, GSK-3β, and β-catenin were downregulated in the HA-ADT group. The data suggest that HA-ADT induces apoptosis via suppressing the AKT/GSK-3β/β-catenin pathway in human HCC cells.
## 3.4. HA-ADT Decreases Autophagy of Human HCC Cells by Inhibiting the TGF-β/Smad2/3 Signaling Pathway
The role of autophagy in the development of cancer is extremely complex [69, 70]. Autophagy is a conservative catabolic process, which plays a dual role in regulating cell growth [71, 72]. Autophagy is an important mechanism by which cellular material is transfered to lysosome for degradation, thus providing energy and allowing the transformation of cellular components [70, 73, 74]. Beclin-1, LC3, and p62 are considered as specific autophagic markers [74]. As shown in Figures 5(a) and 5(b), the expressions of LC3 and beclin-1 in HA-ADT group were lower than those in the control, NaHS, and GYY4137 group, while the expression level of p62 exhibited the opposite trend. TGF-β plays an important role in cell homeostasis, fibrosis, angiogenesis, carcinogenesis, and differentiation. TGF-β can reduce apoptosis by inducing autophagy [75]. It has been shown that Smad$\frac{2}{3}$ is also involved in autophagy [76]. As shown in Figures 5(c) and 5(d), the expression levels of p-Smad$\frac{2}{3}$ and TGF-β in the HA-ADT group were lower than those in the control, NaHS, and GYY4137 group. These results indicate that HA-ADT can inhibit autophagy in human HCC cells via the TGF-β/Smad$\frac{2}{3}$ pathway.
## 3.5. HA-ADT Suppresses the Growth of Human HCC Xenografted Tumors
SMMC-7721 and Huh-7 HCC cells have been successfully used to establish the subcutaneous xenograft models [77, 78]. Compared with the control, NaHS, and GYY4137 group, HA-ADT dramatically suppressed the growth of xenografted tumors (Figures 6(a)–6(e)). In addition, there was no significant difference in body weight between each group (Figures 6(f) and 6(g)). The expression levels of CD31, Ki67, p21, cleaved cas-3, and beclin-1 were further detected by IHC. As shown in Figure 7, the expression levels of CD31, Ki67, and beclin-1 in HA-ADT group were decreased, while p21 and cleaved cas-3 levels were increased in the HA-ADT group. These data demonstrate that HA-ADT can effectively suppress human HCC xenograft tumor growth by promoting apoptosis and reducing autophagy.
## 4. Discussion
At present, H2S is considered the third gaseous transmitter after CO and NO. H2S is involved in many physiological and pathological processes in the human body [21–23, 79]. HA and its derivatives have the characteristics of plasticity, nonimmunogenicity, and good biocompatibility, which are widely adopted in the biomedical field, such as tissue engineering and drug delivery [39–41]. ADT is a methyl derivative of ADT-OH, which is a common H2S donor [42, 79]. In the present study, HA-ADT was synthesized by chemical reaction as previously described [53].
Liver cancer is one of the leading causes of cancer death in the world [1–4]. Liver cancer is difficult to diagnose in the early stage, which will result in the death of patients. In addition, there are few effective treatments for patients with advanced liver cancer [3, 6, 13]. Thus, it is urgent to explore novel drugs to prevent and treat liver cancer. It has been revealed that 25–100 μM·NaHS promotes the growth of HCC cells and blood vessel formation, while 800–1000 μM·NaHS can inhibit angiogenesis and HCC growth [52]. Another study suggests that GYY4137 exhibits potent anti-HCC activity by blocking the signal transducer and activator of transcription 3 pathway [80]. In this study, we examined the roles of HA-ADT in the growth, migration, invasion, and cell cycle of human HCC cells. The results showed that HA-ADT was more effective than both NaHS and GYY4137 in inhibiting the survival, proliferation, migration, invasion, and cell cycle progression of human HCC cells. These results suggest that HA-ADT plays an effective role in inhibiting the development and progression of human HCC cells.
Apoptosis, a form of programmed cell death, is evolutionarily conserved and plays a key role in the homeostasis and development of mammalian tissues [81]. Apoptotic pathways can be divided into two categories: mitochondria-mediated intrinsic pathway and death receptor-mediated extrinsic pathway [82]. Bcl-2 family proteins are pivotal members in the process of apoptosis [53]. Caspases play key roles in apoptotic signaling pathways [83]. Caspases can be activated by many apoptotic stimuli and PARP is cleaved by cleaved caspase-3, resulting in the occurrence of apoptosis [53, 84]. It has been shown that NaHS effectively decreases the growth of C6 glioma cells by inducing caspase-dependent apoptosis [85]. Furthermore, GYY4137 could induce apoptosis in HCC cells by increasing the levels of cleaved cas-9, cas-3 and PARP cleavage [80]. Similarly, our results indicated that HA-ADT can promote apoptosis in HCC cells by up-regulating the levels of cleaved cas-3, 9, and cleaved PARP, indicating the activation of mitochondrial apoptosis. The AKT/GSK-3β/β-catenin pathway is involved in a number of hallmarks of cancer, such as tumor grade and lympho-node metastasis [67, 68, 86]. It has been reported that the AKT pathway is involved in HCC growth and metastasis [68]. In addition, GSK-3β plays a key role in the phosphorylation/degradation of β-catenin in the AKT/GSK-3β/β-catenin pathway [68]. The results suggested that HA-ADT could reduce the expressions of p-AKT, p-GSK-3β, and p-β-catenin in human liver cancer cells. The current research suggests that HA-ADT can inhibit the growth of human HCC cells by inducing apoptosis via inhibition of the AKT/GSK-3β/β-catenin signaling pathway.
Autophagy could be neutral, tumor-promoting, or tumor-suppressive in different contexts in cancer cells [87]. A recent study has shown that diallyl trisulfide, a characterized H2S donor, inhibits the proliferation of urothelial carcinoma cells by promoting apoptosis and inducing autophagy [88]. Our previous study has demonstrated that HA-ADT could suppress the progression of esophageal squamous cell carcinoma via apoptosis promotion and autophagy inhibition [89]. Similarly, in the present study, our data showed that HA-ADT significantly reduced the autophagic level when compared to the control, NaHS, and GYY4137 group. It has been reported that the TGF-β pathway can activate autophagy in many human cancer cells, suggesting that induction of autophagy is a novel biological function of TGF-β [90]. Furthermore, as canonical effectors of TGF-β signaling, Smad$\frac{2}{3}$ are involved in the process of autophagy [91]. Moreover, another study suggests that specificity protein 1-mediated serine/threonine kinase 39 upregulation promotes the proliferation, migration, and invasion of HCC cells by activating the TGF-β1/Smad$\frac{2}{3}$ pathway [47]. Our data indicated that HA-ADT decreased the expressions of p-Smad$\frac{2}{3}$ and TGF-β compared with the control, NaHS, and GYY4137 group. These findings indicate that HA-ADT can inhibit autophagy in human HCC cells through the TGF-β/Smad$\frac{2}{3}$ signaling pathway.
Recent studies suggest that SMMC-7721 and Huh-7 cells have been widely adopted to establish the xenograft tumor models [77, 78]. Therefore, we studied the role of HA-ADT in the growth of HCC xenograft tumor. We observed that HA-ADT exerted more inhibitory effects on HCC xenograft tumor growth than the control, NaHS, and GYY4137 group. Furthermore, there was no obvious change in the body weight in each group. Similar to the in vitro findings, our data suggested that the expressions of Ki67, CD31, and beclin-1 were decreased in HA-ADT group. The levels of p21 and cleaved cas-3 in the HA-ADT group were significantly higher than those in the control, NaHS, and GYY4137 group. The data together indicate that HA-ADT can effectively inhibit the growth of human HCC xenograft tumors.
In sum, HA-ADT can suppress the proliferation, migration and invasion of human HCC cells via inhibition of the AKT/GSK-3β/β-catenin and TGF-β/Smad$\frac{2}{3}$ signaling pathways. HA-ADT might be considered as a promising anticancer candidate for the treatment of HCC.
## Data Availability
All data generated or analyzed in this study are included in this article.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
D.D.W., X.J.Z., and J.C. participated in the conception and design of the experiments. S.F.D., M.M.Z., Q. D., B.Y., W.L., X.Z., H.L.Y., S.H.Z., and N.H.K. carried out the experiments and analyzed the data. D.D.W. and S.F.D. prepared the manuscript. All authors read and approved the final manuscript. S.F.D and M.M.Z equally contributed to this study.
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|
---
title: Longevity, tumor, and physical vitality in rats consuming ginsenoside Rg1
authors:
- Chao-Chieh Hsieh
- Chiung-Yun Chang
- Tania Xu Yar Lee
- Jinfu Wu
- Suchada Saovieng
- Yu-Wen Hsieh
- Maijian Zhu
- Chih-Yang Huang
- Chia-Hua Kuo
journal: Journal of Ginseng Research
year: 2021
pmcid: PMC10014179
doi: 10.1016/j.jgr.2021.04.006
license: CC BY 4.0
---
# Longevity, tumor, and physical vitality in rats consuming ginsenoside Rg1
## Abstract
### Background
Effects of the major ginsenoside Rg1 on mammalian longevity and physical vitality are rarely reported.
### Purpose
To examine longevity, tumor, and spontaneous locomotor activity in rats consuming Rg1.
### Methods
A total of 138 Wistar rats were randomized into 2 groups: control ($$n = 69$$) and Rg1 ($$n = 69$$). Rg1 (0.1 mg/kg per day) were orally supplemented from 6 months of age until natural death. Spontaneous mobility was measured by video-tracking together with body composition (dual energy x-ray absorptiometry) and inflammation markers at 5, 14, 21, and 28 months of age.
### Results
No significant differences in longevity (control: 706 days; Rg1: 651 days, $$p \leq 0.77$$) and tumor incidence (control: $19\%$; Rg1: $12\%$, $$p \leq 0.24$$) were observed between the two groups. Movement distance in the control group declined significantly by ∼$60\%$ at 21 months of age, together with decreased TNF-α ($$p \leq 0.01$$) and increased IL-10 ($$p \leq 0.02$$). However, the movement distance in the Rg1 group was maintained ∼$50\%$ above the control groups ($$p \leq 0.01$$) at 21 months of age with greater magnitudes of TNF-α decreases and IL-10 increases. Glucose, insulin, and body composition (bone, muscle and fat percentages) were similar for both groups during the entire observation period.
### Conclusion
The results of the study suggest a delay age-dependent decline in physical vitality during late life by lifelong Rg1 consumption. This improvement is associated with inflammatory modulation. Significant effects of Rg1 on longevity and tumorigenesis were not observed.
## Graphical abstract
Image 1
## Introduction
Panax ginseng has traditionally been thought to have life-prolonging and vitality-enhancing properties. However, scientific evidence has not been well-established in mammals [1,2]. Such data is also a key reference of toxicity for long-term ginseng use. In vivo studies using ginseng as testing materials suffer inconsistency in major ginsenoside profile due to seasonal changes and geographical variations, leading to uncertain efficacy [3,4]. The major ginsenoside Rg1 and Rb1 show opposing metabolic action [5,6]. Therefore, it is difficult to answer the question of whether ginseng can improve longevity and physical vitality unless its ginsenoside component is well-controlled.
Among the major ginsenoside components in panax ginseng, Rg1 has been reported to reverse loss of spontaneous physical activity of young mice under neuronal damaged condition [7] and increases senescence cell clearance in human muscle after exercise [8]. However, most of the Rg1 supplementation studies in mammals are short-term (<1 month) and were conducted in animals at young age [9,10]. Physical vitality declines during late life and is closely associated with survival time in both animals and humans [11]. It remains unknown whether lifelong Rg1 supplementation can reverse age-dependent decreases in aging mammal’s physical vitality.
Anti-inflammatory action of Rg1 has been previously reported [12], implicating that the beneficial effect of Rg1 supplementation is mediated by immunomodulatory action. An increase in systemic inflammation during advancing age is currently recognized as a common predictor for tumorigenesis and metabolic problems [13]. According to a cross-sectional study, plasma IL-10 and M2 macrophage in tissues of old animals are greater than young animals [14], suggesting a compensatory mechanism to maintain the increasingly growing body size by strengthening the regenerative phase of inflammation [15]. During inflammation, TNF-α increases during the early phagocytic phase, whereas IL-10 increases during the late regenerative phase before resolution of the inflammation [16]. Effect of Rg1 supplementation on trajectories of plasma TNF-α and IL-10 during aging have not previously been reported. To determine the effect of Rg1 on longevity and physical vitality, we measured survival time and spontaneous physical activity in rats consuming ginsenoside Rg1 from 6 months of age until natural death. Inflammatory markers, glucose, insulin, oxidized low density lipoprotein (oxidized LDL), tumor incidence and body fat were assessed at 5 (before treatment), 14, 21, 28 months of age.
## Animals
This study was approved by the Animal Care and Use Committee at the University of Taipei (approval number UT104004) and conformed to the ethical regulation of the Institutional Animal Care and Use Committee (IACUC) and in accordance with the Law of Taiwan in animal protection. Wistar rats were purchased from BioLASCO Taiwan Corporation (Yi-Lan, Taiwan) at 4 weeks with known date of birth. They were housed in the Animal Center of the University of Taipei (Tianmu Campus, Taipei, Taiwan). Animals were maintained in a thermostatic facility with the controlled temperature of 22°C, relative humidity of ∼$50\%$, $\frac{12}{12}$ h light/dark cycle. Rats (two per cage) were provided with standard laboratory chow LabDiet 5001 (LabDiet, Missouri, USA) and tap water ad libitum. Institutional regulation for enforced euthanasia (IACUC decisions after a judge by a veterinarian) are tumor size greater than 5 cm with unhealed wound, weight loss > $20\%$, and decreased physical activity. A total of 13 rats in the control group (first-last: 424-907 d) and 14 rats in the Rg1 group (first-last: 417-907 d) were euthanatized. Euthanatized rats were excluded for longevity analysis (Control: $$n = 13$$; Rg1: $$n = 14$$) and were included in tumor incidence calculation.
## Study design
This study used 138 rats and ranked into 69 weight levels at 1 month of age. They were randomized into the control group ($F = 34$; $M = 35$) and the Rg1 group ($F = 34$, $M = 35$) for each weight level to minimize potential effect of different growth rate on outcome variables. Rats in the control group received $11\%$ high-fructose corn syrup (HFCS) drink, whereas Rg1 group received $11\%$ HFCS drink containing 0.1 mg/kg of Rg1 from 6 months of age until natural death. HFCS drinks were provided when rats reached 3 months of age before Rg1 treatment. Rg1 concentration in the drink was adjusted weekly according to consumed volume of the drink to maintain the targeted dose of Rg1 at 0.1 mg/kg/day. The dosages at both 0.1 mg/kg and 0.01 mg/kg have been previously found to minimize glucose response in oral glucose tolerance test in rats for short-term use [5]. In human study, 0.08 mg/kg has been found to improve exercise-induced cellular senescence in exercised muscle without noticeable side effect [17]. Ginsenoside Rg1 was obtained from NuLiv Science, Inc. (Brea, CA, USA). Rg1 was dissolved using N-methyl-2-pyrrolidone (no-observed effect level of 169 mg/kg/day) in HFCS drink [18]. HFCS drinks for the control group also contained the same amount of N-methyl-2-pyrrolidone.
## Metabolic measures
After a 12-h fasting, blood glucose of rats was measured immediately after sample collection from tail using Accu-chek® performa system (Roche Diagnostics, Indiana, USA). Blood sample was collected in EDTA-contained tubes. Plasma was obtained after centrifugation at 4°C, 3000 rpm for 10 min. Other plasma measures were analyzed using an enzyme-linked immunosorbent assay (ELISA) reader (Tecan GENios, A-5082, Austria) with commercial kits. Plasma insulin was measured using ELISA kit from Mercodia Inc. (#10-1250-01) (Mercodia AB, Uppsala, Sweden). Oxidized LDL was measured using ELISA kits from CusaBio technology LLC (#CSB-E07932r) (Houston, Texas, USA). TNF-α and IL-10 were measured using LEGEND MAX™ Rat TNF-α kit (#438207) from Biolegend Inc (San Diego, California, USA) and Rat IL-10 Quantikine ELISA Kit (#R1000) from R&D Systems (Minneapolis, Minnesota, USA), respectively.
## Spontaneous physical activity
Physical vitality was monitored using Locoscan system (Clever Sys, VA, USA). Rats were transferred from their housing cage to the testing cage (40 cm3 black plastic cage) before activity assessment. One week prior to the assessment, rats were placed in the testing cage for 10 min daily for acclimation. Spontaneous activity was performed in a quiet, dark, and cleaned environment. Rats were placed into the center of testing cage and free to move. A digital infrared camera was mounted above the center of testing cage to record video on their physical activity for 7 min. The middle 5-min recording periods were used from analysis to exclude possible disturbances related to human access to dark room. Rat movement was detected based on video-tracking of multiple individual body parts, posture and frequency of movements. Activity parameters for horizontal traveled distance (mm) and vertical standing frequency (times) were used for analysis.
## Body composition
Body composition of rats was measured at 5, 14, 21, 28 months of age by dual-energy x-ray absorptiometry (DEXA) (Lunar iDXA, GE Medical Systems, WI, USA) with small animal software package. DEXA scans were always performed under Tiletamine/Zolazepam (Zoletil 50, Virbac Lab, France) anesthesia after a 12-h fasting. Water was made accessible during the fasting period. Body composition data included bone percentage, muscle percentage, and fat percentage.
## Tumor assessment
Tumor incidence is based on visibility of X-ray image and reconfirmed by physical appearance and body palpitation.
## Statistical analysis
Longevity was assessed by Kaplan-Meier survival analysis. Independent t test was used to compare difference of all variables between the control and Rg1 groups for each single time point. Chi-square was used to compare the difference in tumor incidence between two groups during the entire treatment period. Two-way analysis of variance (ANOVA) with repeated measure was used to determine the main effect (age and supplement) and interactive effect (age and supplement) for all measures. Probability of type 1 error at $5\%$ is considered significant. SPSS software (12.0 version, Chicago, IL, USA) was used for statistical analysis. Microsoft Excel was used for making figures. All results were expressed as mean ± standard error (SE).
## Results and discussion
Effects of lifelong ginsenoside Rg1 consumption on longevity and physical vitality have not been previously documented in mammals. In the study, longevity outcome was similar for the control and Rg1 groups (Fig. 1). Despite that male and female rats may have different pharmacokinetics and pharmacodynamics after oral Rg1 supplementation, similar results in longevity between both treatment groups were observed for male and female rats (Fig. 1B and C). The key finding of the study is a delayed age-dependent decline in spontaneous physical activity during late age in rats consuming ginsenoside Rg1 for the entire adulthood (Fig. 2). Significantly greater movement distance was observed in the Rg1 group at 21 months of age ($$p \leq 0.01$$), compared with rats in the control group (Fig. 2A). Both female rats (Fig. 2B) and male rats (Fig. 2C) show similar trend. The current data also provide novel evidence of neglectable toxicity of Rg1 for long-term use in adult rats based on Kaplan-Meier survival analysis. Most of the previous studies reporting Rg1 effect on spontaneous physical activity were conducted in young mice within a short period and were mostly under pathological conditions [7,9,10]. Taken together, the present study results suggest that lifelong consumption of Rg1 can improve physical vitality in naturally aging mammals but not longevity. Fig. 1Effect of ginsenoside Rg1 supplementation on longevity. The survival time for both groups is presented for all (A) and separately for female (B) and male (C) rats. The control and Rg1 groups show similar result. Daily Rg1 dosage was 0.1 mg/kg. Fig. 1Fig. 2Effect of ginsenoside Rg1 supplementation on age-dependent declines in spontaneous physical activity during aging. ∗ Significantly difference against the control group, $p \leq 0.05.$ Mean and standard error were calculated from survivors. Daily Rg1 dosage was 0.1 mg/kg. Fig. 2 Fig. 3 shows glucose and insulin trajectories of 21-month survivors after 5 months of age. Both metabolic measures in blood were similar for rats in the control and Rg1 groups across the entire observation period. Insulin resistance associated with overweight and obesity has been recognized as the common origin of metabolic problems and cancer in adults at higher age [13]. In this study, obesity (body fat accumulation) developed during the first 3 quarters of life, while muscle mass percentage decreases during the same period (Fig. 4). Effect of Rg1 supplementation on suppressing obesity has been previously reported [19]. However, we do not find significant effects of Rg1 on obesity and plasma insulin in the lifelong Rg1 supplementation study. The discrepancy may be associated with the fact that most of the previous Rg1 supplementation studies were conducted within a short period with much higher dosage in obese animal models which may be toxic to the animals [19].Fig. 3Effect of ginsenoside Rg1 supplementation on glucose and insulin levels during aging. No difference was found in circulating levels of glucose and insulin at 5, 14, 21, 28 months of age. Mean and standard error were calculated from survivors. Daily Rg1 dosage was 0.1 mg/kg. Fig. 3Fig. 4Effect of ginsenoside Rg1 supplementation on body composition during aging. While bone percentage remained unchanged (A, B, C), body fat percentage increased (D, E, F) and lean mass percentage decreased during aging (G, H, I). No difference between the control and Rg1 groups was observed for all body composition variables at 5, 14, 21, 28 months of age. Daily Rg1 dosage was 0.1 mg/kg. Fig. 4 Weight gain increases cell senescence [20] and cell death [21] in multicellular organisms, which elevates baseline inflammation levels [22]. Inflammation is an immune response which is essential to sustain the increasingly growing body with more cell population [23] by inducing phagocytosis to eliminate unhealthy senescent cells (with increased TNF-α) followed by triggering cell regeneration (with increased IL-10) [16]. Fig. 5 presents trajectory data for biomarkers involving inflammation, which includes plasma oxidized LDL, TNF-α, and IL-10 of survivors at 21 months of age. In support to a cross-sectional study comparing rats between 4 months and 18 months of age [14], we have further observed an increased IL-10 and a decreased TNF-α from 14 to 21 months of age, suggesting a shifting inflammatory balance from phagocytosis to regeneration [24]. This inflammatory shift may have been a compensatory modulation to sustain an increasingly larger cell population with more cell death in growing mammals. Here, we have observed an enhanced IL-10 elevation during aging in rats with Rg1 treatment, compared with that in the control group. This result suggests that Rg1 strengthens the compensatory mechanism in cell regeneration process by modulating the inflammatory balance during aging [16,25]. IL-10 is protective to the aging animals against the formation of foam cells induced by oxidized LDL [26]. Oxidized LDL in the rats consuming Rg1 drinks was significantly higher than the rats consuming control drinks at 21 months of age ($$p \leq 0.02$$), contributed mostly by male rats ($$p \leq 0.007$$). Survival time appears to be unrelated with oxidized LDL. IL-10 is a potent inhibitor for TNF-α expression in immune cells during inflammation [27,28]. Therefore, greater age-dependent increases in IL-10 observed in the study fit well with greater decreases in TNF-α in the Rg1-treated rats compared with the control group during advancing age. Fig. 5Effect of ginsenoside Rg1 supplementation on plasma inflammatory markers during aging. Rats consumed Rg1 show higher levels of oxidized LDL (A) and IL-10 (B) and lower levels of TNF-α (C) compared with the control group. ∗ Significantly difference against the control group, $p \leq 0.05.$ Mean and standard error were calculated from survivors > 21 months (male) or 28 months (female) of age. Daily Rg1 dosage was 0.1 mg/kg. Fig. 5 Weight gain is also the main contributor to tumorigenesis [29]. Tumor incidence increased with age and was similar for the control and Rg1 groups (Fig. 6). The absence of the Rg1 effect is probably associated the similar growth rate between both treatment groups. In the study, tumor incidence increased with age and reaching $19\%$ in the control group and $12\%$ in the Rg1 group of rats at 28 months of age. Difference in tumor incidence across the entire life between the control and Rg1 groups was not significant ($$p \leq 0.24$$).Fig. 6Effect of ginsenoside Rg1 supplementation on tumor incidence during aging. Representative images of visible tumors under X-ray from the same animal (A). No significant difference in tumor incidence between the control and Rg1 groups was observed (B). Daily Rg1 dosage was 0.1 mg/kg. Fig. 6
The major limitation of the current work is that the single-dose study cannot preclude the possibility of a life-prolonging effect of Rg1 at different doses. Hormesis properties of ginsenosides have been reported in the past that antioxidant capacity increases only at low or moderate, not higher doses [30]. This result suggests that a multi-dose study for optimization is needed to conclude the effect of ginsenoside on longevity, tumorigenesis, and metabolic outcomes. Furthermore, Rg1 has been shown to stimulate senescent cell clearance associated with immune cell activation after exercise [8]. Effect of pre-exercise Rg1 supplementation on mortality and physical vitality during late life deserves more investigation. Another limitation of the study is that we could not distinguish whether the Rg1 action is mediated by its metabolites. Rg1 is quickly deglycosylated in vivo [31]. Therefore, further investigation is required to determine what metabolite of Rg1 mediates the improvement in physical vitality.
## Conclusion
Physical vitality, reflected by decreased spontaneous physical activity, declines during late life. The results of the study demonstrated that this age-dependent decline can be effectively attenuated in rats consuming ginsenoside Rg1 during adulthood. This effect is associated with immunomodulatory effect of Rg1. However, the dose used in the study could not produce noticeable effects in longevity, body fat, tumors, and glycemic control.
## Author contributions
CCH, CYC, TXYL, JW, SS, and YWH performed experiments, analyzed data and developed figures. CCH and CYC analyzed data and developed figures. JW, SS, and YWH assisted with biological experiments. CCH and CYC wrote the draft. CHK and CYH designed the study and revised the manuscript. All other authors contributed to draft preparation.
## Declaration of competing interest
CHK and JFW are involved with US patents (10,806,764 B2) for an anti-aging method. This work was funded leading to a designed supplement Senactiv for Nuliv Science, USA.
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|
---
title: 'Impact of pharmacogenetics on aspirin resistance: a systematic review'
authors:
- Gustavo Figueiredo da Silva
- Bruno Mattei Lopes
- Vinicius Moser
- Leslie Ecker Ferreira
journal: Arquivos de Neuro-Psiquiatria
year: 2023
pmcid: PMC10014202
doi: 10.1055/s-0042-1758445
license: CC BY 4.0
---
# Impact of pharmacogenetics on aspirin resistance: a systematic review
## Abstract
Background Pharmacogenetics promises better control of diseases such as cardiovascular disease (CVD). Acetylsalicylic acid, aspirin, prevents the formation of an activating agent of platelet aggregation and vasoconstriction, and it is used to prevent CVD. Nevertheless, patients may have treatment failure due to genetic variants that modify the metabolism of the drug causing aspirin resistance (AR).
Objectives To realize a systematic literature review to determine the impact of genetic variants on AR.
Methods Articles published in the MEDLINE/PubMed, Cochrane, Scopus, LILACS, and SCIELO databases were systematically screened. A total of 290 articles were identified and 269 articles were excluded because they did not comply with the previously established inclusion criteria. A total of 20 case-control studies and 1 cohort was included.
Results *The* genetic variants rs1126643 (ITGA2), rs3842787 (PTGS1), rs20417 (PTGS2), and rs5918 (ITGB3) were the most studied. As for relevance, of the 64 genetic variants evaluated by the articles, 14 had statistical significance ($p \leq 0.05$; $95\%$ confidence interval [CI]) in at least one article. Among them, the following have had unanimous results: rs1371097 (P2RY1), rs1045642 (MDR1), rs1051931 and rs7756935 (PLA2G7), rs2071746 (HO1), rs1131882 and rs4523 (TBXA2R), rs434473 (ALOX12), rs9315042 (ALOX5AP), and rs662 (PON1), while these differ in real interference in AR: rs5918 (ITGB3), rs2243093 (GP1BA), rs1330344 (PTGS1), and rs20417 (PTGS2). As study limitations, we highlight the nonuniform methodologies of the analyzed articles and population differences.
Conclusion *It is* noteworthy that pharmacogenetics is an expanding area. Therefore, further studies are needed to better understand the association between genetic variants and AR.
## INTRODUCTION
Cardiovascular disease (CVD) is the first cause of mortality worldwide, with all the healthcare systems facing this very challenging issue. The World Health Organization (WHO) estimates that $31\%$ of deaths worldwide are due to CVD, with ∼ 17.7 million CVD-related deaths in 2015. Approximately 7.4 million of these deaths were due to heart disease and 6.7 million deaths were due to stroke. 1 Platelet activation plays an important role in the development of CVD. Acetylsalicylic acid (ASA), commonly known as aspirin, is an irreversible inhibitor of platelet cyclooxygenase (COX), which prevents the formation of thromboxane A2 by arachidonic acid and, therefore, prevents the formation of this activating agent of platelet aggregation and vasoconstriction. 2 *Aspirin is* a widely used antiplatelet for primary and secondary prevention of CVD, such as stroke and heart attacks. 3 Nevertheless, several patients may still experience treatment failure with ASA and an increased risk in recurrent stroke events. 4 There are several contributing factors for treatment failure including medication adherence, drug-drug interactions, aspirin-independent thromboxane A2 synthesis and also genetic variations. 2 Even low daily aspirin doses (in the range between 75 and 150 mg) are able to suppress biosynthesis of thromboxane, inhibiting the accumulation of platelets, and reducing the risk of CVD. 5 However, aspirin does not always prevent the formation of thromboxane A2 due to failure to inhibit platelet COX. 6 Because of that, all individuals do not respond to antiplatelet therapy in a similar way. In this sense, the genetic mutations have been related with aspirin resistance (AR) and may cause reduction or increase in drug absorption and metabolism, contributing to AR. 6 7 Aspirin resistance can be diagnosed by clinical criteria or by laboratory tests. Clinically, the patient has a new episode of CVD, despite the regular use of aspirin. While the failure of aspirin to inhibit a platelet function test can be seen by Platelet Function Analyser (PFA-100) or light transmission aggregometry (LTA), for example. 3 The field of pharmacogenetics, which aims to implement specific pharmacological therapies to genetic characteristics with the intention to provide greater efficiency, is a constant target of research. 8 Therefore, several studies have been published about candidate genes associated with the genetic predisposition of resistance to AAS, such as COX-2, GPIIIA, and P2Y1. 9 Resistance to antiplatelet therapy and the indiscriminate use of ASA can increase rates of recurrence and mortality from cardiovascular diseases, such as stroke. 10 Hence, the aim of the present study was to perform a systematic literature review to determine the impact of genetic variants on AR.
## METHODS
The present systematic review was established according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) statement published by Moher et al. [ 2019]. Five following databases were systematically screened: MEDLINE/PubMed, 11 Cochrane, 12 Scopus, 13 LILACS, 14 and SCIELO. 15 The research was restricted to a period of 10 years (December 2009 to December 2019) and the following search terms were applied: Aspirin AND Resistance AND Polymorphism and Aspirin AND Resistance AND Genetic variation.
## Eligibility criteria
Only articles published in English were included in this search. Also, only articles describing the relation between AR, proven by laboratory tests or a new case of CVD, and polymorphisms or genetic variations were included in the present systematic review. The final articles included ($$n = 21$$) in the present review were 20 case-controls and 1 cohort.
## Assessment of risk of bias
The authors, using the combined search terms and based on the inclusion criteria, conducted the primary literature search. In that first moment, titles and abstracts were screened. All reports that appeared in accordance with the inclusion criteria were full-text screened. All studies that did not comply with pre-established eligibility and inclusion requirements were excluded. In a second step, the researchers independently evaluated whether the full-texts previously selected followed the inclusion criteria. In case of disagreement between two authors, a third author was consulted, and a consensus was reached by a meeting between them.
Furthermore, to assess and minimize the presence of potential biases, the Risk of Bias in Systematic Reviews (ROBIS) method was used as a reference. 16
## Data extraction and synthesis
In the primary literature search, a total of 290 articles were found: 178 in SCOPUS, 104 in MEDLINE/Pubmed, 5 in Cochrane, 2 articles in LILACS, and 1 in SCIELO. Of those, 19 were duplicated. Hence, 271 articles were screened for reading of title and abstract, 216 of which were excluded for not meeting our inclusion criteria.
In the next step, the authors independently reviewed 65 full-text articles. Then, 44 articles were excluded for not meeting our inclusion criteria. So, in the end, 21 articles were included in the present systematic review (Figure 1).
**Figure 1:** *Flowchart of selected articles.*
## RESULTS
In the 21 final articles selected, a total of 10,873 patients were analyzed, of which 3,014 were aspirin resistant and 6,882 were aspirin sensitive (some articles brought semiresistance values and were disregarded, and another 2 articles did not classify their patients as sensitive and not sensitive). Of the 21 articles studied, 11 included patients with a cerebrovascular event, totaling 4,835 patients. The other 10 articles mostly analyzed cardiac outcomes. We also emphasize that the clinical conditions of the evaluated patients were varied among the articles, with some articles evaluating patients with > 1 disease: ischemic stroke (10 articles), coronary artery disease [9], peripheral arterial disease [3], acute vascular event [1], age > 80 years old [1], adults [1], and hypertension [1]. Most of the patients in the selected articles are from the Asian continent (9 from China, 4 from India, 2 from Turkey, and 1 from Jordan), and regarding the other works, 3 articles are from the American continent (all from the United States of America), 1 from the European continent (Belgium), and 1 from the African continent (Tunisia).
Among the resistance analysis methods, 4 articles used clinical outcome and 17 used platelet aggregation measurement. Among those who performed platelet aggregation measurement, the most common method was LTA (8 articles), followed by PFA-100 system [3], thromboelastography platelet mapping assay (TEG) [2], VerifyNow [2], PL-11 platelet analyzer [1], TXB2 elisa kit [1] and urinary 11-dehydro TXB2 [1], with some articles using > 1 method.
In Table 1, we detail the following information from the 21 final articles included in the present review: Type of article, country, clinical condition, sample number, number of aspirin resistant patients, number of aspirin sensitive patients, gene, risk allele, protective allele, genetic variant, p-value, Odds Ratio (OR), CI, resistance assessment method, and daily aspirin dose.
**Table 1**
| Author (year) | Type of article | Country | Clinical condition | Sample number* | Aspirin resistant | Aspirin sensitive | Gene | Protective allele | Risk allele | Genetic variation | p-value | OR | CI | Resistance assessment method | Aspirin dose/day |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Patel S. et al (2019) 23 | Case-control | India | Ischemic stroke | 65 | 2 | 62 | CYP2C19 | G | A | rs4244285 (CYP2C19*2) | 0.171 | NI | NI | Platelet Aggregation Measurement - LTA | 75mg |
| Patel S. et al (2019) 23 | Case-control | India | Ischemic stroke | 65 | 2 | 62 | ITGA2B/ITGB3 | T | C | rs5918 (PLA1/A2) | 0.960 | NI | NI | Platelet Aggregation Measurement - LTA | 75mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | PTGS1 | A | G | rs10306114 (A842G) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | PTGS1 | C | T | rs3842787 (C22T) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | PTGS1 | C | A | rs5788 (C644A) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | PTGS1 | C | A | rs5789 (C714A) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | ITGA2 | C | T | rs1126643 (C807T) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | ITGA2 | G | A | rs1062535 (873G/A) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | ITGA2 | C | T | rs1126643 (C807T) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | ITGB3 | T | C | rs5918 (PLA1/A2) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | GP6 | C | T | rs1613662 (C13254T) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | P2RY12 | C | T | rs1065776 (893C > T) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | F13A1 | G | T | rs5985 (V34L) | NI | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Yeo et al. (2018) 35 | Cohort | USA | Peripheral artery disease | 154 | 31 | 123 | PON1 | A | G | rs662 (A576G) | 0.005 | NI | NI | Platelet Aggregation Measurement - VerifyNow Assay | 300mg |
| Wang et al. (2017) 28 | Case-control | China | Ischemic stroke | 97 | 43 | 54 | ITGA2 | C | T | rs1126643 (C807T) | 0.210 | NI | NI | Platelet Aggregation Measurement - PL-11 platelet analyzer | 100mg |
| Wang et al. (2017) 28 | Case-control | China | Ischemic stroke | 97 | 43 | 54 | PTGS2 | G | C | rs20417 (G765C) | 0.69 | NI | NI | Platelet Aggregation Measurement - PL-11 platelet analyzer | 100mg |
| Strisciuglio et al. (2017) 36 | Case-control | Belgium | Stable CAD patients undergoing elective PCI | 597 | NI | NI | NPPA | T | C | rs5065 (T2238C) | 0.7 | NI | NI | Platelet Aggregation Measurement - VerifyNow P2Y12 | 500mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS1 | C | T | rs1236913 | 0.99** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS1 | C | T | rs3842787 | 0.76** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS2 | A | G | rs689466 | 0.89** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS2 | G | C | rs20417 | 0.26** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | TXAS1 | G | A | rs194149 | 0.42** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | TXAS1 | T | C | rs2267679 | 0.53** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | TXAS1 | G | T | rs41708 | 0.72** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | P2RY1 | A | G | rs701265 | 0.48** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | P2RY1 | A | G | rs1439010 | 0.32** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | P2RY1 | C | T | rs1371097 | 0.01** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | P2RY12 | C | T | rs16863323 | 0.21** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | P2RY12 | G | A | rs9859538 | 0.16** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | ITGB3 | A | G | rs2317676 | 0.24** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2017) 19 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | ITGB3 | A | G | rs11871251 | 0.51** | NI | NI | Platelet Aggregation Measurement - LTA | 200mg (14 days) and follow-up with 100mg |
| Peng et al. (2016) 20 | Case-control | China | Ischemic stroke | 283 | 250 | 33 | ABCB1 | C | T | rs1045642 | 0.021 | 0.421 | 0.233–0.759 | Platelet Aggregation Measurement - TXB2 ELISA kit | 100mg |
| Peng et al. (2016) 20 | Case-control | China | Ischemic stroke | 283 | 250 | 33 | TBXA2R | G | A | rs1131882 | 0.028 | 2.712 | 1.080–6.810 | Platelet Aggregation Measurement - TXB2 ELISA kit | 100mg |
| Peng et al. (2016) 20 | Case-control | China | Ischemic stroke | 283 | 250 | 33 | PLA2G7 | A | G | rs1051931 | 0.023 | 8.233 | 1.590–42.638 | Platelet Aggregation Measurement - TXB2 ELISA kit | 100mg |
| Peng et al. (2016) 20 | Case-control | China | Ischemic stroke | 283 | 250 | 33 | PLA2G7 | C | A | rs7756935 | 0.023 | 8.233 | 1.590–42.638 | Platelet Aggregation Measurement - TXB2 ELISA kit | 100mg |
| Peng et al. (2016) 20 | Case-control | China | Ischemic stroke | 283 | 250 | 33 | PEAR1 | G | T | rs12566888 | 0.378 | 0.660 | 0.260–1.671 | Platelet Aggregation Measurement - TXB2 ELISA kit | 100mg |
| Peng et al. (2016) 20 | Case-control | China | Ischemic stroke | 283 | 250 | 33 | PEAR1 | G | A | rs12566888 | 0.378 | 0.660 | 0.260–1.671 | Platelet Aggregation Measurement - TXB2 ELISA kit | 100mg |
| Yi et al. (2016) 8 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS1 | T | C | rs1236913 | 0.95** | NI | NI | Platelet Aggregation Measurement- LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2016) 8 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS1 | C | T | rs3842787 | 0.78** | NI | NI | Platelet Aggregation Measurement- LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2016) 8 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS2 | T | C | rs689466 | 0.82** | NI | NI | Platelet Aggregation Measurement- LTA | 200mg (14 days) and follow-up with 100mg |
| Yi et al. (2016) 8 | Case-control | China | Ischemic stroke | 850 | 175 | 630 | PTGS2 | G | C | rs20417 | 0.42** | NI | NI | Platelet Aggregation Measurement- LTA | 200mg (14 days) and follow-up with 100mg |
| Derle et al. (2016) 3 | Case-control | Turkey | Acute vascular event | 208 | 67 | 141 | ITGB3 | T | C | rs5918 (PLA1/A2) | 0.277 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | 100–300mg |
| Wang et al. (2014) 24 | Case-control | China | > 80 years old | 450 | 236 | 214 | ITGB3 | T | C | rs5918 (PLA1/A2) | 0.002 | NI | NI | Platelet Aggregation Measurement - LTA | 100mg |
| Al-Azzam et al. (2013) 27 | Case-control | Jordan | Adults | 584 | 92 | 492 | ITGA2 | C | T | rs1126643 (C807T) | 0.116 | NI | NI | Platelet Aggregation Measurement - Multiplate Analyzer system | 100mg |
| Al-Azzam et al. (2013) 27 | Case-control | Jordan | Adults | 584 | 92 | 492 | GP1BA | T | C | rs2243093 | 0.003 | NI | NI | Platelet Aggregation Measurement - Multiplate Analyzer system | 100mg |
| Al-Azzam et al. (2013) 27 | Case-control | Jordan | Adults | 584 | 92 | 492 | PTGS2 | G | C | rs20417 | 0.485 | NI | NI | Platelet Aggregation Measurement - Multiplate Analyzer system | 100mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS1 | C | T | rs1888943 | 0.92 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS1 | A | G | rs1330344 | 0.1 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS1 | C | T | rs3842787 | 0.92 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS1 | G | A | rs5787 | 0.92 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS1 | C | A | rs5789 | 1 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS1 | G | A | rs5794 | 1 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS2 | G | C | rs20417 | 1 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | PTGS2 | C | G | rs5277 | 0.24 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Li et al. (2012) 29 | Case-control | China | CAD, stroke, and peripheral artery disease | 431 | 36 | 231 | HO1 | A | T | rs2071746 | 0.04 | NI | NI | Platelet Aggregation Measurement - LTA | 75–160mg |
| Wang et al. (2013) 21 | Case-control | China | Patientsunderwent primary OPCAB | 210 | 62 | 148 | TBXA2R | T | C | rs4523 (T924C) | 0.001 | 4.479 | 1.811–11.077 | Platelet Aggregation Measurement - LTA | 100mg |
| Wang et al. (2013) 21 | Case-control | China | Patientsunderwent primary OPCAB | 210 | 62 | 148 | ITGB3 | T | C | rs5918 (PLA1/A2) | NI | NI | NI | Platelet Aggregation Measurement - LTA | 100mg |
| Wang et al. (2013) 21 | Case-control | China | Patientsunderwent primary OPCAB | 210 | 62 | 148 | P2RY1 | A | G | rs701265 (A1622G) | 0.724 | 1.178 | 0.473–2.934 | Platelet Aggregation Measurement - LTA | 100mg |
| Wang et al. (2013) 21 | Case-control | China | Patientsunderwent primary OPCAB | 210 | 62 | 148 | GP1BA | C | T | rs6065 (C1018T) | NI | NI | NI | Platelet Aggregation Measurement - LTA | 100mg |
| Sharma et al. (2013) 32 | Case-control | India | Ischemic stroke | 450 | 217 | 233 | PTGS2 | G | C | rs20417 (-765G/C) | CC: p = 0.016 GC: p = 0.02 | CC:OR-3.157 GC: OR-1.745 | CC: 1.241–8.033GC: 1.059–2.875 | Clinical outcome | 75–325mg |
| Sharma et al. (2013) 17 | Case-control | India | Ischemic stroke | 610 | 307 | 303 | ALOX5AP | T | A | rs9315042 (SG13S114T/A) | <0.001 | 2.983 | 1.884–4.723 | Clinical outcome | 75–325mg |
| Fan et al. (2012) | Case-control | China | CAD, hypertension, peripheral artery disease and stroke | 431 | 38 | 393 | PTGS1 | A | G | rs1330344 | 0.01 | 1.82 | 1.13–2.92 | Platelet Aggregation Measurement - LTA and TEG Platelet Mapping Assay | 75–100 mg |
| Fan et al. (2012) | Case-control | China | CAD, hypertension, peripheral artery disease and stroke | 431 | 38 | 393 | PTGS1 | C | T | rs1888943 | 0.59 | NI | NI | Platelet Aggregation Measurement - LTA and TEG Platelet Mapping Assay | 75–100 mg |
| Fan et al. (2012) | Case-control | China | CAD, hypertension, peripheral artery disease and stroke | 431 | 38 | 393 | PTGS1 | C | T | rs3842787 | 0.66 | NI | NI | Platelet Aggregation Measurement - LTA and TEG Platelet Mapping Assay | 75–100 mg |
| Fan et al. (2012) | Case-control | China | CAD, hypertension, peripheral artery disease and stroke | 431 | 38 | 393 | PTGS1 | G | A | rs5787 | 0.49 | NI | NI | Platelet Aggregation Measurement - LTA and TEG Platelet Mapping Assay | 75–100 mg |
| Fan et al. (2012) | Case-control | China | CAD, hypertension, peripheral artery disease and stroke | 431 | 38 | 393 | PTGS1 | C | A | rs5789 | 1 | NI | NI | Platelet Aggregation Measurement - LTA and TEG Platelet Mapping Assay | 75–100 mg |
| Fan et al. (2012) | Case-control | China | CAD, hypertension, peripheral artery disease and stroke | 431 | 38 | 393 | PTGS1 | G | A | rs5794 | 1 | NI | NI | Platelet Aggregation Measurement - LTA and TEG Platelet Mapping Assay | 75–100 mg |
| Sharma et al. (2012) 33 | Case-control | India | Ischemic stroke | 560 | 338 | 222 | ABCB1 | C | T | rs1045642 | 0.012 | 1.85 | 1.142–3.017 | Clinical outcome | 75–325 mg/dia |
| Gao et al. (2011) 22 | Case-control | China | Patients underwent primary OPCAB | 262 | 23 | 239 | GP1BA | C | T | rs6065 (C1018T) | 1 | NI | NI | Platelet Aggregation Measurement - LTA | 100mg |
| Gao et al. (2011) 22 | Case-control | China | Patients underwent primary OPCAB | 262 | 23 | 239 | ITGB3 | T | C | rs5918 (P1A1/A2) | 1 | NI | NI | Platelet Aggregation Measurement - LTA | 100mg |
| Gao et al. (2011) 22 | Case-control | China | Patients underwent primary OPCAB | 262 | 23 | 239 | P2RY1 | A | G | rs701265 (A1622G) | 0.991 | NI | NI | Platelet Aggregation Measurement - LTA | 100mg |
| Gao et al. (2011) 22 | Case-control | China | Patients underwent primary OPCAB | 262 | 23 | 239 | TBXA2R | T | C | rs4523 (T924C) | 0.01 | NI | NI | Platelet Aggregation Measurement - LTA | 100mg |
| Chakroun et al. (2011) 31 | Case-control | Tunisia | Stable CAD | 125 | NI | NI | PTGS1 | C | T | rs3842787 (C50T) | Urinary TxB2: 0.1PFA-100: 0.43 | NI | NI | Platelet Aggregation Measurement - PFA-100 system and Urinary 11-dehydro-TXB2 | 250mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | GNB3 | C | T | rs5443 (C825T) | > 0.05 | Black: 1.15 White: 0.93 | Black: 0.71–1.87 White: 0.82–1.07 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | ITGA2 | C | T | rs1126643 (C807T) | > 0.05 | Black: 1.10 White: 0.99 | Black: 0.82–1.46 White: 0.87–1.14 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | ITGB3 | T | C | rs5918 | > 0.05 | Black: 1.03 White: 0.98 | Black: 0.71–1.50 White: 0.85–1.13 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | GP6 | A | G | rs1613662 | > 0.05 | Black: 0.89 White: 0.99 | Black: 0.66–1.20 White: 0.86–1.15 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | GP1BA | T | C | rs2243093 | > 0.05 | Black: 0.84 White: 1.01 | Black: 0.62–1.14 White: 0.86–1.18 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | PEAR1 | A | C | rs2768759 | > 0.05 | Black: 1.05 White: 0.95 | Black: 0.46–2.41 White: 0.83–1.09 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | VAV3 | A | C | rs6583047 | > 0.05 | Black: 1.06 White: 1.02 | Black: 0.80–1.42 White: 0.89–1.16 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | F2R | A | T | rs168753 | > 0.05 | Black: 0.96 White: 1.06 | Black: 0.60–1.54 White: 0.91–1.23 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | THBS1 | A | G | rs2228262 | > 0.05 | Black: 0.68 White: 1.03 | Black: 0.34–1.36 White: 0.88–1.21 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | PTGS1 | C | T | rs3842787 | > 0.05 | Black: 1.29 White: 1.06 | Black: 0.94–1.77 White: 0.88–1.29 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Voora et al. (2011) 26 | Case-control | USA | Coronary stenosis ≥ 75% | 3449 | 865 | 2584 | ADRA2A | G | C | rs1800544 | > 0.05 | Black: 0.98 White: 0.97 | Black: 0.63–1.51 White: 0.85–1.10 | Clinical Outcome | Two groups: < 81mg and > 81mg |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | F5 | G | A | rs6025 (G1691A) | 0.302 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | F5 | A | G | rs1800595 (A4070G - H1299R) | 0.191 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | F2 | G | A | rs1799963 (G20210A) | 0.644 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | F13A1 | G | T | rs5985 (V34L) | 0.480 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | FGB | G | A | rs1800790 (G455A) | 0.814 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | SERPINE1 | A | G | rs1799889 (4G/5G) | 0.656 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | ITGB3 | T | C | rs5918 (HPA1a/b) | 0.623 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | MTHFR | C | T | rs1801133 (C677T) | 0.362 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | MTHFR | A | C | rs1801131 (A1298C) | 0.421 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | ACE | Ins | Del | rs1799752 (ACE I/D) | 0.713 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | APOB | G | A | rs5742904 (R3500Q) | 1 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | APOE | T | C | rs429358 (C112R) | 0.695 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Pamukcu et al. (2010) 25 | Case-control | Turkey | Stable CAD | 126 | 30 | 96 | APOE | T | C | rs429358 (C158A) | 0.695 | NI | NI | Platelet Aggregation Measurement - PFA-100 system | NI (The p-value for the difference between the resistant and sensitive groups was 0.681) |
| Carroll et al. (2010) 34 | Case-control | USA | Candidates for interventional cardiology on aspirin therapy | 81 | 27 | 54 | ALOX12 | A | G | rs434473 | 0.043 | NI | NI | Platelet Aggregation Measurement - TEG Platelet mapping | Not uniform |
| Carroll et al. (2010) 34 | Case-control | USA | Candidates for interventional cardiology on aspirin therapy | 81 | 27 | 54 | ALOX15B | G | A | rs4792147 | 0.440 | NI | NI | Platelet Aggregation Measurement - TEG Platelet mapping | Not uniform |
| Carroll et al. (2010) 34 | Case-control | USA | Candidates for interventional cardiology on aspirin therapy | 81 | 27 | 54 | ALOX12 | G | A | rs1126667 | 0.580 | NI | NI | Platelet Aggregation Measurement - TEG Platelet mapping | Not uniform |
| Carroll et al. (2010) 34 | Case-control | USA | Candidates for interventional cardiology on aspirin therapy | 81 | 27 | 54 | ALOX15 | G | A | rs3892408 | NI | NI | NI | Platelet Aggregation Measurement - TEG Platelet mapping | Not uniform |
In addition, we have highlighted in a separate table the genetic variants with relevant results for AR (Table 2). As for relevance, of the 64 genetic variants evaluated by the articles, 14 had statistical significance ($p \leq 0.05$; $95\%$CI). Among them, the following polymorphisms have had concordant results so far: rs1371097 (P2RY1), rs1045642 (MDR1), rs1051931 and rs7756935 (PLA2G7), rs2071746 (HO1), rs1131882 and rs4523 (TBXA2R), rs434473 (ALOX12), rs9315042 (ALOX5AP), and rs662 (PON1). In turn, these genetic variants differ in real interference in AR: rs5918 (ITGB3), rs2243093 (GP1BA), rs1330344 (PTGS 1), and rs20417 (PTGS2).
**Table 2**
| Biomarker (Pharmacogene) | Alleles | Refs. |
| --- | --- | --- |
| PON1 | rs662 | 35 |
| P2RY1 | rs1371097 | 19 |
| ABCB1 | rs1045642 | 20 33 |
| TBXA2R | rs1131882, rs 4523 | 20 21 |
| PLA2G7 | rs1051931, rs7756935 | 20 |
| ITGB3 | rs5918 | 24 |
| GP1BA | rs2243093 | 27 |
| HO1 | rs2071746 | 29 |
| PTGS2 | rs20417 | 17 |
| ALOX5AP | rs9315042 | 17 |
| PTGS1 | rs1330344 | 29 30 |
| ALOX12 | rs434473 | 34 |
## DISCUSSION
To study the relationship between polymorphisms and AR, it is necessary to consider the resistance analysis mode, which can be performed in two ways: clinical or laboratory. In the first, the patient is considered resistant if there is a negative outcome (death or stroke for example). 17 In the second, several types of tests can be used, such as PFA-100, VerifyNow Aspirin, TEG, PL-11 platelet analyzer, serum and urinary TXB2, LTA, and multiplate analyzer. However, it is important to highlight that the measurement of platelet response to aspirin is highly variable, likely due to differing dependence of the arachidonic acid pathway between techniques. In our research, the most used laboratory method was the LTA, which is considered the gold standard for testing platelet function. 18 The relationship between polymorphisms and AR has been described by Yi et al. This study assessed the interaction with PTGS1 (rs1236913 and rs3842787), PTGS2 (rs689466 and rs20417), TXAS1 (rs194149, rs2267679, and rs41708), P2RY1 (rs701265, rs1439010, and rs1371097), P2RY12 (rs16863323 and rs9859538), and ITGB3 (rs2317676 and rs11871251) gene variants. In the laboratory analysis, only rs1371097 of the P2RY1 gene, comparison CC x TT + CT, obtained statistical relevance ($$p \leq 0.01$$), even after adjusting for other covariates ($$p \leq 0.002$$; OR = 2.35; $95\%$CI: 1.87–6.86). In addition, using the generalized multifactor dimensionality reduction (GMDR) method, the following 3 sets of gene-gene interactions were significantly associated with AR: rs20417CC/rs1371097TT/rs2317676GG ($$p \leq 0.004$$; OR = 2.72; $95\%$CI: 1.18–6.86); rs20417CC/rs1371097TT/rs2317676GG/AG ($$p \leq 0.034$$; OR = 1.91; $95\%$CI: 1.07–3.84); rs20417CC/rs1371097CT/rs2317676AG ($$p \leq 0.0025$$; OR = 2.28; $95\%$CI: 1.13–5.33). These high-risk interactive genotypes were also associated with a bigger chance of early neurological deterioration ($p \leq 0.001$; Hazard Ratio [HR] = 2.47; $95\%$CI: 1.42–7.84). 19 Peng et al. [ 2016] also assessed genes related to thromboxane and others. The analyzed polymorphisms were ABCB1 (rs1045642), TBXA2R (rs1131882), PLA2G7 (rs1051931 and rs7756935) and PEAR1 (rs12041331–rs1256888). There was statistical significance for 3 of them: rs1045642 ($$p \leq 0.021$$; OR = 0.421; $95\%$CI: 0.233–0.759), rs1131882 ($$p \leq 0.028$$; OR = 2.712; $95\%$CI: 1.080–6.810) and rs1051931–rs7756935 ($$p \leq 0.023$$; OR = 8.233; $95\%$CI: 1.590–42.638), 20 while Wang Z. et al [2013] researched the association with TBXA2R (rs4523), ITGB3 (rs5918), P2RY1 (rs701265), and GP1BA (rs6065) polymorphisms. The only polymorphism significantly associated with AR was rs4523 ($$p \leq 0.001$$; OR = 4.479; $95\%$CI = 1.811–11.077). 21 Another study that assessed the TBXA2 and glycoprotein genes was done by Gao et al. GP1BA (rs6065), ITGB3 (rs5918), P2RY1 (rs701265), and TBXA2R (rs4523) genetic variations were researched, but only TBXA2R (rs4523) polymorphism was related ($$p \leq 0.01$$). 22 In addition, Patel et al. also studied the ITGA2B/ITGB3 polymorphisms. They analyzed the relationship with CYP2C19 (rs4244285) and ITGA2B /I TGB3 (rs5918) polymorphisms. However, no association was observed ($$p \leq 0.171$$ and $$p \leq 0.960$$, respectively). 23
Moreover, still in the scope of glycoprotein genes, Derle et al. conducted a study with 208 patients with vascular risk factors. ITGB3 (rs5918) polymorphism was screened, and the results showed that there was no significant difference in the presence of the C allele between the groups ($$p \leq 0.277$$). In addition, in the relationship between the presence of the C allele and atherothrombotic stroke, no significant difference was found ($$p \leq 0.184$$). 3 A study by Wang B et al. also analyzed the rs5918 (PLA1/A2) polymorphism of the ITGB3 gene. All 214 patients in the aspirin sensitive group had the PLA1/A1 genotype and no patients with PLA2/A2 were found. However, of the 236 patients in the AR group, 12 had PLA1/A2 heterozygous genotype ($$p \leq 0.002$$), finding a statistically significant differenc. 24 In the study by Pamukcu et al., 13 polymorphisms of 10 different genes were tested, including ITGB3. *The* genes F5 (rs6025, rs1800595), F2 (rs1799963), F13A1 (rs5985), FGB (rs1800790), SERPINE1 (rs1799889), ITGB3 (rs5918), MTHFR (rs1801133, rs1801131), ACE (rs1799752 - Ins/Del), APOB (rs5742904), and APOE (rs429358 - C112R and C158A) were evaluated. However, there was no significant result for any polymorphism ($p \leq 0.05$). 25 Furthermore, in the case-control study by Voora et al, 11 polymorphisms of 11 different genes were assessed: GNB3 (rs5443), ITGA2 (rs1126643), ITGB3 (rs5918), GP6 (rs1613662), GP1BA (rs2243093), PEAR1 (rs2768759), VAV3 (rs6583047), F2R (rs168753), THBS1 (rs2228262), PTGS1 (rs3842787), and ADRA2A (rs1800544). When comparing the groups, there was no relationship ($p \leq 0.05$). 26 Another research that studied some of the same genes was conducted by Al-Azzam et al.: GP1BA (rs1126643), ITGA2 (rs2243093) and PTGS2 (rs20417). Of these, only the GP1BA (rs2243093) gene was related ($$p \leq 0.003$$), analyzing the presence of the C allele. 27 Additionally, Wang et al. [ 2017] conducted a study about the following polymorphisms: ITGA2 polymorphism gene at rs1126643 and PTGS2 polymorphism gene at rs20417. The authors found no association: $$p \leq 0.21$$ for rs126643 and $$p \leq 0.69$$ for rs20417. 28
Moreover, Yi et al. used Matrix-Assisted Laser Desorption/Ionization-Time Of Flight (MALDI-TOF) to link PTGS1 (rs1236913 and rs3842787) and PTGS2 (rs689466, and rs20417) with AR. The analysis showed that there was no statistical relevance for the relationship. Only when the gene-gene interaction (rs3842787 and rs20417) was evaluated, there was statistical significance: rs3842787/CT + rs20417/CC ($$p \leq 0.016$$; OR = 2.36; $95\%$CI: 1.12–6.86), rs3842787/TT, CT + rs20417/CC ($$p \leq 0.078$$; OR = 1.36; $95\%$ CI: 0.82–2.01), and rs3842787/CT + rs20417/GC ($$p \leq 0.034$$; OR = 1.78; $95\%$CI: 1.04–4.58). Highlighting the fact that, for the second combination, there is an invalid CI. 19 Another study that investigated polymorphisms of the PTGS1 (rs1888943, rs1330344, rs3842787, rs5787, rs5789, rs5794) and PTGS2 (rs20417, rs5277) genes was conducted by Li et al.; in addition to these two genes, a genetic variant of the HO1 gene (rs2071746) was also tested. As a result, only two genetic variations were associated with AR. The rs2071746 polymorphism (HO1 gene) had statistical significance to genotype TT ($$p \leq 0.04$$; OR = 1.40; $95\%$CI = 0.59–3.30) and T allele ($$p \leq 0.04$$; OR = 1.70; $95\%$CI =1.02–2.79), while rs1330344 (PTGS1 gene) had significant results only when G was the risk allele and analyzed separately ($$p \leq 0.02$$; OR = 1.77; $95\%$CI = 1.07–2.92). 29 Still on the PTGS1 gene, Fan et al. investigated several polymorphisms of the PTGS1 gene (rs1888943, rs1330344, rs3842787, rs5787, rs5789, and rs5794), but rs1330344 was the only significantly related to AR ($$p \leq 0.01$$; OR = 1.82; $95\%$CI = 1.13–2.92; allele value) just in LTA + TEG analysis. 30 Moreover, another case-control study by Chakroun et al. investigated the relationship between rs3842787 polymorphism of the PTGS1 gene and AR. Patients with the allele had no statistically significant difference using CEPI-CT ($$p \leq 0.1$$) and uTxB2 ($$p \leq 0.43$$). 31 Sharma et al. evaluated 3 polymorphisms of 3 different genes, PTGS2 (rs20417), ALOX5AP (rs9315042) and ABCB1 (rs1045642), to assess their role in AR. The research was performed in 3 different studies and all studies obtained statistical relevance for the CC allele of rs20417 ($$p \leq 0.016$$; OR = 3.157; $95\%$CI: 1.241–8.033), the GC allele of rs20417 ($p \leq 0.001$; OR = 2.983; $95\%$CI: 1,884–4,723) and for the rs9315042 variant ($p \leq 0.001$; OR = 2.983; $95\%$CI: 1.884–4.723). For the variant rs1045642, 2 comparisons were made, one comparing cases and controls, for the TT x CC alleles ($p \leq 0.001$; OR = 2.27; $95\%$CI: 1.64–3.168), and for the TT x CT + CC alleles ($p \leq 0.001$; OR = 1.72; $95\%$CI: 1.335–2.239) and other comparing AR and sensitive participants ($$p \leq 0.012$$; OR = 1.85; $95\%$CI: 1.142–3.017). 17 32 33
Another study that tested the ALOX gene was done by Carroll et al. The study tested 4 genetic variants: rs434473 and rs1126667 of the ALOX12 gene, rs4792147 of the ALOX15B gene and rs3892408 of the ALOX15 gene. Only the rs434473 polymorphism obtained a significant p -value ($$p \leq 0.043$$). 34 Furthermore, Yeo et al. analyzed some variants of PTGS1 (rs10306114, rs3842787, rs5788, and rs5789), ITGA2 (rs1126643, rs1062535, and rs1126643), ITGB3 (rs5918), GP6 (rs1613662), P2RY12 (rs1065776), and F13A1 (rs5985) genes, but only rs662 (A576G) of PON1 gene was significantly relevant ($$p \leq 0.005$$) to AR. 35 Lastly, a study by Strisciuglio et al. included 450 noncarriers of the T2238C polymorphism (rs5065, NPPA gene) and 147 carriers. The authors concluded that there was no statistical difference when comparing the groups, neither in overall CAD patients ($$p \leq 0.7$$) nor in the diabetic group ($$p \leq 0.6$$). 36 As limitations of the present study, we highlight the nonuniform methodologies of the analyzed articles, as well as population differences. These divergences made it difficult to compare the results of the articles. Among the studies, there was a great difference among the clinical conditions, as well as in the way of analysis of the resistance and in the dosage of aspirin. Unfortunately, meta-analysis was not performed due to such high clinical and methodological heterogeneity of the findings.
Despite the heterogeneity of the findings in terms of methodology and results, it is clear that some polymorphisms are more studied than others. Among them, rs1126643 (ITGA2), rs3842787 (PTGS1), rs20417 (PTGS2), and rs 5918 (ITGB3) were the most studied.
In conclusion, pharmacogenetics is an expanding area that promises a therapy aimed at the individualities of each patient, personalized medicine, for better control of diseases, including cardiovascular diseases, such as stroke.
Finally, further studies are needed to better understand the association between genetic variants and AR and, therefore, the practical application of the findings.
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27. Al-Azzam S I, Alzoubi K H, Khabour O F, Tawalbeh D, Al-Azzeh O. **The contribution of platelet glycoproteins (GPIa C807T and GPIba C-5T) and cyclooxygenase 2 (COX-2G-765C) polymorphisms to platelet response in patients treated with aspirin**. *Gene* (2013.0) **526** 118-121. PMID: 23688555
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|
---
title: Transient receptor potential canonical type 6 (TRPC6) O-GlcNAcylation at Threonine-221
plays potent role in channel regulation
authors:
- Sumita Mishra
- Junfeng Ma
- Desirae McKoy
- Masayuki Sasaki
- Federica Farinelli
- Richard C. Page
- Mark J. Ranek
- Natasha Zachara
- David A. Kass
journal: iScience
year: 2023
pmcid: PMC10014292
doi: 10.1016/j.isci.2023.106294
license: CC BY 4.0
---
# Transient receptor potential canonical type 6 (TRPC6) O-GlcNAcylation at Threonine-221 plays potent role in channel regulation
## Summary
Transient receptor potential canonical type 6 (TRPC6) is a non-voltage-gated channel that principally conducts calcium. Elevated channel activation contributes to fibrosis, hypertrophy, and proteinuria, often coupled to stimulation of nuclear factor of activated T-cells (NFAT). TRPC6 is post-translationally regulated, but a role for O-linked β-N-acetyl glucosamine (O-GlcNAcylation) as elevated by diabetes, is unknown. Here we show TRPC6 is constitutively O-GlcNAcylated at Ser14, Thr70, and Thr221 in the N-terminus ankryn-4 (AR4) and linker (LH1) domains. Mutagenesis to alanine reveals T221 as a critical controller of resting TRPC6 conductance, and associated NFAT activity and pro-hypertrophic signaling. T→A mutations at sites homologous in closely related TRPC3 and TRPC7 also increases their activity. Molecular modeling predicts interactions between Thr221-O-GlcNAc and Ser199, Glu200, and Glu246, and combined alanine substitutions of the latter similarly elevates resting NFAT activity. Thus, O-GlcNAcylated T221 and interactions with coordinating residues is required for normal TRPC6 channel conductance and NFAT activation.
## Graphical abstract
## Highlights
•TRPC6 is constitutively O-GlcNAcylated at T221 to suppress basal conductance•A T221A mutant TRPC6 has much greater conductance and NFAT-activation•Reducing but not raising O-GlcNAc alters T221-dependent TRPC6 function•T221 coordinates with S199, E200, and E246 to control basal channel activity
## Abstract
Biochemistry; Cellular physiology; Cell biology
## Introduction
Transient receptor potential canonical type-6 (TRPC6) is a non-voltage gated transmembrane cation channel conducting primarily Ca2+.1 *It is* expressed at low basal levels in many cell types, and its upregulation and/or post-translational activation is thought to contribute to the pathophysiology of many diseases. They include glomerulosclerosis with podocyte dysfunction,2 pathological cardiac hypertrophy,3,4 dystrophin deficient muscular dystrophy,5,6 wound and pathological fibrosis,7 pulmonary vascular hypertension,8,9,10 wound healing7,11 and autoimmune disease.12 This pleiotropic behavior has led to growing interest in TRPC6 as a pharmacological target, and selective oral inhibitors have been developed13 and are in human trials for renal disease.
TRPC6 is primarily stimulated by G-protein coupled receptor signaling via diacylglycerol and phospholipase C,1 and by mechanical stretch.5 Calcium conducted by TRPC6 stimulates calcium-calmodulin activated phosphatase calcineurin (CaN) to dephosphorylate nuclear factor of activated T-cells (NFAT). This results in NFAT nuclear translocation and altered gene expression.14,15 NFAT consensus sequences within the TRPC6 promoter provide a positive feedback loop that amplifies TRPC6 associated signaling. In humans, TRPC6 gain of function (GOF) mutations result in familial segmental glomerulosclerosis.16,17,18 GOF mutations reside in both cytoplasmic N-terminus ankyrin repeat and C-terminus domains that are physically fairly close,19,20 and can function by disrupting inhibitory calcium binding sites.21 TRPC6 conductance and its associated NFAT activation are normally controlled by post-translational modifications.22,23,24,25 For example, oxidant stress coupled to NADPH oxidases NOX2 and NOX4 stimulates TRPC6 signaling.26,27N-glycosylation at N472 and N56128 and phosphorylation at S1423 are required for normal TRPC6 membrane localization and activity. Phosphorylation can both increase (at T487 by calcium-calmodulin activated kinase, CaMKII),29 or decrease (at T70 and S262 by cGMP-activated kinase, cGK1α)30,31 channel conductance and associated TRPC6 signaling. Hyperglycemia is also associated with TRPC6 and NFAT upregulation in kidney,32,33,34 monocytes,35,36 and platelets.37 This latter observation first led us to speculate TRPC6 may also be modified by O-linked β-N-acetyl glucosamine (O-GlcNAc).38,39,40O-GlcNAc-ylation occurs on serine or threonine residues41,42 and can alter protein activity, protein-protein interactions, structure, subcellular localization, and stability.43,44,45 Two recent comprehensive databases, the O-GlcNAc protein database (www.oglcnac.mcw.edu) and the O-GlcNAc Atlas,46 do not presently report TRPC6 or closely associated TRPC3 and TRPC7 as being O-GlcNAc-modified.
Here, we tested whether TRPC6 is modified by O-GlcNAcylation, and if so where this occurs and what its targeted residues impact. Proteomics identified three TRPC6 residues in the N-terminus to be constitutively O-GlcNAcylated, of which T221 conferred potent basal suppression of channel conductance and corresponding NFAT activity. Threonine 221 resides in the fourth ankyrin repeat domain, and modeling analysis found its O-GlcNAcylation enhances coordination with neighboring residues at Ser199, Glu200, and Glu246 that contribute to the constraint of basal TRPC6 conductance and NFAT signaling. The findings identify a new critical regulatory region of the protein that may help future design of pharmacological modulators.
## TRPC6 is constitutively O-GlcNAcylated in the N-terminus cytoplasmic domain
To examine if TRPC6 is O-GlcNAcylated, HEK-293T cells were transfected with plasmid expressing a TRPC6-YFP fusion protein to facilitate detection of expression and provide a robust epitope for immunoprecipitation (IP). IP was performed using either YFP or O-GlcNAc as bait, and the precipitate then probed with anti-GFP (works as anti-YFP) or monoclonal anti-O-GlcNAc antibodies. The pull-down revealed that recombinant TRPC6 (∼130 kD) is constitutively O-GlcNAcylated (Figures 1A and 1B). Here the control was pcDNA. To confirm YFP itself was not the O-GlcNAc target, we repeated the experiment in cells expressing YFP alone or TRPC6-YFP. Only TRPC6-YFP was found in the oGlcNAc IP lysate (Figure 1C).Figure 1TRPC6 is constitutively OGlcNAcylated(A)HEK-293T expressing TRPC6-YFP or pcDNA were subjected to immunoprecipitation (IP) using O-GlcNAc antibody and then immunoblotted (IB) using GFP antibody (detects YFP-labeled TRPC6). Upper gel shows input, lower IP.(B) Same experiment but using YFP for the IP, and O-GlcNAc for IB. In this study, a group of cells were also exposed to 10 μM Thiamet G (TMG) for 24 h to assess if enhancing O-GlcNAcylation altered the signal associated with TRPC6.(C) Similar experiment using YFP as the control. The input shows either YFP or TRPC6-YFP expressed. After IP for OGlcNAc, IB for YFP shows only in the TRPC6-YFP band region.(D) IP blots with cells pre-treated with 0.2 μg of N-glycan-specific endoglycosidases PNGase F, showing persistence of TRPC6 O-GlcNAcylation. pcDNA serves as the control.(E) HEK-293T cells were co-transfected with TRPC6-YFP and OGT-Flag, and YFP IP performed and probed with OGT Ab. The data shows co-IP of both TRPC6-YFP and OGT-Flag.(F) O-GlcNAc metabolic labeling of TRPC6-YFP by GlcNAz and mutated Gal-T1 to enable click chemistry that adds biotin to O-GlcNAcylated residues. The upper band shows streptavidin beads isolation lysate probed for TRPC6-YFP showing equal input (upper band). For the lower band, GFP-Ab (detecting YFP) was used for the IP, and then probed with streptavidin IRDye to assess for biotinylation. Only TRPC6 subject to the click-chemistry biotinylation based on its having O-GlcNAcylation was detected.(G and H) Results of mass spectroscopy analysis of TRPC6-YFP O-GlcNAcylation revealed three peptides GS14SPRGAAGAAAR,Q70TVLREKGRRLANR,SHDV221TPIILAAHCQEYEIVHTLLR. The position of O-GlcNAc sites are highlighted.
TRPC6 is known to be constitutively N-glycosylated, and to confirm that this was not influencing the O-GlcNAc signal, we repeated the experiment using lysates pre-incubated with PNGaseF to remove this modification. The molecular weight was lower and double-band appearance was less after PNGaseF treatment as expected47 (Figure S1). However, the TRPC6 protein was still bound by antibodies recognizing O-GlcNAc (Figure 1D). To test if the basal levels of TRPC6 O-GlcNAcylation could be augmented by stimulating OGT, we treated cells with TMG (Figure 1B) but found no change in the O-GlcNAc band intensity. Additional support for the targeting of TRPC6 by O-GlcNAcylation is provided by finding co-immunoprecipitation of O-GlcNAc transferase (OGT), the enzyme that generates this modification, with YFP in cells expressing TRPC6-YFP (Figure 1E). Lastly, we performed a Click-itTM assay in HEK-293T cells, in which O-GlcNAcylated protein residues were first selectively labeled by tetra-acetylated azide-modified N-acetylglucosamine (GlcNAz) by Y289L-b4-Gal-T1, and then co-tagged with biotin alkyne by click chemistry. The presence of biotinylation was confirmed by streptavidin-based immunoblot (upper gel), but TRPC6-YFP was only detected in cells treated with Y289L-b4-Gal-T1, further supporting TRPC6 as constitutively O-GlcNAcylated (Figure 1F).
To directly identify O-GlcNacylation of TRPC6 and the residues involved, we performed tandem mass spectrometry. Cell lysates from HEK-293T cells over-expressing TRPC6-YFP were subjected to pull-down using anti-YFP antibody magnetic beads. The immunoprecipitant was subjected to in-gel digestion, and the digests analyzed by nanoUPLC-MS/MS analysis. Three O-GlcNAc-containing peptides in TRPC6 were identified and the modified residues mapped to Ser$\frac{13}{14}$, Thr70 and Thr221 (Figures 1G and 1H). Of these, T221 had not been previously reported as being modified by any PTM, whereas T70 was a known phosphorylation target of cGK1α30 and S14 by cdk5c (Liu et al., 2020).25 T221 in particular resides in the AR4 domain of TRPC6 and is highly conserved across species (Figure S2).
## T221 O-GlcNAcylation is a primary regulator of basal TRPC6 function
To investigate the functional roles of the three O-GlcNAcylated residues in TRPC6, we performed site-directed mutagenesis substituting an alanine at each site to prevent the PTM. Compared to wild-type (WT) TRPC6, S14A and T70A mutants modestly lowered basal NFAT activity by −19 and −$36\%$, respectively (both $p \leq 0.002$). In contrast, the T221A mutation displayed markedly increased promoter activity (11-fold over WT, $p \leq 10$−13, Figure 2A). When all three mutations were introduced, NFAT activity rose further than with T221A alone (Figure 2B). This identifies T221 as the dominant regulating site and shows influences from the other two sites depends on T221 status. Figure 2Substitution of T221 with T221A results in hyperactive TRPC6In all panels showing group data, mean ± SD is displayed for each group.(A) NFAT promoter activity in cells transfected with pcDNA (C), TRPC6-YFP (WT) or TRPC6-YFP mutants (S14A, T70A, T221A) for 24 h (n/group provided in figure). Data are log-transformed, analysis by Brown-Forsythe Welch ANOVA, p-values from Dunnett’s multiple comparison test (MCT).(B) Co-expression of S14A, T70A, and T221A ($$n = 18$$) versus T221A alone ($$n = 21$$). p-value Mann-Whitney test.(C) IP assay in HEK293 cells expressing WT or T221A mutant TRPC6, with IP using OGlcNAc Ab, and the probed for YFP. Cells with WT TRPC6 show O-GlcNAcylated TRPC6, whereas the signal is faint in the T221A mutant.(D) NFAT activity assay in HEK cells expressing WT or T221A TRPC6 in the presence of OGT inhibition (OGT-I) or vehicle (Control). Data are shown normalized to the control in each respective TRPC6 genotype group. Analysis by 2W-ANOVA, Sidak’s multiple comparisons test, ($$n = 13$$–16/group).(E) NFAT promoter activity because of expression of WT or T221A TRPC6 is markedly blunted by selective TRPC6 antagonist, BI 749327 ($$n = 8$$/group, Welch ANOVA, p-values Dunnett’s MCT.).(F) Example current-time-voltage tracings in HEK-293 cells expressing WT or T221A mutant TRPC6.(G) Summary current density versus voltage relations for WT, T221A, and P112Q TRPC6. Current density comparisons for WT and T221A at a transmembrane voltage of +/− 100 mV are shown to the right, p value Mann-Whitney test. The current density plot also shows a gain-of-function mutation P112Q falls in between WT and T221A at positive voltages but has no impact on current density at negative voltages.(H) Gene expression of hypertrophic response genes in cardiomyocytes expressing control (GFP), and WT or T221A mutant TRPC6 adenovirus for 48h. Genes are: A-type and B-type natriuretic peptide (Nppa, Nppb), regulator of calcineurin (Rcan1) and TRPC6 (Trpc6), each normalized to Gapdh. p-values from Dunnett’s MCT following Welch ANOVA; a: 6e-4, b: 8e-5, c:4e-5, d: 0.005, e: 6e-4, f: 8e-5, g:3e-4, h:2e-5. ( $$n = 6$$ for Trpc6, $$n = 7$$ for the rest).(I) *Hypertrophic* gene expression in cardiomyocytes exposed to angiotensin II versus vehicle control, and expressing either WT or T221A TRPC6. AII augments NFAT promoter activity independent of TRPC6 genotype with the genetic mutation increasing activity overall. Results of 2WANOVA, Sidak’s MCT, $$n = 12$$–14/group. AII is p-value for effect of angiotensin II, TRPC6 the effect of the mutation. Interaction was >0.36 (NS) for both genes.
The prominent impact of the T221A mutation could potentially be due to intrinsic changes in the channel function from the mutation independent of its blocking O-GlcNAcylation. To address this, we transfected cells with WT or T221A TRPC6-YFP, performed IP using O-GlcNAc-Ab, and then probed the precipitate for TRPC6-YFP. As before, we observed a signal when the WT channel was expressed but little with the T221A mutant (Figure 2C). In a second experiment, similar cells were incubated with either the OGT-inhibitor (OSMI-1) to reduce O-GlcNAcylation, or vehicle control, and NFAT promoter activity measured (Figure 2D). Here, data are displayed normalized to control for each TRPC6 genotype to facilitate their comparison. In WT-TRPC6 expressing cells, OGT inhibition enhanced NFAT promoter activity, whereas this was blocked in cells expressing T221A-TRPC6 ($$p \leq 0.0004$$ for difference in OGT-inhibitor effect between genotypes by two-way ANOVA).
We next tested whether the T221A mutation disrupted TRPC6 membrane expression or its external structure, using a potent selective TRPC6 antagonist (BI 749327)13 and found this inhibitor significantly reduced NFAT promoter activity in both WT TRPC6 and T221A mutant (Figure 2E). This indicates TRPC6-T221A membrane localization and external structure were likely intact. By contrast, N-glycosylation is required for normal membrane localization.28 To determine the functional impact of T221A mutagenesis, TRPC6 calcium conductance was measured by patch-clamp assay in HEK-293T expressing WT or T221A TRPC6. Figure 2F shows typical raw current-voltage-time tracings, and Figure 2G the summary data. Bidirectional voltage-dependent current increased by 75–$80\%$ in cells expressing T221A-TRPC6 versus WT. This is similar to changes in current induced by angiotensin stimulation of TRPC6 conductance.30 Here we also compared the changes in current density-voltage dependence with T221A to a well described GOF mutation P112Q associated with aggressive renal disease.16 At positive voltages (outward current), the P122Q mutation fell intermediate between WT and T221A, whereas at negative voltages (inward current), P122Q had no impact, whereas this was still markedly increased by T221A (Figure 2G). These data identify T221 as a bidirectional regulator of basal TRPC6 Ca2+ current.
Lastly, we examined the impact of T221 on resting and agonist stimulated NFAT signaling in cardiomyocytes that normally express low levels of TRPC6 but on its activation display pathological hypertrophic signaling.30 Myocytes were transfected with either WT or T221A TRPC6 achieving a high relative level of Trpc6 expression mostly reflecting the very low baseline. We observed increases in two prominent biomarkers of hypertrophic signaling, Nppa (A-type natriuretic peptide) and Rcan1 (regulator of calcineurin-1) in cells expressing the T221A mutant, whereas Nppb (B-type natriuretic peptide) rose similarly with both (Figure 2H). We also tested if expression of T221A in myocytes limits or amplifies enhancement of NFAT signaling induced by angiotensin II stimulation. As before, myocytes expressing T221A TRPC6 exhibited increased expression of both Nppa and Rcan1, but AII stimulation still augmented this further and similarly as in cells with WT TRPC6 (Figure 2I). There was no no significant interaction (2W-ANOVA) between AII or genotype effects. Thus, mutating T221 does not modify channel activation by canonical Gq-coupled DAG-activation.
## Homologous threonines to T221 in related TRPC3 and TRPC7 confers similar activity control
Among the seven-member TRPC channel family, TRPC3, TRPC6, and TRPC7 form a closely related subgroup, with TRPC3 and TRPC6 being most homologous.20 We therefore tested if the functional role of T221 in TRPC6 was shared by homologous threonines in the other channels. Sequence analysis identified T150 in TRPC3 and T166 in TRPC7 as homologous to T221 in TRPC6 (Figure S3). Each of these were then mutated to alanines, expressed in HEK cells, and the impact on NFAT promoter activation determined (Figure 3A). Each T→A mutation was associated with a near 10-fold rise in NFAT promoter activity over the respective WT channel. These data support conservation of this regulatory region in the structure of all three members of this TRPC subfamily. Figure 3Impact of T→A mutation at homologous residues in TRPC3 and TRPC7 to TRPC6-T221, and role of T221 on TRPC6 gain-of-function mutations or hyperglycemiaIn all panels showing group data, mean ± SD is displayed for each group.(A) NFAT promoter activity (log-transformed) in control (C, $$n = 13$$) and cells expressing either TRPC6 (WT versus T221A); TRPC3 (WT versus T150) (both $$n = 21$$) and TRPC7 (WT versus T166) ($$n = 7$$). p-values Dunnett’s MCT, Welch ANOVA.(B) NFAT promoter activity in gain of function TRPC6 mutants +/− stimulated O-GlcNAcylation by combined TMG+OGT overexpression. The stimulation did not alter NFAT promoter activity for WT or any mutant. p-values for mutant versus WT: ∗ - 0.033; ∗∗ 0.004; ∗∗∗ 0.0001 – Dunnett’s MCT/Welch ANOVA.(C) NFAT promoter activity in HEK cells expressing T221A, P112Q, or a double mutant TRPC6. The T221A mutation augments NFAT more than P112Q, but their combination further augments to the same level as with T221A alone. 1WANOVA, p-values by Sidak’s MCT. $$n = 16$$, 16, 14, 16 for groups, respectively.(D) NFAT promoter activation by hyperglycemia (HG) in HEK-293T cells lacking or expressing TRPC6. Mannitol (Man) serves as negative control. ( $$n = 12$$–27, p value for-TRPC6 (CON) +/− HG is Mann Whitney U test; p values for +TRPC6 +/− HG or Man are from Kruskal–Wallis test with p-values by Dunn’s MCT. ∗ - $$p \leq 0.01$$ for interaction of HG and TRPC6 expression by 2W-ANOVA. Sample size for each shown at top.(E)Western blot for total O-GlcNAc in lysates from HEK-293T cells treated with 30 mM HG, 10 μM TMG, or 10 μM Glucosamine (GlcN) for 6h.(F) Quantitation of data from experiment E. ($$n = 5$$/group, Welch ANOVA, p-values Dunnet’s MCT).(G) Corresponding NFAT promoter activity for the same experimental conditions shown in panel (F). Despite similarly increased levels of O-GlcNAcylation, only high glucose significantly increased NFAT promoter activity over TRPC6-vehicle control. ( 1WANOVA, p-values by Sidak’s MCT).
## Independence of TRPC6-T221A NFAT activation on GOF mutations or hyperglycemia
Based on our preceding findings, we speculated that gain-of-function TRPC6 mutations that elevate basal NFAT activity may work in part by interfering with T221 O-GlcNAcylation. To test this, vectors expressing several known potent GOF mutations (P112Q, M132T, and R175Q) that amplify resting NFAT promoter activity were studied (Figure 3B). None of these augmented NFAT promoter activity as much as T221A (e.g., 1-2x versus 11x). If the mutations partially interfered with T221 O-GlcNAcylation, we anticipated by mass action that enhancing it with TMG might lower NFAT activity due to the mutation. However, this was not observed with any of the mutations (Figure 3B). Lack of interaction was further tested by expressing T221A, P112Q, and their combination. All three increased NFAT activity, but T221A alone or combined with P112Q achieved the same level (Figure 3C). This supports the conclusion that such GOF mutations unlikely interfere with T221 O-GlcNAcylation as a mechanism for their NFAT activation.
Our initial impetus for exploring O-GlcNAcylation of TRPC6 was triggered a hypothesis that this PTM might be linked to NFAT activation by hyperglycemia (HG). As displayed in Figure 3D, HG stimulates NFAT promotor activation particularly in HEK293 cells expressing TRPC6 (mannitol is used as a negative control). Our results had shown that O-GlcNAcylation reduced NFAT activation, so the notion this was a mechanism of diabetic TRPC6 signaling was refuted. Here we further tested this independence by increasing total protein O-GlcNAcylation by 200–$300\%$ with either HG, TMG, or glucosamine, and comparing their impact on NFAT activation. ( Figures 3E and 3F). The latter was ony significantly increased by HG despite near identical levels of augmented O-GlcNAcylation by all three interventions (Figure 3G).
## Structural analysis reveals coordinated residues in AK4-linker region that impart basal TRPC6 regulation and O-GlcNAcylation control
To explore how T221-O-GlcNAcylation impacts TRPC6 channel function and identify if other coordinating amino acids are also involved, we turned to recent cryo-electron microscopy structural studies19,48,49 and modeled interactions influenced by adding an O-GlcNAc modification onto residue T221. The model structure of the relevant region is shown in Figure 4A and predicts T221 O-GlcNAcylation fosters electrostatic contacts between residues within the 193-203 loop in the AR4 domain crossing into the linker helix LH1 domain. The model also predicts O-GlcNAc at T221 enhances electrostatic interaction with glutamic acid (E246, purple) and hydrogen bond formation between the side chain of glutamine (Q198, pink) and the backbone amino group of aspartate (D205, light blue). We posited that these interactions hold a portion of the 193-203 loop in place to enhance S199 and E200 for interaction with O-GlcNAc at T221. The side chains of S199 (yellow), E200 (blue), and E246 are predicted to form hydrogen bonds with the hydroxyl of O-GlcNAc. Figure 4T221-coordinating amino acids in 192-203 Ankyrin Repeat-linker loop region are required for normal constrained TRPC6 channel functionIn all panels showing group data, mean ± SD is displayed for each group.(A) Structural model of region linking O-GlcNAc modified T221 with AA193-203 loop near ankyrin repeat domain 4 and linker helix. Electrostatic interactions are between E246-purple, S199- yellow, E200-blue.(B) HEK-293T cells transfected with pcDNA plasmid (Control), WT-TRPC6, and TRPC6 mutants impacting these predicted AA interactions. ( $$n = 33$$ for WT, 8 for T221A, and 12 for all other groups. Kruskal-Wallis test, p-values shown from Dunn’s MCT.(C) Time dependent decline in WT ($$n = 8$$) versus T221A ($$n = 16$$) protein indexed by decline in YFP fluorescence in cells incubated with cycloheximide (CHX). Mean ± SD; p-value for slope difference by analysis of covariance.(D)Western blot of experiment as depicted in panel (C) with protein expression of TRPC6-YFP shown in cells with or without co-treatment with MG132 to inhibit the proteasome.(E) Densitometry of 16 h after CHX data +/− a proteasome inhibitor (MG 132); ($$n = 4$$/group; p value of Mann-Whitney test).
To test this, we selectively mutated each residue to an alanine, expressed the recombinant forms of TRPC6 in HEK-293T cells, and assessed NFAT activity. Figure 4B shows results for NFAT activity. E246A and S199A mutations similarly increased NFAT activity over WT, but both less than with the T221A mutation. Neither Q198A nor E200A alone augmented activity over WT, however when both S199A and E200A were combined, the result was comparable with T221A. Adding E246A to form a triple mutant did not further increase NFAT activity. Figure S4 shows an example immunoblot for protein levels obtained with each mutant transfection, with slight changes in some but far less than their impact on corresponding NFAT activity. Thus, the coordinating hydrogen bonds between S199, E200, and O-GlcNAcylated T221 form critical residue interactions regulating basal TRPC6 conductance and NFAT-activation, with E246 itself also impacting this behavior.
## TRPC6 T221 O-GlcNAcylation enhances protein longevity
Beyond stimulating TRPC6 expression, channel conductance and NFAT signaling, we tested if T221 O-GlcNAcylation stabilizes the protein to protect it from proteasomal degradation. T221A or WT TRPC6 were expressed in HEK-293T cells exposed to cycloheximide to block de novo protein synthesis +/− the proteasome inhibitor MG132. The rate of protein decline was indexed by GFP fluorescence and was significantly faster with the T221A mutant (Figure 4C). This primarily reflected increased proteasome degradation as MG132 restored levels to baseline similarly with the mutant and WT form (Figures 4D and 4E). Thus, O-GlcNAcylation of TRPC6 reduces its proteasomal degradation to improve post-translational longevity.
## Discussion
In this study, we have identified three amino acids in the AR4-LH1 region of the N-terminus of TRPC6 that are constitutively modified by O-GlcNAc, and find that among these, T221 exerts the dominant control over basal channel current and associated NFAT activation. Preventing O-GlcNAcylation at this residue using a T221A mutation results to our knowledge in the highest basal channel conductance and associated NFAT activity yet reported, including from human GOF mutations that cause renal disease or by other activating PTMs. Consistent with its augmentation of NFAT promotor activity, expression of mutant T221A-TRPC6 amplifies cardiomyocyte pathological hypertrophic gene programs while leaving further receptor-operated (Gq-GPCR) stimulation intact. Although the molecular mechanism for T221A GOF remains to be determined, it appears independent of that associated with GOF mutations. We also find O-GlcNAcylation at T221 coordinates with neighboring amino acids required to constrain channel conductance at normal levels, revealing this region as a key regulatory nexus for the channel. Although we had first hypothesized that TRPC6 O-GlcNAcylation might be a mechanism for HG-stimulated TRPC6-dependent NFAT activation, our data refute this by showing that this modification is already present and that reducing not increasing it results in NFAT activation. These results identify a novel regulatory region of the protein that might be leveraged for therapeutics to constrain hyperactive TRPC6 and associated disease.
It is useful to frame our findings in the context of recent cryo-EM structural data regarding TRPC6.19,20,49 Such studies reveal TRPC6 tetramers in a two-layered architecture assembled into an inverted bell-shaped intracellular cytosolic domain (ICD) that caps below the transmembrane domain (TMD). The ICD is assembled through interactions between the four ankyrin repeat domain (residues: 96–243) in the N-terminus, linker helices (residues: 256–393) and a coiled-coil domain in the C-terminus. ARs and LHs are key to inter-subunit interactions and TRPC6 tetramer assembly. Specifically, amino acids of the N-terminal loop (85–94) interact with LHs of the neighboring subunit and the last three LHs pack against the TRP helix providing the major contact site between the ICD and TMD. The ARs are highly conserved helix-turn-helix structural motifs50,51,52 involved in the assembly and stability of multiprotein complexes by forming both intra- and inter-repeat hydrophobic and hydrogen bond interactions.53,54 We find O-GlcNAcylated T221 forms stabilizing electrostatic contacts between AR4, the 193-203 loop near AR4, and loop connecting AR4 to LH1. We believe this helps hold distant regions together and is critical to stabilize the closed state of the channel pore. Many GOF mutations causing renal disease reside in ARs: G109S (AR1), P112Q (AR1), N125S (AR1), M132T (AR2), N143S (AR2), R175Q(AR3), whereas others are found in the C-terminus (e.g. Q899K, R895C, E897K) that is structurally proximate.20,48 Structural studies of these mutants similarly support destabilization of electrostatic interactions at the interface of AR domains and the linker helix that in turn associates with greater current.49 A unifying impact of these mutations appears to be suppression of allosteric inhibition by intracellular calcium.21 Although our data indicate some of these mutations do not prevent T221 O-GlcNAcylation, it remains possible others may, though this seems unlikely or their impact would be so large as to be embryonically lethal. Indeed, TRPC6 T221 mutations have not been reported in humans. Regardless, identification of the residue cluster T221-O-GlcNAc, S199, E200, and E246 and Q198 supports it being a key conserved region required for channel function, linked to constitutive T221 O-GlcNAcylation.
Although attention to O-GlcNAc modifications has often been on its role in disease, it is also known to play constitutive roles.41,55,56 *It is* among the most abundant forms of protein glycosylation,57 with OGT found in all metazoans and expressed in all mammalian tissues.58,59 OGT also has non-catalytic functionality, but only the O-GlcNAcylation function of OGT appears required for cell survival.60 OGT strongly associates with the ribosome and nearly half of ribosomal proteins are O-GlcNAcylated.61 For example, O-GlcNAcylation of Sp1 and Nup62 occurs co-translationally and is key to their stability,62 whereas that of nuclear pore proteins is required for their functionality.63 O-GlcNAcylation is also used to modulate function, as in the case of phospholamban at Ser16 to inhibit myocyte Ca2+ uptake by the sarcoplasmic reticulum,64 STIM1 where it impedes store-operated Ca2+ entry that could influence mechanical and receptor-coupled signaling,65 and calcium-calmodulin stimulated kinase II at Ser279 that activates the enzyme.66 Although we did not find evidence that further enhancement of constitutive TRPC6 O-GlcNAcylation has biological effects, its reduction caused channel activation. Of the reported post-translational modifiers of TRPC6, only N-glycosylation and from the current results O-GlcNAcylation appear required for normal channel localization and conductance.
Although the current results remove TRPC6 O-GlcNAcylation as a mechanism for its increased activity in hyperglycemic conditions, they do provide a strong example of where O-GlcNAc modifications are required for normal protein function. The data also specifically reveals a previously unknown yet highly conserved regulatory region, sharing homology with closely related channels TRPC3 and TRPC7, and this new insight may help provide new pathways for therapeutics to modify channel function.
## Limitations of the study
Although site mutagenesis is commonly used to study the functional impact of targeted post-translational protein modifications, and alanine substitutions will prevent O-GlcNacylation at a given site, it has limitations. Specifically, one may observe changes that are because of substituting the native amino acid to alter protein structure/function itself. Although we cannot fully rule out this possibility we believe the data support a primary effect related to prevention of O-GlcNAcylation. First, we find reducing O-GlcNAcylation with OGT inhibition also increases O-GlcNAcylation of TRPC6 and NFAT activation, yet has significantly less impact on either behavior when the T221A mutation is expressed. Second, the modeling analysis predicts local residue interactions based on T221A being O-GlcNAcylated and not just mutated to alanine, and these predictions are experimentally confirmed. The molecular weights for TRPC6-YFP in our IP gel studies are close to predicted, but did differ somewhat from gel to gel. In particular, we found the weights of the immunoblot from the IP lysates a bit lower than in the input material. This may reflect loss of some of the PTMs on the protein such as N-glycosylation. Still, finding TRPC6 O-GlcNAcylation by IP assay, mass spectrometry, and click-chemistry assay, and reduced changes in the T221A mutation supports this as directly modifying TRPC6. Lastly, the mechanism by which T221 OGlcNAcylation and its interactants control channel conductance and associated NFAT activity remains to be determined, but maybe identified by a future cryo-electron microscopy analysis.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERAntibodiesO-GlcNAc (CTD 110.6) (Mouse Monoclonal antibody)O-GlcNAc Core, JHU, Ma et al. 67N/AOGT (Rabbit Polyclonal antibody)Sigma-AldrichCat# O6264; RRID:AB_532313GFP (Rabbit Polyclonal antibody)Thermo Fisher ScientificCat# A-6455; RRID:AB_221570GFP M-trap beads, Chromo Tek GTD-20Thermo Fisher ScientificCat#17373353IRDye 800CW donkey anti-rabbit 800LI-CORCat#926-32213; RRID:AB_621848IRDye 800CW donkey anti-mouse 680LI-CORCat#926-32212; RRID:AB_621847IRDye 800CW goat anti-rabbit 800LI-CORCat#926-32211; RRID:AB_621843IRDye 800CW goat anti-mouse 680LI-CORCat#926-32210; RRID:AB_621842IRDye 800CW Streptavidin dyeLI-CORCat#926-32230Bacterial and virus strainspAV-YFP adenovirusThis paperN/ApAV-hTRPC6 WT_YFP adenovirusThis paperN/ApAV-hTRPC6 T221A_YFP adenovirusThis paperN/AChemicals, peptides, and recombinant proteinsDulbecco’s modified Eagle’s mediumThermo Fisher ScientificCat#11965084Fetal bovine serumSigma-AldrichCat#F2442GlucoseSigma-AldrichCat#G8270MannitolSigma-AldrichCat#M4125Thiamet GO-GlcNAc Core, JHUN/AGlucosamineSigma-AldrichCat#G4875PBSThermo Fisher ScientificCat#10010023RIPA bufferSigma-AldrichCat#R0278Complete™ protease inhibitor cocktailRoche DiagnosticsCat#11836153001Trizol ReagentThermo Fisher ScientificCat#15596026PNGase FNew England BiolabsCat#P0704SPierceTM Protein A/G Magnetic BeadsThermo Fisher ScientificCat#888024–$15\%$ Criterion™ TGX Stain-Free™ Protein GelsBio-Rad LaboratoriesCat#5678085Trans-Blot® Turbo™ Midi Nitrocellulose Transfer PacksBio-Rad LaboratoriesCat#1704159EDUCycloheximideSigma-AldrichCat#C7698MG132Sigma-AldrichCat#M8699Osmi1Sigma-AldrichCat#SML1621Critical commercial assaysGeneArt Site-Directed Mutagenesis SystemThermo Fisher ScientificCat# A13282Xfect transfection reagentTakara BioCat#631318Dual Luciferase Reporter Assay KitPromegaCat#E1910Click-iT GlcNAz metabolic glycoprotein labeling reagentsThermo Fisher ScientificCat#C33368Thermo Fisher ScientificCat#C33372High-Capacity RNA-to-cDNA KitApplied Biosystems, Thermo fisherCat#4388950Experimental models: Cell linesHEK-293TATCCATTC: CRL-3216Neonatal Rat cardiomyocytes (NRVMs)Primary culturedN/AOligonucleotidesTrpc6Thermo Fisher ScientificAssay ID# Rn00677559_m1NppaThermo Fisher ScientificAssay ID# Rn00664637_g1NppbThermo Fisher ScientificAssay ID# Rn00580641_m1Myh7Thermo Fisher ScientificAssay ID# Rn01488777_g1Rcan1Thermo Fisher ScientificAssay ID# Rn01458494_m1GapdhThermo Fisher ScientificAssay ID# Rn01775763_g1Recombinant DNApcDNA3-human TRPC6-YFPKoitabashi et al.30N/ApcDNA3- YFPThis paperN/ApcDNA3- human TRPC3Seo et al.3pcDNA3- human TRPC7Dr. Steve S Pullen (Boehringer Ingelheim)N/AFLAG-OGTDr. Gerald W Hart, O-GlcNAc Core, JHUN/ApGL3 LucPromegaE1761pGL4.30-NFAT-RE-lucSeo et al.3N/ApGL4.74-TK (thymidine kinase)Seo et al.3N/ATRPC3-YFP T150AThis paperN/ATRPC7-YFP T166AThis paperN/ATRPC6-YFP S14AThis paperN/ATRPC6-YFP T70AKoitabashi et al.30N/ATRPC6-YFP T221AThis paperN/ATRPC6-YFP E246AThis paperN/ATRPC6-YFP Q198AThis paperN/ATRPC6-YFP S199AThis paperN/ATRPC6-YFP E200AThis paperN/ATRPC6-YFP S14AT221AThis paperN/ATRPC6-YFP S14AT70AT221AThis paperN/ATRPC6-YFP S199AE200AThis paperN/ATRPC6-YFP E246AS199AE200AThis paperN/ATRPC6 gene promoter constructThis paperN/ATRPC6-YFP P112QThis paperN/ATRPC6-YFP P112QT221AThis paperN/ATRPC6-YFP M132TThis paperN/ATRPC6-YFP R175QThis paperN/ASoftware and algorithmsAnalyst TF 1.7 softwareAldeghaither et al.68N/AProtein Pilot version 5.0 softwareAldeghaither et al.68N/AParagon and Progroup algortihmsAldeghaither et al.68N/APrism Ver 9.3.1GraphPad.comN/A
## Lead contact
All requests for reagents and resources should be directed to the lead contact, David A Kass ([email protected]).
## Materials availability
*Plasmids* generated in this study are available on reasonable request to the lead contact.
## Cell line
The HEK-293T (ATTC: CRL-3216, (RRID: CVCL_0063) cell line was utilized in the in vitro assays. HEK-293T cells were grown in Dulbecco’s modified Eagle’s medium supplemented with $10\%$ fetal bovine serum, 2 mM glutamine, 1 mM pyruvate and antibiotics.
## Primary cell culture
Some experiments were conducted in primary cultured neonatal rat ventricular myocytes as mentioned in the methods sections. Cells were cultured in DMEM with $10\%$ FBS and $1\%$ penicillin/streptomycin.
## Plasmids
pcDNA3-human TRPC6-YFP plasmid was obtained from Dr. Craig Montell (Koitabashi et al., 2010;30 Kwon et al., 200769), pcDNA3-human TRPC3 plasmid from Dr. Jeffery Molkentin (Seo et al., 2014b),5 pcDNA3-human TRPC7 from Dr. Steve S Pullen (Boehringer Ingelheim) and FLAG-OGT from Dr. Gerald W Hart. pGL4.30-NFAT-RE firefly luciferase (NFAT-luc) driven by the NFAT response element and Renilla luciferase (TK-Rluc) vectors were from Promega (Seo et al., 2014b).5 Alanine substitution mutants: TRPC6-YFP: S14A, T70A, T221A, S14AT221A, S14AT70AT221A, E246A, Q198A, S199A, E200A, S199AE200A, E246AS199AE200A, TRPC3-YFP: T150A; Gain-of-function TRPC6 mutants (P112Q, M132T, R175Q), P112QT221A; TRPC3-YFP: T150A;and TRPC7-YFP: T166A were each generated by PCR-based site mutagenesis (GeneArt Site-Directed Mutagenesis System, A13282) using pcDNA3-human TRPC6-YFP, TRPC3-YFP and TRPC7-YFP as the template. A human TRPC6 gene promoter construct was made by cloning a 1.7kb insert corresponding to the upstream of transcription start site of Human TRPC6 into luciferase reporter plasmid (pGL3 Luc, Promega E1761). pcDNA3 and pcDNA3-YFP vectors served as the control for transfection assays.
## HEK-293T cell transfection and luciferase promotor assay
HEK-293T cells were grown in Dulbecco’s modified Eagle’s medium supplemented with $10\%$ fetal bovine serum, 2 mM glutamine, 1 mM pyruvate and antibiotics. For each well of a 48 well plate, cells were cultured to $70\%$ confluence and transfected with plasmids encoding NFAT-luc (0.25 μg), TK-Rluc (0.02 μg internal control) and wildtype or alanine substituted TRPC mutants (0.25 μg). Transfection was carried out using Xfect transfection reagent following manufacturer’s instruction (Takara Bio, 631318). After transfection, cultures were maintained in serum containing medium for 24 h. After 24h, all treatments were done in serum free culture medium. For High glucose (HG) exposure experiments, DMEM supplemented with 30 mM glucose (Sigma-Aldrich G8270) or 30 mM mannitol (Sigma-Aldrich M4125) (as HG control) was used. OGA inhibitor Thiamet G (TMG) (O-GlcNAc Core, JHU) was used at a concentration of 10 μM and glucosamine (GlcN) (Sigma-Aldrich G4875, 2 mM) was treated for 6h to stimulate O-GlcNAcylation (Chatham and Marchase, 201070). Cells were harvested using passive lysis buffer (Promega E1910). NFAT-luciferase activity was determined using Dual Luciferase Reporter Assay Kit (Promega E1910) using the manufacturer protocol.
## Immunoprecipitation assay
HEK- 293T cells were plated in 10 cm cell culture dishes and grown to $70\%$ confluency. Cells were transfected with 10μg of OGT-Flag and TRPC6-YFP plasmids using Xfect transfection reagent following manufacturer’s instruction (Takara Bio, 631318) and maintained in serum containing medium for 24 h to express the respective proteins. In some studies, cells were further treated with Thiamet G (10 μM) to stimulate O-GlcNAcylation for 24h before harvesting. Cells were washed in ice-cold PBS (ThermoFisher Scientific 10010023), resuspended in RIPA buffer (Sigma-Aldrich R0278) containing Complete™ protease inhibitor cocktail (Roche Diagnostics, 11836153001) and incubated for 20 min on ice to complete lysis. TMG (10 μM) was also added to the lysis buffer in cells pre-treated with TMG. Cell lysates were centrifuged at 12000 rpm × 10minat 4°C and supernatants were used for immuno-precipitation (IP) analysis. In some studies, PNGase F digestion (PNGase F, New England Biolabs P0704S) was done per manufacturer’s specifications to remove N-linked glycans prior to IP studies as indicated in the results section. IP was carried out by incubating lysates with OGT antibody (Sigma-Aldrich O6264), OGlcNAc antibody (CTD 110.6, MABS1254), or GFP M-trap beads (Chromotek GTD-20 serves as YFP capture beads). For OGT and OGlcNAc IP reactions, 50 μL slurry of PierceTM Protein A/G Magnetic Beads (ThermoFisher Scientific 88802) were added to 200 μL of OGT and OGlcNAc IP lysates and all IP samples were incubated for 4h at 4°C with gentle rolling. GFP M-trap beads were directly used for TRPC6-YFP pull down. GFP M-trap beads and OGT IP magnetic beads were washed thrice with 1 mL of washing buffer (20 mM Tris/HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, $0.05\%$ Triton X100, $5\%$ glycerol and Complete™ protease inhibitor cocktail) and proteins were eluted in 30 μL SDS-PAGE loading buffer. O-GlcNAc IP magnetic beads were washed 3x in TBS and eluted with 15 μL of 1M GlcNAc in TBS. Enriched proteins and $10\%$ input samples were boiled in 30 μL of 2× SDS-PAGE loading buffer containing $5\%$ 2-mercaptoethanol for 5 min, and further used for SDS-PAGE analysis.
## SDS/PAGE and western blot analysis
Key resources table contains information of primary and secondary antibodies used in this study. For Western blot analysis, protein samples were separated by precast 4–$15\%$ Criterion™ TGX Stain-Free™ Protein Gels (Bio-Rad Laboratories 5678085), transferred to a nitrocellulose membrane using Trans-Blot® Turbo™ Midi Nitrocellulose Transfer Packs (Bio-Rad Laboratories 1704159EDU) and Trans-Blot® Turbo™ Transfer System (Bio-Rad Laboratories 1704150). The membranes were blocked with $5\%$ non-fat dried skim milk solution for 1 hat 27°C, then incubated overnight at 4°C with primary antibodies diluted in blocking buffer: anti-OGT(1:10,000), anti-O-GlcNAc(1:10,000) and anti-GFP (1:10,000). The next day, fluorescent secondary antibodies (IRDye 800CW donkey anti-rabbit 800(1:20,000), IRDye 800CW donkey anti-mouse 680(1:20,000), IRDye 800CW goat anti-rabbit 800 (1:20,000) and IRDye 800CW goat anti-mouse 680 (1:20,000)), diluted in $1\%$ milk in TBS-T, were added to the membranes for 1 hat room temperature. The membranes were washed 3x in TBS-T for 10 min each and subsequently images were acquired and analyzed using the LI-COR Odyssey Image System.
## O-GlcNAc Click-iT GlcNAz metabolic labeling of WT-TRPC6
O-GlcNAcylation of TRPC6 was assayed by Click-iT GlcNAz metabolic glycoprotein labeling reagents (ThermoFisher Scientific C33368). Lysates (200 μg protein) from HEK-293T cells expressing WT-TRPC6-YFP was incubated with 50 μL GFP M-trap beads (Chromotek GTD-20) to immunoprecipitate TRPC6. Immunoprecipitated protein was enzymatically labeled utilizing the permissive mutant β-1,4-galactosyltransferase (Gal-T1 Y289L) which transfers azido-modified galactose (GalNAz) from UDP-GalNAz to O-GlcNAc residues on the target proteins as per manufacturer’s specifications (ThermoFisher Scientific C33368). The labelled lysate was then clicked on with biotin-alkyne using copper catalyzed azide-alkyne click chemistry reaction protocol according to manufacturer’s instruction (ThermoFisher Scientific C33372). The biotinylation was detected using IRDye 800CW Streptavidin dye and imaged (Odyssey Image System LICOR).
## nanoACQUITY UltraPerformance LC mass spectrometry
pcDNA3-human TRPC6-YFP was over-expressed in HEK-293T cells and immunoprecipitated. The eluate was subjected to SDS-PAGE followed by Commassie Blue staining. The corresponding gel bands were cut out and excised into cubes (ca. 1 × 1 mm) with a razor blade. Gel pieces were de-stained with $50\%$ ACN followed by the addition of 100 μl of 10 mM dithiothreitol (DTT) in 50 mM bicarbonate buffer and incubation at 37°C for 0.5 h. After removal of DTT solution, 100 μL of 30 mM iodoacetamide in 50 mM bicarbonate buffer was added and incubated in dark for 30 min. Proteins were then digested with the addition of sequencing-grade trypsin/Lys-C followed by incubation at 37°C overnight. The yielded peptides were extracted and desalted with C18 Ziptip columns, with elutes dried down with a SpeedVac. Extracted peptides were analyzed with a NanoUPLC-MS/MS system integrating nanoAcquity UPLC (Waters) and a TripleTOF 6600 mass spectrometr (Sciex) (by using similar settings shown in a previous report (Aldeghaither et al., 2019), with some modifications. Specifically, dried peptides were dissolved in $0.1\%$ formic acid and loaded onto a C18 Trap column (Waters Acquity UPLC Symmetry C18 NanoAcquity 10 K 2G V/M, 100 A, 5 μm, 180 μm × 20 mm) at 15 μL/min for 2 min. Peptides were then separated with an analytical column (Waters Acquity UPLC M-Class, peptide BEH C18 column, 300 A, 1.7 μm, 75 μm × 150 mm) which was temperature controlled at 40°C. The flow rate was set as 400 nL/min. A 60-min gradient of buffer A ($2\%$ ACN, $0.1\%$ formic acid) and buffer B ($0.1\%$ formic acid in ACN) was used for separation: $1\%$ buffer B at 0 min, $5\%$ buffer B at 1 min, $45\%$ buffer B at 35 min, $99\%$ buffer B at 37min, $99\%$ buffer B at 40 min, $1\%$ buffer B at 40.1 min, and $1\%$ buffer B at 60 min. Data were acquired with the TripleTOF 6600 mass spectrometer using an ion spray voltage of 2.3kV, GS1 5 psi, GS2 0, CUR 30 psi and an interface heater temperature of 150°C. Mass spectra was recorded with Analyst TF 1.7 software in the IDA mode. Each cycle consisted of a full scan (m/z 400-1600) and fifty information dependent acquisitions (IDAs) (m/z 100-1800) in the high sensitivity mode with a 2+ to 5+ charge state. Rolling collision energy was used.
Data files were submitted for simultaneous searches using Protein Pilot version 5.0 software (Sciex) utilizing the Paragon and Progroup algortihms and the integrated false discovery rate (FDR) analysis function. MS/MS data was searched against the customized human TRPC6 protein database. Trypsin/LysC was selected as the enzyme. Carbamidomethylation was set as a fixed modification on cysteine. HexNAc emphasis was chosen as a special factor. Other search parameters include instrument (TripleTOF 6600), ID Focus (Biological modifications), search effort (Thorough), false discovery rate (FDR) analysis (Yes), and user modified parameter files (No). The proteins were inferred based on the ProGroupTM algorithm using ProteinPilot software. The detected protein threshold in the software was set to the value which corresponded to $5\%$ FDR. Peptides were defined as redundant if they had identical cleavage site(s), amino acid sequence, and modification. All peptides were filtered with confidence to $5\%$ FDR, with the confidence of HexNAc sites automatically calculated. Each of the HexNAc modification sites (>$95\%$ confidence) was then manually confirmed and annotated (Ma and Hart, 2017).67
## Electrophysiology studies – Patch clamp
Patch clamp studies were done as previously described protocol (Galvis-Pareja et al., 201471). Briefly, HEK-293T cells were plated in glass coverslips in a 24 well plate and transfected with 1 μg of TRPC6 channel plasmids (WT, T221A and P112Q) using Xfect transfection reagent according to manufacturer’s protocol (Takara Bio 631318). Cells expressing wildtype and mutant channels were identified by YFP fluorescence. The bath solution was 140mM NaCl, 5mM CsCl2, 1 mM MgCl2, 10 mM HEPES, and 10 mM glucose with pH of 7.4. Borosilicate glass capillary pipettes (World Precision Instr.) were used with ∼3MΩ resistance when filled with solution containing 5mM NaCl, 40 mM CsCl2, 80mM Cs-glutamate, 5mM Mg-ATP, 5 mM EGTA, 1.5 CaCl2 (free calcium concentration was 100 nM). Currents were obtained using a voltage step-pulse protocol or ramp protocol from -100 mV to +100 mV applied every 2s for 500 ms from holding potential of -60 mV. Current recordings were in a whole-cell configuration using Axopatch 200A amplifier (Axon Instruments, Molecular Devices).
## Neonatal Rat ventricular myocyte isolation and adenoviral transfection
Neonatal Rat cardiomyocytes (NRVMs) were freshly isolated as previously described (Mishra et al., 202172) and cultured at 1 million cells per well in six-well plates for 24 h in DMEM with $10\%$ FBS and $1\%$ penicillin/streptomycin. Adenoviruses were developed expressing the GFP-tagged wild-type sequence of human TRPC6 or TRPC6 T221A. NRVMs were infected with an MOI of 10 with the respective viruses for 48 h before performing the downstream assays.
## RNA isolation and gene expression analysis
Total RNA from NRVMs was extracted using Trizol Reagent (Cat. No. 15596026, Invitrogen, Thermofisher, USA) per manufacturer’s instructions. High-Capacity RNA-to-cDNA Kit (Cat. No. 4388950, Applied Biosystems, Thermofisher, USA) was used to reverse transcribe the RNA into cDNA as described before (Mishra et al., 202172). Quantitative real time PCR analysis was carried out using TaqMan specific primers for: Trpc6, Nppa, Nppb, Myh7, Rcan1, and Gapdh. Primer information are displayed in the key resources table. The threshold cycle (Ct) values were determined by crossing point method and normalized to GAPDH (Applied Biosystems) values for each run.
## Protein degradation measurement
A fluorescent microplate-based assay was used to measure TRPC6-WT versus TRPC6-T221A-GFP signal intensity decay. HEK-293T cells seeded on 96 well plates were transfected with 0.1 μg of the respective plasmids. 24 h after transfection, cells were treated with cycloheximide (100 μg/mL), and fluorescence intensities of the wells were measured at the indicated time points. T221A-GFP decline was measured in cycloheximide treated cells in the presence or absence of proteasome inhibitor MG132 (10 μM). For western blot experiments, cells were plated in 6 well dishes and transfected with 5μg plasmids per well. 24 h after transfection, HEK cells were incubated with 100 μg/mL of cycloheximide. Cells were harvested using100 μL RIPA cell lysis buffer at the indicated time points as shown in Figure 4D, followed by SDS-PAGE and western blotting to visualize TRPC6 T221A protein levels.
## Statistical analysis
Statistical analysis was performed using Prism Ver 9.3.1. All of the individual tests used for each datafigure in the study are provided in their respective figure legend along with sample size per group. For multiple groups, 1-way ANOVA, a Welch ANOVA (if test for variance difference between groups was positive), or non-parametric Kruskal Wallis (if non-normally distributed) was used. A 2-Way ANOVA was also used in some testing as indicated. Two-group comparisons used non-parametric tests (Mann Whitney U test). All precise p values are provided for statistical testing in the figures and/or legends.
## Supplemental information
Document S1. Figures S1–S4
## Data and code availability
All data reported in this paper will be shared by the lead contact upon request. This paper does not report original code.
## Author contributions
S.M. performed the majority of the experiments, data analysis, figure and manuscript preparation; D.M. performed a number of cell and molecular assays for the study, J.M. performed the mass spectrometry studies and contributed to manuscript editing; M.S. generated the site mutation plasmid vectors, F.F. performed the patch clamp electrophysiological studies and analysis; R.C.P. performed the molecular structural analysis and interacting residue predictions; M.J.R. assisted with molecular assays and editorial input into the manuscript; N.Z. provided expertise in O-GlcNAc biology and experimental design and interpretation; and D.A.K. supervised the project, provided input into study design, data analysis, and edited the manuscript.
## Declaration of interests
D.A.K. receives grant support from Boehringer Ingelheim pursuing the use of a selective TRPC6 antagonist for treatment of Duchenne Muscular Dystrophy.
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|
---
title: Deep Proteome Profiling of White Adipose Tissue Reveals Marked Conservation
and Distinct Features Between Different Anatomical Depots
authors:
- Søren Madsen
- Marin E. Nelson
- Vinita Deshpande
- Sean J. Humphrey
- Kristen C. Cooke
- Anna Howell
- Alexis Diaz-Vegas
- James G. Burchfield
- Jacqueline Stöckli
- David E. James
journal: 'Molecular & Cellular Proteomics : MCP'
year: 2023
pmcid: PMC10014311
doi: 10.1016/j.mcpro.2023.100508
license: CC BY 4.0
---
# Deep Proteome Profiling of White Adipose Tissue Reveals Marked Conservation and Distinct Features Between Different Anatomical Depots
## Body
White adipose tissue is one of the most adaptive tissues in mammals and can expand to account for greater than $70\%$ of total body mass in extreme cases of sustained positive energy balance. Adipose tissue expandability is crucial to accommodate the storage of excess lipids in times of plenty and mobilize nutrients for use by tissues throughout the body in times of limited food availability. However, in the case of sustained positive energy balance, adipose tissue stores can be overwhelmed, resulting in spill over and accumulation of harmful ectopic lipids in other tissues such as cardiovascular tissue, skeletal muscle, liver, and the pancreas. Intriguingly, in humans, there is a strong relationship between visceral adiposity and metabolic disease risk [1]. Conversely, individuals with a preponderance of subcutaneous fat are often protected from metabolic disease. Many theories have been proposed to explain these observations. Subcutaneous fat has higher neural innervation and, as a consequence, is enriched with “beige” adipocytes, which have elevated thermogenic capacity and so are protective against excess weight gain [2, 3, 4]. Visceral fat, on the other hand, has higher infiltration of immune cells such as macrophages particularly in response to obesogenic environments [2, 5], which is associated with systemic inflammation and insulin resistance in other tissues [6]. Therefore, it will be critical to understand the molecular underpinnings that confer depot specificity.
Gene expression analysis has yielded important insights into key differences between adipose depots [7, 8]; however, mRNA and protein levels display considerable discordance [9]. Thus, it is critical to investigate adipose tissue composition at the proteome level to uncover depot-specific biology. Several studies have explored white adipose tissue proteomes in either humans who display distinct metabolic phenotypes [10, 11, 12] or in mice that were exposed to different environments such as cold exposure [13], diet [14, 15]), or aging [16]. However, these studies have examined adipose tissue rather than adipocytes. Recently, two studies interrogated adipocyte proteomes from distinct anatomical adipose depots in obese subjects and found protein signatures dictated by depot [17, 18]. To our knowledge, our study is the first to examine adipose tissue and adipocytes from distinct depots in both the lean and obese states in a pair-wise manner.
It is unclear which aspects of the proteome define adipocytes from different depots in a lean, healthy context, or how different adipocyte proteomes adapt to obesogenic conditions. One possibility is that the proteomes from adipocytes from different depots are highly conserved and functional differences may be conferred by interactions with the microenvironment established by the surrounding stromal vascular cells. Since adipose tissue is a milieu of many cell types including fibroblasts and immune cells, it is crucial to compare both the adipocyte and whole tissue proteomes to link molecular differences between depots to physiologic consequences. Here we report a deep proteomics analysis of different depots using both whole tissue and isolated adipocytes from lean and diet-induced obese mice by Western diet (WD) feeding. In lean mice, we discovered that only $3\%$ of the adipocyte proteome differs between the two depots, and we revealed that adipocytes from subcutaneous adipose tissue (SAdis) had enhanced capacity for catabolic processes, while adipocytes isolated from visceral adipose tissue (VAdis) were equipped for lipid storage and cell expansion. WD caused a greater divergence in the adipocyte proteomes, with the greatest changes occurring in visceral adipocytes and tissue compared to subcutaneous, including immune cell infiltration, downregulation of ‘adipocyte’ processes such as glucose metabolism, and upregulation of ‘fibroblastic’ processes including collagen deposition. Furthermore, we uncovered a pro-apoptotic proteomics signal in the VAdi after WD feeding pointing to severe mitochondrial dysfunction in these adipocytes. These data provide an invaluable adipose-centric resource for the metabolic research community by highlighting both similarities and key differences that emerge between the biology of each adipose depot, and how each depot adapts to overnutrition.
## Abstract
White adipose tissue is deposited mainly as subcutaneous adipose tissue (SAT), often associated with metabolic protection, and abdominal/visceral adipose tissue, which contributes to metabolic disease. To investigate the molecular underpinnings of these differences, we conducted comprehensive proteomics profiling of whole tissue and isolated adipocytes from these two depots across two diets from C57Bl/6J mice. The adipocyte proteomes from lean mice were highly conserved between depots, with the major depot-specific differences encoded by just $3\%$ of the proteome. Adipocytes from SAT (SAdi) were enriched in pathways related to mitochondrial complex I and beiging, whereas visceral adipocytes (VAdi) were enriched in structural proteins and positive regulators of mTOR presumably to promote nutrient storage and cellular expansion. This indicates that SAdi are geared toward higher catabolic activity, while VAdi are more suited for lipid storage. By comparing adipocytes from mice fed chow or Western diet (WD), we define a core adaptive proteomics signature consisting of increased extracellular matrix proteins and decreased fatty acid metabolism and mitochondrial Coenzyme Q biosynthesis. Relative to SAdi, VAdi displayed greater changes with WD including a pronounced decrease in mitochondrial proteins concomitant with upregulation of apoptotic signaling and decreased mitophagy, indicating pervasive mitochondrial stress. Furthermore, WD caused a reduction in lipid handling and glucose uptake pathways particularly in VAdi, consistent with adipocyte de-differentiation. By overlaying the proteomics changes with diet in whole adipose tissue and isolated adipocytes, we uncovered concordance between adipocytes and tissue only in the visceral adipose tissue, indicating a unique tissue-specific adaptation to sustained WD in SAT. Finally, an in-depth comparison of isolated adipocytes and 3T3-L1 proteomes revealed a high degree of overlap, supporting the utility of the 3T3-L1 adipocyte model. These deep proteomes provide an invaluable resource highlighting differences between white adipose depots that may fine-tune their unique functions and adaptation to an obesogenic environment.
## Graphical Abstract
## Highlights
•Adipocyte proteomes were highly conserved between white depots in lean mice.•Sustained obesogenic environment caused mitochondrial stress in visceral adipocytes.•Subcutaneous adipose tissue adaptations could not be detected at the adipocyte level.•3T3-L1 total proteome was a good representation of white adipocytes from lean mice.
## In Brief
Madsen et. al measured deep proteomes of visceral and subcutaneous adipose tissue and isolated adipocytes in a lean and obesogenic state. In the lean state, the adipocyte proteomes from distinct depots were remarkably similar. A sustained obesogenic diet caused the proteomes to diverge, driven by the visceral adipocytes which presented a maladaptive mitochondrial stress signature. Comparing dietary changes in tissue and adipocytes revealed that changes to the subcutaneous adipocyte proteome could not explain whole tissue changes within the depot.
## Animals
C57BL/6J male mice were obtained from Australian BioResources (Moss Vale). Mice were maintained at 23 °C on a 12-h light-dark cycle and ad libitum access to food and water. From weaning, mice were fed standard laboratory chow [containing $12\%$ calories from fat, $65\%$ calories from carbohydrate, $23\%$ calories from protein (‘Irradiated Rat and Mouse Diet’, Specialty Feeds)]. For WD studies, mice were fed a diet made in-house containing $45\%$ fat, $35\%$ carbohydrate, and $20\%$ protein as we have described [19] for 9 months from 11 to 14 weeks of age. Mice were weighed once per week. Body composition was analyzed at 43 weeks of age using quantitative magnetic resonance technology (EchoMRI Body Composition Analyser, EchoMRI). For glucose tolerance tests, 43-week-old mice were fasted for 6 h and received an oral bolus of glucose (1 g/kg lean mass). Blood was sampled from the tail vein at indicated time points using an Accu-Check II glucometer (Roche Diagnostics). For insulin measurements, whole blood was collected from the tail vein at basal and 15 min after oral glucose into wells of commercially available ELISA kit (Crystal Chem) containing sample buffer, then the assay was carried out according to the manufacturer’s specifications. Results were multiplied by a factor of 2 to estimate the concentration of insulin in the plasma. For histological assessment of white adipose tissue, 8- to 10-week-old C57BL/6J mice were fed either chow or WD for 8 weeks. Experiments were performed in accordance with NHMRC (Australia) guidelines approved by The University of Sydney Animal Ethics Committee (#$\frac{2014}{694}$ ethics protocol covered animal for proteomics and #$\frac{2017}{1274}$ for animals used for histology).
## Tissue Collection and Primary Adipocyte Isolation
Mice were sacrificed by cervical dislocation. The epididymal fat pad was taken as ‘visceral’ adipose and was excised carefully to avoid the testes. The inguinal fat pad was taken as ‘subcutaneous’ adipose, from which lymph nodes were removed following excision. Fat pads were rinsed in PBS and either snap frozen in liquid nitrogen or stored in fresh Hepes buffer (120 mM NaCl, 30 mM Hepes, 10 mM NaHCO3, 5 mM glucose, 4.7 mM KCl, 2 mM CaCl2, 1.18 mM KH2PO4, 1.17 mM MgSO4.7H2O, $1\%$ BSA, pH 7.4) for immediate adipocyte isolation. The two diet groups were subdivided into three groups each and pooled for the isolation of primary adipocytes. Each group of mice [chow 1 ($$n = 4$$), chow 2 ($$n = 4$$), chow 3 ($$n = 3$$); WD 1 ($$n = 5$$), WD 2 ($$n = 5$$), WD 3 ($$n = 5$$)] represents one pooled data point for proteomics analysis. Adipose tissues were minced in HEPES buffer until pieces were approximately <1 mm2 in size. Collagenase (Type I, Worthington) was added at 0.5 mg/ml for visceral and 1 mg/ml for subcutaneous adipose tissue and digested for 1 h at 37 °C. Samples were passed through a 250 μm (chow adipocytes) or 300 μm (WD adipocytes) nylon mesh (Spectrum Labs) and washed three times with HES buffer (250 mM sucrose, 20 mM Hepes, 1 mM EDTA, pH 7.4). Between washes, adipocytes were left to float. Lysis buffer [$2\%$ sodium deoxycholate (Sigma), 200 mM Tris HCl, pH 8.5] was added and stored at −80 °C until further processing.
## Cell Culture—3T3-L1 Adipocytes
3T3-L1 fibroblasts (a gift from Howard Green, Harvard Medical School) were grown in Dulbecco’s modified Eagle’s medium (DMEM) containing $10\%$ fetal bovine serum (FBS) (Sigma) and 2 mM GlutaMAX (Gibco) in $10\%$ CO2 at 37 °C. For differentiation into adipocytes, cells were re-seeded and rapidly grown to confluence within 24 h, then treated with DMEM/FBS containing 4 μg/ml insulin, 0.25 mM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine, and 100 ng/ml d-biotin. After 72 h, the differentiation medium was replaced with fresh FBS/DMEM containing 4 μg/ml insulin and 100 ng/ml d-biotin for a further 3 days, then replaced with fresh FBS/DMEM. Adipocytes were re-fed with FBS/DMEM every 48 h and used for experiments 10 days after initiation of differentiation. Cells were routinely tested for mycoplasma. Prior to harvesting, 3T3-L1 adipocyte cell monolayers were washed 5× with ice-cold PBS.
## Sample Preparation—3T3-L1 Adipocytes, Adipocyte, and Adipose Tissue Proteomes
Adipocytes were processed according to the in-StageTip protocol [20]. Briefly, samples were lysed in an equal volume of SDC lysis buffer ($2\%$ sodium deoxycholate (Sigma), 200 mM Tris HCl, pH 8.5) with boiling at 95 °C for 5 min with mixing at 1000 rpm on a ThermoMixer (Eppendorf), cooled on ice, sonicated using a tip probe sonicator (1 × 20 s, $90\%$ output), then spun at 21,000g for 15 min at 4 °C. For tissue samples, 100 to 600 mg tissue was added to 1.5 ml lysis buffer, lysed using a tip probe sonicator (4-5 × 20 s, $90\%$ output), and spun at 21,000g for 30 min at 4 °C. After centrifugation, fat layers were carefully removed and discarded, and an aliquot was taken from which protein quantification performed using the Pierce BCA Protein Assay (Thermo Fisher Scientific). 60 μg of protein was extracted into clean tubes and samples topped with lysis buffer to obtain equal volumes. Proteins were reduced and alkylated with the addition of TCEP (Thermo Fisher Scientific) and CAA (Sigma) to final concentrations of 10 mM and 40 mM respectively at 95 °C for 5 min at 1000 rpm on a ThermoMixer. Trypsin (Sigma) and LysC (Wako) were added in ratio of 1 μg enzyme to 50 μg protein, and samples digested at 37 °C overnight for 18 h with mixing at 2000 rpm on a ThermoMixer. Digested peptides were diluted 1:1 with water and then desalted on SDBRPS StageTips as follows. Samples were diluted $50\%$ with $99\%$ EA (ethyl acetate)/$1\%$ TFA (trifluoracetic acid), vortexed thoroughly, and loaded onto StageTips packed with 2× disks SDBRPS material (3M Empore). StageTips were washed 1× with 100 μl $99\%$ ethyl acetate/$1\%$ TFA, and 2× with 100 μl $0.2\%$ TFA, then eluted with $5\%$ ammonia/$80\%$ ACN (acetonitrile) and dried in a vacuum concentrator (Eppendorf) prior to fractionation.
## Offline Peptide Fractionation by StageTip-Based SCX (Adipocyte and Adipose Tissue Samples)
Peptides derived from mouse adipocyte and adipose tissue samples were separated into three fractions using StageTip-based SCX fractionation [21]. Briefly, approximately 30 μg of peptides were resuspended in $1\%$ TFA and loaded onto StageTips packed with 6× disks of SCX material (3M Empore). Peptides were eluted and collected separately with increasing concentrations of ammonium acetate (150 mM and 300 mM) in $20\%$ ACN, followed by $5\%$ ammonia/$80\%$ ACN. Collected peptide fractions were dried in a vacuum concentrator (Eppendorf) and resuspended in MS loading buffer ($2\%$ ACN/$0.3\%$ TFA).
## Offline Peptide Fractionation by High pH Reversed-Phase HPLC (3T3-L1 Adipocytes)
Peptides derived from 3T3-L1 adipocytes were separated into 24 fractions using concatenated high pH reverse phase fractionation, as previously described [22]. Briefly, peptides were fractionated using an UltiMate 3000 HPLC (Dionex, Thermo) with a XBridge Peptide BEH C18 column, (130A°, 3.5 mm 2.1 3250 mm, Waters). 30 μg of peptides were resuspended in buffer A and loaded onto the column that was maintained at 50 °C using a column oven. Buffer A comprised 10 mM ammonium formate/$2\%$ ACN and buffer B 10 mM ammonium formate/$80\%$ ACN, and buffers were adjusted to pH 9.0 with ammonium hydroxide. Peptides were separated by a gradient of 10 to $40\%$ buffer B over 4.4 min, followed by 40 to $100\%$ buffer B over 1 min. Peptides were collected for a total duration of 6.4 min, with 72 fractions concatenated directly into 24 wells of a 96-well deep-well plate (three concatenated fractions per well) using an automated fraction collector (Dionex, Thermo) maintained at 4 °C. After fraction, samples were dried down directly in the deep-well plate and resuspended in MS loading buffer ($2\%$ ACN/$0.3\%$ TFA) prior to LC-MS analysis.
## Mass Spectrometry Analysis—Adipocyte and Adipose Tissue Proteomes
For the mouse adipocyte and adipose tissue proteomes, peptides were analyzed by mass spectrometry using a Dionex Ultimate 3000 UHPLC coupled to a Q Exactive Plus benchtop Orbitrap Mass Spectrometer (Thermo Fisher Scientific). Peptides were loaded onto an in-house packed 75 μm ID x 50 cm column packed with 1.9 μm C18 material (Dr Maisch, ReproSil Pur C18-AQ) and separated with a gradient of 5 to $30\%$ ACN containing $0.1\%$ FA over 95 min at 300 nl/min, and column temperature was maintained at 60 °C with a column oven (Sonation). MS1 scans were acquired from 300 to 1650 m/z (35,000 resolution, 3e6 fill target, 20 ms maximum fill time), followed by MS/MS data-dependent acquisition of the top 15 ions using high-energy dissociation, with MS2 fragment ions read out in the Orbitrap (17,500 resolution, 1e5 AGC, 25 ms maximum fill time, 25 NCE, 1.4 m/z isolation width).
## Mass Spectrometry Analysis—3T3-L1 Adipocyte Proteomes
For the 3T3-L1 proteome samples, peptides were analyzed by mass spectrometry using a Dionex Ultimate 3000 coupled to a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific). Peptides were loaded onto an in-house packed 75 μm ID x 50 cm column packed with 1.9 μm C18 material (DrMaisch, ReproSil Pur C18-AQ) maintained at 60 °C with a column oven (Sonation). Peptides were eluted with a gradient of 5 to $30\%$ buffer B ($80\%$ ACN/$0.1\%$ formic acid) over 40 min at a flow rate of 300 nl/min and analyzed by data-dependent acquisition with one full scan (350–1400 m/z; $R = 60$,000 at 200 m/z) at a target of 3e6 ions, followed by up to 20 data-dependent MS2 scans using high-energy dissociation (target 1e5 ions; max. IT 28 ms; isolation window 1.4 m/z; NCE $27\%$; min. AGC target 1e4), detected in the Orbitrap mass analyzer ($R = 15$,000 at 200 m/z). Dynamic exclusion (15 s) was switched on.
## Data Processing
Raw data were processed using MaxQuant (version 1.5.9.1) [23]. The built-in Andromeda search engine scored MS2 spectra against fragment masses of tryptic peptides that were derived from a mouse reference proteome containing 58,268 entries including isoforms (UniProt January $\frac{2016}{01}$ release) and a list of 245 potential contaminant proteins. The search space was restricted to peptides with a minimum length of seven amino acids, a maximum peptide mass of 4600 Da, and two missed cleavage sites. Carbamidomethylation of cysteine was specified as a fixed modification, and methionine oxidation and acetylation at protein N termini as variable modifications. MaxQuant uses individual peptide mass tolerances, while initial maximum precursor mass tolerances were 20 ppm in the first search, and fragment ion mass tolerances were set to 20 ppm. False discovery rate was controlled using a target-decoy approach at <$1\%$ for peptide spectrum matches and <$1\%$ for protein group identifications. Match between runs was enabled to facilitate the transfer of MS/MS identifications between equivalent and adjacent fraction measurements. The MaxLFQ algorithm was employed for label-free protein quantification as described [24] and MS/MS was required for all LFQ comparisons.
Intensity values were normalized using total reporter area sum normalization. Data were filtered to remove contaminants and proteins that were not quantified in any sample (supplemental Tables S1 and S2). Protein intensities were median normalized to account for differences between protein loading of tissue and cell-based samples (supplemental Fig. S1A).
## Network Analysis
To find associations between lipolysis, lipids synthesis, and glucose uptake, STRING: Pubmed query in Cytoscape (v3.8.2) was used to identify the top 50 proteins within each term with a confidence score of 0.7 or greater. These three networks were then merged and filtered for differentially regulated protein for both VAdi and SAdi.
## Estimating Cell Type Proportions in Whole Adipose Tissue Proteomics
The R package BisqueRNA [25] was used for reference-based decomposition of the whole tissue proteomics data with ‘marker = NULL’ and ‘use.overlap = FALSE’. As reference, we utilized murine single RNA sequencing data [26]. Seurat formatted data were downloaded (https://gitlab.com/rosen-lab/white-adipose-atlas), and data from all male mice were selected as input for cell type estimation.
## Gene Set Enrichment Analysis
Gene set enrichment was performed with the web-based GEne SeT AnaLysis Toolkit [27] with the minimum number of genes in a pathway specified as 15 and maximal as 200 within the Gene Ontology Resource or using the Reactome database (reactome.org). Pathways with $p \leq 0.05$ false discovery rate were considered to be significantly overrepresented.
## Experimental Design and Statistical Rationale
The aim of this study was to examine adipose depot adaptations to a sustained obesogenic challenge. To this end, the duration of feeding was selected based on prior time course data in C57Bl6/J fed similar diets [28]. Adiposity reached a plateau at approximately 40 weeks (10 months) of WD feeding without changes to lean mass, indicating the time at which adipose storage capacity is exceeded. Therefore, we selected 9 months of WD feeding for this study to maximize WD expansion but before complete exhaustion of adipose fat storage capacity. To achieve measurement of the adipocyte proteome, the biological replicates analyzed comprised pooled isolated adipocytes from three to five mice. Pooling was necessary to ensure sufficient protein yields for proteomics analysis. “ Enrichment” was considered at a fold change of ±2 and $p \leq 0.05$ unless otherwise stated. For histology, each group contained five animals, except for visceral adipose tissue (VAT) from chow fed animals, which contained three animals.
## General Data Analysis
Bioinformatics, statistical analyses, and plot generation were performed within the R statistical environment. Differential expression analysis was performed using the LIMMA package [29]. Two-way ANOVAs were performed using a standard linear model function. All p values were adjusted for multiple testing using the Benjamini & Hochberg or Tukeys HSD method.
## Adipose Tissue Histology and Adipocyte Area
Formalin-fixed epididymal adipose tissue was paraffin embedded, sectioned, mounted on coverslips, and stained with H&E. Coverslips were scanned to digital images using an Aperio ScanScope. Adipocyte cell area was then analyzed in ImageJ version 1.51 as described in [19] with the following modifications. Images were converted to 8 bit, and the threshold was adjusted so cell membranes were identified as signal, and cell contents were identified as background in ImageJ, version 1.51. Identification of cell membranes was performed in Ilastik (version 1.3.3) using supervised machine learning-based image segmentation. A sample of the images to be analyzed were selected as the training set and the object classification workflow was used to label regions of the images as object types (cell membrane, cytoplasm, or artifact). Using the uncertainty overlay, areas of high uncertainty were defined until the prediction layer showed satisfactory identification of the object types. The trained classifier was then run on all images, and the object classification data were saved as simple segmentations of the original image in black (cytoplasm) and white (cell membranes). In ImageJ, the “Analyze Particles” built-in function was then applied to calculate cell areas with a defined size range of 400 to 80000 μm2 and a circularity range of 0.40 to 1.00. An entire cross section for each mouse was analyzed so that >5000 adipocytes were quantified per mouse.
## Comprehensive Deep Proteome Analysis of Depot Specific Adipose Tissue and Adipocytes
We employed a deep proteomic analysis of whole-tissue and isolated adipocytes from subcutaneous and visceral white adipose depots of middle-aged C57Bl/6J mice fed chow or WD for 9 months. WD-fed mice ($$n = 11$$) were obese and glucose intolerant compared to chow fed animals ($$n = 15$$) (supplemental Fig. S1. B–F). The cohort was divided into three biological replicates from each diet matched by metabolic parameters, allowing comparison of adipose tissue and adipocyte proteomes from two white adipose depots across two diets (Fig. 1A). A total of 7655 protein groups were quantified in at least one sample, and over half of the proteome (4178 protein groups) was quantified across all 24 samples (Fig. 1B). After filtering for proteins quantified in $\frac{2}{3}$ biological replicates within depot and diet, we quantified 6580 and 5507 proteins groups in the adipocyte or whole tissue proteomes, respectively. This difference can partly be explained by a greater missingness in whole tissue proteomes, where missing values ranged from 16 to $28\%$ compared to 10 to $14\%$ in the adipocyte proteomes (supplemental Fig. S1G). The overlap between the adipocyte and tissue proteomes was 5129 proteins (Fig. 1B). We were initially surprised by this high degree of overlap between the adipocyte and whole-tissue proteomes, particularly since the adipocyte fractions were highly enriched as described below. We reasoned that this might reflect the fact that most cells express a core set of proteins that encode generic functions, and such proteins represent a large proportion of all total cellular proteomes. To address this, we next compared the adipocyte proteome with deep proteomes from hepatocytes [30] and pancreatic islets [22]. Consistent with our speculation, there was a high degree of overlap between these proteomes and the adipocyte proteome (Fig. 1C) supporting the notion that distinct cell types do share a substantial proportion of their proteomes. Protein abundances spanned six orders of magnitude (Fig. 1D), making this proteome coverage the deepest reported for adipose tissue or adipocytes. Biological replicates were tightly correlated, with correlation coefficients above 0.93 (supplemental Fig. S1H). Principal component analyses revealed clustering of replicates, which separated by sample type (whole tissue and adipocytes) in principal component (PC) 1 and by diet in PC2 (Fig. 1E), highlighting a distinction between the whole tissue and adipocyte proteomes. Adipocyte-specific markers and lipid droplet proteins, such as PLIN1, perilipin-4, hormone-sensitive lipase, adipose triglyceride lipase (ATGL), and the insulin-dependent glucose transporter GLUT4 were enriched in the adipocyte proteome by up to 5-fold (Fig. 1F). This indicates the high degree of purity of our adipocyte preparation as previous studies have suggested that adipocytes comprise only around $30\%$ of all cells found in adipose tissue [31]. Overall, ∼1500 proteins showed >2 fold increase in the adipocyte proteomes. These proteins were enriched for adipocyte-specific functions such as lipid and amino acid metabolism, as well as mitochondrial processes. Furthermore, PLIN1, fatty acid-binding protein (FABP4), adiponectin (ADIPOQ), and hormone-sensitive lipase were ranked in the top $3\%$ most abundant adipocyte proteins. Fig. 1Deep proteomics coverage of adipose tissue and isolated adipocytes. A, schematic workflow for adipose tissue processing and proteomic analysis. B, quantification of proteins across proteomes. C, overlap between the adipocyte proteome and isolated pancreatic islets and primary hepatocytes. D, dynamic range of quantified proteins across all samples, based on intensity based absolute quantification (iBAQ). E, principal component analysis of across all samples. F, protein abundance of adipocyte markers in whole-tissue and isolated adipocytes from two visceral and subcutaneous adipose tissue from chow-fed animals. ATGL, adipose triglyceride lipase; ADIPOQ, adiponectin; FABP4, fatty acid-binding protein; HSL, hormone-sensitive lipase; PLIN, perilipin; V, visceral; S, subcutaneous.
## Adipocytes From Different Depots are Strikingly Similar Under Chow Conditions
In chow-fed mice, we observed striking concordance between subcutaneous and visceral adipocyte (SAdi and VAdi, respectively) proteomes ($R = 0.98$; Fig. 2A). Remarkably, just $3\%$ of the total proteome was different between adipocytes from the two depots, such that 146 proteins were exclusively identified in adipocytes from one depot, and an additional 89 proteins were differentially expressed (Fig. 2B). Gene set enrichment analysis revealed that the most defining depot-specific features were an enrichment of select mitochondrial proteins in SAdi, particularly complex I of the electron transport chain and cytochrome complex assembly proteins, and an enrichment of cytoskeletal proteins in VAdi (Fig. 2C). These processes likely reflect differences in cell morphology and metabolic demand between the two depots. Fig. 2Comparison of isolated adipocytes from subcutaneous and visceral depots from chow fed animals. A, label-free quantitation (LFQ) of visceral adipocyte (VAdi) versus subcutaneous adipocyte (SAdi) proteomes. B, differences in protein abundances between VAdi and SAdi. C, fold-changes of proteins associated with mitochondrial complex I and cytoskeletal remodeling. D–G, protein abundances of selected proteins. MBP, myelin basic protein; MPZ, myelin protein zero.
We identified 77 proteins exclusively expressed in SAdi, and only an additional 38 proteins that were significantly enriched compared to VAdi. Two proteins involved in calcitonin-like ligand receptors signaling, receptor activity-modifying protein 2 (RAMP2) and calcitonin gene-related peptide type 1 receptor (CALCRL), were quantified exclusively in SAdi. RAMP2 is a membrane-spanning protein that interacts with the receptor CALCRL enabling higher affinity binding of adrenomedullin (ADM) over calcitonin [32]. We also observed that the orphan G protein–coupled receptor GPR182, which has been suggested although not confirmed to be part of the ADM signaling apparatus [33, 34], was only present in SAdi. These findings support previous evidence that ADM regulates adipocyte beiging [35, 36]. Consistent with the unique role of SAdi in beiging, we observed expression of a number of beiging-related proteins, such as CREB regulated transcription coactivator 3 [37, 38], exclusively in SAdi. UCP1, a classic marker for adipocyte beiging, trended to be enriched in SAdi versus VAdi, although not significantly (Fig. 2E), likely because the mice from which these adipocytes were obtained were housed at room temperature. Strikingly, markers of adipose tissue myelinated neurons myelin protein zero and myelin basic protein [39] were 320- and 21-fold higher in the SAdi proteome, respectively, highlighting greater neural innervation to SAdi (Fig. 2D), which is a major driver of adipocyte beiging [40].
We identified 69 proteins exclusively expressed in VAdi, and an additional 51 proteins enriched in adipocytes from this depot. This included the two ‘atypical’ subunits of extracellular matrix (ECM) protein collagen VI, collagen alpha-5(VI) chain (COL6a5), and alpha-6(VI) chain (COL6a6), which were 28- and 5-fold higher in VAdi compared to SAdi, respectively (Fig. 2F). Interestingly, the typical collagen VI isoforms (COL6a1, COL6a2, and COL6a3) were not different between SAdi and VAdi (Fig. 2F). This suggests that there is a unique ECM composition between these different adipocytes even under chow-fed conditions, which involves the atypical, but not the typical, collagen VI isoforms. Also noteworthy, cyclin-dependent kinase 6 (CDK6) was 15-fold more abundant in VAdi (Fig. 2G). CDK6 has been associated with expansion of adipocyte size [41] and suppression of adipose beiging [42] in part via crosstalk with the nutrient sensor mammalian target of rapamycin (mTOR). In line with this, sodium-coupled neutral amino acid transporter 9, which is a lysosomal amino acid transporter that communicates amino acid availability to mTOR to induce its activity [43, 44], was quantified exclusively in VAdi. These data indicate that these processes may operate synergistically to promote hypertrophy of VAdi during tissue expansion. To further investigate adipocyte size between the two different depots, we performed histological examination of fixed visceral and subcutaneous adipose depots in C57Bl/6J mice fed chow or WD for 8 weeks (Fig. 3, A and B). This revealed that VAdi were significantly larger than SAdi in chow-fed mice and, upon WD feeding, underwent a 2.6-fold expansion in area compared to only a 1.7-fold expansion in SAdi, which supports the hypothesis that VAdi are primed to expand via hypertrophy. We next performed targeted analysis of exclusively or differentially expressed proteins between SAdi and VAdi from chow-fed mice in several metabolic pathways integral to adipocyte metabolism. Analysis of the glycolysis pathway revealed that the enolase class of enzymes were enriched in SAdi. ENO1 was exclusive to SAdi, ENO3 was significantly more abundant, and ENO2 trended higher ($$p \leq 0.052$$) in SAdi compared to VAdi. The enolase enzymes catalyze the interconversion of 2-phosphoglycerate and phosphoenolpyruvate in the penultimate step of glycolysis. As there were no other differences in levels of glycolytic proteins, including the insulin-responsive glucose transporter SLC2A4 (GLUT4), this may denote a separate function for enolase in adipocytes, as certain moonlighting functions for enolase have been described including stress response and tissue remodeling [45]. In contrast to differences in glucose metabolism, we observed no significant differences in the levels of proteins involved in lipid synthesis or storage (i.e., CD36, DGAT 1 and 2, LPL, FABP4, LDLR, PLN 1, 2, and 3, ACC1, ACLY, FASN, and SCD2). Finally, both adipocyte types had similar levels of adipokines including adiponectin (ADIPOQ), leptin (LEP) and resistin (RETN). Taken together, this comparison of SAdi and VAdi from lean mice reveals that these adipocyte proteomes are highly conserved, with depot-specific differences including select metabolic processes, extracellular matrix and regulators of cell size. Fig. 3Visceral adipocytes are larger than subcutaneous and show greater expansion with WD feeding. A, representative H&E stained sections of fixed visceral and subcutaneous adipose tissue of male C57Bl/6J mice fed chow or WD for 8 weeks (scale bars represent 100 μm). B, density plots with medians of adipocyte areas pooled by depot and diet groups. SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; WD, Western diet.
## VAT Adipocytes are More Diet Responsive Compared to SAT Adipocytes
We next focused on diet-induced changes by comparing chow versus WD within each adipocyte type. Upon WD feeding, $16\%$ (1046 proteins) of the total proteome changed in VAdi, in contrast to $7\%$ (411 proteins) in SAdi (Fig. 4, A and B). There was striking overlap between the diet-regulated proteins in adipocytes between the different depots ($54\%$ of proteins) (Fig. 4C); thus, this conserved regulation likely defines core adaptive mechanisms across white adipocytes. Upregulated core proteins were enriched for ECM remodeling and organization (Fig. 4D), reflecting adipocyte hypertrophy. The majority of core-regulated ECM remodeling proteins were also regulated in a depot-dependent manner (marked ∗) with greater expression in VAdi. Downregulated core proteins were enriched for fatty acid metabolism, cellular detoxification, and branch chain amino acid catabolism. Coenzyme Q (CoQ) is a mitochondrial cofactor synthesized in mitochondria that is essential for mitochondrial respiration [46] and depletion of the CoQ biosynthesis machinery has been shown in a range of insulin resistance models in adipocytes [47] and, notably, many proteins involved in mitochondrial CoQ biosynthesis (COQ4-7, 9, 10A and 10B) were decreased in adipocytes from both depots (Fig. 4D). Given the prominent role of mitochondria in insulin resistance [48] and the pronounced downregulation of the mitochondrial CoQ biosynthesis pathway (Fig. 4D), we next determine whether diet induced a selective or generalized depletion of mitochondrial proteins. Our adipocyte proteome covered $70\%$ of the mitochondrial proteins described in the MitoCarta3.0 database [49]. Sustained WD induced an overall decrease in mitochondrial protein abundance regardless of depot, with the majority of complex I (NDUFA1-3;5–13, NDUFB2-11, NDUFS1-8, and NDUFV1-3), complex II (SDHA-D), and complex III (Uqcrc1-2;11;B;C:FS1;H) downregulated and, to a lesser extent, complex IV (COX4I1;6A1;6B1) and complex V (ATPC1;5days;5O). Interestingly, a significantly larger proportion of mitochondrial proteins were decreased in VAdi compared to SAdi ($31\%$ versus $15\%$, χ2 test, $p \leq 0.0001$) suggesting that the mitochondrial proteome exhibits differential sensitivity to WD depending on depot (Fig. 4E). The electron transport chain (ETC) complexes reflected this relationship, as there was a striking reduction in proteins related to complex I, II, III, and IV in VAdi after WD relative to SAdi on the same diet (Fig. 4F), suggesting that depletion of ETC subunits may contribute to mitochondrial dysfunction in VAdi. Since adipocyte death may be caused by excessive stress during obesity-related adipose tissue remodeling [50], we next explored the abundance of pro-apoptotic and anti-apoptotic proteins in this dataset. Two stress-associated pro-apoptotic proteins which are localized to the outer mitochondrial membrane, BCL-2–associated X (BAX) and BCL-2-related ovarian killer protein, were regulated in a depot-dependent manner in response to WD. We detected no change in expression of the counter-regulatory anti-apoptotic proteins [B-cell lymphoma 2 (BCL2) or MCL1 apoptosis regulator], indicating the vulnerability of VAdi from WD-fed mice to apoptotic signaling (Fig. 4G). Interestingly, BCL-2–associated X and BCL-2-related ovarian killer protein have a role in mitophagy suggesting a possible interaction between mitophagy and cell death in adipocytes [51]. Expression of the key adipogenic regulator peroxisome proliferator–activated receptor gamma (PPARG) is decreased in hypertrophic adipocytes, which can cause mitochondrial dysfunction [52]. We found that PPARG was regulated in a depot-dependent manner in response to diet, where PPARG was below the limit of detection in VAdi upon WD only, which points toward loss of adipocyte identity in VAdi (Fig. 4H). Furthermore, two targets of PPARG, BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3) and the E3 ubiquitin ligase membrane-associated ring-CH-type finger 5, were regulated in a similar manner (Fig. 4H). Since BNIP3 is required for optimal mitophagy [53], it is possible that BNIP3 downregulation induces the accumulation of dysfunctional mitochondria [52, 54], increasing the susceptibility of VAdi to pro-apoptotic stimuli. Fig. 4Adipocytes from the visceral depot are more diet-responsive than adipocytes from the subcutaneous depot. A, Venn diagram showing differentially regulated proteins in response to WD in isolated adipocytes from the two depots. B, distribution of log2 fold changes in response to WD in visceral and subcutaneous adipocytes (VAdi and SAdi, respectively). C, proteins abundances after WD across all proteins in VAdi (x-axis) and SAdi (y-axis) with correlation coefficient for direction and magnitude of changes. D, Z-scored protein abundances of selected proteins within pathways that change in both depots in response to WD. E, changes in all mitochondrial proteins detected in VAdi or SAdi after WD. F, abundances of proteins in complexes I-VI of the electron transport chain in VAdi after WD relative to SAdi. G–H, label-free quantitation for BCL-2-associated X (BAX), BCL-2-related ovarian killer protein (BOK), apoptosis regulator Bcl-2 (BCL2), *Myeloid leukemia* cell differentiation protein Mcl-1 homolog (MCL1), peroxisome proliferator-activated receptor gamma (PPARG), BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3) and E3 ubiquitin-protein ligase MARCHF5 (MARCHF5). I, Z-scored protein abundances of selected proteins within pathways uniquely changing in VAdi in response to WD. J, protein differences in smooth-muscle proteins in VAdi and SAdi in mice fed either chow or WD. K, Difference in protein abundance of typical smooth muscle proteins in adipocytes from different depots within same diet. C, Chow; W, WD, Western diet.
PPARG is a major regulator of lipid metabolism and glucose homeostasis; therefore, we further interrogated these processes by constructing a protein–protein interaction network of proteins involved in lipolysis, lipid synthesis and storage, and glucose uptake, then filtered these for diet-responsive proteins in VAdi or SAdi (Fig. 4I). Key receptors modulating glucose and fat metabolism, including the insulin receptor (INSR) and the beta-3 adrenergic receptor (ADRB3), were downregulated in adipocytes from both depots after sustained WD feeding. However, several additional key metabolic proteins were downregulated selectively in VAdi, including the lipolytic proteins phosphodiesterase 3B (PDE3B; 3-fold) and ATGL (4-fold), proteins involved in the glucose transport pathways such as GLUT4 (4-fold), RAB10 (2-fold) and TBC1D4 (3-fold). The lipogenic proteins fatty acid synthase (FASN; 4-fold) and acetyl-CoA carboxylase 2 (5-fold) were also downregulated in VAdi, whereas we did not observe diet regulation of fatty acid transport proteins including CD36, FABP4, or LDLR. Further pathway analysis of proteins that were regulated by WD specifically in VAdi revealed a decrease in proteins involved in branched-chain amino acid catabolism and several Rab GTPase proteins and other vesicle trafficking proteins and increased actin polymerization (Fig. 4J). The VAdi from WD mice was also enriched in proteins involved in inflammation and immune-related processes, such as ‘leukocyte proliferation’ and ‘adaptive immune response’. This was surprising, as the adipocytes were isolated by collagenase digestion and flotation. One possibility is that fat-laden immune cells, such as macrophage foam cells, co-fractionate with adipocytes during flotation. It should be noted that similar enrichment of immune-related proteins in VAdi has been reported by others [7]. Our observations in VAdi of increased expression of cytoskeletal and ECM proteins and decreased expression of proteins specific to adipocyte function, together with a decrease in PPARG, reveal a transition of VAdi to a fibroblast-like state.
Interestingly, we identified several proteins enriched in the SAdi proteome from WD-fed animals which are typically associated with smooth muscle function, such as myosin light chain 1 (MYL1), myosin light chain 4 (MYL4), myosin-4 (MYH8), skeletal muscle actin alpha 1 (ACTA1), alpha-actinin-3 (ACTN3), troponin T2 (TNNT2), and the sarcoplasmic/endoplasmic reticulum calcium ATPase 1 (ATP2a1), with differences in expression ranging from 6 to 120-fold compared to VAdi from the same animals. This was specifically an adaptation to WD, as this disparity was not observed between adipocytes from chow-fed animals (Fig. 4K). The enrichment of muscle-associated proteins in SAT is consistent with this depot’s unique Prx1-expressing smooth muscle lineage [55], and this signature resembles that of beige adipocytes residing in SAT [56]. A mechanical actomyosin response is important for adrenergic stimulation of brown adipocytes to maintain their stiffness and uncoupling capacity [57], suggesting this is an important mechanism for SAdi to maintain cytoskeletal stiffness independent of the ECM during nutrient overload. Recently, it was speculated that these proteins are involved in adipose tissue innervation, as overexpression of the fat-derived neurotrophic factor neurotrophin 3 increases adipose tissue innervation and a subsequent increase in striated muscle specific proteins [58], so this may reflect differences in neural innervation between depots to confer beta-adrenergic responsiveness.
## Whole-Tissue Proteomics in VAT, but not SAT, Reflects Changes to the Adipocyte Proteome Upon WD Feeding
Adipose tissue contains numerous cell types in addition to adipocytes [26]. Cell type deconvolution of subcutaneous and visceral depots at the whole-tissue level (SAT and VAT, respectively) utilizing an adipose single nuclei transcriptome database [25, 26] (Fig. 5A) revealed that preadipocytes, macrophages, and adipocytes were the predominant cell types, with the only difference between the depots being significantly higher dendritic cells in VAT. WD was associated with a significant increase in macrophages and a decrease in adipocytes in VAT, but not SAT (Fig. 5A). To more closely assess diet-induced changes in the two tissues, we selected tissue-enriched proteins (>1.5 fold higher expression in tissue, $p \leq 0.01$) or proteins exclusively expressed in tissue versus adipocytes. Of these, we identified 56 tissue-enriched proteins in SAT and 91 in VAT that had increased expression with WD. Seven proteins were upregulated in both tissues, representing conserved mechanisms for adipose tissue adaptation to WD. These included proteins involved in cytoskeletal organization (SSH3, CDAN1, and SCAMP2), ECM formation (PXDN1), estradiol metabolism (CYP1B1), lipoprotein metabolism (APOA4), and a DNA polymerase subunit (POLE3). In SAT, 84 proteins were upregulated specifically in response to WD, with a particular enrichment of the E3 ubiquitin ligase pathway (RNF146, UBE2W, FBXO7, and FBXW17). In VAT, subunit 2 of the NFκB complex was upregulated with WD, as well as BCL10, which is essential for lymphocyte-induced activation of NFkB. NFκB activation in adipose tissue is important for recruitment of immune cells [59, 60], and may represent a depot-specific mechanism for the observed diet-induced recruitment of macrophages in VAT.Fig. 5WD causes a shift in visceral adipose tissue toward macrophage infiltration and fibrosis, and WD adaptations in subcutaneous adipose tissue occur primarily in non-parenchymal cells. A, cell type deconvolution using proteomic data from subcutaneous or visceral whole tissue of mice fed chow (C) or Western diet (W). ∗ indicates significant difference by 2-way ANOVA with Tukey’s post hoc test; $p \leq 0.05.$ B, overlap of proteins changing with Western diet (WD) in isolated adipocytes and whole-tissue in the visceral or subcutaneous depots. C, correlation of changes in protein abundance after WD across all proteins in tissue and adipocytes within each depot. D, Gene ontology of proteins changing with WD in visceral and subcutaneous adipose tissue (VAT and SAT, respectively) (showing pathways which are significant after false discovery correction $p \leq 0.05$). SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue.
Due to the difficulty of isolating primary adipocytes, many studies only examine changes in the whole tissue and extrapolate these to estimate changes in adipocytes. In examining the validity of this approach, we observed that the overlap between diet-responsive proteins from VAdi and VAT was 350 proteins (Fig. 5B). In the case of the visceral depot, the direction and magnitude of changes in response to diet between adipocytes and whole tissue were highly correlated ($R = 0.48$ $p \leq 2.2$e-16) (Fig. 5C), indicating that VAdi adaptations to WD could be reasonably detected at the tissue level. Pathways that were commonly upregulated between VAdi and VAT included cell growth (e.g., pathways associated with mRNA processing and actin filament organization) and immune system (Fig. 5D). Commonly downregulated pathways included fatty acid and lipid metabolic processes, fat cell differentiation, amino acid metabolic processes, and energy-related pathways (Fig. 5D). In the case of the subcutaneous depot, however, only 51 diet-regulated proteins overlapped between SAdi and SAT (Fig. 5B), and there was no correlation in diet response (Fig. 5C). These data suggest that the overall proteomics changes at a tissue level within SAT does not reflect the changes to adipocytes of the same tissue.
## Proteome Comparison of the 3T3-L1 Adipocyte Model With Primary Adipocytes
3T3-L1 adipocytes are the standard model system to study adipocyte biology. Based on the depth of our murine proteome (Fig. 1B), we reasoned it would be valuable to compare these with the 3T3-L1 adipocyte proteome to determine the robustness of this model. To this end, we generated the deepest 3T3-L1 adipocyte proteome to date containing 8862 proteins across four biological replicates. $90\%$ of proteins identified in the isolated adipocyte proteome were identified in the 3T3-L1 proteome. The intensity-based absolute quantification values of the overlapping proteins were strongly correlated between adipocytes from both VAT and SAT ($R = 0.75$; $p \leq 2.2$e-16 for VAdi; $R = 0.73$; $p \leq 2.2$e-16 for SAdi), indicating similar relative expression levels on an individual protein basis. Thus, many important adipocyte-specific processes were highly conserved in the 3T3-L1 adipocyte model at the proteome level.
Given the high concordance between 3T3-L1 and isolated adipocyte proteomes, we next ascertained differences that may be important to consider when translating findings from 3T3-L1 adipocytes to primary adipocytes. We designated proteins with less than 5-fold difference in expression in primary adipocytes versus 3T3-L1 adipocytes to be ‘normal range’, as the abundances of $99.5\%$ of the proteins in the VAdi and SAdi proteomes from chow-fed mice were within 5-fold (Fig. 2B). Of proteins identified in both the 3T3-L1 and primary adipocyte proteomes, $75\%$ of proteins were within a 5-fold range (Fig. 6, A and B), indicating good conservation between the two proteomes. However, pathway analysis revealed that there was a clear enrichment of ETC proteins and especially for proteins within complex I in isolated adipocytes independent of depot (Fig. 6C). Furthermore, several immune-related processes were enriched in the isolated adipocyte together with proteins promoting angiogenesis, annexin A1 and A3, fibroblast growth factor 1 and 2 and integrin beta 1. We also observed several pathways intrinsic to adipocyte biology that were not conserved in 3T3-L1 adipocytes, such as fatty acid and glycerolipid metabolism, including FABP4, phosphoenolpyruvate carboxykinase 1, CD36, PLIN1, and ATGL. Notably, leptin was exclusively expressed in primary adipocytes, and resistin was 20 to 30 times more abundant compared to 3T3-L1 adipocytes. Thus, the increased expression of these proteins in primary adipocytes correlates with the higher lipid content and larger, unilocular lipid droplets compared to 3T3-L1 adipocytes [61]. Lastly, several proteins within cAMP signaling, most notably exemplified with the beta 3 adrenergic receptor, were highly enriched in isolated adipocytes. In contrast, proteins with higher abundance in 3T3-L1 adipocytes were enriched for chromatin assembly, ribosome assembly, and RNA splicing pathways, features which are characteristic of proliferative cells. These data highlight potentially important considerations when using the 3T3-L1 adipocyte model. However, overall, the 3T3-L1 adipocyte proteome is highly representative of primary adipocytes. Fig. 6Comparison of the 3T3-L1 and primary murine adipocyte proteomes. A and B, ranked protein intensities based absolute quantification (iBAQ) of 3T3-L1 adipocytes and (A) visceral adipocytes (VAdi) or (B) subcutaneous adipocytes (SAdi) from chow-fed C57Bl/6J mice. Colors denote fold changes in protein abundance between primary murine adipocytes and 3T3-L1 adipocytes. C, gene ontology of proteins that were differentially regulated between 3T3-L1 and primary murine adipocytes.
## Discussion
SATs and VATs have well-documented differences that have been implicated in their differential association with metabolic disease. This study presents a global and unbiased proteomic analysis of these two different depots in order to define the molecular features that might govern these functional differences. These data should serve as an invaluable resource for researchers interested in adipose biology.
Several studies have addressed white adipose depot and/or diet differences by proteomics analysis [14, 15, 18]; however, our study is the first to assess both whole-tissue and isolated adipocytes from two depots across an obesogenic WD or a lean control diet. By studying the proteomes of isolated adipocytes from visceral and subcutaneous depots, we have found that the major depot-specific differences are encoded by just $3\%$ of the proteome. Nevertheless, several major biological processes are encoded within this small subdomain of the proteome. These include various components of the mTORC1 pathway such as sodium-coupled neutral amino acid transporter 9 and CDK6, and cytoskeletal components such as COL6a5 and COL6a6 that are enriched specifically in VAdi. COL6a5 and COL6a6 atypical collagens can replace the typical collagen VI isoforms (a1, a2, and a3) in the collagen VI monomers. Collagen VI is implicated in a deleterious fibrotic response in adipose [62, 63], and inhibition of COL6a5 is associated with improvements in lipid metabolism in adipocytes [64]. We observe that visceral adipocytes in C57Bl/6 mice are significantly larger than those from the subcutaneous depot, and mTOR is known to play a role in controlling adipocyte cell size [65, 66]. Furthermore, larger cells require profound shifts in cytoskeleton to support size expansion. Conversely, SAdi were enriched in mitochondrial proteins, particularly proteins that comprise complex I of the ETC, indicating gearing of energy machinery toward carbohydrates as a fuel source [67] and possibly rendering SAdi more metabolically flexible [68]. Taken together with the trend for increased expression of major lipolysis and beiging proteins in SAdi, these processes are likely coordinately regulated and complementary to subcutaneous adipocyte metabolism.
In contrast to visceral, subcutaneous adipocytes are capable of undergoing browning or beiging. One of the signature features of this process is the increased expression of the uncoupling protein UCP1, which provides the molecular basis for increased thermogenesis. Interestingly, we observed several other molecular features that may participate in this unique beiging process in subcutaneous adipocytes. For example, an enrichment in muscle-associated actomyosin proteins, which are important for the mechanical aspect of adrenergic stimulation [57] which controls lipolysis and beiging; increased expression of proteins involved in neural innervation exemplified with structural components of myelin sheath of neurons (MPZ and MBP) [39, 69], and enrichment of several subunits of the ADM receptor including RAMP2 and CALCRL [35]. Importantly, RAMP2 expression is associated with beneficial metabolic effects, as single nucleotide polymorphisms in the RAMP2 gene have been associated with changes in BMI and T2D in humans, and ADM has been shown to improve insulin sensitivity when administered to WD-fed mice [36]. Taken together, we observed few, but potentially important, differences that define lean adipocytes between these two depots.
Both adipose depots underwent profound changes in a sustained obesogenic environment by WD feeding. We combined our whole-tissue proteomics data with single-RNA sequencing data from murine adipose tissue [25, 26] to achieve a more holistic overview of these depots. This approach shows that VAT increased in both preadipocytes and immune cells (macrophages) in response to sustained WD feeding. These data are in line with the ‘adipose tissue expandability model’, which identifies limited lipid storage capacity during energy surplus followed by adipose tissue dysfunction [70], which has been reported previously in C57Bl/6 mice [71]. Furthermore, by overlaying the proteomics changes with diet in whole adipose tissue and isolated adipocytes, we uncovered concordance between adipocytes and tissue only in the visceral depot. This is an important observation for two reasons. First, this distinction is crucial for future studies of adipose tissue biology, as assumptions of adipocyte biology may be distorted by whole tissue approaches, and second, dietary responses in subcutaneous tissue cannot be explained by the adipocytes themselves, warranting future research. Notably, a large part of the proteomic adaptations to diet within SAdi also occur in VAdi, which we define as a set of core additive proteins albeit VAdi displayed a much greater diet response than SAdi. Mitochondrial dysfunction is thought to be a hallmark of adipose dysfunction [48, 72]. During mitochondrial stress, both apoptotic signaling and mitophagy are activated, where enhanced mitophagy facilitates cell survival by removing damaged mitochondria [73]. Intriguingly, a large part of the mitochondrial proteome was decreased with sustained WD in adipocytes independent of depot. However, this decrease was greater in VAdi with a concurrent increase in apoptotic signaling and a decrease in the level of a key regulator of mitophagy, BNIP3 [53], pointing toward severe mitochondrial stress in VAdi. Interestingly, PPARG was selectively downregulated in VAdi upon WD together with many proteins involved in highly adipocentric pathways, such as lipid breakdown and storage and glucose uptake, whereby VAdi take on a fibroblast-like cell identity. PPARG regulates BNIP3 [54] and in line with this, PPARG agonists ameliorate mitochondrial function in obese white adipose tissue [74]. These data highlight a depot-dependent protein fingerprint in response to WD, where VAdi showed signs of mitochondrial dysfunction, possibly through downregulation of PPARG.
This analysis also provided an opportunity to investigate the suitability of 3T3-L1 adipocytes as a model of adipocyte biology by proteomics profiling. Strikingly, 3T3-L1 and isolated adipocytes share ∼$90\%$ overlap at the proteome level supporting the robustness of this in vitro cell mode, and their proteomes were highly correlated. However, there were some key differences: first, the isolated adipocytes were enriched in proteins related to organismal biology, highlighted by immune-related proteins, but also proteins involved in angiogenesis stating that isolated adipocytes receive cues from multiple cell types. Second, isolated adipocytes were enriched in adipocyte proteins, such as ATGL, PLIN1, and the beta 3 adrenergic receptor, and third, 3T3-L1 protein profile point toward a more proliferative cell type. The last two presumably work cooperatively giving rise to larger lipid droplets in isolated adipocytes.
This comparison of the adipocyte-specific and adipose tissue proteomes from lean and obese mice has revealed that the adipocytes from subcutaneous and visceral white adipose depots are profoundly similar. However, we identified several important biological processes unique to each adipocyte type, which are likely important regulators of adaptation to WD and resultant systemic effects associated with the different adipose depots. Notably, we observed unique tissue-specific processes which support the functions of the resident adipocytes and likely drive many unique adipose depot functions. This deep proteomics analysis will serve as a resource to complement other studies that have compared the proteomes or transcriptomes of different adipose tissue depots from mice or humans to better understand adipose biology.
In conclusion, our global proteomic analysis of the adipocyte proteome indicates that the major differences between adipocytes from different depots is encoded by changes in a relatively small proportion of the overall proteome. The majority of these changes represent differential expression of proteins found in both depots with relatively few proteins being found exclusively in one depot. Part of the reason for this high degree of overlap is because a large proportion of cellular proteomes is devoted to generic functions such as organelle-specific proteins, intermediary metabolism, and the cytoskeleton. This was illustrated by the high degree of proteomic overlap even between cells of quite distinct specificity and lineage, such as hepatocytes and pancreatic islets. One of the limitations of proteomics is that extremely low abundance proteins or even very small peptides may be beyond the limit of detection and so we cannot exclude the possibility that we have underestimated the contributions of such proteins to cellular identity. Future studies will be required to explore this possibility.
## Data Availability
All mass spectrometry data and MaxQuant-processed proteomic data, including peptide and protein levels identifications, have been deposited in the PRIDE proteomeXchange repository [75]. The mouse adipose tissue/adipocyte data can be accessed with accession PXD039523 and the 3T3-L1 proteome with PXD039532.
## Supplemental data
This article contains supplemental data.
## Conflict of interest
The authors declare no competing interests
## Supplemental Data
Supplemental figure 1 Supplemental Table 1 Supplemental Table 2
## Funding and additional information
This work was supported by $\frac{10.13039}{501100000923}$Australian Research Council project grant FL200100096 (to D. E. J.). D. E. J. is supported by an ARC Laureate Fellowship.
## Author contributions
D. E. J., S. J. H., and V. D. conceptualization; V. D., K. C. C., A. H., S. J. H., and J. S. investigation. S. M., M. E. N., A. D. V., and J. G. B. formal analysis. S. M., M. E. N., and D. E. J. writing—original draft; A. D. V., J. G. B., V. D., K. C. C., A. H., S. J. H., M. E. N., S. M., D. E. J., and J. S. writing—review and editing.
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|
---
title: 'The effect of a low renal acid load diet on blood pressure, lipid profile,
and blood glucose indices in patients with type 2 diabetes: a randomized clinical
trial'
authors:
- Maryam Armin
- Zahra Heidari
- Gholamreza Askari
- Bijan Iraj
- Cain C. T. Clark
- Mohammad Hossein Rouhani
journal: Nutrition Journal
year: 2023
pmcid: PMC10014397
doi: 10.1186/s12937-023-00849-6
license: CC BY 4.0
---
# The effect of a low renal acid load diet on blood pressure, lipid profile, and blood glucose indices in patients with type 2 diabetes: a randomized clinical trial
## Abstract
### Background
Observational studies have reported that dietary renal acid load has an important role in insulin resistance and metabolic factors. The aim of the present study was to assess the effect of a low renal acid load diet (LRALD) on blood pressure, lipid profile, and blood glucose indices in patients with type 2 diabetes.
### Methods
In this parallel randomized clinical trial, 80 patients with type 2 diabetes were randomly assigned to the LRALD ($$n = 40$$) or control ($$n = 40$$) groups, for 12 weeks. Both groups received a balanced diet and a list of nutritional recommendations based on healthy eating behaviors. In the LRALD group, food items with low renal acid load were prescribed. Primary outcomes including: fasting blood glucose (FBG), hemoglobin A1c (HbA1c), fasting serum insulin, quantitative insulin sensitivity check index (QUICKI), homeostatic model assessment for insulin resistance (HOMA) and secondary outcomes including: weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL). were measured at baseline and end of the study. The present trial was registered at IRCT.ir (IRCT20130903014551N5).
### Results
Seventy subjects completed the study ($$n = 35$$ in control group and $$n = 36$$ in LRALD). Weight ($P \leq 0.001$), body mass index ($P \leq 0.001$), FBG ($P \leq 0.001$), HbA1c ($P \leq 0.001$), SBP ($$P \leq 0.004$$), and TG ($$P \leq 0.049$$) were reduced and HDL ($$P \leq 0.002$$) was increased in both groups, compared with baseline. After adjusting for baseline values, DBP ($$P \leq 0.047$$) was reduced in the LRALD group compared with control group. Results had no changes after using intention to treat analysis.
### Conclusion
A LRALD may decrease DBP in type 2 diabetic patients. However, it elicited no significant effect on lipid profile compared with a healthy diet.
### Trial registration
This randomized clinical trial was registered at IRCT.ir (IRCT20130903014551N5).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12937-023-00849-6.
## Introduction
Type 2 diabetes is one of the most prevalent non-communicable metabolic disorders, that results in a high rate of morbidity and mortality, worldwide [1], and, by 2030, the number of people with type 2 diabetes is estimated to exceed 552 million [2]. Uncontrolled type 2 diabetes may lead to retinopathy, nephropathy, heart diseases, stroke, and reduced life expectancy [2], whilst lifestyle modification, including dietary intervention, has an important role in management of type 2 diabetes [3].
Dietary intake is a determinant of acid production and may influence on acid–base balance in the body [4]; indeed, foods rich in components metabolized to acid precursors (i.e., sulfur and cationic amino acids including cysteine, methionine, taurine, lysine and arginine) may increase acid production [5]. In contrast, potassium, magnesium, and calcium are considered as alkali nutrients [5]. Since acid producing foods, including animal proteins and processed foods, were rarely consumed before the industrial revolution, it seems that humans may not have adapted to the contemporary acid producing dietary pattern, which may be a contributing factor to the current epidemics of chronic diseases, such as type 2 diabetes and obesity [6].
One of the main indicators used to estimate the acid renal load is potential renal acid load (PRAL), which refers to the intestinal absorption of five nutrients, including protein, potassium, calcium, phosphorus, and magnesium. A positive PRAL indicates acid-inducing and a negative score indicates alkali-inducing properties [7]. Net endogenous acid production (NEAP) is another index of dietary renal acid load that indicates the ratio of protein to potassium content of foods [8].
Several observational studies have been conducted to investigate the relationship between dietary acid load and glycemic control and metabolic indices. A prospective cohort study conducted on middle-aged subjects reported that a high acid-load diet was associated with a higher risk of type 2 diabetes [9]. A cross-sectional study conducted on participants aged 40 to 85 years revealed a positive relationship between dietary acid load and cardiovascular disease [10], whilst dietary renal acid load has been directly associated with gestational type 2 diabetes [11]. Furthermore, a meta-analysis, that pooled the results of the seven observational studies, reported that a high acid-load diet increased the risk of type 2 diabetes [12].
Although the number of observational studies that assessed the association between dietary acid load and glycemic control and metabolic factors are informative, no well-designed interventional study has been performed to detect the effect of dietary renal acid load on metabolic factors in type 2 diabetic patients. Therefore, in the present study, we aimed to evaluate the effects of a low renal acid load diet (LRALD) on, blood glucose, and insulin resistance as primary outcomes and anthropometric variables, blood pressure and lipid profiles as secondary outcomes in patients with type 2 diabetes.
## Subjects
This parallel randomized clinical trial was conducted from June to September 2020. Adults with type 2 diabetes were recruited from a governmental type 2 diabetes center. Individuals were included if they were: 1) between 20 to 65 years old, 2) type 2 diabetic patients not on insulin therapy, 3) not pregnant or lactating, 4) not on glucocorticoids and 5) not underweight (body mass index (BMI) > 18.5 kg/m2). Subjects who started using a new blood glucose-lowering drug, changed the dose of medications, or had a diabetic ketoacidosis attack during the study were excluded. The number of subjects was calculated based on fasting blood glucose (FBG) by using following formula: $$n = 2$$ [(Z (1-α/2) + Z(1-β))2 × S2] / Δ2, where α = 0.05 (type I error), β = 0.20 (type II error), Δ = 15.63 mg/dl and $S = 32.91$ mg/d [13]. Therefore, the minimum required sample size for the present study was 70 ($$n = 35$$ in each group). Accordingly, we recruited 40 subjects in each group at baseline, to account for loss to attrition. The aims and details of the study were individually explained for each volunteer. All participants signed an informed written consent forms before study commencement. This study was approved by the Research Council and Ethical Committee of the School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran and the Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Present trial was registered at IRCT.ir (IRCT20130903014551N5).
## Study procedure and dietary intervention
Both groups were recommended a balanced diet modified for type 2 diabetic patients (such as carbohydrate counting) Energy intake was calculated for normal weight and overweight/obese subjects using current body weight and adjusted ideal body weight, respectively. Energy requirement was calculated using the Mifflin St Jeor Equation [14]. The diet in both groups contained 52–$53\%$ of carbohydrates, 17–$18\%$ of protein, and 30–$31\%$ of fat. In order to prevent ketosis, the carbohydrate content of the diets was above 130 g/d. Volunteers in both groups received a list of nutritional recommendations based on healthy eating behaviors including: 1) Meals should be small, frequent and used regularly, 2) Do not change carbohydrate content of your diet without consulting your dietician 3) Restrict intake of refined carbohydrate, Whole grains are preferable to refined grains, 4) Eat vegetables frequently, 5) Use fruits with skin if possible and 6) Fruits are preferable to fruit juice. Daily meal plans were designed according to the potential renal acid load (PRAL) of food items [15] only in LRALD group. Foods with high acid load (PRAL > 4), except chicken meat, were excluded from the diet of LRALD group. Chicken meat, one of the most frequently consumed foods, was limited to one serving per day. Also, two fixed snacks contained very low PRAL foods (foods with PRAL < -4 such as spinach, celery, squash, and raisins) were prescribed in LRALD group. A colored list of the food items was provided, in which the red color was used for foods with high PRAL and low-PRAL foods were demarcated by the color green. Subjects in the LRALD group were educated to select green items and limit red foods. Dietary recommendations for two group are reported in (Supplementary 1). Compliance with prescribed diets were assessed by food records. PRAL Table of foods are reported in (Supplementary 2). In the present study, dietary acid load indices were estimated using the following formulas [16]:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{PRAL}\left(\mathrm{mEq}/\mathrm d\right)=0.4888\times\mathrm{protein\ intake}\left(\mathrm g/\mathrm d\right)+0.0366\times\mathrm{phosphorus}\left(\mathrm{mg}/\mathrm d\right)-0.0205\times\mathrm{potassium}\left(\mathrm{mg}/\mathrm d\right)-0.0125\times\mathrm{calcium}\left(\mathrm{mg}/\mathrm d\right)-0.0263\times\mathrm{magnesium}(\mathrm{mg}/\mathrm d),$$\end{document}PRALmEq/$d = 0.4888$×proteinintakeg/d+0.0366×phosphorusmg/d-0.0205×potassiummg/d-0.0125×calciummg/d-0.0263×magnesium(mg/d),and [17]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{NEAP}\left(\mathrm{mEq}/\mathrm d\right)=\left[54.5\times\mathrm{protein\ intake}\left(\mathrm{mg}/\mathrm d\right)\;\div\;\mathrm{potassium\ intake}\;\left(\mathrm{mEq}/\mathrm d\right)\right]-10.2$$\end{document}NEAPmEq/$d = 54.5$×proteinintakemg/d÷potassiumintakemEq/d-10.2
## Confounding variables
Potential confounding variables in this study were physical activity and food intake.
## Dietary intakes and physical activity
Six, one-day (4 weekdays and 2 weekends), food diaries were completed by participants at baseline and during the study. The validity and precision of the dietary record is high and it is often considered as a reference method in validation studies. Nevertheless, we checked all dietary records to complete unclear and incomplete reports by each participant. Also, we excluded dietary records that reported < 800 or > 4200 kcal energy intake per day. We guided participants to how complete a food record by several images regarding portions sizes. Also, a completed food record was provided for each participant as a sample. We checked all dietary records to complete unclear and incomplete reports by each participant. Food diaries were converted to the macro/micronutrients by Nutritionist IV using the USDA database [18, 19].
Participants were asked to complete 4 daily physical activity records during the study. Physical activity was calculated using Metabolic Equivalent per hour per day (MET.hd) [19]. Patients invited to meetings scheduled at baseline and 3-, 7- and 10 weeks to assess compliance. In addition, the subjects were individually monitored every week.
## Anthropometric measurements and blood pressure
Body weight was measured at the baseline and on the 12th week, where subjects wore lightweight clothes and were unshod, using a standard scale, to the nearest 0.1 kg. Height was measured at baseline, using a wall-mounted stadiometer, with participants standing upright and unshod. Seated blood pressure was measured using a mercury sphygmomanometer, after 10 min of rest. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded by the first sound and the fade of the sound, respectively.
## Biochemical measurements
In the early morning, a fasting (12 h) blood sample was drawn. Serum was separated by centrifuging at 3,000 × g for 10 min. Enzymatic colorimetric method was performed to measure FBG, serum concentration of triglyceride (TG) and total cholesterol (TC) (Pars Azmoon, Tehran, Iran). Similarly, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were measured after blocking other cholesterol containing components by photometric methods. Glycosylated hemoglobin (HbA1c) was assessed using ion exchange chromatography method. Fasting serum insulin was measured by enzyme-linked immunosorbent assay (ELISA) (Monobind Inc,Costa Mesa, CA, USA). To estimate insulin resistance, Quantitative insulin sensitivity check index (QUICKI) and homeostatic Model Assessment for Insulin Resistance (HOMA) were calculated by following formulas [20]:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{HOMA}}= ({\mathrm{fasting\ glucose\ }}[{\mathrm{mmol}/\mathrm{L}}] \times {\mathrm{fasting\ insulin\ }}[{\mathrm{\mu U}/\mathrm{mL}}])/22.5$$\end{document}HOMA=(fastingglucose[mmol/L]×fastinginsulin[μU/mL])/22.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{QUICKI }=1/ (\mathrm{log\ fasting\ glucose\ }[\mathrm{mg}/\mathrm{dL}] +\mathrm{ log\ fasting\ insulin\ }[\mathrm{\mu U}/\mathrm{mL}])$$\end{document}QUICKI=1/(logfastingglucose[mg/dL]+logfastinginsulin[μU/mL])
## Confounder variables
We considered age, sex, body mass index and physical activity as potential confounding variables. Previous studies revealed that age was related to the lipid profile [21] and blood glucose indices [22]. Also, serum lipids and glucose hemostasis may be affected by gender [23, 24]. Obesity is considered as a risk factor for high blood glucose [25] and abnormal blood lipids [26]. Evidence showed that physical activity may improve glucose hemostasis [27] and lipid profile [28].
## Statistical analysis
Normality of the data was checked using the Kolmogorov–Smirnov test and Q-Q plot, and results showed that the distribution of HOMA-IR, QUICKI, and TG was not normal. Therefore, we used the log transformed version of these variables. Chi-square tests were used to compare qualitative variables between the LRALD and control groups. Quantitative variables were reported as percentages. To compare baseline and endpoint values within each group, Paired T test analysis was used. Quantitative variables were compared between two groups using Independent Student t-test. To adjust for confounding variables (energy intake and baseline values), analysis of covariance (ANCOVA) was applied. To report primary and secondary outcomes, both per-protocol and intention to treat (ITT) analysis were used. Missed data were treated according to linear regression method. Continuous data were reported as mean ± standard deviation. The log-transformed variables were reported as geometric mean ± standard deviation. All data analyses were conducted using SPSS version 21 statistical software, with an a priori alpha level of 0.05.
## Results
A flow diagram of the study procedure is illustrated in Fig. 1. Of 350 subjects were screened at baseline according to the inclusion and exclusion criteria, 80 patients who met these criteria and completed the informed written consent form. Then they were randomly assigned to the LRALD and control groups. During the study, 5 patients in the control group were excluded due to positive COVID-19 test ($$n = 3$$), taking a new drug ($$n = 1$$), and not following the prescribed diet correctly based on the patient's own confessions ($$n = 1$$). Four subjects were excluded from LRALD group due to positive COVID-19 test ($$n = 2$$), heart attack ($$n = 1$$), and low adherence to the prescribed diet based on the patient's own confessions ($$n = 1$$). Finally, data of 71 patients ($$n = 35$$ in control group and $$n = 36$$ in LRALD) were statistically analyzed. The number of the subjects who completed the study was greater than minimum required sample size reported in method section. Fig. 1Flow Diagram illustrating participant selection process and study procedure General characteristics of the subjects are reported in Table 1. There was no significant difference in age, sex, height, weight, BMI, and physical activity between two groups at baseline. Table 1Baseline characteristics of participantsVariableLRALD group($$n = 36$$)Control group($$n = 35$$)P value aAge (y)50.4 ± 10.547.6 ± 7.90.23Male (%)30.335.50.43Height (m)1.6 ± 0.11.6 ± 0.00.67Weight (kg)77.5 ± 15.673.8 ± 11.20.27Body mass index (kg/m2)28.7 ± 4.827.7 ± 3.70.33Normal Weight (%)$18.2\%$$22.6\%$0.48Overweight (%)$45.5\%$$54.8\%$Obese (%)$36.4\%$$22.6\%$Physical Activity (MET hour/day)33.4 ± 3.833.4 ± 2.40.98All continuous variables are reported as Mean ± SDLRALD Low renal acid load dietaP values for continues and nominal variables were calculated by Independent t-test and Chi-square, respectively Comparison of dietary intake during the study between LRALD and control groups is shown in Table 2. Results showed that intake of cholesterol, saturated fatty acid, polyunsaturated fatty acid, monounsaturated fatty acid, vitaminB3, calcium, magnesium, zinc, and phosphorus was significantly less in the LRALD group compared with the control group. In contrast, subjects in the LRALD group consumed more amounts of iron, potassium, beta-Carotene, folate, vitamin C, vitamin B1, vitamin E), vitamin A, and dietary fiber in comparison with control group. PRAL and NEAP, indicators of adherence to the LRALD, were significantly lower in the LRALD group compared with control group. Table 2Food intake during the study in low renal acid diet and control groups based on food recordsNutrientsLRAL diet($$n = 36$$)Control($$n = 35$$)P value bEnergy (kcal)1854 ± 3421848 ± 2490.94Carbohydrate (g/d)233a ± 5234a ± 50.30Protein (g/d)83a ± 583a ± 50.62Fat (g/d)66a ± 365a ± 30.21Cholesterol (mg/d)103a ± 122398a ± 122 < 0.001SFA (g/d)9.03a ± 213a ± 2 < 0.001PUFA (g/d)13a ± 415a ± 40.04MUFA (g/d)9a ± 2142a ± 2 < 0.001Dietary Fiber (g/d)25a ± 817a ± 8 < 0.001VitaminA (RE/d)1132a ± 427481a ± 427 < 0.001VitaminE (mg/d)19a ± 514a ± 5 < 0.001VitaminC (mg/d)86a ± 2428a ± 24 < 0.001VitaminB1 (mg/d)2a ± 02a ± 0 < 0.001VitaminB2 (mg/d)2a ± 02a ± 00.84VitaminB3 (mg/d)20a ± 322a ± 30.01VitaminB6 (mg/d)1a ± 11a ± 10.14Folate (µg/d)478a ± 120246a ± 120 < 0.001Beta-Caroten (µg/d)980a ± 454121a ± 454 < 0.001Potassium (mg/d)3261a ± 4972844a ± 4970.001Iron (mg/d)16a ± 113a ± 1 < 0.001Calcium (mg)967a ± 1551095a ± 1550.001Zinc (mg/d)8a ± 19a ± 1 < 0.001Magnesium (mg/d)265a ± 42308a ± 42 < 0.001Phosphorus (mg)1109a ± 1361202a ± 1360.008Selenium (mg)1a ± 01a ± 00.35Chromium (mg)1a ± 21a ± 20.34PRAL(mEq/d)-6.5 ± 11.53 ± 7 < 0.001NEAP(mEq/d)36.8 ± 14.145 ± 80.003All variables are reported as Mean ± SDLRAL Low renal acid load, SFA Saturated fatty acid, PUFA Polyunsaturated fatty acid, MUFA monounsaturated fatty acid, PRAL Potential renal acid load, NEAP *Net endogenous* acid productionaValues were adjusted for energy intakebCalculated by ANCOVA except for energy, PRAL and NEAP calculated by independent t-test As shown in Table 3, the results of comparing anthropometric indices, lipid profile, fasting blood sugar, and blood pressure in the LRALD and control groups before and after the study. Table 3Comparison of anthropometric indices, lipid profile, fasting blood sugar, and blood pressure in the low renal acid diet and control groups before and after the studyLRAL diet($$n = 36$$)Control($$n = 35$$)PcPdBaselineEnd of trialChangePbBaselineEnd of trialChangePbBMI (kg/m2)28.7 ± 4.827.3 ± 4.57-1.4 < 0.00127.7 ± 3.726.8 ± 3.6-0.9 < 0.0010.210.21Weight (kg)77.6 ± 15.674.5 ± 14.68-3.0 < 0.00173.8 ± 11.271.5 ± 10.1-2.3 < 0.0010.460.46FBG (mg/dl)192.3 ± 68.2155.0 ± 51.45-37.2 < 0.001190.5 ± 78.2160.1 ± 66.4-30.50.010.590.59HbA1C (%)8.7 ± 2.27.9 ± 1.98-0.8 < 0.0018.2 ± 2.37.7 ± 1.8-2.30.010.660.66Insulin (mIU/L)7.5 ± 2.47.5 ± 2.590.70.947.9 ± 2.35.8 ± 2.4-3.50.010.080.08HOMA-IR3.4 ± 2.52.7 ± 2.63-0.60.193.5 ± 2.42.1 ± 2.3-30.50.0010.080.08QUICKI0.3 ± 0.10.3 ± 0.050.00.240.3 ± 0.00.3 ± 0.0-0.90.0010.250.27SBP (mm Hg)127 ± 13.8108 ± 35.8-18.30.004128.8 ± 17.6110.2 ± 43.2-3.50.010.990.99DBP (mm Hg)77 ± 6.370 ± 20.1-6.5 < 0.0579.4 ± 11.680.0 ± 11.60.60.780.050.05TC (mm Hg)187.9 ± 50.1183.0 ± 47.3-4.90.50178.7 ± 45.7180.2 ± 49.51.50.690.550.57HDL (mg/dl)41.7 ± 7.750.2 ± 12.58.50.00240.4 ± 9.949.3 ± 12.08.90.0050.7530.753LDL (mg/dl)98.3 ± 31.895.0 ± 25.7-3.30.4296.2 ± 33.790.8 ± 28.89-5.450.0140.4810.479TG (mg/dl)163.1 ± 1.6148.5 ± 1.6-13.7 < 0.05147.1 ± 1.7127.58 ± 1.68-24.930.0030.3030.302BMI Body mass index, FBG Fasting blood glucose, HbA1C Hemoglobin A1C, SBP Systolic blood pressure, DBP Diastolic blood pressure, TC Total cholesterol, HDL High-density lipoprotein, LDL Low-density lipoprotein, TG Triglyceride, HOMA-IR Homeostatic model assessment for insulin resistance, QUICKI Quantitative insulin sensitivity check indeaVariables are expressed as mean ± SD except for insulin, HOMA-IR and TG reported as geometric mean ± SDbComparison between baseline and endpoint, obtained from Paired T testcObtained from ANCOVA, adjusted for baseline valuedObtained from ANCOVA, adjusted for baseline value after intention to treat
## Primary outcomes
Blood glucose indices showed that the changes in FBG and HbA1C were significantly changedwithin both groups. Insulin, HOMA-IR, QUICKI and LDL were significantly decreased compared with baseline in the control group. Analysis were repeated after using ITT method (Table 3). Nevertheless, similar findins were observed.
## Secondary outcomes
Weight, BMI,, SBP, HDL and TG were significantly changed within both groups. In the LRALD group, DBP was significantly decreased after intervention. LDL was significantly decreased compared with baseline in the control group. After adjusting for baseline measurements, the comparison of the final values in the two groups showed that a LRALD marginally decreased DBP compared with the control diet (Table 3). Similar findings were observed after using ITT method (Table 3).
## Discussion
In the present study, two dietary interventions were compared in type 2 diabetic patients. The most important innovation of the present study was the utilization of dietary acid load as a nutritional intervention. a finding of the present study was a significant reduction in DBP after adherence to a LRALD compared with control intervention. Also, SBP was significantly reduced after following a LRALD compared with the beginning of the study. While, in the control group, only a decrease in SBP was observed. Indeed, these findings indicated that a LRALD had more beneficial effects than a usual diabetic diet on blood pressure in patients with type 2 diabetes. The prevalence of hypertension in patients with type 2 diabetes is notably prevalent compared with healthy individuals [29]; indeed, most type 2 diabetic patients have a high blood pressure at type 2 diabetes diagnosis. Also, there is a direct relationship between blood pressure and risk of nephropathy, retinopathy, neuropathy, and cardiovascular disease in patients with type 2 diabetes [30]; therefore, it is plausible that a LRALD could play an important role in reducing the side effects of type 2 diabetes by lowering blood pressure.
A possible mechanism of the decreasing impact of a LRALD on blood pressure is the presence of abundant phenolic compounds in plants found in high amounts in this diet [31]. The hypothesis of the effect of phenolic compounds on blood pressure has various mechanisms, including the effect of phenolic acids on NO-mediated vasodilatory response of endothelial wall, decreasing oxidative stress by reduction in NAD (P) H-dependent (nicotinamide adenine dinucleotide phosphate) super oxide products, and inhibiting the activity of the angiotensin-converting enzyme [32]. It has previously been observed that a diet with a high acid load can induce the glutaminase enzyme and activate the renin-angiotensin system, leading to an increase in blood pressure [33]. Moreover, blood uric acid is directly related to high blood pressure and a diet with low acid content can lead to an increase in urinary uric acid clearance and a decrease in blood uric acid [34].
Previous studies have reported findings similar to results of the present study regarding the effect of a LRALD on blood glucose indices. A cross-sectional study in Japan found that there was no correlation between dietary acid load, FBG and HBA1c [35]. Indeed, these results are consistent with the findings of the present study.
The findings of the current study regarding the effect of an LRALD on blood pressure has been confirmed by previous studies. A cross-sectional study reported that PRAL and NEAP were positively associated with DBP in men and SBP in women [36], whilst a meta-analysis that pooled the results of the 14 studies reported that there was a positive association between dietary acid load and blood pressure. In this meta-analysis, it was revealed that each 20 units increase in PRAL elevated the risk of hypertension by $3\%$, also, a significant nonlinear relationship was found between NEAP and blood pressure [37].
The present study showed a no significant effect of LRALD on lipid profile, similar to a previous study which found no relationship between NEAP and HDL, LDL, and TC [17]. It should be acknowledged that the concentration of LDL, TC, HDL, and TG was in normal range at baseline, and it is unlikely that dietary interventions can change blood lipids within normal physiological range. Nevertheless, some studies have reported that other healthy diets similar to LRALD, such as DASH diet, failed to change lipid profile in normal range [38].
Three dietary patterns, including LRALD, DASH, and Mediterranean diet, have numerous similarities; for example, high-fat cheeses, red meat, and egg yolks are limited in all three diets. Instead of red meat, the consumption of legumes and chicken and fish in DASH and LRALD is recommended, also, olive oil is recommended in these three dietary patterns. Sodium restriction is also encouraged in all three diets [39]. Therefore, it is suggested that future research examine the effects of combined dietary patterns, such as the low renal acid load DASH diet or the low renal acid load Mediterranean diet, to provide patients with all the benefits of these diets.
Although we have provided a novel addition to literature, which may be a practical relevance to prescribing clinicians and patients, there are limitations that should be acknowledged. One of the limitations of this study is the lack of examining of individuals' compliance with the intervention via biomarker. Assessment of compliance with the LRALD requires measuring urine pH over 24 h, however, collecting 24-h urine sample is difficult and it may affect the reliability of the results [40]. Therefore, multiple food diaries were used to assess compliance in the present study. Another limitation of the present study was the loss of some participants due to the prevalence of COVID-19 and lockdown. Nevertheless, it should be noted that the number of subjects who completed the study was more than minimum required sample size. Social stress in people during the COVID-19 outbreak can also be one of the co-variants that leads to impaired blood glucose regulation in type 2 diabetic patients, and it may have a negative effect on the results.
## Conclusion
This study showed that a LRALD had beneficial effects on blood pressure in type 2 diabetic patients compared with the usual diabetic diet, but its effect on blood glucose control factors and lipid profile was not significant.
## Supplementary Information
Additional file 1: Supplementary 1. Dietary recommendations for control and LRAL group. Additional file 2: Supplementary 2. Potential renal acid load score of foods.
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|
---
title: 'Insulin resistance, lipids and body composition in patients with coronary
artery disease after combined aerobic training and resistance training: a randomised,
controlled trial'
authors:
- Tim Kambic
- Mojca Božič Mijovski
- Borut Jug
- Vedran Hadžić
- Mitja Lainscak
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10014406
doi: 10.1186/s13098-023-01017-w
license: CC BY 4.0
---
# Insulin resistance, lipids and body composition in patients with coronary artery disease after combined aerobic training and resistance training: a randomised, controlled trial
## Abstract
### Background
The effect of resistance training (RT) in cardiac rehabilitation (CR) on insulin resistance remains elusive. We examined whether the addition of high-load (HL) or low loads (LL) RT has any effect on the levels of insulin resistance and lipids versus aerobic training (AT) alone in patients with coronary artery disease (CAD).
### Methods
Seventy-nine CAD patients were randomised to HL-RT [70–$80\%$ of one repetition maximum (1-RM)] and AT, LL-RT (35–$40\%$ of 1-RM) and AT or AT (50–$80\%$ of maximal power output), and 59 patients [$75\%$ males, $15\%$ diabetics, age: 61 [8] years, left ventricular ejection fraction: 53 [9] %] completed the study. Plasma levels of glucose, insulin, blood lipids [total cholesterol, triglycerides, high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL)] cholesterol and body composition were measured at baseline and post-training (36 training sessions).
### Results
Training intervention had only time effect on lean mass ($$p \leq 0.002$$), total and LDL cholesterol levels (both $p \leq 0.001$), and no effects on levels of glucose and insulin resistance (homeostatic assessment 2-insulin resistance). Total and LDL cholesterols levels decreased following AT [mean difference ($95\%$ confidence interval); total cholesterol: − 0.4 mmol/l (− 0.7 mmol/l, − 0.1 mmol/l), $$p \leq 0.013$$; LDL: − 0.4 mmol/l (− 0.7 mmol/l, − 0.1 mmol/l), $$p \leq 0.006$$] and HL-RT [total cholesterol: − 0.5 mmol/l (− 0.8 mmol/l, − 0.2 mmol/l), $$p \leq 0.002$$; LDL: − 0.5 mol/l (− 0.7 mmol/l, − 0.2 mmol/l), $$p \leq 0.002$$]. No associations were observed between post-training change in body composition and post-training change in blood biomarkers.
### Conclusions
RT when combined with AT had no additional effect beyond AT alone on fasting glucose metabolism, blood lipids and body composition in patients with CAD.
Trial registration number NCT04638764.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-023-01017-w.
## Background
Exercise training is a core component of cardiac rehabilitation (CR) [1, 2] and has been associated with improvements of physical performance, body composition and quality of life, as well as blood pressure, glucose metabolism and lipid control [1, 3, 4]. While the effects of multicomponent exercise-based CR on physical performance, body composition and quality of life are evident [5–7], less is known about CR effects on insulin resistance and lipids in patients with CAD [4], despite high prevalence of diabetes and dysmetabolism ($54\%$) among patients enrolled in an early phase II CR [8].
During the early stage of phase II CR, the standard care is focused mainly on clinical assessments of cardiac function and risk factors, and optimisation of pharmacological therapy [9], while less emphasis is given on the initial implementation of progressive training programmes with optimal training loading due to the lack of exact training recommendation [10], which would otherwise greatly improve the efficacy of CR efficacy. Therefore, previous studies in patients with CAD have applied only low-load (LL) to moderate-load RT [50–$65\%$ of one repetition maximum (1-RM)] in combination with moderate to high intensity AT and mostly showed no additional benefits on glucose metabolism and blood lipids when compared with control [11, 12] and/or AT alone [12, 13]. Whilst only the superior effects on maximal muscle strength were established following combined AT with high-load (HL) in our recent study [14], such efficacy over combined AT with LL-RT or AT alone remains to be established on insulin resistance and lipids.
Since the recent recommendations for patients with CAD and coexisting diabetes advocates for the use of combined AT and RT at the highest intensity possible to achieve optimal control of glucose metabolism, dyslipidaemia and body composition in early phase of CR [9], our study aimed to determine whether the dose-dependent relationship between RT load (LL-RT vs HL-RT) and improvements of glucose metabolism and lipids profile exists in patients with CAD.
## Study design
This study presents a prespecified secondary analysis of a randomised controlled trial (ClinicalTrials.gov Identifier: NCT04638764). Patients with CAD were cluster randomised to three parallel training interventions (Fig. 1): combination of HL-RT with AT; combination of LL-RT with AT; and solely AT as a standard care. The study was designed in accordance with the CONSORT guidelines [15] and Declaration of Helsinki, and was approved by the National Medical Ethics Committee (registration number: 0120-$\frac{573}{2019}$/15). The study protocol, the feasibility and safety, and the primary outcomes of the study were published previously [14, 16, 17].Fig. 1CONSORT flow chart of the study. HL-RT high-load resistance training, LL-RT low loads resistance training, AT aerobic training, COVID-19 Coronavirus disease-19 The outcomes of this secondary analysis of the randomised controlled trial were: change in glucose, insulin and insulin resistance [homeostatic model assessments of insulin resistance (HOMA-IR)], blood lipids (cholesterol, HDL, LDL and triglycerides), and body composition (body weight, lean mass and fat mass), following training intervention. The assessor of the study was not blinded to group allocation due to COVID-19 outbreak staff reassignments.
Patients were assessed at baseline and post-training (> 24–36 training sessions). Body composition was measured at baseline (7–10 days prior to enrolment) and post-training (3–7 days after last training session) to exclude potential false muscle hypertrophy due to acute muscle swelling post last RT session. Blood samples were collected after overnight fast (≥ 10 h) in the morning prior to first and last training sessions (48–72 h after the last session) In addition, maximal aerobic capacity and lower limb muscle strength were assessed at baseline and after seven weeks (only maximal leg press strength) to determine AT and RT workloads.
## Study sample
The study recruited patients with CAD (acute coronary syndrome and/or percutaneous coronary intervention) from the Division of Cardiology, General Hospital Murska Sobota, Slovenia. Inclusion criteria were age (18–85 years), left ventricular ejection fraction ≥ $40\%$, documented CAD (≥ 1 month after clinical event), referral to phase II outpatient CR, and completion of a baseline cardiopulmonary exercise test [2]. Exclusion criteria were aligned with standard recommendations for participation in RT [4, 18]. Prior to enrolment, all patients received verbal and written information about the study aims, procedures and potential risk during the study and were asked to sign a written informed consent before beginning study procedures.
## Training protocol
Training intervention was embedded in a standard phase II out-patient CR consisted of three weekly training sessions for 12 weeks (i.e., 36 training sessions), with 48–72 h rest between sessions.
All patients performed aerobic interval cycling (3–5 min workload cycling/2 min unloaded cycling, a total of 40 min/session) starting from the initial $50\%$ of maximal workload achieved at baseline cardiopulmonary exercise test and progressively increasing every two weeks to $80\%$ maximal workload [17]. Cycling cadence was set at 50–60 revolutions per min [2].
Patients randomised to RT completed a total of 36 sessions on a leg-press machine (three 1-RM tests and 33 RT sessions). In HL-RT group the workload was increased from an initial three sets at intensity $70\%$ of 1-RM (6–11 repetitions/set) to $80\%$ of 1-RM (6–8 repetitions per set) in the first seven weeks of the CR, while the workload in the LL-RT group increased from the initial $35\%$ of 1-RM (12–22 repetitions/set) to $40\%$ of 1-RM (12–16 repetitions per set). Similar progression in both RT groups was applied following 1-RM re-evaluation after 7 weeks of training [17, 19–21]. A lifting cadence of 1 s: 1 s (concentric and eccentric contraction) was used, with 90 s rest between sets [22]. Each RT session lasted for 7–10 min and was performed between intervals of unloading intervals of AT in a changing, randomised order for all patients in each training group to eliminate potential effects of fatigue. The entire study protocol is available elsewhere [17].
## Maximal aerobic capacity
Maximal aerobic capacity was measured using an adjusted ramp protocol [23] on a Schiller ERG 911 ergometer and using mask connected to a breath-by-breath Cardiovit CS-200 Excellence ErgoSpiro system (Schiller, Baar, Switzerland). Patients were first instructed and familiarised with correct breathing technique followed by a spirometry test. Afterwards, patients remained seated for determination of baseline gas exchange and hemodynamic (heart rate and blood pressure) values. Maximal aerobic capacity was assessed using adjusted ramp protocol by increasing workload every minute for an additional 10–25 W until exhaustion or other contraindication [17, 23].
## Maximal leg strength
Submaximal strength test assessments and RT were performed using a Life Fitness Leg Press Pro 2 (Life Fitness Inc., Rosemont, Illinois, USA). Patients were first familiarised with correct lifting and breathing technique, which was followed by two warm-up sets comprising of eight and six repetitions at $50\%$ and $70\%$ of patients` perceived 1-RM, respectively. Afterwards, the weight was progressively increased until reaching the workload that could be lifted three to five times (3–5 RM). Trials were separated with a two to three min rest [20]. The 1-RM was calculated using the established 1-RM prediction equation (predicted 1-RM = maximal load lifted/1.0278–0.0278 × number of repetitions) [24].
## Blood biomarkers
Blood samples were drawn from the right antecubital vein using 21-gauge needle (40 mm) into 2,5 mL and 10 mL BD Vacutainer® vacuum serum tube with silica particles coating (Becton, Dickinson and Company, Vacutainer System Europe, Heidelberg, Germany). Serum samples were prepared with 10-min centrifugation at 2700 rpm and 20 °C using Eppendorf 5810 R centrifuge (Eppendorf Ag, Hamburg, Germany). After centrifugation, 2,5 mL serum tubes were immediately used for analysis of glucose, triglycerides, total cholesterol, high-density lipoprotein [HDL] cholesterol and low-density lipoprotein [LDL] cholesterol concentrations using Roche Cobas 8000–1 modular analyser (Roche Diagnostics Ltd., Rotkreuz, Switzerland). From 10 mL vacuum tubes serum was aliquoted into 1, 8 mL Sarstedt cryovials (Sarstedt Ag and Company, Nümbrecht, Germany) and stored ≤ − 70 °C within two hours until further analysis of insulin levels. Insulin levels were measured in a thawed serum aliquote with the Luminex’s xMAP® technology utilizing magnetic beads coupled with specific antibodies, with allowed multiplexing. Analysis was performed using a MagPix analyzer in line with manufacturer’s instructions (all R&D Systems, Abingdon, United Kingdom). Homeostatic model assessment 2 of insulin resistance (HOMA2-IR) was calculated using values glucose and insulin levels using well established HOMA2 equation [25] and calculator.1
## Anthropometry and body composition
Body height and mass were measured on Marsden DP3810 weighing scale and stadiometer (Marsden Weighing Group, Rotherham, UK), and body lean mass and body fat mass were measured using bioimpedance measurement with a Bodystat Quadscan 4000 Touch (Bodystat, Douglas, Isle of Man, UK). Measurement of body composition was performed in the morning (before 10 a.m.) in line with reported protocol [17]. Post-training measurements were performed during the same time of the day as were at baseline (± 2 h).
## Statistical analysis
Sample size was calculated for primary outcomes [maximal aerobic capacity (ml/kg/min) and maximal voluntary contraction (Nm)], and the exact calculations were previously published [14, 17]. This study presents pre-specified secondary outcomes of body composition, glucose and insulin metabolism and blood lipids, which should be only interpreted as exploratory.
Data are presented as numbers and percentages for descriptive variables and as means (standard deviations) or medians (interquartile ranges) (according to the normality of distribution) for numeric variables. Numeric variables were screened for normality of distribution (Shapiro–Wilk test), homogeneity of variances (Levene test) and sphericity (Mauchly test). Data were analysed using per-protocol analysis [17], and we included all patients who completed > 24 training sessions in the final analysis. Between-group differences in baseline were assessed using one-way analysis of variance (ANOVA) or the Kruskall–Wallis test (depending on the assumptions), with additional post-hoc analysis using Tukey or Bonferroni tests. Training effect was assessed using two-way repeated measures ANOVA or analysis of covariance (ANCOVA), in case of significant baseline difference between groups. The within-group training effect was calculated using Bonferroni adjustment for multiple comparisons within two-way ANOVA. In addition to ANCOVA, paired sample t-tests or Wilcoxon tests was used, accordingly, to assess within-group improvement following training. The reported effect size is partial eta squared (η2). Comparison between training groups in categorical outcomes was calculated using Chi-square test or Fisher exact test. Correlation between post-training difference (post-training difference = post-training-baseline value) in body composition and blood biomarkers was calculated using Spearman rank correlation coefficient. Comparison between baseline and post-training values of blood markers in patients with and without diabetes was calculated using paired-samples t-test or Wilcoxon rank test. IBM SPSS 25 software (SPSS Inc., Armonk, NY, USA) was used for the analysis at a level of statistical significance set at alpha < 0.05.
## Results
One hundred and fifty-four patients with CAD were screened for eligibility and 79 were included in the study (Fig. 1). During the study, 20 patients were lost to follow-up, mainly due to personal and medical reasons, and 59 patients were included in the final per-protocol analysis. On average, patients were 61 [8] (years old, had left ventricular ejection fraction 53[9] %), and were mostly males ($75\%$) and non-smokers or ex-smokers ($83\%$). In the AT group, more patients were diagnosed with atrial fibrillation than in the HL-RT and LL-RT groups ($$p \leq 0.038$$). Otherwise, there no between-group differences in baseline anthropometry, clinical characteristics, smoking status and pharmacological therapy (Table 1). Following the training intervention, the dose of statins or ezetimibe significantly increased in all three training groups (AT: + 7 mg, $$p \leq 0.010$$; LL-RT: + 7 mg, $$p \leq 0.023$$; HL-RT: + 11 mg, $p \leq 0.001$), with no significant time x group interaction (Additional file 1: Table S1). There was also no difference between training groups in lipid lowering drug at baseline ($$p \leq 0.836$$) and post-training ($$p \leq 0.426$$) (Additional file 1: Table S2). Training adherence AT and RT was very high, only eight patients completed less than 36 AT sessions (one patient completed 35 sessions in AT group; one patient completed 34 sessions and four patients completed 35 sessions in LL-RT group; two patients completed 35 sessions in HL-RT group), and only one patient failed to complete all HL-RT sessions (35 completed sessions).Table 1Anthropometry, clinical characteristics and cardiovascular risk factors at baselineVariableSample ($$n = 59$$)AT group ($$n = 19$$)LL-RT group ($$n = 19$$)HL-RT group ($$n = 21$$)pAge (years)61 [8]61 [9]61 [7]62 [8]0.910Sex [males, (%)]44 [75]14 [74]15 [79]15 [71]0.931Anthropometry Height (cm)172.1 (8.4)170.4 (8.8)172.8 (8.6)172.9 (7.9)0.582 Weight (kg)85.47 (15.43)90.94 (19.04)81.46 (13.37)84.15 (12.56)0.148Clinical data LVEF (%)53 [9]50 [45,60]55 [50, 60]50 [45,58]0.454 Time from clinical event to inclusion in CR (months)2.0 (1.5, 3.0)2.0 (2.0,2.5)2.5 (1.5, 3.0)2.0 (1.5, 2.8)0.832Myocardial infarction, f (%) NSTEMI25 [42]9 [47]8 [42]8 [38]0.947 STEMI24 [41]7 [37]7 [37]10 [48] Unstable AP/PCI10 [17]3 [16]4 [21]3 [14]Comorbidities and risk factors, f (%) Arterial hypertension41 [70]15 [79]11 [58]15 [71]0.383 Hyperlipidemia49 [83]16 [84]14 [74]19 [91]0.384 Diabetes9 [15]4 [21]3 [16]2 [10]0.602 Atrial fibrillation5 [9]4 [21]1 [5]0 [0]0.038 Thyroid disease5 [9]2 [11]2 [11]1 [5]0.727 Renal disease4 [7]0 [0]2 [11]2 [10]0.534Smoking, f (%) Non-smoker14 [24]3 [16]3 [16]8 [38]0.346 Ex-smoker35 [59]13 [68]11 [58]11 [52] Current smoker10 [17]3 [16]5 [26]2 [10]Pharmacological therapy, f (%) ASA57 [97]17 [90]19 [100]21 [100]0.200 Beta blocker59 [100]19 [100]19 [100]21 [100]1.000 ACE inhibitor/ARB58 [98]19 [100]18 [95]21 [100]0.644 Statin/Ezetimibe59 [100]19 [100]19 [100]21 [100]1.000 Antiplatelets58 [98]18 [95]19 [100]21 [100]0.644 Anticoagulation5 [9]3 [16]1 [5]1 [5]0.509 Diuretic5 [9]4 [21]0 [0]1 [5]0.071Data are presented as mean (standard deviation) or as median (first quartile, third quartile)AT aerobic training, LL-RT low-load resistance training, HL-RT high-load resistance training, LVEF left ventricular ejection fraction, (N)STEMI (non)ST-segment-elevated myocardial infarction, AP angina pectoris, PCI percutaneous coronary intervention, ASA acetylsalicylic acid, ACE angiotensin-converting enzyme, ARB angiotensin II receptor blockers With exception of significant difference between groups in baseline triglycerides ($$p \leq 0.014$$), training groups did not differ in baseline glucose and insulin levels, HOMA2-IR and other blood lipids (Table 2). After adjusting for baseline difference, there was no significant difference between groups in post-training triglycerides levels ($$p \leq 0.927$$). Two-way ANOVA has shown a significant effect of time on total cholesterol and LDL (both $p \leq 0.001$), but no effects of time x group interaction on glucose levels, insulin levels, HOMA2-IR and blood lipids (all interaction $p \leq 0.326$). When compared with baseline, total cholesterol and LDL were significantly lower following AT [total cholesterol: − 0.4 mmol/l (− 0.7 mmol/l, − 0.1 mmol/l), $$p \leq 0.013$$; LDL: − 0.4 mmol/l (-0.7 mmol/l, − 0.1 mmol/l), $$p \leq 0.006$$] and HL-RT [total cholesterol: − 0.5 mmol/l (− 0.8 mmol/l, − 0.2 mmol/l), $$p \leq 0.002$$; LDL: − 0.5 mol/l (− 0.7 mmol/l, − 0.2 mmol/l), $$p \leq 0.002$$].Table 2Baseline and post training levels of glucose, insulin resistance and blood lipidsBlood markerGroupNBaselinePost-training2-way ANOVA/ANCOVATime effect/effect of baselineInteraction/post-training differenceGlucose (mmol/l)AT156.0 (1.2)6.1 (1.4)$$p \leq 0.741$$η2 = 0.002p = 0.791η2 = 0.010LL-RT165.6 (0.6)5.5 (0.7)HL-RT195.6 (0.5)5.7 (0.5)Insulin (pmol/l)AT1595 [46]98 [58]$$p \leq 0.923$$η2 = 0.000p = 0.885η2 = 0.005LL-RT1678 [38]77 [31]HL-RT1974 [56]70 [44]HOMA2-IR (units)AT151.82 (0.86)1.90 (1.12)$$p \leq 0.965$$η2 = 0.000p = 0.880η2 = 0.005LL-RT161.49 (0.71)1.46 (0.58)HL-RT191.40 (1.05)1.34 (0.82)Total cholesterol (mmol/l)AT193.8 (1.1)3.4 (0.9)$p \leq 0.001$η2 = 0.013p = 0.492η2 = 0.025LL-RT193.2 (0.7)3.0 (0.5)HL-RT213.6 (0.9)3.2 (0.5)HDL (mmol/l)AT191.2 (0.5)1.3 (0.4)$$p \leq 0.961$$η2 = 0.000p = 0.573η2 = 0.020LL-RT191.2 (0.4)1.2 (0.3)HL-RT211.2 (0.3)1.2 (0.3)LDL (mmol/l)AT192.0 (1.0)1.6 (0.7)$p \leq 0.001$η2 = 0.260p = 0.499η2 = 0.025LL-RT191.6 (0.5)1.4 (0.4)HL-RT212.0 (0.7)1.5 (0.4)Triglycerides (mmol/l)AT191.8 (1.0)1.7 (0.9)$p \leq 0.001$η2 = 0.649p = 0.927η2 = 0.003LL-RT191.3 (0.4)1.3 (0.5)HL-RT211.3 (0.4)1.2 (0.4)Data are presented as mean (standard deviation) or as median (first quartile, third quartile). Text in bold presents ANCOVA results. Glucose, insulin and HOMA2-IR are analysed only for nondiabetic patientsHOMA2-IR homeostatic model assessment for insulin resistance, HDL high density lipoprotein, LDL low density lipoprotein, AT aerobic training, LL-RT low-load resistance training, HL-RT high-load resistance training, ANOVA analysis of variance, ANCOVA analysis of covariance, η2 partial eta squared (effect size) Table 3 presents the change of body mass, lean mass and fat mass following training intervention in all groups. Training groups significantly differed in baseline fat mass (LL-RT vs AT = − 8.20 kg, $$p \leq 0.035$$). After adjusting for baseline difference, there was no significant differences between groups in post-training fat mass. Two-way repeated measures ANOVA has shown a significant time effect for lean mass, but no effects of time x group interaction on any of the body composition variables. When compared with baseline, AT group significantly increased fat % [mean difference ($95\%$ Confidence interval for mean difference), + $1\%$ ($0\%$, + $2\%$), $$p \leq 0.048$$], decreased lean mass % [− $1\%$ ($0\%$, − $2\%$), $$p \leq 0.048$$] and lean mass [− 1.05 kg (− 1.89 kg, − 0.20 kg), $$p \leq 0.016$$] following the training intervention. Similarly, HL-RT group significantly decreased lean mass [− 1.05 kg (− 1.87 kg, − 0.22 kg), $$p \leq 0.014$$].Table 3Baseline and post-training body compositionBody composition measureNBaselinePost-training2-way ANOVA/ANCOVATime effect/effect of baselineInteraction/post-training differenceBody mass (kg)AT1990.94 (19.04)90.49 (17.87)$$p \leq 0.187$$η2 = 0.031p = 0.974η2 = 0.001LL-RT1981.46 (13.37)80.91 (13.90)HL-RT2184.15 (12.56)83.47 (13.48)Fat (%)AT1928.2 (9.2)29.2 (8.7)$$p \leq 0.500$$η2 = 0.008p = 0.138η2 = 0.070LL-RT1922.3 (4.7)22.0 (5.2)HL-RT2024.9 (8.4)24.7 (7.6)Fat (kg)AT1926.0 (11.0)26.7 (10.3)$p \leq 0.001$η2 = 0.900p = 0.095η2 = 0.083LL-RT1917.8 (3.3)17.6 (4.5)HL-RT2021.0 (8.3)20.5 (7.5)Lean (%)AT1971.8 (9.2)70.8 (8.7)$$p \leq 0.497$$η2 = 0.008p = 0.139η2 = 0.069LL-RT1977.7 (4.7)78.0 (5.2)HL-RT2075.1 (8.4)75.3 (7.6)Lean (kg)AT1964.9 (13.6)63.9 (13.1)$$p \leq 0.002$$η2 = 0.166p = 0.354η2 = 0.037LL-RT1963.6 (12.5)63.3 (12.4)HL-RT2063.4 (11.7)62.4 (11.5)Data are presented as mean (standard deviation) or as median (first quartile, third quartile). Text in bold presents ANCOVA resultsLL-RT low load resistance training, HL-RT high load resistance training, AT aerobic training, ANOVA analysis of variance, ANCOVA analysis of covariance, η2 partial eta squared (effect size) Additional exploratory analysis of associations between post-training difference in blood markers and post-training difference body composition revealed no significant correlation when calculated on a whole sample and in patient subgroups with and without diabetes (Table 4). In absence of significant time x group interaction, additional comparison between baseline and post-training levels of glucose and insulin metabolism also showed no improvement in patients with ($$p \leq 0.220$$–0.910) and without diabetes ($$p \leq 0.713$$–0.953) (Table 5). In addition, the exploratory analysis of associations between post-training difference in glucose levels and post-training difference in statin dose showed only significant positive correlation following HL-RT (Spearman`s correlation coefficient = 0.471, $$p \leq 0.049$$) (Additional file 1: Table S3).Table 4Correlations between post-training difference in body composition and blood markersInsulin differenceGlucose differenceHOMA-IR differenceTotal cholesterol differenceHDL differenceLDL differenceTriglycerides differenceNon-diabetic ($$n = 49$$)Fat mass differenceSpearman rho− 0.0060.067− 0.0160.0710.0460.0320.000p0.9690.6450.9150.6300.7520.8251.000Lean mass differenceSpearman rho0.007− 0.0630.017− 0.076− 0.048− 0.039− 0.005p0.9630.6660.9080.6030.7440.7930.973Diabetic ($$n = 9$$)Fat mass differenceSpearman rho− 0.2330.250− 0.1330.008− 0.420− 0.0430.417p0.5460.5160.7320.9830.2600.9130.265Lean mass differenceSpearman rho0.233− 0.2500.133− 0.0080.4200.043− 0.417p0.5460.5160.7320.9830.2600.9130.265Sample ($$n = 58$$)Fat mass differenceSpearman rho− 0.0220.149− 0.016− 0.006− 0.038− 0.0460.090p0.8680.2640.9030.9650.7750.7330.503Lean mass differenceSpearman rho0.025− 0.1460.0190.0020.0370.041− 0.092p0.8550.2750.8880.9880.7800.7580.491Spearman rho Spearman correlation coefficient, HOMA-IR homeostatic model assessment for insulin resistance, HDL high density lipoprotein, LDL low density lipoproteinTable 5Baseline and post-training glucose and insulin metabolism in coronary disease patients with and without diabetesBaselinePost-trainingtpNon-diabetic patientsGlucose (mmol/l)5.7 (0.8)5.7. ( 0.9)− 0.3700.713Insulin (pmol/l)69 [48, 116]73 [47, 111]− 0.0650.953HOMA2-IR (unit)1.34 (0.93, 2.19)1.34 (0.89, 2.13)− 0.1210.909Diabetic patientsGlucose (mmol/l)8.6 (3.8)7.2 (2.0)1.3300.220Insulin (pmol/l)101 [80, 173]111 [76, 461]− 0.5330.652HOMA2-IR (unit)2.00 (1.70, 4.25)2.38 (1.46, 8.09)− 0.1780.910Data are presented as mean (standard deviation) or as median (first quartile, third quartile). HOMA2-IR homeostatic model assessment for insulin resistance, t-test statistic of paired samples t-test or Wilcoxon rank test (bold text)
## Discussion
This study is one of the first to compare the dose-dependent relationship between RT load and improvements in insulin resistance and lipids profile in patients with CAD enrolled in early phase II CR. The addition of RT to AT, regardless of the RT load showed no additional benefits on insulin resistance and lipids. However, HL-RT and AT decreased total cholesterol and LDL following training intervention, whereas there were no differences between training modalities in body composition or blood biomarkers. In addition, there was also no relationship between post-training difference in body composition and post-training difference in blood markers in patients with CAD.
The levels of insulin resistance were not improved with the addition of RT to AT, likely due to lower HOMA2-IR values than are cut-off values for determining potential metabolic risk in nondiabetic individuals (HOMA2-IR > 1.8) [26]. This contrasts with previous studies, which mostly included patients with obesity, the metabolic syndrome and diabetes with worse metabolic and body composition status (e.g., higher body fat % and body mass index) in comparison to our sample of patients with CAD [27–32]. Moreover, fewer multiple exercise interventions were performed in patients with CAD [11–13, 33, 34]. After exercise-based CR, studies showed no difference between combined AT and RT, and AT, RT or usual care alone in glucose, insulin resistance and/or blood lipids, similarly, as observed in our study. Most interventions with longer training duration (> 8 weeks), regardless of training modality (AT, RT or combined AT and RT), improved insulin resistance and blood lipids levels [33, 34], which partially corroborates with benefits observed following AT and HL-RT in our study. Otherwise, shorter training intervention (< 6 weeks) failed to elicit any between-group or within groups improvements [12, 13], despite using similar RT loads as longer training interventions ($60\%$-$65\%$ of 1-RM). In our study, the use of only single lower limb resistance exercise likely elicited suboptimal stimulus for any additional cardiometabolic benefits.
With a high prevalence of co-exiting diabetes in patients with CAD [8], our findings can be compared with similar interventions in patients with metabolic syndrome, prediabetes or diabetes. The studies that enrolled similarly aged patients with metabolic syndrome have demonstrated absence of between-group difference and only post-training improvements in insulin resistance and/or blood lipids following each training modality [27, 28]. On contrary, one study has shown superiority of combined RT and AT over AT alone on insulin resistance in middle-aged obese individuals [30]. When compared with our findings, the authors measured insulin resistance only 12 h after the last training session, which may in combination with more metabolically demanding protocols of AT (weekly exergy expenditure of 14 kcal/kg of body weight) and HL-RT (multiple whole body resistance exercises) explain the discrepancy between studies. In addition, the worse metabolic clinical status of the participants cannot be ruled out [30], as our patients entered the CR with optimized drug therapy and with lower prevalence of diabetes as expected, thus, the improvement in insulin resistance was harder to achieve, regardless of the training modality. Furthermore, our findings are also in line with a recent meta-analysis of patients with type II diabetes that showed similar effects of LL-RT and HL-RT when compared with AT on glycated hemoglobin, insulin levels and insulin resistance [32]. In contrast to our findings, the analysis even showed a superior effects of HL-RT over usual care in reduction of fasting glucose (− 0.92 mmol/l). In addition, LL-RT was associated with a greater decrease in insulin levels than HL-RT when compared to usual care [32], which suggests that gains in muscle endurance may play a superior role over maximal muscle strength gains in improvement of insulin metabolism. However, direct comparison with our study cannot be made due to only indirect comparison between HL-RT and LL-RT in the meta-analysis [32] and due to lower prevalence of diabetes in our study ($15\%$). The effects of dose-dependent relationship between RT load and improvement in insulin resistance also remains to be established in similarly aged older adults without type II diabetes, whereas previous interventions have only combined LL- to moderate load-RT with AT and have showed an improvement in insulin resistance over AT alone [35, 36].
The effect of combined AT and RT on body mass differed among previous studies in patients with CAD [21, 33, 37, 38], with two similarly designed studies supporting our findings [21, 37]. Most previous interventional studies showed decreased fat mass and/or fat % following combined AT and RT [33], which was superior to AT alone [21, 37–39]. In contrast, we have demonstrated an increase in fat % following AT, and maintained fat % following both LL-RT and HL-RT. Our results can be partially explained by an increased energy demands when participating in RT, as similarly increased metabolism after RT was observed in healthy older adults (up to $15\%$ of total daily energy expenditure) [40]. Furthermore, muscle hypertrophy was evident following most combined AT and RT interventions in patients with CAD [21, 37–39], with greater stimulus achieved following moderate-to HL-RT [21] or HL-RT [38] and after longer intervention (> 24 weeks) [21, 37]. Since most of the previous studies applied multiple RT exercises for upper and lower extremities [21, 37, 38], it seems that using only one lower extremity RT exercise may have provided inadequate stimulus to promote superior effects on lean body mass when compared with solely AT. In addition, the discrepancies between the findings can also be attributed to type of measure, as most of the previous studies measured body composition with Dual-energy X-ray absorptiometry, which is more accurate than bioimpedance [41] used in our study.
Our findings, although novel, need to be interpreted with regards to following limitations. Firstly, our study was primarily powered only for primary study outcomes (maximal aerobic capacity and maximal voluntary contraction) [14], therefore all secondary outcomes of this study must be interpreted as exploratory, especially HOMA2-IR. Nevertheless, our sample size was comparable to some of the previous studies in patients with CAD [13, 33]. Secondly, coronavirus-19 pandemic restriction prevented blinding of the outcome assessors. In addition, the staff relocations to other departments also limited the inclusion of more than one lower limb exercise (e.g., leg press) in RT, which may elicit greater changes in body composition, and glucose and lipids metabolism, and consequently distinguished the effects between training interventions. Thirdly, the prevalence of diabetes was low in our sample ($15\%$) compared with recent EUROASPIRE IV survey cohort [8], therefore, our results cannot be directly translated in CAD patients and predominately co-exiting diabetes. Future multimodal training intervention should therefore include more CAD patients with metabolic syndrome or with diabetes. Lastly, higher doses of lipid lowering drugs were likely superior over exercise training effects.
## Conclusions
In conclusion, the combination of RT with AT, regardless of RT load, does not enhance benefits on HOMA2-IR and lipids when compared with solely AT. Therefore, AT alone with a combination of optimal pharmacological therapy and lifestyle modifications (dietary and physical activity advice) presents an adequate training modality to optimally control insulin resistance and blood metabolism. Otherwise, the addition of HL-RT or LL-RT to AT may still provide greater benefits on maximal muscle strength and physical performance over AT alone, as shown in our previous reports [14, 42], and should be therefore applied according to patients` abilities. Despite favorable implications of our RT protocols, future training interventions should include more patients with CAD and diabetes and apply multiple upper and lower limb RT exercise to examine whether greater training workloads would elicit additional benefits on metabolic control compared to AT alone in patients with CAD.
## Supplementary Information
Additional file 1: Table S1. Statin or ezetimibe dose at baseline and post-training. Table S2. Lipids lowering drugs at baseline and post-training. Table S3. Correlations between post-training difference in glucose levels and statin dose.
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|
---
title: 'Young Adults’ Perspectives on the Implications of an Augmented Reality Mobile
Game for Communities’ Public Health: A Qualitative Study'
authors:
- Yuk Chiu Yip
- Ka Huen Yip
- Wai King Tsui
journal: International Journal of Public Health
year: 2023
pmcid: PMC10014459
doi: 10.3389/ijph.2023.1605630
license: CC BY 4.0
---
# Young Adults’ Perspectives on the Implications of an Augmented Reality Mobile Game for Communities’ Public Health: A Qualitative Study
## Abstract
Objectives: Several physical, psychological, and social health consequences are caused by smartphone users’ addiction to games. A location-based game (LBG), Pokémon GO, recently garnered significant attention from young people. This study aims to explore their experiences with this game and motivations for playing, investigating their perspectives on the game’s implications for themselves and the public health of their communities.
Methods: Ten qualitative focus group interviews were conducted. Young adults, aged 18–25 years ($$n = 60$$), were recruited in Hong Kong. Data were analyzed using a thematic approach.
Results: Five themes emerged: 1) missing out or self-regulation, 2) childhood memories of Pokémon, 3) extending virtual-reality exploration, 4) spending more time outdoors walking and exercising, and 5) getting together with others and social interaction.
Conclusions: This study showcases the motivational factors of young adults and their cohorts in societies worldwide. LBGs may impact players’ physical and social activity levels, and behavior. Nonetheless, certain negatives were identified (i.e., addiction and behavior resulting from a loss of self-control). These negatives deserve health practitioners’ attention and future studies should explore possible public health interventions
## Introduction
The last decade witnessed an emergence of the prevalent use of smartphones. Such devices have become a vital component of daily life, and their integration has been facilitated by their low cost and technological advances. Smartphones with sophisticated mobile operating systems have characteristics similar to those of handheld personal computer systems [1]. In Hong Kong (HK), the demographic that mostly uses and owns smartphones involves young people, with nearly $92\%$ over the age of 10 [2]. Internet services, online entertainment, games, telecommunications, and social connections are all possible via multifunctional smartphone devices (3–8). The psychological and physical health of smartphone users who play online games remains an active area of public health research (9–15).
Location-based games (LBGs), a mobile technology enabling people to experience immersive game environments, provides augmented reality environments in which digital images are layered onto reality [16, 17]. Pokémon GO is an augmented reality game with an LBG component [4, 18, 19]. Players participate in a virtual environment through their smartphones, in which they hunt for and catch digitally animated characters. Real geographical locations, tied to virtual environments, are a distinctive characteristic of LBGs. Various animated characters are displayed in real-world locations via augmented reality, which draws in young users. After downloading the game or application, users search for the animated characters in the physical environment that is familiar to them, that is, their neighborhood. Thus, physical exercise in the form of walking around one’s neighborhood is stimulated and encouraged, as many people are drawn into the game and its mechanics. However, the physical and mental health of an individual might also be negatively impacted by the excessive time spent playing LBGs [3, 12, 17, 19].
Consequently, it is crucial to explore how the physical and mental wellbeing of young people are impacted by LBG engagement. In terms of the social, psychological, and physical impacts of gaming addiction, multiple effects on health are noted in the literature (7, 9, 12, 17, 19–23). On the one hand, depression can be alleviated by such engagement, and the benefits of physical exercise can mitigate Type 2 diabetes and reduce the issues caused by obesity (9, 24–29).
On the other hand, players may become addicted to games (30–35). When enthusiasm gives way to addiction, studies have shown that the health consequences may become decidedly negative [16, 36, 37]. Nonetheless, individual health can be beneficially affected by games that encourage healthy lifestyles [38]. Patients suffering from pain have reported the utility of LBGs in distracting them from their discomfort [4, 39]. Depression, social anxiety, and other psychological disorders experienced by players can also be alleviated through game engagement [40, 41]. Furthermore, the reduction of sedentary behavior and elevated physical activity are direct outcomes for anyone who engages with Pokémon GO, regardless of age (42–44). This is exemplified by the approximately 2,500 steps taken daily by LBG players, per statistics provided by location-based technology [45, 46].
Following a thorough literature review, we found that the health-related implications associated with the continual engagement in LBGs have not been studied extensively among young adults. Thus, this qualitative study aims to explore the insights of their experiences with Pokémon GO as a well-known LBG and their motivations for playing, in order to investigate their perspectives on the game’s implications on the public health of their communities. In alignment with the above-mentioned aims, the research objectives of this qualitative study are as follows: 1) to explore the underlying factors that constitute the primary driving force for young adults to develop continual engagement with LBGs—in our case, Pokémon GO, and 2) to examine the implications of the continual engagement of LBGs for the young adults in terms of their social, psychological, and physical wellbeing, and in relation to the public health of their communities at large.
## Methods
Focus groups (FG) were used to collect data from group interactions and discussions providing diverse explanations and outlooks concerning the central topics [47]. This study was approved by the Committee on the Use of Human and Animal Subjects in Teaching and Research at the Tung Wah College (Ref. no. RESC2017003). Participants provided written consent for their participation in this study and were assured that their experiences and interview content would be reported anonymously in an international journal. The details of the methodological considerations were provided in Table 1.
**TABLE 1**
| Participants | The inclusion criterion for selection as a participant encompassed being a recent or currently active player of Pokémon GO. Interest in study participation was ascertained by contacting potential participants individually; the eligibility criteria for participant selection, the objective, and methodology of the paper were communicated to the participants. The time and venue for data collection procedures, assessment of eligibility, and elucidation of the study’s objectives were discussed in interactions with potential participants. Regarding the demographic information of the participants, the occupations of these participants were not collected as the research team believed that this socio-demographic characteristic did not seem relevant to the current research’s aims and objectives. Thus, based on the ethical principle to avoid collecting unnecessary participant information, the research team decided to not include the question of occupation in the data collection process. |
| --- | --- |
| Data collection | Prior to the consent process, the participants were permitted to ask questions. The risks, methodology, and objectives of the study were communicated to them at the beginning. The audio of the FG sessions was recorded with the permission of the participants. The focus group questions in Table 2 were used to determine what factors in social, individual, interpersonal, and environmental domains relate to the insights of young people’s experiences with Pokémon GO, their motivations for playing, and their perspectives on the game’s implications for the public health of their communities (48, 49). The FGs were moderated by the second author (KH), who has extensive experience in administering qualitative health research techniques. To ensure that the questions, the flow of the discussion, and the content were well received, a pilot FG was conducted by the research team (YC, KH, and WK). Viewpoints following LBG engagement and alterations in physical activity were discussed with the participants. The discussion ended after further details and interpretations were sought through additional inquiries. Based on the availability of participants, FG interviews were conducted in a quiet and comfortable room of a local academic institution. |
| Data analysis | Codes corresponding to 1) the underlying factors that constitute the primary driving force for young adults to develop continual engagement with LBGs and 2) the implications of the continual engagement of LBGs were determined via an analysis of the transcripts during the initial coding phase conducted by YC and KH, thus enacting a deductive process (48, 50). Additionally, an inductive process was undertaken by WK to examine the transcripts (50). As part of the study’s analysis strategy, themes and secondary codes, in line with the ecological model, were generated following a research team discussion (YC, KH, and WK). The final coding was cross-checked by different team members. Finally, the themes, topics, and insights outlined in the FGs were subject to a final check by YC and KH, to ensure that all possible areas were covered. |
| Ethics statement | This study was approved by Tung Wah College, Committee on the Use of Human and Animal Subjects in Teaching and Research (HASC) (Ref. no. RESC2017003). All participants provided written consent for their participation in this study. All participants were assured that the experiences they share, and their interview content would be reported in an international journal anonymously. |
## Participants
The snowball sampling technique was used to recruit 18- to 25-year-old participants. In this study, a total of 60 participants were recruited. A large proportion ($75\%$) of the participants included males, although the applied sampling strategy did not preclude the participation of female participants. The average age of the participants was 20.9 years.
## Data Collection
Participant recruitment and data collection were conducted by YC, KH, and WK. Between July 2017 and July 2018, ten FG discussions were conducted. Each discussion lasted approximately 45–60 min, consisting of five to seven participants per group. The basic demographic information of the participants (such as the age and sex) and the participants’ activity on Pokémon GO were collected prior to the commencement of the FG.
A literature review was performed by the research team (YC, KH, and WK) on online games and LBGs, including the psychosocial and physical health perceptions linked to gaming. The review served as the basis for the discussion guide formulated in this paper, as shown in Table 2 (4, 23, 51–54). Data saturation was confirmed by all researchers at the completion of the eighth FG discussion, with no novel data arising from subsequent rounds of the FG [55].
**TABLE 2**
| - | Can you describe your experiences of playing Pokémon GO outside? |
| --- | --- |
| - | Since the game launched, what do you think its impact on daily life has been from a public health perspective? |
| - | What attributes of the game do you find most appealing? |
| - | How has your social life changed since you began playing Pokémon GO? |
| - | From a public health perspective, how do you think the game alters your interactions with friends, family, and strangers? |
| - | From a third-person perspective, can you share how your family and acquaintances rate the Pokémon GO game? What do you think about their opinions? |
| - | If there were no Pokémon GO now, how would you use the time that you used to devoted to it? |
| - | Overall, based on your perceptions of public health, what do you think are the negatives and positives of playing Pokémon GO? |
## Data Analysis
The thematic analysis method was used to examine the verbatim transcriptions of the audio recordings of the FG sessions [50]. Initial coding and code refinement of the transcripts were facilitated using the NVivo 12 qualitative software (QSR International, Melbourne, Australia). The themes ultimately derived from the analysis process were discussed in the results section.
## Results
As Table 3 illustrates, the Pokémon GO trainer level of the participants was as follows: two were between trainer levels 16–20; four between trainer levels 21–25; nine between trainer levels 26–30; nine between trainer levels 31–35; 25 between trainer levels 36–40; and 11 above trainer level 41. Table 3 also illustrates the amounts of money the participants spent on Pokémon GO as at the interview day: nine spent no money; three spent between 1 and 12 USD; nine spent between 13 and 64 USD; 27 spent between 65 and 130 USD, and 12 spent over 130 USD. The average playing time, per day, was 4.9 h and the average duration spent playing was 5.3 months.
**TABLE 3**
| Measure | n | Percentage (%) |
| --- | --- | --- |
| Sex (Number of participants) | Sex (Number of participants) | Sex (Number of participants) |
| Female | 15 | 25 |
| Male | 45 | 75 |
| Age (Number of participants) | Age (Number of participants) | Age (Number of participants) |
| 18–19 | 12 | 20 |
| 20–21 | 24 | 40 |
| 22–23 | 15 | 25 |
| 24–25 | 9 | 15 |
| Number of months spent playing Pokémon GO | Number of months spent playing Pokémon GO | Number of months spent playing Pokémon GO |
| 1 | 0 | 0 |
| 2 | 3 | 5 |
| 3 | 6 | 10 |
| 4 | 6 | 10 |
| 5 | 18 | 30 |
| >6 | 27 | 45 |
| Average number of hours spent per day | Average number of hours spent per day | Average number of hours spent per day |
| <1 | 0 | 0 |
| 1–2 | 6 | 10 |
| 3–4 | 12 | 20 |
| 5–6 | 22 | 36.7 |
| >6 | 20 | 33.3 |
| Trainer level (in the Pokémon GO) | Trainer level (in the Pokémon GO) | Trainer level (in the Pokémon GO) |
| 1–5 | 0 | 0 |
| 6–10 | 0 | 0 |
| 11–15 | 0 | 0 |
| 16–20 | 2 | 3.3 |
| 21–25 | 4 | 6.7 |
| 26–30 | 9 | 15 |
| 31–35 | 9 | 15 |
| 36–40 | 25 | 41.7 |
| >41 | 11 | 18.3 |
| Money spent (USD) as at the interview day | Money spent (USD) as at the interview day | Money spent (USD) as at the interview day |
| 0 | 9 | 15 |
| 1–12 | 3 | 5 |
| 13–64 | 9 | 15 |
| 65–130 | 27 | 45 |
| >130 | 12 | 20 |
| Number of venues for playing | Number of venues for playing | Number of venues for playing |
| 1 | 0 | 0 |
| 2 | 0 | 0 |
| 3 | 0 | 0 |
| 4 | 3 | 5 |
| 5 | 3 | 5 |
| 6 | 21 | 35 |
| All locations | 33 | 55 |
## Theme 1: Missing Out or Self-Regulation
Addiction, harming oneself or others by colliding with objects on the street, or social disturbance were among the potentially harmful impacts that every participant acknowledged as a possibility resulting from playing LBGs.
Many participants reported that they had had similar experiences as they reflected on their memories of accidents that had occurred when players were focusing on hunting for Pokémon. They recognized that players engrossed in the game in public might disturb others.
Some conflicting opinions were expressed concerning players’ lack of self-control. Apart from mobility and improved sociability, participants who were loyal fans of Pokémon GO, devoted a huge sum of their pocket money to make in-game purchases. Participants agreed that, once an individual demonstrated behavior that reflected a compromise in self-control (an excessive devotion of time to the game at the expense of sleep and rest time, an urge to devote money to “equip” the game character in order to dominate in the game, etc.), it would indicate the start of an “addiction.” Some of them also reflected that this uncontrollable behavior affected their job performance.
## Theme 2: Childhood Memories of Pokémon
According to the participants, the nostalgia elicited by the connection of childhood memories, from watching the Pokémon TV show and movies, to playing the LBG in reality, was the principal appeal. Their smartphone devices enabled virtual participation in the Pokémon world, but the pursuit of the Pokémon occurs physically in the real world; combined with nostalgia, this provides a pleasurable experience. Given that Pokémon was one of the most popular television series during the participants’ childhood, it is an attractive and exciting proposition. Half of the participants were inspired to begin playing Pokémon GO for this reason.
Many participants felt that the Pokémon were actually living in the real world, as the digital image of the character appeared in the setting where they were standing when the smartphone camera was on; they would then be required, in the virtual world, to throw a ball at the image to catch it. This interactive gaming experience represents a secondary factor, which motivates the participants to devote time and energy to the game, because they described it as “like I am the real character, as in the game when the character is adventuring, and you do not know what will happen next.” The surprise that comes with the adventure drives participants to explore the world in the LBG by walking around in the real world.
## Theme 3: Extending Virtual-Reality Exploration
According to several participants, reality and the virtual world are connected when playing Pokémon GO. Searching HK within one’s neighborhood is incentivized for Pokémon GO players. Legendary Pokémon characters reside only in specific famous locations around the world. Consequently, the game encourages traveling to other countries or new locations nearby. At times, health campaigns and activities may be noted by the participants, increasing their awareness of some population health issues.
Some participants were active players, who expressed the enjoyment they received from hunting for Pokémon in a variety of places, while aiming to be one of the world’s master trainers. They emphasized that doing so could be a useful tool for acquiring an in-depth understanding of the geography and culture of their neighborhood.
## Theme 4: Spending More Time Outdoors Walking and Exercising
According to every participant, the objective of seeking out Pokémon GO’s characters tangibly altered their daily routines, modifying their walking habits and increasing the amount of exercise they undertook. The majority of participants considered it possible that people who were socially withdrawn could, in a certain sense, be brought out of their shells by Pokémon GO. A positive reaction from an autistic child was witnessed by one of the participants, who noted the child’s joy at acquiring a new character after stepping out the front door with her mother.
Some of the participants experienced difficulties in altering their daily habits. Acquiring a Pokéstop or a Pokémon egg, was achieved by many participants through small alterations to their routines, such as walking to certain locations or getting off one stop earlier on their public transportation route. Particularly for age groups that were frequently physically inactive, such as the parents of the participants, exercise was promoted by the LBG engagement.
## Theme 5: Getting Together With Others and Social Interaction
Inter-generational connection, increased peer interactions, the congregating of like-minded people, and increased social interaction are among the game’s benefits, owing to the common objective shared by all players.
All participants expressed that there was a common goal whenever one was playing the game and that large groups of people would never miss the opportunity to hunt for a Pokémon.
Most participants (nearly $91\%$) observed that they shared a common goal, which favored meeting up with or reuniting with others. They felt that the game had the potential to improve their social skills, to make them better at communicating with others.
Most of them (approximately $86.6\%$) also noted that a variety of age groups participated in this game. The common goal generated communication and interaction between different generations.
## Discussion
The cognitive, psychological, and physical health of Pokémon GO players may be positively affected in their day-to-day lives by playing the LBG [56]. For positive outcomes to occur in these aspects of health, the level of players’ engagement in a particular LBG over time is an important question for researchers who intend to apply LBG as a health promotion tool at a population level. Though the nature of this study as a qualitative inquiry may limit our ability to answer that important question, our study revealed that the extent of the players’ engagement and motivation to play the game is largely determined by their childhood memories (as Pokémon in this case). This finding implicates that, if a LBG is to be constructed for the purposes of health promotion, a careful selection of games (and/or the characters in games) may be required; these games and/or characters should be able to stimulate players’ memories and thereby internally motivating the players to engage in the gaming activities over time. To confirm this argument, the comments from 51 participants in our study may be relevant. They noted repeatedly in the interviews that the “mental connection [that is, how a player of LBG perceives and experiences the game]” plays a major role in maintaining the engagement in the gaming activities through time. Our study found that the gaming design of Pokémon GO enables players to feel the experiences and sentiments of an adventurous and interactive journey via augmented reality technology. As the participants experienced the sensation of becoming a Pokémon trainer in real life, specific memories are generated. These memories serve as a driving force to keep them engaging in the game, leading to alterations in cognitive behaviors through time (for example, an increase in the player’s social interactions with individuals in their neighborhood). Our study confirms the findings from similar research that the positive influence on mental and physical health appeared to take effect through various engaging features of LBG, namely, human interaction, community integration, and physical participation [38].
Our study findings suggested that walking more than usual was incentivized, the formation of exercising habits was stimulated, and prolonged game engagement can lead to high levels of daily physical exercise. For example, the physical activities such as walking, and running are the core of the treasure-hunt tasks required by the players in Pokémon GO. In the game, encountering other characters or stimulating the hatching of eggs, is more likely when walking than when using various forms of transportation, which led to some participants increasing the amount they walked [57]. Similar to our findings, research in other countries also pointed out that, after playing the game, an average increase of between 1,000 and 2,000 steps was observed by the players [58, 59]. To support the notion that exercising habits were stimulated through gaming experiences, a majority of the participants in our study mentioned that when searching for a Pokémon that was only available for a limited time, they would run toward the target. They also observed that many players develop this habit of running in their rapid pursuit of rare Pokémon characters. As pointed out in the literature [60], the increases in physical activity of an engaging player may range from moderate to substantial. However, as a word of caution, our research team also noted the drawbacks of excessive engagement of LBG using smartphones [38, 61], and these findings should be considered when LBG is used for the purposes of health promotion.
An important finding of this paper is that the application of an LBG may help integrate a community, which may have implications when LBG is used to promote health at a population level. The design and objective of LBG technology is in part conveyed through the treasure-hunt mechanic, which led the players (and the participants in our study) to explore novel locations within their communities [4, 60, 62, 63]. Specifically, owing to the game requirement to search for Pokémon characters in multiple locations, participants reported an increased desire to visit places that they never or rarely would have. Increased knowledge and familiarity with one’s neighborhood or city are encouraged via the repeated walks through the community. As a result, approximately $80\%$ of our participants noted that, because of their perceived closeness with the community, they are more aware of the health promotional campaigns in their cities. Other studies also indicate the effects of LBG in terms of community integration [4, 64].
Pokémon GO enables the possibility of fortifying and building human relationships, implicating that positive effects on the population health may not solely at the physical level, but also at a psychosocial level. Our study confirms that rather than electronic communication via text, in-person social interactions are incentivized via the gameplay mode [3, 4, 65]. Similar to other studies [3, 66], our study found that participants engage with a common goal without social anxiety when they are virtually battling in gyms in the same geolocation. In pursuit of this common goal, players unknown to one another can communicate with each other if they are in the same vicinity.
The recent surge in Pokémon popularity has led to multiple age groups engaging with the free-to-play game (67–69). Our study reveals that players are stimulated to develop positive relationships with not only other players, but also their parents through game engagement within a family. According to the participants, common issues like generation gaps, were bridged and relationships were developed. This positive benefit on family health may be explained by that the participants’ childhood memories of Pokémon resurfaced through playing the game. Such recaptured memories are not merely enjoyed again but are lived out and reflected through playing among people of different generations, leading to the closing of communication gaps between generations (70–72).
Nonetheless, our study also highlighted one possible drawback of the application of LBG in promoting activity levels. Social disturbance and other risks within the community have been caused by the significant influx of players pouring into the streets, while being focused on the gameplay on their smartphones. Thus, players blindly crossing the street, colliding with objects in the street, and running into people, are among the physical drawbacks that other studies have identified as a result of distracted players [4].
## Limitations
One of the limitations of this study involved its qualitative methodological nature, which does not contain any statistical tests that may help in isolating the major contributive factors motivating young adults to play LBGs. Nonetheless, in terms of achieving the research aims, our study design enabled us to explore, with open-ended questions, the in-depth feelings and emotions of the players developing continual engagement with the LBG. In this sense, the research objectives were adequately addressed, according to the research team, through authentic and detailed descriptions of the participants’ perspectives and insights within contexts. Another limitation of this study may be associated with the relative proportion of male participants. Although the sampling strategy of our study did not preclude any female participants from participating (nor did the research team impose any selection criteria on the sex of the participants), a larger proportion of male participants may have certain effects on the narratives (possibly because of the differences in gaming behaviors of males and females, if any). Nonetheless, our research team ensured that data saturation was achieved, and that during the coding process, we did not detect any significant divergence of views and perspectives in terms of the participants’ sex.
## Conclusion
Our findings shed light on the novel phenomena in terms of individual and community health underlying LBG engagement. The experiences and behavioral trends of Pokémon GO players in real-life settings were revealed. It is evident that both positive and negative impacts arising from LBG engagement exist. For example, through game engagement within a family, players are stimulated to develop positive relationships with not only other players, but also their parents. On the contrary, walking more than usual was incentivized. Players’ lack of self-control, addiction, harming oneself or others by collisions with objects on the street, or social disturbance, were the potentially harmful impacts. The findings are particularly relevant to future endeavors concerning the development of public health interventions with the use of LBG with augmented virtual technology to improve population health.
## Ethics Statement
This study involving human participants was reviewed and approved by Committee on the Use of Human and Animal Subjects in Teaching and Research (Ref. no. RESC2017003) Tung Wah College (Hong Kong). The participants provided their written informed consent to participate in this study.
## Author Contributions
KY: conceptualisation, methodology, formal analysis, investigation, writing the original draft, supervision, and project administration. YY: methodology, formal analysis, investigation, writing, reviewing and editing. WT: investigation, reviewing and editing. All authors have read and agreed to the published version of the manuscript.
## Conflict of Interest
The authors declare that they do not have any conflicts of interest.
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|
---
title: Weakly acidic microenvironment of the wound bed boosting the efficacy of acidic
fibroblast growth factor to promote skin regeneration
authors:
- Qiao Pan
- Ruyi Fan
- Rui Chen
- Jiayi Yuan
- Shixuan Chen
- Biao Cheng
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC10014462
doi: 10.3389/fbioe.2023.1150819
license: CC BY 4.0
---
# Weakly acidic microenvironment of the wound bed boosting the efficacy of acidic fibroblast growth factor to promote skin regeneration
## Abstract
The pH value within the wound microenvironment influences indirectly and directly all biochemical reactions taking place in the process of skin wound healing. Currently, it is generally believed that a low pH value, such as it is found on normal skin, is favorable for wound regeneration, while some investigations have shown that in fact alkaline microenvironments are required for some healing processes. The role of growth factors in promoting wound healing requires a specific microenvironment. In wound microenvironments of different pH, growth factors with different isoelectric points may have different effects. To explore whether the application of FGF with different isoelectric points in wounds with different pH values interferes with the healing process to different degrees, GelMA hydrogels with different pH values were prepared to maintain the wounds microenvironment with the same pH values, in which aFGF and bFGF were loaded as well. The results show that GelMA hydrogels of different pH values maintained the same pH of the wound microenvironment sustainably on the 4th day. Moreover, aFGF and bFGF promoted skin wound healing to varying degrees in different pH wound microenvironments. In particular, aFGF significantly promoted wound re-epithelialization in a weak acidic microenvironment, while bFGF promoted collagen synthesis and deposition in the early stage of weak acid wounds. In addition, aFGF plays a superior role in inhibiting inflammation in weak acidic wounds.
## 1 Introduction
Skin wound healing is a dynamic and sequential process with multiple events, including the secretion and release of cytokines, and various cell activities (e.g., proliferation, migration, differentiation, as well as the synthesis and remodeling of extracellular matrix) (Gillitzer and Goebeler, 2001; Gurtner et al., 2008). During this process, the pH value of the local microenvironment is a very important factor that can indirectly or directly affect a series of repair reactions during the process of wound healing (Schneider et al., 2007; Schreml et al., 2010). The pH value of normal skin is between 4.8 and 6.0 due to the organic acid secreted by keratinocytes. Bennison et al. [ 2017] When the integrity of the skin is damaged, alkaline tissue fluid and the plasma in the broken capillaries are extravasated, the pH value of the wound area will significantly increase. Milne and Connolly [2014] Previous studies have shown that a low pH value of the wound microenvironment is more conducive to wound healing, especially when it is closer to normal skin. Sim et al. [ 2022] Under the acidic microenvironment of the wound, not only the infection risk is low, but also the granulation tissue formation and neovascularization are effectively promoted. While in the alkaline wound microenvironment, bacterial infection and biofilm are more easily formed. In addition, protease hydrolysis is further intensified, especially in a chronic wound, which is harmful to wound healing.
Growth factors, including fibroblast growth factor (FGF), platelet-derived growth factor-BB (PDGF-BB), Epidermal growth factor (EGF), Vascular endothelial cell growth factor (VEGF), and so on are essential to wound healing (Barrientos et al., 2008; Schultz and Wysocki, 2009; Behm et al., 2012). Among these growth factors, FGFs are not only the most common mitogens, but also are multifunctional growth factors (Turner and Grose, 2010; Ornitz and Itoh, 2015). In terms of wound healing, FGFs are all-powerful growth factors. It is capable of regulating several important biological events of wound healing, such as granulation tissue formation, angiogenesis, and re-epithelialization (Hui et al., 2018; Guo et al., 2019). In humans, 23 members of the FGF family have been identified, Ornitz and Itoh [2001] and FGF1 and FGF2 are the most common to know. The FGF1 is also known as the acidic fibroblast growth factor (aFGF), and FGF2 is also known as the basic fibroblast growth factor (bFGF). Both the aFGF and bFGF have been widely encapsulated into hydrogels, particles and nano/micro-fibers for the application of acute and chronic wound repair. However, these studies did not consider the influence of local pH value of wounds and substrates. The changes in pH value of the microenvironment may affect the effectiveness of growth factors with different isoelectric points.
Herein, we hypothesized that aFGF or bFGF with different isoelectric points applied to the wound bed with different pH may interfere with the healing process to varying degrees. GelMA hydrogel is widely used in various drug release systems and wound biological dressing materials due to its suitable biological properties, adjustable physical properties, and basic characteristics that are similar to the natural extracellular matrix. To verify the above hypothesis, GelMA hydrogel was selected as the carrier of two growth factors to extend their action time, we also adjusted the pH value of the hydrogel to regulate the pH value of the wound microenvironment, so that to observe the healing role of aFGF and bFGF in different microenvironments during the process of wound healing.
## 2.1 Preparation of acidic- and basic- FGF-loaded GelMA hydrogels with different pH values
GelMA was dissolved in phosphate-buffered saline (PBS) to prepare GelMA solution at a certain concentration of $6\%$ (w/v). The solution was stirred in a water bath at 37 °C and 500 r/min until the GelMA solid completely dissolved. All GelMA solutions contained 2,959 (Irgacure2959, Sigma-Aldrich, United States, $98\%$ pure) at a concentration of $0.5\%$ (w/v), and the pH value of the solution was adjusted to 6.4, 7.4, and 8.4, respectively. The aFGFs and bFGFs were separately added to the GelMA solutions with different pH values at a concentration of 4ug/ml. Then solutions of 6.4-GelMA/aFGF, 7.4-GelMA/aFGF, 8.4-GelMA/aFGF, 6.4-GelMA/bFGF, 7.4-GelMA/bFGF, and 8.4-GelMA/bFGF were sterilely filtered through a 25 µm microporous membrane. 50μl of GelMA solution was placed into each cylindrical mold with an inner diameter of 8 mm and a height of 1 mm and each hydrogel scaffold containing 200 ng of FGF was subsequently fabricated by photo cross-linking.
## 2.2 SEM observation
The internal structure of the freeze-dried GelMA hydrogels was observed using a scanning electron microscope (Hitachi S-3400N, Japan). Before observation, platinum was coated by ion sputtering (Hitachi E−1010) for 60 seconds. Before taking digital photomicrographs from randomly selected areas of the samples with a magnification of ×100 and 400×, at an acceleration voltage of 5 kV.
## 2.3 Swelling ratio test
The swelling behaviors of GelMA hydrogel with different pH values were measured by weighing both the initial and swollen hydrogels. Briefly, the initial weights of the hydrogel scaffolds (W0 values) were measured. The hydrogel scaffolds were then placed in PBS (Gibco, United States) at 37 °C and allowed to swell for 15, 30, 45, 60, 90, 120, and 180 min. The wet weights of each of the hydrogel scaffolds (Wt values) were measured at each time point. The swelling ratios were calculated using the equation below. For this test, $$n = 10$$, and each measurement was repeated 3 times. Esr %=Wt –W0/W0×$100\%$ Here, Esr (%) is the swelling rate of the hydrogel, *Wt is* the wet weight of the hydrogel after absorbing water, and W0 is the initial dry weight of the hydrogel.
## 2.4 Drug release test
The release test of aFGFs and bFGFs from GelMA hydrogels with different pH values was performed. Briefly, the hydrogel was placed in 2 ml PBS in a 37 °C incubator and 50 ul of PBS was replaced by the same volume of fresh PBS at distinct time points (1,3,5,9,14 days). The concentration of aFGF and bFGF in removed PBS was measured by the ELISA Kit (Bioswamp) and Microplate Reader at 450 nm. For this test, $$n = 4$$, and each measurement was repeated 3 times.
## 2.5 Wound healing
Eight-week-old specific-pathogen-free (SPF)-class female C57BL/6 mice were purchased from the Zhejiang Laboratory Animal Center. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at Wenzhou Institute, University of Chinese Academy of Sciences. For wounding, the mice were anesthetized with isoflurane. The hair was removed from the dorsal region, and the skin was prepared with betadine and $70\%$ ethanol. A skin biopsy punch was used to make a circular full-thickness skin wound with a diameter of 8 mm on both sides of the dorsal skin. A splint was spread with an instant bonding adhesive and placed around the wound and secured to the skin with eight interrupted 6–0 nylon sutures.
The post-surgery mice were randomly assigned to one of seven groups of three mice each, treated with 6.4-GelMA/aFGF, 7.4-GelMA/aFGF, 8.4-GelMA/aFGF, 6.4-GelMA/bFGF, 7.4-GelMA/bFGF, 8.4-GelMA/bFGF hydrogel scaffold, and no hydrogel scaffold. Scaffolds were attached to the wound site immediately after the wounding. The wounds and splints were covered with Tegaderm (3M) sterile transparent dressing. A digital camera was used to record the wounds on each mouse on days 0, 4, 8 and 12 after treatment. The wound-healing rates were calculated by the following equation: wound-healing rate = (the area of the original wound - the area of the unhealed wound)/the area of the original wound $100\%$.
## 2.6 Histological analysis
On postoperative days 4, 8, and 12, mice were sacrificed and a complete wound with a 0.5 cm margin was removed. After being fixed in $4\%$ paraformaldehyde solution (PFA) for 2 days, the samples were rinsed in PBS, $50\%$, $70\%$, $80\%$, $90\%$, $95\%$, and $100\%$ ethanol solution for gradient dehydration. After xylene clear treatment and gradient wax dipping, embedded samples in paraffin to make 5-μm sections for hematoxylin and eosin (H&E) staining. The wound re-epithelialization rate was calculated by the following formula: Rate of re-epithelialization (%) = St/S0 $100\%$ S0 is the initial wound distance for the section and *St is* the keratinocytes covering the wound distance at the indicated time for that section. Masson’s trichrome staining was performed by Masson’s trichrome staining kit (Cat# MST-8004, MaiXin-Bio, China) according to the manufacturer’s instructions.
## 2.7 Immunohistochemical staining
Deparaffinized tissue sections were heated in citrate buffer to retrieve the antigens and then treated with $3\%$ H2O2 for 15 min to inactivate endogenous peroxidase activity. Sections were then blocked with goat serum and BSA solution for 1 h and incubated with primary antibodies overnight at 4 °C. The primary antibodies included α-SMA (Boster, China, Catalog#BM0002), CD31(Proteintech, China, Catalog#28083-1-AP), CCR7 (Abcam, United States; Catalog#EPR23192-57), and CD206 (Abcam, United States; Catalog#EPR25215-277). The next day, after being treated with biotinylated secondary antibodies for 1 h at room temperature, the peroxidase activity of tissue section was determined with diaminobenzidine (DAB). Image-Pro Plus was used to analyze the average numbers of the positive cells for α-SMA, CD31, CCR7, and CD206 expression.
## 2.8 Statistical analysis
Data are presented as mean ± standard error of the mean. Statistical significance was determined by one-way analysis of variance (ANOVA) followed by Tukey’s posttest using GraphPad Prism software (version 8.0). A value of $p \leq 0.05$ was considered statistically significant (significance levels: *$p \leq 0.05$, and **$p \leq 0.01$).
## 3.1 Preparation of GelMA hydrogels with different pH values
GelMA is an widely used biomaterials with excellent biocompatibility in the field of tissue regeneration (Yue et al., 2015). Thus, we choose the GelMA hydrogel as a substrate. As shown in Figure 1A, hydrochloric acid and sodium bicarbonate were used to adjust the pH values of GelMA hydrogels to 6.4, 7.4 and 8.4 respectively. Then the aFGF and bFGF were mixed into hydrogel solution, and hydrogels were crosslinked by UV irradiation. In this study, the effects of the micro environment created by GelMA hydrogel with different pH values on the efficacy of aFGF or bFGF were explored in a skin wound healing model (Figure 1B). The GelMA hydrogels with different pH values showed a porous structure, and there was no difference on the pore size among the three different hydrogels (Figure 2A), resulting in these hydrogels showed a similar swelling behaviors (Figure 2B), the final swelling rate of the GelMA hydrogels with different pH values range from 6.4 to 8.4 were (532.029 ± 55.869)% (547.569 ± 92.310)%, and (521.103 ± 79.775)% respectively. The release curves discovered that the release of aFGF from weakly acidic GelMA hydrogel (pH 6.4) was faster than it released from neutral (pH 7.4) and weakly alkaline (pH 8.4) GelMA hydrogels (Figure 2C). The cumulative release of aFGF from the pH 6.4 GelMA hydrogel ((88.965 ± 1.157) %) was higher than it released from the pH 7.4 GelMA hydrogel ((75.465 ± 7.151) %) and pH 8.4 GelMA hydrogel ((74.068 ± 4.228) %). There was no difference on the release of bFGF from weakly acidic, neutral and weakly alkaline GelMA hydrogels (Figure 2C).
**FIGURE 1:** *The schematic illustrates the preparation processes and their application on skin wound healing of acidic- and basic-fibroblast growth factors loaded GelMA hydrogels with different pH values. (A) The preparation processes of acidic- and basic-fibroblast growth factors loaded GelMA hydrogels with different pH values. The pH value of GelMA hydrogel was precisely controlled by adding hydrochloric acid and sodium bicarbonate (B) The application of acidic- and basic-fibroblast growth factors loaded GelMA hydrogels with different pH values on skin wound healing.* **FIGURE 2:** *The internal structure and release profiles of aFGFs and bFGFs loaded GelMA hydrogels with different pH values. (A) The internal porous structure of GelMA hydrogels with different pH values. (B) The swelling behaviors of GelMA hydrogel with different pH value. (C) The release profiles of aFGFs and bFGFs from GelMA hydrogels with different pH values.*
## 3.2 aFGF loaded GelMA hydrogel (pH 6.4) promotes wound contraction
The purpose of this study is to explore the effects of the micro environment created by GelMA hydrogel with different pH values on the efficacy of aFGF or bFGF were explored in a skin wound healing model. Thus, the pH value of micro environment created by the hydrogel is very important. Firstly, we examined whether the GelMA hydrogels with different pH values are able to control the desirable pH values. As shown in Figure 3A, the GelMA hydrogel (pH 6.4) could maintain a weakly acidic environment, the GelMA hydrogel (pH 7.4) could maintain a weakly acidic environment, the GelMA hydrogel (pH 7.4) could maintain a neutral environment, and GelMA hydrogel (pH 8.4) could maintain a weakly alkaline environment. We speculate that the maintenance of local pH values of wound site is achieved by the sustained release of hydrochloric acid and sodium bicarbonate from GelMA hydrogel. After applying the aFGF or bFGF loaded GelMA hydrogels with different pH value, we found the wounds treated with aFGF loaded GelMA hydrogel (pH 6.4) exhibited fastest wound closure rate and achieved wound healing on Day 8 (Figures 3B,C). The H&E staining further revealed that the wounds treated with aFGF loaded GelMA hydrogel (pH 6.4) also showed a fastest re-epithelialization rate when compared with other groups on both Day 4 and Day 8 (Figure 3B). Wound contraction is drove by the myofibroblasts, which highly expressed α-SMA. The immunohistochemical staining results discovered that the expression of α-SMA of the wounds treated with aFGF loaded GelMA hydrogel (pH 6.4) was significantly increased than the other groups (Figures 3E,F). According to the reported studies, both aFGF and bFGF were all able to promote wound contraction (Wu et al., 2016; Choi et al., 2018; Hui et al., 2018). However, there are few reports on the effect of the pH values of the micro environment on FGF activity. In this study, we found weakly acidic environment is more conducive to the function of aFGF. It may be associated with the physical stabilization of aFGF which induced by the weakly acidic environment (Volkin et al., 1993).
**FIGURE 3:** *The application of aFGF- or bFGF- loaded GelMA hydrogels with different pH values on wound closure. (A) The maintenance of weak acid, neutral, and alkaline microenvironments of wound sites filled with GelMA hydrogels with different pH values. (B) The photographs and H&E staining of the healed wound treated with aFGF- or bFGF- loaded GelMA hydrogels with different pH values for 8 days. (C) The wound closure rate of aFGF- or bFGF- loaded GelMA hydrogels with different pH values after 4 and 8 days of treatment. (D) The H&E staining results of aFGF- or bFGF- loaded GelMA hydrogels with different pH values after 4 and 8 days of treatment. (E) The re-epithelialization rate of aFGF- or bFGF- loaded GelMA hydrogels with different pH values after 4 and 8 days of treatment. (F,G) The expression of α-SMA within the wound area of aFGF- or bFGF- loaded GelMA hydrogels with different pH values after 4 and 8 days of treatment. *p < 0.05, **p < 0.01, ***p < 0.001.*
## 3.3 aFGF loaded GelMA hydrogel (pH 6.4) accelerates angiogenesis
Vascularization plays important role during wound healing, it can provide enough nutrients for cells involved in wound repair (Wan et al., 2019; Chen et al., 2020). As shown in Figure 4A, the trichrome staining results showed lots of new blood vessels in the wound bed of aFGF loaded GelMA hydrogel (pH 6.4) when compared with six groups on Day 4. These new blood vessels provide enough nutrients in the early stage of wound healing, resulting in accelerating wound healing. The number of blood vessels of wound site will gradually decrease after wound closure on Day 8. CD31 is a specific marker of vascular endothelial cells (Sun et al., 2011). The CD31 immunohistochemical staining further confirmed the histological observations. The expression of CD31 on aFGF loaded GelMA hydrogel (pH 6.4) treated wound was higher than the other six groups on Day 4, and the expression of CD31 in aFGF loaded GelMA hydrogel (pH 6.4) treated group on Day 8 was reduced than it on Day 4 (Figure 4B; 4C).
**FIGURE 4:** *The granulation tissue formation of the wounds treated with aFGF- or bFGF- loaded GelMA hydrogels with different pH values for 4 and 8 days. (A) The trichrome staining of the wounds treated with aFGF- or bFGF- loaded GelMA hydrogels with different pH values for 4, 8, and 12 days. (B) The immunohistochemical staining of α-SMA within the wounds treated with aFGF- or bFGF- loaded GelMA hydrogels with different pH values for 4 and 8 days. (C) The quantification of the α-SMA expression of the (B). *p < 0.05, **p < 0.01, ***p < 0.001.*
## 3.4 aFGF loaded GelMA hydrogel (pH 6.4) regulates the immune responses to a pro-regenerative status
Immune response is capable of determining the status of wound healing (Jiang et al., 2018; Chen et al., 2019). For example, if the inflammatory response of the wound is in a pro-inflammatory state, it is harmful to wound healing, such as diabetic foot ulcers. If the inflammatory response of the wound is in a state of pro-regenerative status, it will help to promote wound healing. The status of inflammation depends on the involved inflammatory cells. Macrophages are the main cells of the inflammatory response and play a major role in controlling the entire inflammatory process (Kim and Nair, 2019). As shown in Figures 5A,C, the expression of CD206 (a marker of M2 macrophages) of the wounds treated with aFGF loaded GelMA hydrogel (pH 6.4) was significantly higher than the other six groups on Day 4. While the expression of CCR7 (a marker of M1 macrophages) of the wounds treated with aFGF loaded GelMA hydrogel (pH 6.4) was obvious lower than the other six groups on Day 4 (Figures 5B,D). M2 macrophages play pro-regenerative role, while M1 macrophages play pro-inflammatory role. The increased M2 macrophage expression and decreased M1 macrophage expression co-determined the direction of inflammatory response is pro-regenerative status (Jiang et al., 2018; Chen et al., 2019). In addition, the expression of CD206 in aFGF loaded GelMA hydrogel (pH 6.4) treated group was reduced when compared its expression on Day 4. Because the wounds already completed wound healing, the status of wound healing has changed from proliferative phase to remodeling phase that the role of macrophages go down.
**FIGURE 5:** *The expression of macrophages within the wounds treated with aFGF- or bFGF- loaded GelMA hydrogels with different pH values for 4 and 8 days. (A,B) The expression of CD206 (M2 macrophages) within the wounds treated with aFGF- or bFGF- loaded GelMA hydrogels with different pH values for 4 and 8 days. (C,D) The expression of CCR7 (M1 macrophages) within the wounds treated with aFGF- or bFGF- loaded GelMA hydrogels with different pH values for 4 and 8 days.*
## 3.5 The exploration of potential mechanism
As shown in Figure 6, The volcano plot and heatmap discover the differentially expressed genes (Figures 6A,B), including Apold1, Epx, F830016B08Rik, Gm12185, Tnfsf14, il15, Oas3, Rnf213. For example, Apold1 is an endothelial cell early response protein that may play a role in regulation of endothelial cell signaling and vascular function (Stritt et al., 2022). The upregulation of Apold1 can promote angiogenesis, which is consistent with the results of trichrome staining and the expression of CD31. The residual down-expressed genes, including Epx, il15, Oas3, Tnfsf14 were able to regulate the immune responses. It is consistent with the increased expression of M2 macrophages and decreased expression of M1 macrophages. In addition, the significant enrichment analysis of GO function revealed that protein ubiquitination, regulation of protein localization, positive regulation of cytokine production and immune responses (Figure 6C). The KEGG enriched signaling pathway analysis discovered the immune regulation related signaling pathways, such as NF-kappa B signaling pathway and TNF signaling pathway, were the most significant (Figure 6D). Immunomodulation plays critical role during the wound healing, Chen et al. [ 2023] thus the improved immune response is capable of accelerating wound healing.
**FIGURE 6:** *RNA-seq analysis of differentially expressed genes and signaling pathways of the wounds treated with aFGF loaded GelMA hydrogel with 6.4 and 7.4 pH value. (A,B) Volcano plot and heatmap discover the differentially expressed genes. (C,D) The potential signaling pathway involve in the wound healing processes.*
## 4 Conclusion
In this study, we have tried to explored the influence of the local pH value of wound site on efficacy of fibroblast growth factor. We found aFGF and bFGF played similar role on promoting wound healing under neutral and alkaline conditions. However, aFGF exhibited significant promoting effects than the bFGF when the local pH value of wound site was adjusted to acidic conditions. The local acidic pH value helped fibroblast growth factors to play significant role in promoting wound contraction, granulation tissue formation, vascularization, re-epithelialization.
## Data availability statement
The RNA-seq data presented in the study are deposited in the GEO repository, accession number GSE225505.
## Ethics statement
The animal study was reviewed and approved by Institutional Animal Care and Use Committee (IACUC) at Wenzhou Institute, University of Chinese Academy of Sciences.
## Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: Aberrant lipid accumulation in the mouse visceral yolk sac resulting from maternal
diabetes and obesity
authors:
- Man Zhang
- J. Michael Salbaum
- Sydney Jones
- David Burk
- Claudia Kappen
journal: Frontiers in Cell and Developmental Biology
year: 2023
pmcid: PMC10014468
doi: 10.3389/fcell.2023.1073807
license: CC BY 4.0
---
# Aberrant lipid accumulation in the mouse visceral yolk sac resulting from maternal diabetes and obesity
## Abstract
Maternal diabetes and obesity in pregnancy are well-known risk factors for structural birth defects, including neural tube defects and congenital heart defects. Progeny from affected pregnancies are also predisposed to developing cardiometabolic disease in later life. Based upon in vitro embryo cultures of rat embryos, it was postulated that nutrient uptake by the yolk sac is deficient in diabetic pregnancies. In contrast, using two independent mouse models of maternal diabetes, and a high-fat diet-feeding model of maternal obesity, we observed excessive lipid accumulation at 8.5 days in the yolk sac. The numbers as well as sizes of intracellular lipid droplets were increased in yolk sacs of embryos from diabetic and obese pregnancies. Maternal metabolic disease did not affect expression of lipid transporter proteins, including ApoA1, ApoB and SR-B1, consistent with our earlier report that expression of glucose and fatty acid transporter genes was also unchanged in diabetic pregnancy-derived yolk sacs. Colocalization of lipid droplets with lysosomes was significantly reduced in the yolk sacs from diabetic and obese pregnancies compared to yolk sacs from normal pregnancies. We therefore conclude that processing of lipids is defective in pregnancies affected by maternal metabolic disease, which may lead to reduced availability of lipids to the developing embryo. The possible implications of insufficient supply of lipids -and potentially of other nutrients-to the embryos experiencing adverse pregnancy conditions are discussed.
## Introduction
Maternal diabetes and maternal obesity during pregnancy are known risk factors for structural birth defects, such as cardiovascular malformations and neural tube defects (NTDs) (Mills, 1982; Martinez-Frias, 1994; Martinez-Frias et al., 2005; Helle and Priest, 2020). The causative mechanisms underlying the elevated risk conferred in these pregnancies have not been identified, although it is generally accepted that nutrient excess, such as in hyperglycemia of the mother, plays a critical role.
During the formation of heart and neural tube, the embryo receives nutrients through the yolk sac, which serves as the ‘primitive placenta’ during the early post-implantation development (Zohn and Sarkar, 2010). The yolk sac tissues are comprised of the outer parietal yolk sac and the inner visceral yolk sac. The visceral yolk sac consists of a layer of endodermal cells facing the yolk sac cavity and underlying mesodermal cells that face the embryonic cavity. The visceral yolk sac thus functions as the site for the uptake and processing of nutrients by endocytic cells in the endodermal layer, while the transport of nutrients to the embryo is accomplished by vessels in the mesodermal layer that form from a common precursor of endothelial and hematopoietic progenitor cells (Zohn and Sarkar, 2010).
Prior studies on rat conceptuses cultured in vitro have provided evidence that exposure to high glucose concentrations was associated with yolk sac morphological abnormalities (Pinter et al., 1986b), reduced uptake of horseradish peroxidase (as an indicator for endocytosis) (Reece et al., 1989), reduced fatty acid uptake from the medium (Pinter et al., 1988) and decreased lipid droplet content (Reece et al., 1994). Several of these outcomes could be alleviated by the addition of arachidonic acid (Pinter et al., 1986a; Pinter et al., 1988). These authors also reported that vessel coverage of the yolk sac surface was reduced by excessive glucose in the culture medium (Pinter et al., 1986b). Culture of mouse conceptuses yielded confirming observations of vasculopathy (Pinter et al., 1999), which could be ameliorated by addition of an external nitric oxide donor (Nath et al., 2004). Although some similarities between yolk sacs freshly isolated from diabetic pregnant dams and cultured high-glucose-exposed specimens were observed in both the rat and mouse models (Reece et al., 1994; Pinter et al., 1999; Nath et al., 2004), the culture of post-implantation conceptuses in vitro is confounded by addition of adult serum, and an excessively high oxygen atmosphere. In contrast, implantation sites in vivo are in hypoxic conditions, which stimulate and are required for vasculogenesis (Ryan et al., 1998).
In the present study, we therefore sought to re-appraise the in vivo manifestation of yolk sac abnormalities in two mouse models of diabetic pregnancy, spontaneously occurring diabetes in the non-obese diabetic (NOD) mouse strain (Leiter, 1989; Atkinson and Leiter, 1999), and Streptozotocin-induced diabetes in the FVB/N mouse strain (Pani et al., 2002; Qi et al., 2005; Pavlinkova et al., 2009). In order to model the hyperlipidemia that is characteristically associated with diabetic pregnancy, we also investigated pregnancies in FVB/N mice fed a high-fat-high-sucrose diet for 4 weeks prior to mating (Salbaum and Kappen, 2012). The specific focus on yolk sac was prompted by our earlier findings of layer-specific expression of glucose and fatty acid transporters in visceral yolk sac endodermal and mesodermal cells (Kappen et al., 2022). Since expression of these transporters at the mRNA level was unaffected by exposure to maternal diabetes in decidua, embryos, and yolk sacs, we hypothesized that abnormalities in nutrient uptake, processing and/or transport caused by the exposure should be evident at the functional level.
## Animals
All animal experiments were performed with prior approval of the Pennington Biomedical Research Center Institutional Animal Care and Use Committee (IACUC) in accordance with the “Guide for the care and use of laboratory animals” of the United States National Institutes of Health. The experimental model consisted of female mice of the FVB strain and the Non-Obese Diabetic (NOD) strain. Mice of the FVB inbred strain were obtained from Charles River Laboratories at the age of 5–6 weeks, and NOD strain animals were obtained from The Jackson Laboratories at the age of 7–8 weeks. Mice were accommodated to the animal facility for 1 week before experimentation.
Two diabetic mouse models were used in our experimentation. Diabetes in FVB strain was induced by three i. p. Injections within a week of Streptozotocin at a dose of 100 mg/kg body weight, as previously described (Kappen et al., 2011, 2012). Females whose blood glucose levels exceeded 250 mg/dl were considered diabetic and were set up for mating no earlier than 7 days after the last STZ injection. The NOD strain of mice is an established model for human type I diabetes (Leiter, 1989; Leiter, 2005), in which individuals turn diabetic spontaneously. Approximately $60\%$ of females of this strain exhibit profound hyperglycemia between 12–17 weeks of age, so that normoglycemic non-diabetic females of comparable age can be used as controls. When blood glucose levels exceeded 250 mg/dl, females were designated hyperglycemic and were mated to normoglycemic chow-fed males of the same strain, respectively.
The maternal obesity model was set up by using the FVB strain: females were fed with high-fat-high-sucrose diet (D12331, Research Diets, New Brunswick, NJ) for 4 weeks prior to mating, a regimen that we previously showed produces about $25\%$ adiposity while preserving fertility (Salbaum and Kappen, 2012). The diet consists of 16.4 kcal% protein, 25.5 kcal% carbohydrate (sucrose content is $18.4\%$ of total weight), 58.0 kcal% fat, and is replete for minerals, Vitamins and micronutrients and was fed through pregnancy until sacrifice. Diabetic and high-fat-diet-fed females were bred to normoglycemic males of the respective strain that had been fed regular mouse chow diet (LabDiet 5001, LabDiet, St. Louis, MO). Vaginal plugs were checked in the morning after mating, and the day of detection of a plug was counted as day 0.5 of gestation.
## Bodipy staining and three-dimensional image analysis
Conceptuses at E7.5 were isolated from normoglycemic and diabetic NOD pregnancies, and the parietal yolk sacs were removed. Staining with Bodipy was done as described below, with counterstain for Actin filaments (F-actin) by Phalloidin (Abcam, Waltham, MA). Conceptuses at E8.5 were recovered from normal pregnancies, diabetic pregnancies, and high-fat-diet (HFD) pregnancies, and those yolk sac tissues that could be dissected away from the embryo were freshly isolated. The developmental stage of cognate embryos was recorded based upon the. number of somite pairs, and somite stages were variable between individuals from a given litter, as expected. In the aggregate, the distribution of stages between the groups of normal samples was comparable to that of samples from diabetic and of high-fat diet-fed pregnancies within the same strain, respectively.
Following fixation in $4\%$ PFA for 30 min, yolk sacs were washed 3 times in phosphate-buffered saline (PBS) for 10 min. Bodipy (Invitrogen, Waltham, MA, 1:1000) was diluted in PBS at a concentration of 1 mg/ml and applied to the yolk sacs at 37°C for 15 min, followed by the incubation of 4’,6-diamidino-2-phenylindole (DAPI; 1:1000 in PBS) at room temperature for 5 min. Serial Z-stack fluorescence images from each sample were obtained by a Leica SP5 spectral scanning confocal microscope (Leica, Wetzlar, Germany). Imaris software was employed to reconstruct three-dimensional images and quantify the individual three-dimensional lipid droplets and their volumes in yolk sac samples. Specifically, contour surfaces were created for both Bodipy and DAPI fluorescence, individual fluorescence particles were identified and their volumes calculated, respectively. Then the fluorescence intensities were quantified by summing up of all the particle volumes, as implemented in the Imaris package. The relative fluorescence volumes were calculated as the ratio of the Bodipy and DAPI total volumes.
## Immunohistochemistry (IHC) staining
Paraffin embedded yolk sac sections were used for IHC staining. For deparaffinization, slides were heated for 60 min at 65°C, followed by three treatments with $100\%$ xylene, then two times with $100\%$ ethanol, one time with $95\%$ ethanol and $70\%$ ethanol, for 5 min per step. For antigen retrieval, slides were incubated with 0.01M citrate buffer (pH 6.0) at 90°C in a pressure cooker for 20 min. Then, the slides were treated with a $3\%$ H2O2 solution for 15 min to saturate endogenous peroxidases. After blocking with horse serum for 1 h at room temperature, slides were incubated overnight at 4°C with primary rabbit antibodies against ApoA1 (Thermo Fisher Scientific, Waltham, MA, 1:200), or ApoB (Abcam, Cambridge, United Kingdom, 1:200), or SR-B1 (Abcam, Cambridge, United Kingdom, 1:100). On the second day, after washing three times with PBS, the slides were incubated with HRP micropolymer-conjugated horse anti-rabbit IgG (Vector Laboratories, Burlingame, CA) at room temperature for 1 h. Slides were then washed with PBS and incubated with DAB, an HRP substrate (Vector Laboratories, Burlingame, CA) for 2 min, followed by dehydration, including one time of $70\%$ ethanol and $80\%$ ethanol, two times of $95\%$ ethanol and $100\%$ ethanol, and $100\%$ xylene for 10 min. The slides were coverslipped with permanent mounting medium (Vector Laboratories, Burlingame, CA) and imaged under bright field illumination on an optical microscope (Zeiss, Oberkochen, Germany).
## Immunofluorescence (IF) staining and quantification
Paraffin embedded tissue sections were used for IF staining. After deparaffinization and antigen retrieval procedures (described in the IHC staining method above), slides were incubated in $5\%$ donkey serum in $1\%$ BSA for 60 min for blocking, then washed in PBS three times. Slides were incubated overnight at 4°C with primary rabbit antibodies against ApoA1 (Thermo Fisher Scientific, Waltham, MA, at 1:100 dilution), ApoB (Abcam, Cambridge, United Kingdom, at 1:100 dilution), SR-B1 (Abcam, Cambridge, United Kingdom, at 1:100 dilution) and Folate receptor α (Thermo Fisher Scientific, Waltham, MA, at 1:50 dilution). The following day, slides were washed three times with PBS and incubated at room temperature for 60 min with donkey anti-rabbit Alexa Fluor 488-conjugated (Thermo Fisher Scientific, Waltham, MA, 1:500) and donkey anti-sheep Alexa Fluor 555-conjugated secondary antibodies (Thermo Fisher Scientific, Waltham, MA, 1:200). Slides were then washed three times in PBS and covered with antifade mounting medium with DAPI (Vector Laboratories, Burlingame, CA). Slides were stored in the dark at 4°C until imaged with the Sp5 confocal microscope (Leica, Wetzlar, Germany). Total fluorescence intensities were quantified for lipid transporter proteins and DAPI by ImageJ. The relative fluorescence intensities were calculated as the ratio of total fluorescence intensity of these lipid transporter proteins over DAPI.
## LysoTracker staining and colocalization analysis
Fresh yolk sacs were collected from normal, diabetic and obese pregnancies at embryonic day 8.5 (E8.5). LysoTracker™ Deep Red (Thermo Fisher Scientific, Waltham, MA, 1:10,000) and Bodipy (Invitrogen, Waltham, MA, 1:1000) were diluted in Opti-MEM (Gibco, Waltham, MA) and applied to the yolk sacs at 37°C for 25 min. Following washing with Opti-MEM for 5 min three times, the yolk sacs were immobilized in 35 mm glass bottom dishes (Mattek, Ashland, MA) by a method modified from a previous study (Aoyama et al., 2012). $0.2\%$ gellan gum (Sigma, St. Louis, MO) was mixed in $40\%$ glycerol in PBS and microwaved for 30 s 500–1000 μl of hot gellan gum solution was poured into a 35 mm glass bottom dish (Mattek, Ashland, MA). The dishes were stored in the refrigerator to further harden after solidification. A small cut was made in the middle of the gellan gum gel using a syringe needle, and the yolk sacs were placed beneath the cut gel. Immobilized yolk sac tissue stained with Lysotracker and Bodipy was imaged by a Leica SP5 spectral scanning confocal microscope (Leica, Wetzlar, Germany). The ImageJ plugin “Colocalization Image Creator” was used for quantification of colocalization of both stains at default settings. Areas where Bodipy staining was colocalized with LysoTracker staining, and the individual lysosome areas were identified and quantified by ImageJ. A colocalization area fraction was calculated by the ratio of colocalized regions and the corresponding lysosome region.
## Next-generation sequencing (RNAseq.)
The technical details of these experiments are described elsewhere (J.M. Salbaum, K. Stone, C. Kruger, and C. Kappen, manuscript submitted); the results have been deposited in the Gene Expression Omnibus database under accession number GSE197396. Briefly, yolk sacs were isolated from E8.5 pregnant dams of the NOD mouse strain that were either normoglycemic or diabetic. Individual samples were processed for RNAseq., sequence read counts were normalized to the size of each sequencing library, and analysis was performed using DESeq2. p-values for comparison of averages between control and experimental group were adjusted for multiple comparisons within the entire dataset, as implemented in DESeq2. For this manuscript, the gene ontology (GO) annotations of this dataset were queried with the search terms lipid, droplet, lipoprotein, lysosome, lipolysis, autophagy, Lrp, Snx, Perilipin, Cubilin, Enpp, and FolR1, and genes with low abundance (normalized read count below 50) were eliminated.
## Statistical analysis
Data are representative of at least three independent experiments unless otherwise specified. Quantitative data are expressed as average ±SD; samples from the same pregnancy were first normalized to each pregnancy, before averages were tallied by experimental group. Student’s t-test was used to evaluate statistical significance between two groups, based upon two-tailed distributions and assumption of unequal variances. All statistical analyses were done using Graph Pad Prism software, and the criterion for significance was considered $p \leq 0.05.$
## Results
Morphometric parameters of the mouse models employed in these studies are depicted in Figure 1. Body weight was recorded at the time of inspection for a copulation plug (days 0.5 of gestation, Figure 1, Panel A, light bars), and at the time of sacrifice at 8.5 days of gestation (E8.5, filled bars). Diabetic FVB females started the pregnancy slightly lighter (compare open red bar to open black bar, $$p \leq 0.017$$), but gained weight to the extent that the difference was not statistically significant at the time of euthanasia. FVB females fed a high-fat-high-sucrose diet for 4 weeks gained more than 5 g body weight on average (compare dark to light turquoise bars; ranging from 2.7–9.3 g gained individually) up to the time of mating compared to the chow-fed FVB controls; they gained another 2 g on average during the pregnancy; no single individual lost weight. The NOD females also gained weight during pregnancy, and while the diabetic group started the pregnancy slightly heavier than the normoglycemic controls ($$p \leq 0.023$$), the differences were no longer statistically significant at the time of euthanasia.
**FIGURE 1:** *Maternal parameters in two experimental models of diabetic pregnancy and a mouse model of obesity in pregnancy. Panel A: FVB females entered the experiments at the age of 10–12 weeks, after diabetes induction by STZ, whereas NOD females were used between the ages of 12–17 weeks, depending on development of diabetes. FVB females with high-fat diet feeding entered the feeding stage at ∼8 weeks of age, with matings performed at ∼12 weeks of age. Maternal body weights were recorded at entry into the feeding phase, at mating and at sacrifice at gestational day E8.5. Diabetic cohorts are labeled in red, FVB animals are represented by solid bars, NOD mice by striped bars. Panel B: Blood glucose levels were measured in FVB dams before and after STZ-induction, and at mating and sacrifice. In NOD females, blood glucose levels were monitored weekly after the age of 12 weeks. Females were declared diabetic and mated when their blood glucose levels exceeded 250 mg/dl; their glucose levels increased further throughout the pregnancy. Glucose measurements on HFD-fed females were taken at mating and sacrifice, and remained normal throughout the pregnancy. Panel C: Litter sizes were reduced in FVB diabetic females, and remained unaffected by HFD and in the NOD strain. Number of pregnant females/group: FVB normoglycemic n = 17; FVB diabetic n = 16; FVB HFD: n = 12; NOD normoglycemic n = 16; NOD diabetic n = 16.*
Consistent with our definition of hyperglycemia beyond 250 mg/dl blood glucose content, both the FVB and NOD diabetic groups at E8.5 had high blood glucose levels that even exceeded the upper sensitivity of the glucometer in 3 NOD females (Figure 1, Panel B). High-fat diet-fed females were normoglycemic throughout the experiment. Diabetic FVB females had significantly smaller litter sizes compared to normoglycemic controls ($$p \leq 0.028$$), which was not observed in the NOD strain (Figure 1, Panel C), which had generally smaller litters. Notably, high-fat-diet-feeding had no effect on litter size.
## Excessive lipid accumulation in yolk sacs of pregnancies affected by type I diabetes
Upon dissection of E7.5 conceptuses under the microscope (for other experiments), we observed greater deflection of light and reduced translucency when the samples came from a diabetic pregnancy. This prompted us to stain whole conceptuses for lipids by using Bodipy, and for actin as a general marker for cells by using Phalloidin, after removal of the parietal yolk sac. In the examples shown in Figure 2, greater intensity of Bodipy fluorescence was evident in the sample from a diabetic NOD pregnancy when compared to a sample from a normal NOD female at E7.5, suggesting that there was excessive accumulation of lipids in the yolk sac in the diabetic condition.
**FIGURE 2:** *Accumulation of lipid droplets in yolk sac of diabetes-exposed embryos. Conceptuses were isolated from normoglycemic and diabetic pregnant NOD dams at gestational day E7.5, and dissected free of the parietal yolk sac. They were stained with Phalloidin and Bodipy. The conceptus from a diabetic pregnancy displays more lipid droplets in the visceral yolk sac.*
In order to quantify lipid abundance, we chose to investigate conceptuses at the E8.5 time point, which facilitates dissection and provides larger tissue samples. Visceral yolk sacs were isolated by microdissection, and stained with Bodipy (lipid droplets) and DAPI (nuclei). Three-dimensional renderings were created by Imaris software from serial Z-stack fluorescence images of each sample. The individual fluorescence particles from both Bodipy and DAPI staining were identified and quantified in volume. The total fluorescence volume was calculated by summing up of all particle volumes for a given fluorescence channel. The fraction of total fluorescence volume of Bodipy over that of DAPI represents the lipid accumulation in each sample. Fluorescence images are shown in Figure 3 Panel A, and quantification data in Figure 3 Panel B. There was significantly increased lipid accumulation in yolk sacs from FVB-STZ diabetic pregnancies, compared to yolk sacs from normal FVB pregnancies. In addition, there was a greater number of lipid droplets, and the sizes of lipid droplets were larger in diabetic conditions, as shown in Figure 3 Panel C, where the size of lipid droplets is color-coded along the spectrum shown at the bottom of each image. Frequencies of occurrence of each droplet size range were quantified, and droplets of larger sizes than normal were more abundant in diabetic pregnancies (Figure 3 Panel D). Similarly, we found elevated lipid accumulation in NOD-diabetic pregnancies, again with increased droplet size and number (Figure 3 Panels E–H). The results demonstrate, in two mouse models of diabetic pregnancy, that in diabetic conditions the conceptus contains excess accumulation of lipids in the visceral yolk sac, characterized by increased number and volume of lipid droplets.
**FIGURE 3:** *Excess lipid accumulation in yolk sacs from diabetic and obese pregnancies. Confocal imaging was performed on E8.5 yolk sacs stained with Bodipy (lipids) and DAPI (DNA) for all three experimental models. Panels A, E, and I: Three-dimensional renderings of signals for lipids and DNA. Panels B, F, and J: Voxels detected in each channel were quantified, and the ratio of lipid over DAPI voxels was calculated. This ratio was significantly higher in visceral yolk sac from diabetic FVB and NOD pregnancies, and in pregnancies where the dam was fed high-fat diet. Panels C, G and K: Volumes of lipid droplets were pseudo-colored for imaging according to size, following the scale at the right bottom of each image, purple-blue = smaller, yellow-red = larger volume. Yolk sacs from diabetic pregnancies and from dams fed HFD display appreciably more droplets with colors representing larger volume ranges. Panels D, H and L: Quantification of the numbers of lipid droplets within a given size range; note that the scale of the X-axis is logarithmic (log2), and that X and Y axis scales differ when comparing D to H and L. Frequencies of occurrence are shifted towards lipid droplets of larger volumes in yolk sacs from diabetic FVB and NOD dams, and HFD pregnancies. Sample size: FVB Normal = 25 samples representing 14 yolk sacs from 4 pregnancies, FVB diabetic = 43 samples representing 21 yolk sacs from 7 pregnancies; NOD Normal = 26 samples representing 14 yolk sacs from 5 pregnancies, NOD diabetic = 36 samples representing 25 yolk sacs from 9 pregnancies; FVB Normal = 42 samples representing 14 yolk sacs from 4 pregnancies, FVB-HFD = 68 samples representing 24 yolk sacs from 7 pregnancies. Data were averaged for each yolk sac (where multiple fragments were analyzed), and then averaged over all yolk sacs from a given pregnancy, after which averages were calculated for all pregnancies in the respective experimental group; Averages are presented as mean ± SD. Asterisks indicate p < 0.05, when comparing experimental and control groups using two-tailed t-tests.*
## Excessive lipid accumulation in yolk sacs of pregnancies affected by obesity
Applying the same experimental approach, we also observed increased lipid accumulation in yolk sacs from FVB pregnancies where the dam was fed a high-fat-high-sucrose diet for 4 weeks prior to mating, and through gestation day E8.5. In these pregnancies as well, numbers of lipid droplets and their sizes were significantly elevated compared to normal FVB pregnancies (Figure 3, Panels I–L). Taken together, our results revealed excessive lipid accumulation in visceral yolk sac under conditions of diabetic pregnancy, and upon HFD feeding.
## Expression of lipid transporter proteins in yolk sacs from pregnancies affected by maternal diabetes and obesity
We then investigated whether lipid accumulation could be caused by aberrant expression of lipid transport proteins. We focused on ApoA1, ApoB and SR-B1, based upon prior reports of yolk sac expression, and the appearance of neural tube defects in mouse mutants for ApoB and SR-B1, which is a major transporter of cholesterol. The cellular localization of lipid transport proteins was revealed by immunohistochemistry staining for ApoA1, ApoB and SR-B1, on serial sections of entire paraffin-embedded decidua. Figure 4 Panel A depicts immunohistochemical staining for ApoA1 and ApoB, which are widely expressed in the decidua, as well as embryonic tissues and the yolk sac. Within the visceral yolk sac, ApoA1 was found expressed in endoderm as well as mesodermal cells (see magnification), whereas more intense ApoB signal was observed at the mesodermal yolk sac surface. SR-B1 was not broadly expressed in decidua, but exhibited prominent staining in trophoblasts; within the visceral yolk sac, a moderate signal was observed, which was specifically localized to the endodermal layer of the yolk sac (see magnification).
**FIGURE 4:** *Lipid transporter protein expression in yolk sacs is unaffected by maternal metabolic disease or high fat diet-feeding. Expression of lipid transporter proteins was detected by immunohistochemistry and immunofluorescence on serial sections from paraffin-embedded whole decidua isolated at E8.5. The apical surface of the endodermal layer of the yolk sac is facing towards the right in all images. Panel A: Immunohistochemical staining for ApoA1, ApoB, and SR-B1 in specimen from a normal FVB pregnancy. Areas of magnification (lower images) are indicated by frames in the upper images. Abbreviations: hf = head fold of embryo; d = deciduum; t = trophoblast layer; vys = visceral yolk sac; mes = mesodermal layer of visceral yolk sac; endo = endodermal layer of visceral yolk sac. Panel B: Positive identification of the endodermal layer of the visceral yolk sac by immunofluorescence staining for Cubilin and Folate Receptor 1 (here detected with an antibody against FRα). Cubilin is very selectively located only on the apical surface of the visceral endoderm, whereas Folate Receptor 1 staining detects the entire endoderm layer and was therefore used in all immunofluorescence assays. Panel C: Immunofluorescence detection of ApoA1 expression in yolk sac from FVB pregnant mice. ApoA1 expression is present in endodermal, Folate Receptor 1-positive cells as well as FolR1-negative cells of mesodermal origin. Panel D: Quantification of fluorescence for each lipid transporter relative to the DAPI signal intensity in cells expressing the respective protein. No significant differences were found for lipid transporter expression between yolk sacs from diabetic compared to normoglycemic FVB dams. N = six to eight samples from 3 normoglycemic FVB pregnancies, n = seven to nine samples from 2 diabetic FVB pregnancies, sample numbers varied depending on protein investigated. Panel E: Immunofluorescence detection of ApoB in yolk sac from NOD pregnant mice. Panel F: Quantification of relative expression levels did not reveal statistically significant differences in yolk sac lipid transporter expression between diabetic and normoglycemic NOD pregnancies. N = 7–20 samples from 3 normoglycemic NOD pregnancies, n = 3–16 samples from 2 diabetic NOD pregnancies. Panel G: Immunofluorescence detection of SR-B1 expression in yolk sac from FVB dams where one group had been fed a high fat diet for 4 weeks prior to mating and until sacrifice. SR-B1 signal is predominantly located at the apical surface of the endodermal cells layer, coincident with enriched Folate Receptor 1 localization. An apparently stronger signal for SR-B1 in the HFD condition was only detected in this sample, and was not consistent after quantification of multiple specimen (see Panel H). N = 12–15 samples from chow-fed FVB pregnancies, n = 8–17 samples from high-fat-diet-fed FVB pregnancies. Panel H: Quantification of relative expression levels did not reveal statistically significant differences in yolk sac lipid transporter expression between high-fat-diet-fed and chow-diet-fed dams. Results were first averaged over each pregnancy and then the respective experimental group, presented as mean ± SD, and statistical significance at p < 0.05 would be indicated by asterisks if detected.*
To identify positively the endodermal layer upon fluorescence staining, we used antibodies against Folate receptor 1, which we had previously shown to be exclusively expressed in visceral endoderm cells at this developmental stage (Salbaum et al., 2009). Figure 4 Panel B depicts Folate Receptor 1 staining in endodermal cells, in which expression of the Vitamin B12 receptor *Cubilin is* selectively localized only at the apical surface; we therefore continued to use Folate Receptor 1 to visualize the whole endodermal cell layer. While all three lipid and both vitamin transporters were examined in the three experimental models, Figure 4 Panels C, E, and G depict representative staining images for just one each of the lipid transporters in either of the experimental models, with quantification of all transporters in all the experimental models depicted in Panels D, F, and H. We did not observe altered cellular localization under conditions of exposure to maternal metabolic disease when compared to normal pregnancies, indicating that the cell-type specificity of expression was preserved for all examined nutrient transporters. Quantification was achieved by determining fluorescence intensities of lipid transporter staining, and for cells expressing the transporters, the intensities of DAPI staining were also determined, employing NIH ImageJ. Relative fluorescence intensities were calculated as the ratio of total fluorescence intensity of these lipid transporter proteins over DAPI intensity in those cells expressing the respective protein. No significant differences of expression levels were observed for ApoA1, ApoB, SR-B1 and Cubilin in the yolk sacs from normal, diabetic and HFD pregnancies (Figure 4 Panels D, F, and H, respectively). Taken together, these results indicate that metabolic status or high-fat-diet-feeding of the dam did not affect lipid transporter expression in the yolk sac at E8.5 days of gestation.
These results are consistent with expression at the RNA level, as represented in an RNAseq. dataset derived from yolk sacs of NOD diabetic and normoglycemic pregnancies at E8.5 (Table 1) that was queried for lipid transporters, and other genes involved in lipid droplet formation and processing. None of the 77 genes in this list exhibit statistically significant differences between metabolic conditions on the basis of adjusted p-values. When raw p-values were considered, only the first 9 genes (including Cideb) displayed significance, but their fold-changes were moderate at best, and expression levels low. Notably, among the highly expressed genes were ApoE, ApoA1, ApoA2, ApoA4, ApoB, ApoM, HDL binding protein, Cubilin and Megalin (Lrp2), Sortin nexins 1, 3, 5 and 6, and Microsomal triglyceride transfer protein. Yet, their expression did not significantly differ between metabolic conditions. When interrogated for all genes with Slc (solute carrier) nomenclature, this dataset also did not reveal significant differences for over 200 nutrient transporter genes expressed at this stage (Kappen et al., 2022). Taking together our quantitative and qualitative data, they do not provide evidence that altered gene expression in lipid-related pathways could be invoked as causes for the excess lipid accumulation in any of our three experimental models.
**TABLE 1**
| MGI acc. # | Symbol | Description | meanCtrl | meanTreat | FC | Padj |
| --- | --- | --- | --- | --- | --- | --- |
| 96828 | Lrp1 | low density lipoprotein receptor-related protein 1 | 61.62 | 77.05 | 1.25 | 0.23 |
| 1915091 | Atg3 | autophagy related 3 | 185.08 | 160.34 | -1.15 | 0.23 |
| 1914776 | Atg12 | autophagy related 12 | 260.7 | 226.11 | -1.15 | 0.26 |
| 2443882 | Snx30 | sorting nexin family member 30 | 133.17 | 154.43 | 1.16 | 0.27 |
| 2387801 | Snx17 | sorting nexin 17 | 89.29 | 104.9 | 1.17 | 0.31 |
| 893578 | Scarb1 | scavenger receptor class B, member 1 | 188.06 | 214.59 | 1.14 | 0.38 |
| 1916428 | Snx5 | sorting nexin 5 | 1388.8 | 1270.25 | -1.09 | 0.39 |
| 1277186 | Atg5 | autophagy related 5 | 117.78 | 104.01 | -1.13 | 0.39 |
| 1270844 | Cideb | cell death-inducing DNA fragment. factor, α subunit-like effector B | 86.79 | 69.85 | -1.24 | 0.41 |
| 1918190 | Dap | death-associated protein | 231.23 | 249.87 | 1.08 | 0.43 |
| 2137642 | Snx18 | sorting nexin 18 | 144.3 | 157.18 | 1.09 | 0.44 |
| 1923159 | Vmp1 | vacuole membrane protein 1 | 479.03 | 418.94 | -1.14 | 0.45 |
| 1916400 | Snx4 | sorting nexin 4 | 387.77 | 354.81 | -1.09 | 0.45 |
| 96765 | Ldlr | low density lipoprotein receptor | 88.45 | 109.61 | 1.24 | 0.47 |
| 1931027 | Stx12 | syntaxin 12 | 256.67 | 234.69 | -1.09 | 0.47 |
| 1919433 | Snx6 | sorting nexin 6 | 631.0 | 569.02 | -1.11 | 0.47 |
| 1929480 | Lrp10 | low-density lipoprotein receptor-related protein 10 | 107.38 | 120.58 | 1.12 | 0.48 |
| 1933830 | Enpp5 | ectonucleotide pyrophosphatase/phosphodiesterase 5 | 55.98 | 48.42 | -1.16 | 0.48 |
| 1891421 | Mesd | mesoderm development LRP chaperone | 343.36 | 317.54 | -1.08 | 0.48 |
| 1860508 | Abcb10 | ATP-binding cassette, sub-family B (MDR/TAP), member 10 | 170.29 | 153.53 | -1.11 | 0.48 |
| 2444575 | Soga1 | suppressor of glucose, autophagy associated 1 | 48.05 | 58.0 | 1.21 | 0.49 |
| 1915054 | Snx2 | sorting nexin 2 | 333.99 | 298.15 | -1.12 | 0.49 |
| 97783 | Psap | Prosaposin | 121.0 | 147.65 | 1.22 | 0.49 |
| 1919331 | Snx12 | sorting nexin 12 | 399.97 | 362.69 | -1.1 | 0.51 |
| 1351617 | Abca3 | ATP-binding cassette, sub-family A (ABC1), member 3 | 103.74 | 115.82 | 1.12 | 0.51 |
| 1916823 | Hilpda | hypoxia inducible lipid droplet associated | 179.63 | 201.11 | 1.12 | 0.57 |
| 1891828 | Becn1 | beclin 1, autophagy related | 195.76 | 182.52 | -1.07 | 0.57 |
| 1347061 | Abcg2 | ATP binding cassette subfamily G member 2 (Junior blood group) | 566.04 | 527.8 | -1.07 | 0.57 |
| 1914090 | Wdr45b | WD repeat domain 45B | 353.17 | 326.68 | -1.08 | 0.58 |
| 2140175 | Ldlrap1 | low density lipoprotein receptor adaptor protein 1 | 125.57 | 144.1 | 1.15 | 0.58 |
| 1923811 | Snx7 | sorting nexin 7 | 54.2 | 47.67 | -1.14 | 0.6 |
| 109533 | Abcb7 | ATP-binding cassette, sub-family B (MDR/TAP), member 7 | 61.15 | 56.46 | -1.08 | 0.61 |
| 1196458 | Scarb2 | scavenger receptor class B, member 2 | 49.56 | 53.44 | 1.08 | 0.62 |
| 88051 | Apoa4 | apolipoprotein A-IV | 614.66 | 683.86 | 1.11 | 0.63 |
| 1921968 | Snx16 | sorting nexin 16 | 126.22 | 116.38 | -1.08 | 0.63 |
| 2443816 | Snx8 | sorting nexin 8 | 472.71 | 425.94 | -1.11 | 0.64 |
| 2138856 | Ldlrad3 | low density lipoprotein receptor class A domain containing 3 | 46.09 | 52.74 | 1.14 | 0.64 |
| 88055 | Apoc3 | apolipoprotein C-III | 67.97 | 61.57 | -1.1 | 0.65 |
| 1349216 | Abcd3 | ATP-binding cassette, sub-family D (ALD), member 3 | 405.09 | 382.79 | -1.06 | 0.66 |
| 95794 | Lrp2 | low density lipoprotein receptor-related protein 2 | 681.95 | 635.23 | -1.07 | 0.66 |
| 1351658 | Abcf1 | ATP-binding cassette, sub-family F (GCN20), member 1 | 310.29 | 338.78 | 1.09 | 0.69 |
| 1298218 | Lrp6 | low density lipoprotein receptor-related protein 6 | 127.84 | 135.65 | 1.06 | 0.71 |
| 1914155 | Plin3 | perilipin 3 | 452.75 | 432.97 | -1.05 | 0.71 |
| 1928395 | Snx1 | sorting nexin 1 | 604.79 | 562.58 | -1.08 | 0.74 |
| 1915065 | Sec14l2 | SEC14-like lipid binding 2 | 336.7 | 316.78 | -1.06 | 0.74 |
| 87920 | Plin2 | perilipin 2 | 358.57 | 337.58 | -1.06 | 0.77 |
| 1915367 | Apool | apolipoprotein O-like | 58.1 | 53.76 | -1.08 | 0.77 |
| 88050 | Apoa2 | apolipoprotein A-II | 565.05 | 613.99 | 1.09 | 0.79 |
| 106926 | Mttp | microsomal triglyceride transfer protein | 611.78 | 590.73 | -1.04 | 0.79 |
| 1913865 | Atg4b | autophagy related 4B, cysteine peptidase | 111.02 | 106.61 | -1.04 | 0.8 |
| 1915368 | Atg101 | autophagy related 101 | 130.47 | 124.81 | -1.05 | 0.81 |
| 96829 | Lrpap1 | low density lipoprotein receptor-related protein associated protein 1 | 380.07 | 362.5 | -1.05 | 0.82 |
| 1919232 | Snx10 | sorting nexin 10 | 84.36 | 87.3 | 1.03 | 0.82 |
| 1352447 | Abcc2 | ATP-binding cassette, sub-family C (CFTR/MRP), member 2 | 133.9 | 142.19 | 1.06 | 0.83 |
| 1195458 | Abce1 | ATP-binding cassette, sub-family E (OABP), member 1 | 505.65 | 486.33 | -1.04 | 0.83 |
| 99256 | Hdlbp | high density lipoprotein (HDL) binding protein | 993.29 | 974.33 | -1.02 | 0.84 |
| 96820 | Lpl | lipoprotein lipase | 49.52 | 52.55 | 1.06 | 0.84 |
| 88057 | Apoe | apolipoprotein E | 4485.1 | 4625.62 | 1.03 | 0.85 |
| 1351656 | Abcf3 | ATP-binding cassette, sub-family F (GCN20), member 3 | 47.84 | 49.52 | 1.04 | 0.85 |
| 1927471 | Lsr | lipolysis stimulated lipoprotein receptor | 480.94 | 497.06 | 1.03 | 0.85 |
| 2155664 | Snx14 | sorting nexin 14 | 71.85 | 69.39 | -1.04 | 0.87 |
| 108498 | Nbr1 | NBR1, autophagy cargo receptor | 203.86 | 199.29 | -1.02 | 0.87 |
| 1931256 | Cubn | cubilin (intrinsic factor-cobalamin receptor) | 772.08 | 744.74 | -1.04 | 0.9 |
| 1923809 | Atg2b | autophagy related 2B | 77.4 | 80.25 | 1.04 | 0.9 |
| 1914421 | Dram2 | DNA-damage regulated autophagy modulator 2 | 71.97 | 69.26 | -1.04 | 0.91 |
| 95568 | Folr1 | folate receptor 1 (adult) | 334.0 | 325.21 | -1.03 | 0.92 |
| 88053 | Apoc1 | apolipoprotein C-I | 116.26 | 120.19 | 1.03 | 0.92 |
| 88049 | Apoa1 | apolipoprotein A-I | 1988.87 | 1938.81 | -1.03 | 0.94 |
| 1278315 | Lrp5 | low density lipoprotein receptor-related protein 5 | 56.13 | 57.65 | 1.03 | 0.94 |
| 1924290 | Atg16l1 | autophagy related 16-like 1 (S. cerevisiae) | 60.69 | 60.28 | -1.01 | 0.96 |
| 1351644 | Abcc5 | ATP-binding cassette, sub-family C (CFTR/MRP), member 5 | 254.45 | 253.78 | -1.0 | 0.96 |
| 88052 | Apob | apolipoprotein B | 1775.25 | 1812.33 | 1.02 | 0.97 |
| 1860188 | Snx3 | sorting nexin 3 | 696.45 | 706.37 | 1.01 | 0.97 |
| 1351657 | Abcf2 | ATP-binding cassette, sub-family F (GCN20), member 2 | 85.4 | 87.06 | 1.02 | 0.97 |
| 2443111 | Abcc4 | ATP-binding cassette, sub-family C (CFTR/MRP), member 4 | 224.01 | 222.07 | -1.01 | 0.98 |
| 1923992 | Snx27 | sorting nexin family member 27 | 354.27 | 353.6 | -1.0 | 0.99 |
| 1930124 | Apom | apolipoprotein M | 1215.12 | 1213.62 | -1.0 | 1.0 |
## Decreased colocalization of lipid droplets and lysosomes in yolk sacs of diabetic pregnancies and with high-fat diet feeding
We then considered the possibility that lipid processing could be altered in yolk sacs with excessive lipid accumulation. Lipid mobilization from droplets involves the autophagosome and lysosomes (Brent et al., 1990; Zohn and Sarkar, 2010). To investigate lipid processing in lysosomes, fresh visceral yolk sacs were isolated at E8.5 days of gestation from normal, diabetic and HFD pregnancies. Lysosomes were stained with LysoTracker dye, and lipids with Bodipy, respectively. Figure 5 Panel A shows that in normoglycemic pregnancy, many lipid droplets are colocalized with the lumen of lysosomes in visceral yolk sac cells. In yolk sac from FVB-diabetic pregnancies, however, more lipid droplets are present, often clustering together, and fewer of them are associated with lysosomes. A corresponding situation is detected in NOD diabetic pregnancies (Figure 5 Panel C). The areas of colocalization, and the corresponding individual lysosome areas were identified and quantified, by using the NIH ImageJ plugin Colocalization Image Creator, and then we calculated the ratio of colocalized lipids relative to the corresponding individual lysosome area size. The fraction of colocalization was significantly decreased in yolk sacs from FVB-diabetic pregnancies compared to normal pregnancies (Figure 5 Panel B). Likewise, there was significantly decreased colocalization of lipid droplets with lysosomes in yolk sacs from NOD-diabetic pregnancies (Figure 5 Panel D). These results are consistent with a lower rate of incorporation of lipid droplets into lysosomes, suggesting that lipid accumulation could be due to abnormal lipid processing in yolk sac cells.
**FIGURE 5:** *Decreased colocalization of lipid droplets and lysosomes in yolk sacs from diabetic and obese pregnancies. Live yolk sac tissue was stained with LysoTracker dye and Bodipy. Panel A: Yolk sac from normoglycemic and diabetic FVB pregnancies; bigger lipid droplets were observed in the lumen of lysosomes in the normal yolk sac. Panel B: The areas depicting lipid droplets colocalized with lysosomes were identified and quantified, and the areas of the corresponding individual lysosomes were measured. The ratios of the areas occupied by colocalized lipid droplets over the corresponding individual lysosome areas were calculated (depicted in %). Lipid droplet colocalization with lysosomes was significantly decreased in yolk sacs from diabetic FVB dams. Sample size: N = 18 yolk sacs from 5 normoglycemic FVB dam pregnancies, n = 16 yolk sacs from 6 diabetic FVB dam pregnancies. Panel C: Yolk sacs from normoglycemic and diabetic NOD pregnancies were analyzed for lipid droplet colocalization with lysosomes (smaller magnification than Panel A). Panel D: Quantification reveals that lipid droplet colocalization with lysosomes was significantly decreased in yolk sacs from diabetic NOD dams (n = 18 yolk sacs from 6 diabetic NOD pregnancies) when compared to normoglycemic NOD pregnancies (n = 21 yolk sacs from 5 pregnancies). Panel E: Lipid droplet colocalization with lysosomes analyzed in yolk sacs from chow- and high-fat-diet-fed FVB pregnant dams. Panel F: Lipid droplet colocalization with lysosomes was significantly decreased in yolk sacs from HFD-fed pregnant FVB dams (n = 14 yolk sacs from 4 pregnancies) when compared to yolk sacs from chow-fed FVB pregnancies (n = 18 yolk sacs from 5 pregnancies). Data presented as mean ± SD, first averaged over each pregnancy, then for each respective experimental group; asterisks indicate p < 0.05, when comparing experimental and control groups using two-tailed t-tests.*
Notably, in FVB diabetic pregnancies, the lysosomes appeared to be larger, and more uniformly stained when compared to normal yolk sac (Figure 5 Panel A, lower middle). The number of lysosomes also seemed to be reduced in NOD diabetes-exposed yolk sacs (Figure 5 Panel C lower middle), although we were unable to quantify these observations due to the lack of a reference for normalization to cell size or number when staining live tissue. In contrast, in yolk sac from dams fed high-fat diet, the size and number of lysosomes, as well as their staining appearance is indistinguishable from control yolk sac cells (Figure 5 Panel E, middle images), with appreciable but -albeit reduced-colocalization of lipid droplets compared to control (Figure 5, Panel F). Thus, even though appreciable numbers of lysosomes are present, they contain fewer lipid droplets. Taken together, our results are indicative of a reduced rate of lipid processing in yolk sacs with excess lipid accumulation, which conceivably would be exacerbated by a lower number of lysosomes in yolk sacs from diabetic pregnancies.
## Discussion
Our study reveals excessive lipid accumulation in pregnancies affected by both maternal diabetes and obesity, using in vivo-derived visceral yolk sacs isolated at gestational day E8.5. A particular strength of our approach were the large sample numbers, which allowed us to balance the biological variability inherent in mouse pregnancies. Furthermore, in addition to microscopic identification of structures and cells, we quantified lipid droplets based upon volumetric rendering from consecutive (Z-stack) confocal images. These data revealed both increased quantity and larger sizes of lipid droplets in yolk sac cells that were exposed to maternal metabolic disease. The use of two different mouse strains and of two independent models of diabetic pregnancy make our findings particularly compelling. We acknowledge as weaknesses of the present study the lack of functional measurements of nutrient transport, as well as the limited information on relevance of lipid accumulations for cellular metabolism in the yolk sac itself, and on consequences for nutrient metabolism in the embryo.
Our results differ from a previous report that observed fewer lipid droplets in yolk sacs from E10 rat conceptuses that were cultured in vitro for 2 days in medium containing high levels of glucose (Reece et al., 1994). This was associated with release of oleic acid into the medium, interpreted by the authors as evidence for potential lipid deficiency in the embryo (Pinter et al., 1988). Subsequently, it was suggested that the yolk sac in diabetic pregnancy may also be deficient in essential fatty acids (Pinter et al., 1986a; Reece et al., 1994). In contrast, our work demonstrates excessive lipid accumulation in yolk sac freshly isolated from diabetic pregnancies, albeit at earlier stages than investigated previously. Thus, lipids are either taken up from the maternal circulation, where they are in excess supply due to maternal hyperlipidemia in diabetic pregnancies, or they would have to be synthesized in visceral yolk sac from the excess msternal glucose under hyperglycemic conditions. We have previously shown that in addition to Glut3, the visceral yolk sac expresses the high-capacity glucose transporter Glut2, specifically in the endodermal cell layer (Kappen et al., 2022), lending plausibility to excessive glucose uptake. We here show that lipid transporters ApoA1, ApoB, SR-B1 and Cubilin are also expressed in the visceral yolk sac, with Cubilin and SR-B1 restricted to the endodermal layer, while ApoA1 and ApoB are also found in the mesodermal compartment of the visceral yolk sac, consistent with prior literature (Shi and Heath, 1984; Farese et al., 1996; Terasawa et al., 1999; Strope et al., 2004; Santander et al., 2013). Maternal diabetes did not affect expression of these lipid transporters, but this does not exclude the possibility that they could be importing lipids or fatty acids at a higher rate under conditions of hyperlipidemia. When dams had normal blood glucose levels but were fed a high-fat content diet, we also observed excess lipid accumulation in yolk sacs from these pregnancies. Our finding that excess lipid accumulation can occur with normoglycemia suggests that excess lipid supply might be associated with excess lipid supply, either through diet or maternal hyperlipidemia, with glucose playing only a minor role.
Taken together, these results argue against an endocytic deficit in the yolk sac, as was previously postulated for diabetic pregnancies, based upon reduced Horseradish peroxidase uptake (Reece et al., 1989) by yolk sac in rat conceptuses cultured in vitro in high concentrations of glucose. For mouse conceptuses at early somite stages, amino acid and protein uptake into the visceral yolk sac was found reduced only after prolonged exposure to high concentrations of glucose (Hunter and Sadler, 1992). Thus, we cannot rule out that the excess accumulation of lipids at the stages we investigated here could potentially lead to decreased nutrient yolk sac uptake at later stages; additional gestational time points would have to be investigated to address this possibility.
Lipids taken up by or synthesized by the yolk sac, or present in lipid droplets, require processing before they can be transported to the embryo (Zohn and Sarkar, 2010). Autophagy is required for lipid droplet turnover in yolk sac cells, as evidenced by increased lipid accumulation in yolk sacs of embryos lacking the essential autophagy gene, Atg7 (Singh et al., 2009). Lipophagy is a selective form of autophagy particularly of intracellular lipid droplets. The process of lipophagy begins with formation of double membraned autophagosomes that engulf the lipid droplets, and then merge with lysosomes to form the autolysosome for further degradation (Singh, 2010). For other cell types, it was shown that excessive size of lipid droplets inhibits uptake into the autophagosome (Singh et al., 2009) and prevents lipophagy (Schott et al., 2019). Thus, the larger lipid droplets accumulated in our models may also prevent proper lipid processing under conditions of maternal metabolic disease. Defective autophagy and lipophagy therefore result in excessive lipid accumulation (Singh, 2010), and excessive lipid loads -in a negative feedback-loop- then further inhibit lipophagy (Knaevelsrud and Simonsen, 2012). Our results, namely that fewer lipid droplets colocalize with lysosomes, are suggestive of similar defects in lysosomal lipid processing in yolk sac cells in conditions of maternal diabetes or obesity.
It is currently unknown to what extent the increased lipid accumulation is detrimental to the yolk sac endoderm cells themselves. As lipid droplets are enveloped by perilipins (Sztalryd and Brasaemle, 2017) and other lipid-droplet-associated proteins, yolk sac cells undergoing excess lipid accumulation most likely have to divert considerable resources to the synthesis of these proteins. Then, it is conceivable that yolk sac cells with excessive lipid accumulation -owing to defective lipid processing- experience an energy deficit, which may affect their capacity to transport nutrients to the embryo. Intriguingly, reduction of rough endoplasmic reticulum, ribosomes and mitochondria number have been observed in yolk sac cells of rat conceptuses cultured in high glucose (Pinter et al., 1986a), consistent with biosynthetic insufficiency. Additionally, the mesodermal compartment of the yolk sac is affected in diabetic pregnancies at later stages, in that vascularization was reported to be reduced in diabetic pregnancies of mice (Pinter et al., 1999; Pinter et al., 2004) and rats (Zabihi et al., 2007).
Lipids are required for membrane and organelle synthesis in normal embryo development, and in yolk sac cells. Nascent VLDL was found by ultrastructural analysis within the luminal spaces of the rough endoplasmic reticulum and *Golgi apparatus* in yolk sac visceral endoderm cells at E7.5 and E8.5, suggesting a vital role of yolk sac in providing lipids and lipid-soluble nutrients to embryos during the early phases of mouse development (Terasawa et al., 1999). Transport of lipids out of yolk sac cells involves ApoB and the microsomal triglyceride transfer protein (MTP), encoded by the mouse *Mttp* gene. MTP is required for assembly of ApoB-containing lipoprotein complexes, and Mttp-knockout mice exhibit embryonic lethality (Raabe et al., 1998), with marked lipid accumulation at the basolateral surfaces of yolk sac visceral endodermal cells at E9.5. Homozygous mutant embryos displayed retarded growth, and failure of anterior neural tube closure by E10.5, as well as exencephaly at E14.5. ( Raabe et al., 1998). Likewise, ApoB is critical to lipoprotein assembly in visceral yolk sac endodermal cells (Farese et al., 1996), and deficiency of ApoB is associated with failure to close the neural tube, exencephaly, hydrocephaly, embryonic lethality, and embryo apoptosis and resorption, with the spectrum of phenotypes depending on the particular mouse model (Homanics et al., 1993; Farese et al., 1995; Huang et al., 1995). In the ApoB mutants, excessive lipid droplet accumulation in yolk sac endodermal cells was accompanied by deficiencies of Vitamin E and of cholesterol in the embryo (Farese et al., 1996), and embryonic defects could be ameliorated by alpha-tocopherol supplementation (Homanics et al., 1993; Farese et al., 1996). Mouse embryos deficient for the HDL transporter SR-B1 experience Vitamin E-deficiency, and are prone to develop neural tube defects (Santander et al., 2013; Santander et al., 2017). Shortage of maternal cholesterol also has been shown to result in multiple congenital birth defect in the offspring, including holoprosencephaly, neural tube defects, and heart and limb anomalies (Cooper et al., 1998). Based upon these findings, we expect that embryos whose yolk sacs undergo excessive lipid accumulation in diabetic pregnancies would also exhibit deficiencies of cholesterol and Vitamin E. The benefit of Vitamin E supplementation in reducing the incidence of developmental defects in such pregnancies (Sivan et al., 1996) provides strong support for this notion; cholesterol supplementation has not been reported in diabetic pregnancies to date.
In previous studies, supplementations of Arachidonic acid and its derivative prostaglandin E2 (PGE2) were shown to reduce the incidence of embryonic malformation caused by maternal diabetes in mouse and rat embryos in vivo, and in vitro cultures under high glucose high oxygen conditions (Goldman et al., 1985; Pinter et al., 1988; Baker et al., 1990; Goto et al., 1992). Lower levels of prostaglandin E2 were detected in rat embryos from diabetic pregnancies (Wentzel and Eriksson, 2005), and in rat embryos cultured in high glucose (Wentzel and Eriksson, 2005). Embryos contained less Arachidonic acid within the phospholipid fraction when conceptuses were cultured in high glucose (Pinter et al., 1988), but Arachidonic acid was increased in the yolk sacs under these conditions, indicating that it may also be captured in the accumulating lipid droplets, and possibly unavailable as a precursor for PGE2 production. In humans, significant reduction in yolk sac levels of PGE2 in early pregnancy of diabetic women has been reported (Schoenfeld et al., 1995). Consistent with this, single lipid species supplementation has been shown to reduce embryonic malformations (Reece et al., 1996; Wiznitzer et al., 1999; Wentzel and Eriksson, 2005; Reece et al., 2006) in animal models. Taking our results together with prior evidence, it appears that multiple lipid species could be of insufficient availability for optimal growth of embryos in diabetic pregnancies.
While our result indicate that lipid processing is defective in yolk sac endodermal cells under diabetic and obesity conditions, it remains to be determined to which extent the processing of other nutrients, such as proteins or glucose for examples, is also altered. For examples, in yolk sacs from mice with a targeted disruption of the gene encoding ADP-ribosylation factor-like 8B, the trafficking of lysosomes is defective, leading to accumulation of proteins in late endocytic organelles and reduced availability of free amino acids to the developing embryo (Oka et al., 2017). Impairment of transport function has also been described for yolk sac deficient in Pax-Interacting Protein 1-associated glutamate rich protein 1a (Pagr1a), due to an absence of apical vacuoles (Kumar et al., 2014). An excess number of apical vacuoles is also associated with defects in lysosomal processing in the yolk sac of mutants with ablation of both Sorting nexins 1 and 2 (Snx1, Snx2 double mutants), resulting in developmental delay and neural tube defects in the mutant embryos (Schwarz et al., 2002). Moreover, Autotaxin, an ectonucleotide pyrophosphatase/phosphodiesterase, encoded by the *Enpp2* gene, is required for conversion of smaller lysosomes to larger size (Koike et al., 2009), which is essential for proper nutrient processing in yolk sac endodermal cells. Enpp2 germ-line mutant embryos suffer growth retardation, failure of turning and embryonic lethality (Koike et al., 2010), and embryos with disruption of Enpp2 in the neural tube (via Sox1-driven cre-mediated recombination) exhibit neural tube defects (van Meeteren et al., 2006). Taken together, these evidences underscore the importance of effective lysosome processing for supply of nutrients to the developing embryo. Furthermore, insofar the multiple mouse mutant models discussed here have demonstrated causal links between defective yolk sac lipid metabolism and neural tube closure, they provide support for our conclusion that defective lipid processing in yolk sac cells possibly underlies the risk for neural tube defects in pregnancies affected by maternal diabetes, and by inference, obesity.
Previous studies have observed developmental delay and growth retardation of the embryos from diabetic mice and rats (Eriksson et al., 1984; Zhao et al., 2017), which would be consistent with generalized nutrient deficit. If a wide variety of essential nutrients is in limited supply, this -conceivably- could be the cause of embryonic growth restrictions, and the reduced growth of placenta in diabetic pregnancies that we reported previously (Kappen et al., 2011; Salbaum et al., 2011). Interestingly, growth restriction in placenta and embryos was more pronounced when the diabetic pregnant dams were fed a diet with higher lipid content (Kappen et al., 2011, 2012), and associated with a higher rate of neural tube defects (Kappen et al., 2011). It remains to be determined whether the extent of lipid accumulation is even more excessive under these dietary conditions, and whether the very high-fat-diet we showed here to be sufficient for excessive lipid droplet accumulation in yolk sac in normoglycemic FVB dams would exacerbate the lysosomal dysfunction in diabetic pregnancies.
Finally, it is intriguing to speculate whether compounds can be identified that either stimulate lipid release from droplets, or promote lipid processing in the yolk sac, and as a consequence, might enhance lipid transport to the embryo. The expectation would be that restored supply of vital nutrients to embryos in diabetic pregnancies should also lower the incidence of neural tube defects. Considering that neural tube closure proceeds within a relatively short time frame, even short-term treatment might be effective if given just before the neural tube closure process commences.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
## Ethics statement
The animal study was reviewed and approved by Pennington Biomedical Research Center Institutional Animal Care and Use Committee.
## Author contributions
MZ performed experiments, collected and analyzed data, and drafted the manuscript. JMS supervised embryo dissections and conducted bioinformatics analyses. SJ handled the animal husbandry and assisted with conceptus recovery. DB instructed and supervised imaging experiments. CK directed the project, acquired funding for the study, and edited the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Engineered multi-functional, pro-angiogenic collagen-based scaffolds loaded
with endothelial cells promote large deep burn wound healing
authors:
- Hengyue Song
- Kewa Gao
- Dake Hao
- Andrew Li
- Ruiwu Liu
- Bryan Anggito
- Boyan Yin
- Qianyu Jin
- Vanessa Dartora
- Kit S. Lam
- Lucas R. Smith
- Alyssa Panitch
- Jianda Zhou
- Diana L. Farmer
- Aijun Wang
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10014525
doi: 10.3389/fphar.2023.1125209
license: CC BY 4.0
---
# Engineered multi-functional, pro-angiogenic collagen-based scaffolds loaded with endothelial cells promote large deep burn wound healing
## Abstract
The lack of vascularization associated with deep burns delays the construction of wound beds, increases the risks of infection, and leads to the formation of hypertrophic scars or disfigurement. To address this challenge, we have fabricated a multi-functional pro-angiogenic molecule by grafting integrin αvβ3 ligand LXW7 and collagen-binding peptide (SILY) to a dermatan sulfate (DS) glycosaminoglycan backbone, named LXW7-DS-SILY (LDS), and further employed this to functionalize collagen-based Integra scaffolds. Using a large deep burn wound model in C57/BLK6 mice (8–10 weeks old, 26–32g, $$n = 39$$), we demonstrated that LDS-modified collagen-based Integra scaffolds loaded with endothelial cells (ECs) accelerate wound healing rate, re-epithelialization, vascularization, and collagen deposition. Specifically, a 2 cm × 3 cm full-thickness skin burn wound was created 48 h after the burn, and then wounds were treated with four groups of different dressing scaffolds, including Integra + ECs, Integra + LDS, and Integra + LDS + ECs with Integra-only as the control. Digital photos were taken for wound healing measurement on post-treatment days 1, 7, 14, 21, 28, and 35. Post-treatment photos revealed that treatment with the Intgera + LDS + ECs scaffold exhibited a higher wound healing rate in the proliferation phase. Histology results showed significantly increased re-epithelialization, increased collagen deposition, increased thin and mixed collagen fiber content, increased angiogenesis, and shorter wound length within the Integra + LDS + ECs group at Day 35. On Day 14, the Integra + LDS + ECs group showed the same trend. The relative proportions of collagen changed from Day 14 to Day 35 in the Integra + LDS + ECs and Integra + ECs groups demonstrated decreased thick collagen fiber deposition and greater thin and mixed collagen fiber deposition. LDS-modified Integra scaffolds represent a promising novel treatment to accelerate deep burn wound healing, thereby potentially reducing the morbidity associated with open burn wounds. These scaffolds can also potentially reduce the need for autografting and morbidity in patients with already limited areas of harvestable skin.
## 1 Introduction
According to a report published by the World Health Organization (WHO), approximately 180,000 people die worldwide each year from burn injuries, while approximately 11 million people required medical care from burns in 2004 (World Health Organization B, 2018). Since large deep burn wounds (>$20\%$ TBSA) often lack the adequate amount of perfused soft tissue to sustain a skin autograft, it requires a greater metabolic effort and more time to develop an appropriate wound bed for autografting (Porter et al., 2016). These contribute to the overall morbidity of large surface area deep burns, which includes insensible fluid and heat losses, increased metabolic demand, increased risk of infection, and overall increased risk of poor scar formation (Gauglitz et al., 2011; Mason et al., 2019). All of this aligns with the WHO’s report, which describes non-fatal burn injuries as severe causes of morbidity, including hospitalization, disfigurement, and disability (World Health Organization B, 2018).
The deep burn wound healing process consists of four phases: haemostasis, inflammation, proliferation, and remodeling. Each of these phases involves different cell types, such as keratinocytes, fibroblasts, endothelial cells, and macrophages (Urciuolo et al., 2019; Vig et al., 2019). Of interest to our study, endothelial cells, keratinocytes, and fibroblasts play the predominant role in the proliferation phase, which includes connective tissue formation, granulation tissue formation, angiogenesis, and epithelialization. Angiogenesis is a prominent feature in proliferation in the wound healing process. This process increases the number of blood vessels and is necessary for the proper delivery of essential oxygen and nutrients through new blood vessels to the wound site, potentially improving the wound healing process. Deep burn wounds with a large surface area, often lack vascularity in the center of the wound, given that the center of burn wounds usually corresponds to the epicenter of the burn zone of injury. Therefore, the center of burn wounds often is dependent on perfusion through the intact blood vessels from the margins of the wound and by diffusion through the uninjured interstitium (Robson et al., 2001; Hunt et al., 2022; Phillips, 2022). Endothelial cells (ECs) lined in the inner layer of blood vessels are an essential cell type for neovascularization. In the center of larger surface area, deep burn wounds, ECs are activated by the hypoxia-driven growth factors such as vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF), breakdown ECM in granulation tissue, proliferate, and migrate and form new capillaries (Eilken and Adams, 2010a). In the remodeling phase, growth factors, matrix metalloproteinases (MMPs), and tissue inhibitors of metalloproteinases (TIMPs) aid in granulation tissue maturation and ECM remodeling. Scar formation is more likely to occur in deep burn wounds and create complications during the deep burn wound healing process. Currently, there are no adequate treatments for deep burn wounds that accelerate wound healing and help prevent the aforementioned complications (Phillips, 2022).
In the clinical setting, burn wound barrier dressings in conjunction with topical antimicrobial agents are utilized as a conservative and traditional method to treat deep burn wounds to physically protect the wound from insensible fluid losses and reduce bacterial bioburden. Allogeneic and xenogeneic skin grafts are also used for wound bed preparation before autografting as another burn wound management strategy to help in cases of large surface area burns that create a severe limitation in the area of donor skin. Modern treatments overcome the deficiency of autografting by utilizing artificial bioengineered dermal templates and scaffolds, such as Integra® and AlloDerm®. Integra, an FDA-approved, biocompatible three-dimensional structure scaffold made from bovine collagen type I crosslinked with chondroitin sulfate from shark cartilage, provides a dressing for vascularization and remodeling in the operative setting for wound treatment. It has been used for over 30 years in burn wound healing, scar and keloid repair, cutaneous tumor reconstruction, giant congenital melanocytic nevus repair, and abdominal reconstruction after necrotizing fasciitis, et. due to its good property of cell migration, proliferation, and blood vessel formation to form neo dermis (Abai et al., 2004; Clayman et al., 2006; Su et al., 2015; Eugénie et al., 2020; Prezzavento et al., 2022). Some have combined this scaffold with cellular elements such as stem cells, keratinocytes, fibroblast, or endothelial cells to accelerate wound healing rate (Kremer et al., 2000; Danner et al., 2012; Banakh et al., 2020; Piejko et al., 2020). However, one of the drawbacks of collagen-based scaffolds is the lack of innate signaling to recruit enough endothelial progenitor cells (EPCs) to promote revascularization (He et al., 2022).
Previous studies identified that LXW7 as a cyclic peptide ligand for the VEGF receptor 2 (VEGF-R2) on ECs and EPCs, which increases VEGF-R2 phosphorylation and activates ERK$\frac{1}{2}$ mitogen-activated protein kinase (Hao et al., 2017; Olsson et al., 2017; Soldi et al., 2022). Overall, LXW7 shows a strong, stable, and specific ability to retain ECs/EPCs, and provides sufficient endogenous EC recruitment and exogenous EC binding and proliferation. A synthetic collagen-binding peptidoglycan DS-SILY, composed of dermatan sulfate (DS) and collagen-binding peptides SILY was designed to mimic the native decorin, a member of the small leucine-rich proteoglycan (SLRP) family, which consists of a protein core containing leucine repeats with a glycosaminoglycan (GAG) chain consisting of either chondroitin sulfate (CS) or dermatan sulfate (DS) (Stuart et al., 2011). Decorin is related to fibrillogenesis, keloid scar, and hypertrophic scar formation. Recent studies demonstrate that decorin has the ability to prevent collagen from collagenase degradation and decrease scar formation (Khorramizadeh et al., 1999; Armstrong and Jude, 2002; Geng et al., 2006; Mukhopadhyay et al., 2022; Sayani et al., 2022; Scott et al., 2022; Zhang et al., 2022). Therefore, DS-SILY can bind to the collagen scaffold and inhibit collagen degradation.
In this project, we designed a multi-functional collagen-based Integra® scaffold modified by LXW7-DS-SILY (LDS) and loaded with ECs to evaluate the treatment potential benefits in a mouse deep burn wound model. We demonstrate that the LDS modified Integra® not only promotes the ability of proliferation and survival of exogenously seeded ECs, but also accelerates the recruitment of endogenous ECs and angiogenesis in deep burn wounds to build a better-quality wound base and improve wound healing.
## 2.1 Cell characterization and expansion
C57BL/6 mouse primary bone marrow-derived endothelial cells (ECs) were purchased from Cell Biologics, Inc. (C57-6221). ECs were maintained and cultured in a mouse endothelial cell medium (M1168, Cell Biologics). The passage number of ECs used in all experiments was between passages five and eight in this study. Dil-Ac-LDL staining, immunostaining of CD31 and VE-Cadherin, flow cytometry of CD31, CD34, CD45, CD144, CD90, and tube formation assay were used for mouse EC characterization.
## 2.2 Lentiviral vector transduction
The lentiviral constructs were generated at the University of California, Davis Institute for Regenerative Cures (IRC) Vector Core. ECs were transduced with pCCLc-MNDU3-LUC-PGK-EGFP-WPRE or pCCLc-MNDU3-LUC-PGK-Tomato-WPRE. pCCLc is the backbone of the lentivirus; MNDU3 is a ubiquitous promoter driving the luciferase (LUC) expression. The PGK is another promoter driving the expression of enhanced green fluorescent proteins (EGFP) or td-Tomato fluorescent protein (Tomato). WPRE represents an enhancer that could boost transgene expression. Lentiviral vector was added in transduction media consisting of mouse basal endothelial cell medium, $5\%$ FBS, and 20 μg/ml protamine sulfate (MP Biomedicals) at a multiplicity of infection (MOI) of 10 for 48 h. The transduction medium was then replaced with a complete mouse endothelial cell medium, and the cells were cultured for an additional 72 h.
## 2.3 Acetylated low-density lipoprotein uptake assay
ECs were cultured in serum-free medium for 24 h with the density of 1.2 × 104/well in 24-well plate and then incubated with 10 μg/ml Dil-Ac-LDL (L3484, Invitrogen) for 4 h at 37°C, $5\%$ CO2. Cells were then washed with Dulbecco’s Phosphate buffered saline (DPBS, HyClone) and fixed with $10\%$ formalin (ThermoFisher Scientific) at room temperature (RT) for 5 min, following 3 times wash with DPBS and imaged with a Zeiss Observer Z1 microscope.
## 2.4 Tube formation assay
A 24-well plate was fully covered with 200 μL chilled Matrigel (354234, Corning) per well and incubated at 37°C, $5\%$ CO2 to gel for 1 h 6 × 104 ECs were then seeded onto the Matrigel-coated wells and incubated at 37°C for 18 h. Phase contrast images were taken using a Zeiss Observer Z1 microscope.
## 2.5 Immunofluorescent staining of mouse ECs
5 × 104 ECs were cultured in 24-well plates for 24 h and fixed with $10\%$ formalin for 10 min and blocked with blocking buffer containing $5\%$ bovine serum albumin (BSA, bioWORLD) in 1X DPBS to block non-specific binding sites for 1 h at RT. The cells were then stained with either 0.01 mg/ml PE Rat Anti-Mouse CD31 (553373, BD Biosciences) or 0.01 mg/ml PE Rat Anti-Mouse CD144 (562243, BD Biosciences) antibodies in a staining buffer containing $1\%$ BSA in 1X DPBS and incubated at 4°C overnight. The cell nuclei were stained with 1:5000 DAPI for 5 min and washed 3 times. Then images were taken using a Zeiss Observer Z1 microscope.
## 2.6 Immunostaining analyses of ligand-cell binding affinity
2 × 104/well ECs were cultured in an 8-well cell chamber (80807, ibidi) for 24 h and fixed with acetone at −20°C for 15 min, and non-specific binding sites were blocked with blocking buffer for 1 h at RT. The cells were then stained with 0.001 mg/ml Cy3 Rabbit Anti-Mouse αvβ3 (C02329Cy3, Signalway Antibody) or 1 μM LXW7-FITC or mixed 0.001 mg/ml Cy3 Rabbit Anti-Mouse αvβ3 with 1 μM LXW7-FITC in staining buffer and incubated at 4°C overnight. 1μM LXW7-Scramble-FITC was used as control. The cell nuclei were stained with 1:5000 DAPI for 5 min and imaged using a Nikon A1 confocal microscope.
## 2.7 Attachment and proliferation assay of EC binding on LXW7 modified culture surface
To modify the culture surface with LXW7, a 24-well plate was coated with 150 μL of 10 μg/ml Avidin (Thermo Fisher) and incubated at 37°C for 1 h. Wells coated with Avidin were washed with 1X DPBS 3 times and treated with 150 μL M equivalents (1 μM) LXW7-Biotin and incubated at 37°C for 1 h. D-Biotin was used as a negative control. All treated wells were washed with 1X DPBS 3 times and blocked with blocking buffer at 37°C for 1 h. After all the wells were washed with 1X DPBS 3 times, 1 × 105 ECs were seeded into the wells and incubated at 37°C for 1 h. After incubation, unattached cells were washed with 1X DPBS 3 times. Images were taken by using a Zeiss Observer Z1 microscope. Cell counter ImageJ plugin was used to determine the cell numbers in three randomly selected fields from each independent experiment.
Same as the treatment of the 24-well plate for ligand attachment assay, 96-well plates were coated with 50 μL of 10 μg/ml Avidin and incubated at 37°C for 1 h. After being washed with 1X DPBS 3 times, wells were treated with 50 μL 1 μM LXW7-Biotin or D-Biotin and incubated at 37°C for 1 h then all treated wells were washed and blocked with blocking buffer at 37°C for 1 h 2 × 103 ECs were seeded into each well and cultured at 37°C for the next 7 days. MTS assay (G3580, Promega) was performed following the manufacturer’s instructions. Absorbance was measured at 490 nm and 630 nm using a SpectraMax i3 plate reader instrument (Molecular Devices LLC).
## 2.8 Synthesis and characterization of peptide-hydrazides
Hydrazide-modified peptides RRANAALKAGELYKSILYGSG-hydrazide (SILY-hydrazide) and cGRGDdvc (AEEA)2WG-hydrazide (LXW7-hydrazide, wherein AEEA is short PEG linker) were synthesized using standard Fmoc solid-phase peptide synthesis following previously established protocols with modification (Paderi and Panitch, 2008; Xiao et al., 2010). In brief, Cl-TCP(Cl) ProTide Resin (loading 0.4–0.6 mmol/g, CEM Corporation) was rinsed three times with dichloromethane (DCM, Fisher Scientific) and N, N-Dimethylformamide (DMF, Fisher Scientific) and expended in $50\%$ DCM/DMF for 1 h. As soon as the resin was swollen, it was reacted twice with $10\%$ hydrazine hydrate (Sigma) in DMF and 0.057MN, N-Diisopropylethylamine (DIPEA, Fisher Scientific) for 2 h at RT each. To cap any unreacted chloride groups, $10\%$ methanol (Fisher Scientific) in DMF was used, and the resin was then washed three times in DMF, as well as 3 times in DCM. After this, the resin was treated with four equivalents of the first Fmoc-amino acid, four equivalents of OxymaPure, N,N-diisopropylcarbodiimide (DIC, Fisher Scientific), and ten equivalents of DIPEA in DMF for 4 hours, followed by three repetitions of the wash. Using a Liberty Blue microwave peptide synthesizer (CEM Corporation), subsequent amino acids were coupled for 10 min each at 50°C with 5 equivalents of Fmoc-amino acids, DIC, and OxymaPure containing 0.1 M DIPEA. The deprotection process was carried out using $20\%$ piperidine in DMF. To cleave peptides off beads, this reaction was conducted using trifluoroacetic acid (TFA, Fisher Scientific), phenol (Sigma), and water ($5\%$ H2O) for 3 h. Fresh peptides were precipitated using cold diethylether (Acro Organics) and then allowed to dry before dissolving in $5\%$ acetonitrile/water for purification. The cysteine residues in LXW7 were first oxidized using ClearOx resin (Peptides International) in accordance with the manufacturer’s guidelines before purification. Using an AKTApure 25 FPLC (GE Healthcare) and an acetonitrile gradient, peptides were purified through a C18 prep column against an acetonitrile gradient and verified using MALDI-TOF-MS (Bruker). Peptides were purchased from InnoPep Inc. for some experiments.
## 2.9 Synthesis and characterization of molecule variants
As described in our previous studies (Walimbe et al., 2021), LDS was synthesized by conjugating SILY-hydrazide and LXW7-hydrazide to a dermatan sulfate (DS) backbone using carbodiimide chemistry. DS (average molecular weight 41,816 Da, Celsus Laboratories) was reacted with peptide-hydrazides by 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide hydrochloride (EDC, ThermoFisher Scientific) in 0.1 M MES [2- (N-morpholino) ethanesulfonic acid] buffer with 8 M urea (Sigma) and $0.6\%$ NaCl (Sigma) titrated to pH 4.5. First, SILY-hydrazide was conjugated to the DS for 4 h using 0.01 mM EDC. To stop the reaction, pH eight was titrated. A tangential flow filtration (Spectrum labs) was used to purify the product with a 10 kDa column prior to lyophilization. In a similar manner, 0.1 mM EDC was used to sequentially conjugate LXW7-hydrazide to DS-SILY constructs for 24 h prior to purification. To verify peptide conjugation, standard curves were created using concentration-dependent 280 nm absorbance of aromatic amino acids, and absorbances of synthesized molecules were extrapolated using readings obtained on a NanoDrop UV–Vis spectrophotometer (Thermo Fisher).
## 2.10 Preparation of LDS-modified integra scaffold with or without ECs
6 mm diameter punch-outs of Integra were cut using a sterile biopsy punch and placed into two 35 mm dishes with the collagen side facing up and incubated with 20 μL of 10 μM LDS or 1X DPBS at 37°C for 1h. The scaffold was washed 3 times with 1x DPBS and soaked in the ECs culture medium at 37°C for 1h. The scaffold was then placed separately into each well of the 48-well plate. 1 × 106 cells/cm2 ECs were suspended in 20 μL of ECs culture media per scaffold and carefully loaded onto the surface of LDS-modified Integra and unmodified Integra. The plate was placed in a 37°C, $5\%$ CO2 incubator, incubated for 1h for cells to adhere to the scaffold, and then added 100 μL culture media into each well.
To prepare the scaffold for surgery, 2 × 3 cm2 Integra was cut by sterile scissors, placed in 35 mm dishes separately with the collagen side facing up, and incubated with 500 μL of 10 μM LDS or 1X DPBS for 1h at 37°C. The scaffold was rinsed three times with DPBS and soaked in the culture medium at 37°C for 1h 1 × 106 cells/cm2 ECs were suspended in 200 μL of ECs culture media per scaffold and carefully loaded onto the surface of LDS-modified Integra and unmodified Integra. Then scaffolds were cultured for 24h at 37°C, $5\%$ CO2 incubator prior to surgery.
## 2.11 Attachment and proliferation assay of EC binding on LXW7 modified collagen base scaffold
To further explore the effect of LXW7 on mouse EC binding efficacy on a collagen-based scaffold, 1 × 106 cells/cm2 mouse ECs were seeded on the LDS-modified or untreated 6 mm diameter Integra scaffold and incubated for 1 h. After incubation, unattached cells were washed off with 1X DPBS 3 times. Images were taken using a Nikon A1 confocal microscope. Cell counter ImageJ plugin was used to determine the cell numbers in three randomly selected fields from each independent experiment.
A cell proliferation assay was performed using 1 × 106 mouse ECs/cm2 seeded on the LDS modified or untreated 6 mm diameter Integra scaffold and incubated for 5 days. MTS assay was performed following the manufacturer’s instructions. Absorbance was measured at 490 nm and 630 nm using a SpectraMax i3 plate reader instrument (Molecular Devices LLC).
## 2.12 Mouse large deep burn wound healing model
All animal procedures were approved by the University of California, Davis (UCD) Institutional Animal Care and Use Committee (IACUC). Male C57BLK/6 mice (Jackson Lab, 8–10 weeks old) were used in this project. The dorsal hair of the mice was shaved and cleaned with $70\%$ ethanol right before burn injury creation. Mice were anesthetized with $3\%$ isoflurane and placed dorsal skin exposed in a 2 cm × 3 cm window of a rack. Full-thickness skin burn wound was created by immersing the bottom of the rack in 65°C water for 20 s (Nguyen et al., 2022; Shen et al., 2022). 48 h after the burn wound injury, the burned skin (2 cm × 3 cm) was removed and different groups of scaffolds, including Integra only, Integra + ECs, Integra + LDS and Integra + LDS + ECs were placed on the wound area. Buprenorphine (0.03 mg/mouse) and $0.9\%$ saline (1 mL) was given intraperitoneally for analgesia and fluid resuscitation immediately after injury. On days 1, 7, 14, 21,28, and 35 post-treatments, macroscopic photos were taken of all wounds for further measurements.
## 2.13 Histological analyses
Animals were euthanized at two time points, day 14 and day 35 after treatment. The 2 cm full-thickness wound skin tissue samples were collected within the wound area at the center of each tissue sample. All tissue samples were fixed in $4\%$ paraformaldehyde for 24 h, dehydrated in $30\%$ sucrose for 48 h, embedded in O.C.T. compound (Sakura Finetek USA), and stored in −80°C. 10μm thickness sections were cut and prepared by the Cryostat (Leica CM3050S). Hematoxylin and Eosin (H&E) staining was performed to observe the wound tissue formation. Masson Trichrome (22110648, Epredia™) staining was used to evaluate the collagen deposition. All the images were captured and analyzed by the 4x lens of ImageXpress Pico Automated Cell Imaging System (Molecular Devices). Picro Sirius Red staining (ab245876; Abcam) was performed to observe the different collagen alignment in the wound area at different timepoints. Polarized images were captured by 10x lens of Leica DMi8 microscope under linearly polarized light by inserting a rotating polarizer into the beam path before and after the section, respectively. Once Picro Sirius Red staining images were captured, they were processed in MATLAB for analysis and graphing (Rich and Whittaker, 2017).
For immunostaining images, tissue sections were washed with 1X DPBS, permeabilized with $0.5\%$ Triton X-100 for 10 min, blocked with blocking buffer for 1h, and stained by the following primary antibodies by incubating in 4°C overnight: PECAM-1(1:200, goat, AF3628, R&D Systems), α-SMA (1:200, rabbit, ab5694, abcam). Sections were then incubated with their respective secondary antibodies diluted at 1:500 for 1 h, counterstained with DAPI (1:5000) for 5 min, and mounted with Prolong Diamond Antifade Mountant (P36961, Invitrogen). Nikon A1 laser-scanning confocal microscope was used to acquire images. The number of blood vessels (α-SMA positive) per field was counted.
## 2.14 Statistics
Data are reported as mean ± standard deviation (SD) for cell attachment and MTS assay and as mean ± standard error of mean (SEM) for healing rate and histological analysis. Statistical analysis of cell attachment assay was performed using unpaired two-tailed distribution, equal variance Student’s t-test. Analyses of histological analysis including wound length, re-epithelialization, collagen volume fraction and number of blood vessels were performed using one-way ANOVA. MTS assay and Picro Sirius Red Staining was performed by two-way ANOVA. Healing rate was performed by mixed-effects analysis. All statistical analyses were performed using PRISM 8 (GraphPad Software Inc.), and differences were considered significant when $p \leq 0.05.$
## 3.1 Characterization and transduction of mouse bone marrow ECs
ECs derived from mouse bone marrow exhibited typical EC morphology and were efficiently transduced with the lentiviral vector expressing fluorescent marker GFP for analysis. A significant positive expression of CD31, CD34, CD144, as well as a negative expression of CD45 and CD90 in flow cytometry, demonstrated the EC characteristics (Figure 1A). A high level of expression of EC markers CD31 and CD144 in immunofluorescence staining verified their EC characteristics (Figures 1B, C). In mouse ECs co-cultured with DiI-Ac-LDL, positive staining for DiI-Ac-LDL was observed (Figure 1D). In vitro tube formation assays demonstrated their ability to form tubules in the presence of basement membranes (Figure 1E). Based on these results, mouse bone marrow derived ECs and human ECs have similar phenotypes and functions.
**FIGURE 1:** *Characterization of mouse bone marrow derived endothelial cells (ECs). (A) Flow cytometry of CD31, CD34, CD45, CD144 and CD90 expression and GFP transduction on ECs. (B, C) Immunofluorescence staining results of expression of CD31 (B) and VE-Cadherin (C). (D) Acetylated low-density lipoprotein uptake by ECs. (E) Representative phase contrast image of in vitro tube formation of ECs. Scale bar = 200 μm.*
## 3.2 LXW7 ligands accelerated mouse EC attachment and proliferation
To demonstrate the abilities of LXW7 to enhance mouse EC attachment and proliferation, we tested the cell-ligand binding ability in different situations with tissue culture plate and collagen-based scaffold Integra. To modify the tissue culture plate surface, we used LXW7-Biotin as treatment and D-Biotin as a negative control. To further explore the ability of cell-ligand binding ability on scaffold, we modified the Integra with LDS to allow the LXW7 to functionalize in the scaffold. After seeding mouse ECs on the tissue culture plate and collagen-based Integra scaffold, our results demonstrated that LXW7 can aid in the attachment of mouse ECs to the tissue culture surface, while LDS can accelerate the attachment of mouse ECs to the scaffold (Figures 2A, D, G). The remaining cells on the modified plate and Integra were higher than those on the plate and Integra without LWX7 or LDS treatment (Figures 2B, E).
**FIGURE 2:** *Effects of LXW7 and LDS on the attachment and proliferation of ECs. (A, D) Representative images of ECs attached on D-Biotin(control) or LXW7-Biotin treated culture plate and Integra (control) (A) or LDS treated Integra scaffold (D) after 30 min incubation. (B, E) Quantitative data and analysis of remaining cells on culture plate (B) and Integra scaffold (E). (C, F) Proliferation and viability of ECs on D-Biotin (control) or LXW7-Biotin treated culture plate (C) and on Integra scaffold or LDS treated Integra scaffold (F) were measured by MTS assay at different timepoints. (G) Scheme of design of ECs attachment assay on culture plate or Integra scaffold. (H) Representative images of integrin αvβ3 expression on ECs (i), LXW7 binding efficiency on ECs (ii), and integrin αvβ3+LXW7 co-staining (iii). Scale bar = 50 μm. Data are expressed as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.*
To further clarify the ability of proliferation, we performed the MTS assay both on tissue culture plate surface and collagen-based Integra scaffold. After incubating mouse ECs on LWX7-Biotin or D-Biotin treated tissue culture plate for 7 days, we observed that the cell proliferation rate was rapid from Day 1 to Day 5, while from Day 5 to Day 7 the rate decreased. Compared to the D-Biotin treated group, the LXW7 treated surface significantly enhanced cell proliferation from Day 2 (Figure 2C). After incubating mouse ECs on collagen-based scaffold for 5 days, we observed the same trend as the tissue culture plate, that LDS modified Integra can improve mouse EC proliferation (Figure 2F). The results clearly demonstrate that the LXW7 modified surface and LDS-functionalized scaffold are effective in promoting mouse EC attachment and proliferation.
To clarify the ligand-cell binding affinity, we stained mouse ECs with integrin αvβ3 antibody and LXW7 to evaluate the ligand binding to the mouse ECs through integrin αvβ3 (Figure 2H). Mouse ECs showed high expression of integrin αvβ3 (Figure 2Hi). LXW7 also showed high binding efficiency on ECs. After incubating integrin αvβ3 antibody and LXW7 with mouse ECs at the same time, LXW7 colocalized with the integrin αvβ3 on the mouse ECs, suggesting that LXW7 has high binding efficiency to mouse ECs through integrin αvβ3.
## 3.3 LDS accelerated the wound healing rate in the mouse large deep burn wound model
The large deep burn wound model was used to evaluate the effect of different treatment groups on large deep burn wounds in vivo. The group compositions are as follows: Integra only (negative control); Integra + ECs; Integra + LDS; Integra + LDS + ECs. Digital photos were taken at Days 1, 7, 14, 21, 28, and 35 (Figure 3A). The wound area decreased in all groups over time. The average wound healing rate was significantly increased at week two in Integra + LDS + ECs ($46.049\%$ ± $5.3\%$) compared to both Integra ($29.678\%$ ± $3.925\%$) and Integra + LDS ($31.78\%$ ± $2.744\%$). In week 3, the wound healing rate was accelerated in Integra + LDS + ECs ($63.018\%$ ± $2.65\%$) compared to both Integra ($43.186\%$ ± $6.153\%$) and Integra + ECs ($45.687\%$ ± $6.443\%$). In week 4, the wound healing rate was higher in Integra + LDS + ECs ($86.253\%$ ± $2.176\%$) and Integra + ECs ($84.33\%$ ± $5.853\%$) in comparison to Integra ($71.928\%$ ± $5.671\%$). On Day 35, there was no significant difference among four groups (Figure 3B). Overall, Integra + LDS + ECs shows great potentials in the proliferation and early remodeling stage of large deep burn wound healing.
**FIGURE 3:** *Characterization of wound healing in large deep burn wound model. The wounds were treated with Integra with ECs, Integra with LDS, or Integra with ECs and LDS, or the Integra only as standard of care. (A) Representative images of wounds in all groups during healing over 35 days. Relative wound area is indicated with white dotted line. Scale bar = 1 cm. (B) Quantitative data of wound healing rate of four groups at different time points. Data are expressed as mean ± SEM. *p < 0.05, **p < 0.01, and ***p < 0.001.*
## 3.4 LDS accelerated deep burn wound re-epithelialization and decreased wound length
To further evaluate the wound healing qualities at different time points, H&E staining was performed on large deep burn wound tissue at Day 14 and Day 35 (Figures 4A, B). It showed that Integra + LDS + ECs had the shortest wound length among all groups, followed by Integra + ECs, Integra + LDS and Integra groups at Day 35 (Figure 4E). At Day 14, the same trend occurred among these groups. The Integra + LDS + ECs group exhibited the shortest wound length in all groups. And Intgra + ECs showed longer wound length compared to Integra + LDS + ECs. Integra only showed longer wound length in Day 14 and Day 35 among four groups (Figure 4C). Also, the Integra + LDS + ECs was fully covered with neo epidermis and showed significantly better re-epithelialization out of all four groups, while the Integra + ECs, Integra + LDS, and Integra only groups were partially covered with neo epidermis at Day 35 (Figure 4F). At Day 14, the Integra + LDS + ECs group showed the trend of improved re-epithelialization among all groups (Figure 4D). Integra only showed more un-epithelialized area in Day 14 and Day 35. Overall, the Integra + LDS + ECs group has better wound healing qualities at Day 14 and Day 35.
**FIGURE 4:** *Histological evaluation of wound regeneration at Day 14 and Day 35. (A,B) Hematoxylin and eosin (H&E) staining was performed at Day 14 and Day 35 post-wounding for four groups. The wound area and normal tissue were separated by yellow dot lines. The un-epithelialized area and re-epithelialized area were separated by black dot lines. Scale bar = 1 cm. (C,D) Quantification of the wound length at Day 14 and Day 35. (E,F) Quantification of the re-epithelialization at Day 14 and Day 35. Data are expressed as mean ± SEM. *p < 0.05, **p < 0.01, and ***p < 0.001.*
## 3.5 LDS modified integra scaffold increased collagen deposition and resulted in optimal collagen composition
Masson trichrome and Picro Sirius Red staining were conducted on large deep burn wound samples at Day 14 and Day 35 in order to evaluate the quality of collagen in different groups at different time points (Figures 5A, B). Collagen stains blue, nuclei stain dark brown, whereas muscles, cytoplasm, and keratinocyte stain red. In all groups, Masson Trichrome staining showed that newly formed collagen was arranged in the regenerated tissue at Day 14 and Day 35. A typical collagen fiber, with densely packed and basket-weave patterns, was observed at Day 14 for Integra + LDS and Integra + LDS + ECs groups; and at Day 35 for Integra + LDS + ECs and Integra + ECs groups. At Day 14, the collagen deposition of Intgera + LDS + ECs was accelerated compared to the Integra only and Integra + ECs groups (Figure 5C). At Day 35, the collagen volume fraction of Integra + LDS + ECs was significantly higher than the Integra only and Integra + LDS groups (Figure 5D). Picro Sirius Red staining showed different collagen types in deep burn wounds at different time points. The red birefringence represents thick fibers (Type I), while the green birefringence represents thin fibers (Type III). The yellow birefringence represents mixed fiber, which is the combination of collagen I and collagen III. The type and amount of collagen change during wound healing, which determines the tensile strength of skins. In the early stages of wound healing, collagen III is synthesized first, followed by collagen I, the dominant type of collagen in the skin. As shown in Figure 5E, at Day 14, the proliferation stage of wound healing, the Integra + LDS + ECs group induced significantly higher collagen III deposition compared to the other three groups. Also, the mixed collagen in the Integra + LDS + ECs treated group was higher than Integra only group. At Day 35, the main portion of collagen in the wound was collagen I. The Integra + LDS + ECs group and the Integra + LDS group showed the trend of increasing mixed collagen compared to other two groups (Figure 5F). These results indicated that the Integra + LDS + ECs group showed promising results in collagen deposition, collagen proportion and potentially decreased scar formation.
**FIGURE 5:** *Histology evaluation of collagen deposition and portion of wounds treated by different groups at Day 14 and Day 35. (A) Representative images of Masson Trichrome staining at Day 14 and Day 35. Collagen stains blue, nuclei stain dark brown, whereas muscles, cytoplasm, and keratinocyte stain red. Scale bar = 1 cm. (B) Representative images of Picro Sirius Red staining under linear polarized light microscope at Day 14 and Day 35. Red birefringence represents thick fibers (Type I), while green birefringence represents thin fibers (Type III). Yellow birefringence represents mixed fiber. Scale bar = 20 μm. (C, D) Quantification of the collagen volume fraction at Day 14 and Day 35. (E, F) Quantification of the percentage of collagen type of total collagen at Day 14 and Day 35. Data are expressed as mean ± SEM. *p < 0.05, **p < 0.01, and ***p < 0.001, and ****p < 0.0001.*
## 3.6 LDS promoted angiogenesis in the large deep burn wound model
To measure the angiogenesis in large deep burn wound at different time points, we stained deep burn wound tissue with CD31 and α-SMA to evaluate the number of blood vessels in the wound center at Day 14 and Day 35 (Figure 6A). At Day14 and Day 35, the Integra + LDS + ECs group showed higher blood vessel number than other three groups. Integra + LDS and Integra + LDS + ECs group showed a significantly higher number of blood vessels compared to Integra group at Day 35 (Figures 6B, C). Only Integra + LDS + ECs group indicated the increase of blood vessels from Day 14 to Day 35 (Figure 6D). In addition, the blood vessels in Integra + LDS + ECs group show more mature and completed structures. Integra + LDS + ECs showed the highest CD31 density among all groups, followed by Integra + ECs, Integra + LDS and Integra groups at Day 14. Also, CD31 in all four groups exhibited a from Day 14 to Day 35.
**FIGURE 6:** *Immunofluorescence staining of CD31 and α-SMA in wound samples on Day 14 and Day 35. (A) Representative images of CD31 and α-SMA co-immunostaining in all groups at Day 14 and Day 35. Scale bar = 50 μm. (B–D) Quantitative data of newly formed vessels per field on Day 14 and Day35. Data are expressed as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.*
## 4 Discussion
The wound healing process involves various cell types, cytokines, and chemokines and consists of four overlapping phases: homeostasis, inflammation, proliferation, and remodeling. To further explore how LDS performs on a large wound model, we created large deep burn wound area on the dorsal of C57 BLK6 mice. The wound area is about 20 times larger than a 6 mm punch wound. Unlike normal wound healing, large deep burn wound has a unique and complex healing process. Thermal injury potentially increases the damage to the area after injury, causing hypoxia, damage of blood vessels, and skin tissue loss. Due to the large amounts of tissue loss in the deep burn wound area, these complex wounds lead to insensible fluid and heat loss, higher infection rate, prolonged time for wound bed preparation before autografting, and increasing need for skin autografting. These can lead to suboptimal scar formation that can be painful and eventually lead to debilitating contractures that require long term physical therapy.
One of the key processes in deep burn wound healing is rebuilding the vasculature network in the wound bed to deliver oxygen, nutrients, and other biological moieties to allow cellular proliferation, migration, and tissue regeneration. Endothelial cells serve a critical role in efficient vascularization in wound healing. Activated ECs break down ECM in the granulation tissue, proliferate, migrate, form new cell-cell junctions, and branch out to form new capillaries (van Hinsbergh and Koolwijk, 2008; Vestweber, 2008; Dejana et al., 2009; Arroyo and Iruela-Arispe, 2010).
LXW7, discovered by screening OBOC libraries, has a high binding affinity to αvβ3 integrin on ECs/EPCs. In our previous study, due to increased phosphorylation of VEGF receptor 2 (VEGF-R2) and activation of ERK$\frac{1}{2}$, LXW7 helps ECs/EPCs proliferation, migration, and recruitment (Hao et al., 2017). Meanwhile, in vitro data supports that mouse ECs have highly improved attachment and proliferation abilities on both LXW7 treated culture plate and LDS treated Integra scaffold (Figure 2), which shows that scaffolds treated with LDS have the potential to help EC survive, proliferate and attach. On Day 14, both Integra + ECs and Integra + LDS + EC groups show higher wound healing rates compared to the other two groups, indicating exogenous ECs are essential to the large deep burn wound at the early stage. In vivo, the ability of wound healing arose in group Integra + LDS on Day21, which indicates that the LXW7 potentially recruits endogenous ECs to the wound site to accelerate angiogenesis and wound healing. Because of the lack of blood supply, it takes time for adequate numbers of endogenous ECs to migrate to the wound site and take effect in the neovascularization process. LXW7 cooperated with exogenous ECs to aid in proliferation, retention, and functionalization at the wound site. Some studies have shown that EC transplantation may benefit skin wound healing by promoting the recruitment of monocytes/macrophages and increasing neovascularization at the wound site (Suh et al., 2005). The binding of growth factors to proteoglycans and, thereby, physical linkage to matrix scaffolds is essential for the generation of growth factor gradients, which are critical for filopodia extension and the directional growth of endothelial sprouts in neovascularization (Ruhrberg et al., 2002; Gerhardt et al., 2003; Eilken and Adams, 2010b). IHC shows that on Day 35, the number of endothelial cells decreased. Of interest is that the signal of transplanted ECs disappeared on day 35. This may have been because, during the remodeling phase, the cellular processes activated in the acute phase following injury are downregulated and eventually halted. There are few cells in the wound bed, consisting primarily of collagen and other proteins that make up the extracellular matrix, as endothelial cells, macrophages, and myofibroblasts undergo apoptosis or migrate out of the affected area (Gurtner et al., 2008). Another interesting observation is that on the Day35, the Integra + ECs and Integra + LDS + ECs groups showed better-wound healing, though there was no significant difference between the four groups. Some studies posit that angiogenesis is a critical determinant of wound healing (Arnold and West, 2022; Folkman, 2022; Swift et al., 2022), while others, like Jacobi, J. observed that Wound healing was not affected by the same degree of impairment in wound angiogenesis (Jacobi et al., 2004). It is important to note that in the large deep burn wound, wound closure is not a consistently used endpoint for wound healing. After wound closure, the remodeling and maturation often continue for months to years. As such, it is especially important to determine how to optimize the remodeling phase to minimize scarring.
We accomplished this by studying not only vascularization but also collagen deposition. We used Day 14 and Day 35 to represent time points within the proliferation and remodeling phases, respectively (Baldassarro et al., 2022). In a previous study, DS-SILY was shown to decrease collagen degradation by inhibiting MMP, thus, reducing dermal scarring (Stuart et al., 2011). Quantification in Masson Trichrome staining on Day 14 in Figure 5C indicates that at an early stage of collagen formation, DS-SILY may offer better support for collagen from MMP degradation. The collagens form a relaxed network of cross-linked long-chain fibers to give the strength and elasticity of healthy skin and scar tissue. The two dominant types of collagens in wound repair are collagen I and III. Remodeling involves the active remodeling of the acellular matrix from one primarily made up of type III collagen to one primarily composed of type I collagen (Lovvorn et al., 2022). It is known that, the ratio of collagen I to collagen III is essential in scar formation. In hypertrophic scar and keloid, this ratio is often perturbed, arising up to 17:1, almost 3 times higher than in normal skin (6:1) (Verhaegen et al., 2009; Wolfram et al., 2009; Xue and Jackson, 2015; Uitto J Fau - Perejda et al., 2022). Studies have shown that collagen III is essential for the non-scar healing process (Liu et al., 2013; Wang et al., 2018; Wang et al., 2019). At Day 14, the collagen III content in the wound model was higher in the Ingtegra + LDS + ECs group, also at Day 35, the portion of mixed collagen I and III was higher in the Integra + LDS + ECs and the Integra + LDS groups, suggesting large deep burn wounds treated with LDS with ECs may have formed less scarring. Unlike human skin, rodent animal has a panniculus carnosus layer to heal the wound by contraction. The wound healing process in humans is related to re-epithelialization and granulation tissue formation. Like collagen formation, normal tissue in rodents has a reticular collagen structure, whereas scar tissue has large parallel bundles of collagen arranged at approximately right angles to the basement membrane. In humans, instead of a random basketweave formation of the collagen fibers found in normal tissue, scars contain collagen that forms cross-links to align in a single direction parallel to the skin, opposite to the rat. In addition, scars have greater collagen density and larger fiber size compared to normal tissue (Whitby and Ferguson, 1991; Armour et al., 2007; Wolfram et al., 2009). In the future, we will consider moving to a porcine model to mimic more closely large wounds of the human body.
In conclusion, this study demonstrates a novel approach to treating large deep burn wounds by functionalizing endothelial cells with LDS on a collagen scaffold. For future studies, we intend to create large deep burn wounds on a porcine model to mimic human skin and further explore applications for LDS. At the same time, exploring the mechanism of LDS in different healing phases, in its interaction with other cell types, and how it regulates scar formation will be of clinical interest.
## 5 Conclusion
This study provides a promising novel treatment to accelerate large deep burn wound healing, thereby potentially reducing the morbidity of open burn wounds, such as insensible fluid losses and infection. Moreover, our scaffolds present the potential for treating large areas of deep burns by reducing and potentially obviating the need for autografting and its accompanying morbidity in patients with already limited areas of harvestable skin.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by the University of California, Davis (UCD) Institutional Animal Care and Use Committee (IACUC).
## Author contributions
HS performed the conceptual design of the in vitro and in vivo studies, troubleshooting, experimental execution, interpretation of data, manuscript writing, figure creation and result discussion. KG assisted with the mouse surgery, conceptual design, and data analysis design. BA, QJ, and BY assisted the in vitro test. AL, RL, KL, VD, LS, and AP provided the resources. AL, KG, DH, BA, KL, AP, and DF were involved in the results discussion and manuscript revisions. JZ assisted with the data analysis and manuscript revision. AW was responsible for conceptualization, results, discussion, and revision of the manuscript.
## Conflict of interest
KL, AP, and AW are founders of VasoBio and VasoBio reserves the right to license this technology.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Analysis of risk factors for the increased incidence of gallstone caused by
hepatectomy: A retrospective case–control study'
authors:
- Jiangmin Zhou
- Lin Chen
- Zhiwei Zhang
- Biao Wu
journal: Frontiers in Surgery
year: 2023
pmcid: PMC10014528
doi: 10.3389/fsurg.2023.1097327
license: CC BY 4.0
---
# Analysis of risk factors for the increased incidence of gallstone caused by hepatectomy: A retrospective case–control study
## Abstract
### Background
An increased risk of gallstones has been observed in patients undergoing hepatectomy. This study attempted to analyze the risk factors for gallstones after hepatectomy.
### Methods
From January 2013 to December 2016, clinical data of 1,452 eligible patients who underwent hepatectomy were consecutively reviewed. According to the imaging, including gallbladder ultrasound, computerized tomography, and magnetic resonance imaging, all patients were divided into the gallstone group and the nongallstone group. Univariate and multivariate logistic regression analyses were performed to select indicators associated with gallstone formation among patients after hepatectomy.
### Results
In the total sample of included patients, there were 341 patients with gallstones and 1,147 patients without gallstones. The incidence of gallstones was $23.5\%$ ($\frac{341}{1}$,452). The incidence of gallstones in the primary liver cancer group was higher than that in the benign liver tumor group ($25.7\%$ vs. $18.9\%$, $$P \leq 0.004$$). Univariate and multivariate logistic regression analyses showed that female gender, high body mass index, tumor located in S5, and severe postoperative complication were factors related to gallstones in patients with benign liver tumors after hepatectomy. In addition, Child–Pugh B, low albumin, liver cirrhosis, and transcatheter arterial chemoembolization (TACE) after recurrence were factors related to gallstones in patients with primary liver cancer after hepatectomy.
### Conclusions
Hepatectomy increased the risk of gallstones in benign or malignant liver tumors, especially when the tumor was located in S5. TACE further increased the risk of gallstones in patients with postoperative recurrence.
## Introduction
The incidence of gallstones in adults is $10\%$–$20\%$ (1–3). The morbidity of gallstones in patients with chronic liver disease (CLD) ranges from $3.6\%$ to $46\%$, with a 1.2- to 5-fold increase compared with the general population (1, 3–5). Clinically, we have found that the incidence of gallstones is significantly increased in those patients who have undergone liver resection or other therapies such as transcatheter arterial chemoembolization (TACE). Previous studies have shown that any factors involving changes in the composition of hepatic bile and gallbladder hypomotility will facilitate gallstone formation (6–8). The anatomy of the intrahepatic bile duct is complex and closely related to liver tissue. It is inevitable that hepatectomy or TACE treatment will lead to iatrogenic biliary tract injury including the nourishing blood vessels of the biliary tract or the function of the gallbladder's vagus. In addition, whether postoperative complications such as abdominal adhesion or abdominal infection can lead to impaired function of contraction of the gallbladder still needs to be further verified. Therefore, we retrospectively analyzed the risk factors for gallstone formation after hepatectomy.
## Patients and definitions
This was a retrospective case–control study. We consecutively reviewed 1,452 eligible patients who underwent hepatectomy between January 2013 and December 2016 in the Hepatic Surgery Center of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. According to the imaging, including gallbladder ultrasound, computerized tomography (CT), and magnetic resonance imaging (MRI), all patients were divided into the gallstone group and the nongallstone group. The flow chart of patients is displayed in Figure 1.
**Figure 1:** *Flow diagram for enrolling patients.*
The inclusion criteria are as follows: [1] Child–Pugh A or B; [2] primary liver cancer Barcelona Clinic Liver Cancer (BCLC) A/B stages; and [3] benign tumor of liver including hepatic adenoma, hepatic hemangioma, and hepatic focal nodular hyperplasia (FNH). The exclusion criteria are as follows: [1] metastatic liver cancer; [2] history of cholecystectomy; [3] already existing cholecystitis or gallstones; [4] intrahepatic bile duct stones; [5] consistent use of hormone replacement therapy; and [6] abdominal radiotherapy.
The gallbladder is attached to S5 of the liver. *In* general, anatomical hepatectomy usually combines with cholecystectomy when a tumor is located in S5 of the liver. When the tumor is small and nonanatomic hepatectomy is performed, gallbladder preservation is feasible. Thus, there is a considerable number of patients with tumors located in S5 of the liver in our study who underwent hepatectomy with gallbladder preservation.
Postoperative complications are common after hepatectomy. The severity of complications was classified according to the Clavien–Dindo classification system [9]. Grade I complications included mild pleural, peritoneal effusion, electrolyte disturbance, and other deviations from the normal postoperative course. Grade II complications included blood transfusion, plasma infusion, or albumin infusion. Grade III covered severe pleural and peritoneal effusion requiring percutaneous aspiration or drainage. Grade IV complications covered severe lung infections, respiratory failure, or kidney failure, requiring intubation or dialysis treatment.
## Surgical methods
Hepatectomy was accomplished in a hepatic surgery center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. All patients were given general anesthesia. The methods of hepatectomy included open and laparoscopic hepatectomy. Partial resection or anatomic resection of the liver was performed based on the tumor location. Complete removal of at least one Couinaud segment containing the focus and the portal vein in the drainage area of the lesion was defined as an anatomic resection. A complete tumor plus a rim of non-neoplastic liver parenchyma was considered a nonanatomic resection. Partial resection makes preservation of the gallbladder possible when the surgeon removes the small lesions located at S5.
The study was approved by the Ethical Committee of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology. All procedures performed in this study abided by the Declaration of Helsinki.
## Statistical analysis
Continuous variables were presented as the median and interquartile range (IQR), and categorical data as were presented numbers and percentages. A χ2 test and the Mann–Whitney U test were used to compare groups where appropriate. Univariate and multivariate logistic regression analyses were performed to evaluate risk factors for gallstone development. $P \leq 0.05$ was considered statistically significant. Statistical analyses were performed using SPSS version 19.0 for Windows (SPSS, Chicago, IL, United States).
## Clinical characteristics of patients
Figure 2 shows that 1,452 eligible patients were stratified according to the malignant or benign tumor. In the total sample of included patients, there were 981 patients with primary liver cancer and 471 patients with benign tumors, including 24 patients with hepatic adenoma, 387 patients with hepatic hemangioma, and 60 patients with FNH.
**Figure 2:** *Proportion of gallstones in malignant and benign tumor groups.*
The median age was 56 years, and $85.5\%$ (1,$\frac{241}{1}$,452) of patients were men. In total, $96.6\%$ (1,$\frac{403}{1}$,452) of patients were Child–Pugh A. The etiology of patients was 776 ($53.4\%$) positive for hepatitis B virus (HBV), 18 ($1.2\%$) positive for hepatitis C virus (HCV), 41 ($2.8\%$) for other causes, and 617 ($42.6\%$) with no hepatitis background. A higher body mass index (BMI), total bilirubin, tumor size, and lower albumin were observed in the gallstone group. The proportion of the female gender, diabetes, Child–Pugh B, hepatitis B surface antigen (HBsAg)-positive, liver cirrhosis, S5 liver resection, multiple tumor, and postoperative complications was higher in the gallstone group (Table 1).
**Table 1**
| Clinical characteristics | Gallstone (n = 341) | No gallstone (n = 1,111) | P |
| --- | --- | --- | --- |
| Age (years), M (IQR) | 57 (49–65) | 56 (47–67) | 0.252 |
| Sex, female, N (%) | 85 (25.1) | 126 (11.3) | <0.001 |
| BMI (kg/m2), M (IQR) | 25.1 (23.4–28.6) | 22.5 (21.1–23.5) | 0.015 |
| Diabetes, yes, N (%) | 45 (15.8) | 67 (6.0) | <0.001 |
| Smoking, yes, N (%) | 152 (44.6) | 456 (41.0) | 0.248 |
| Drinking, yes, N (%) | 120 (35.2) | 378 (34.0) | 0.691 |
| Child–Pugh, B, N (%) | 32 (9.4) | 17 (1.5) | <0.001 |
| HBsAg, positive, N (%) | 310 (90.9) | 466 (41.9) | <0.001 |
| ALT (U/L), M (IQR) | 34 (19–53) | 32 (22–53) | 0.247 |
| AST (U/L), M (IQR) | 31 (16–49) | 29 (18–54) | 0.145 |
| Albumin (g/L), M (IQR) | 35.1 (33.8–38.4) | 37.1 (35.1–40.2) | 0.021 |
| TBiL (μmol/L), M (IQR) | 14.2 (10.6–19.6) | 10.2 (7.2–16.2) | <0.001 |
| Liver cirrhosis, yes, N (%) | 315 (92.5) | 445 (40.1) | <0.001 |
| Resection types, AR, N (%) | 98 (28.7) | 333 (30.0) | 0.663 |
| Surgical methods, open N (%) | 140 (41.1) | 467 (42.0) | 0.749 |
| Liver resection, S5, N (%) | 119 (34.9) | 88 (7.9) | <0.001 |
| Tumor size (cm), M (IQR) | 5.6 (3.8–6.4) | 4.2 (3.0–5.5) | 0.017 |
| Tumor number, multiple, N (%) | 106 (31.1) | 100 (9.0) | <0.001 |
| Complications, yes, N (%) | 219 (64.2) | 267 (24.0) | <0.001 |
The overall gallstone incidence was $23.5\%$ ($\frac{341}{1}$,452), and the asymptomatic gallstone incidence was $83.6\%$ ($\frac{285}{341}$). The incidence of gallstones in the primary liver cancer group was $25.7\%$ ($\frac{252}{981}$) higher than that in the benign tumor group [$18.9\%$ ($\frac{89}{471}$), $$P \leq 0.004$$]. Asymptomatic gallstones accounted for $81.7\%$ ($\frac{206}{252}$) of all gallstones in the primary liver cancer group and $89.2\%$ ($\frac{79}{89}$) of all gallstones in the benign tumor group ($$P \leq 0.11$$).
In 4 years, 41 out of 341 patients ($12.0\%$) with gallstones underwent cholecystectomy. All patients with cholecystectomy had significant right upper abdominal pain, and their Murphy sign was positive. In addition, CT indicated thickening of the gallbladder wall and chronic cholecystitis. No cholecystectomy was performed for all asymptomatic and some symptomatic gallstones. Moreover, we had not observed any serious disease caused by gallstones, including acute pancreatitis and gallbladder cancer.
## Factors associated with a gallstone in patients with benign liver tumors
Univariate and multivariate logistic regression analyses showed that female gender (OR: 1.547, $95\%$ CI: 1.358–2.356, $$P \leq 0.018$$), BMI (OR: 3.255, $95\%$ CI: 2.789–4.589, $$P \leq 0.009$$), liver resection at S5 (OR: 3.687, $95\%$ CI: 2.987–4.698, $$P \leq 0.012$$), and postoperative complication (OR: 3.684, $95\%$ CI: 3.489–4.398, $P \leq 0.001$) were factors related to gallstone in patients with benign liver tumors after hepatectomy (Table 2).
**Table 2**
| Variables | Univariate analysis | Univariate analysis.1 | Multivariate analysis | Multivariate analysis.1 |
| --- | --- | --- | --- | --- |
| Variables | OR (95% CI) | P | OR (95% CI) | P |
| Age (years), M (IQR) | 0.998 (0.997–1.002) | 0.854 | | |
| Female gender, N (%) | 1.297 (1.181–1.727) | 0.021 | 1.547 (1.358–2.356) | 0.018 |
| BMI (kg/m2), M (IQR) | 4.512 (3.212–6.589) | <0.001 | 3.255 (2.789–4.589) | 0.009 |
| Diabetes, N (%) | 1.214 (1.123–1.689) | 0.019 | 1.123 (0.998–1.136) | 0.148 |
| Smoking, N (%) | 1.112 (0.987–1.245) | 0.265 | | |
| Drinking, N (%) | 1.089 (0.975–1.102) | 0.239 | | |
| ALT (U/L), M (IQR) | 0.989 (0.971–1.036) | 0.687 | | |
| AST (U/L), M (IQR) | 0.997 (0.974–1.021) | 0.876 | | |
| Albumin (g/L), M (IQR) | 1.258 (1.025–1.359) | 0.025 | 1.025 (0.996–1.285) | 0.365 |
| TBiL (μmol/L), M (IQR) | 1.021 (0.991–1.121) | 0.129 | | |
| Resection types, AR, N (%) | 1.009 (0.986–1.158) | 0.259 | | |
| Surgical methods, Open N (%) | 1.008 (0.965–1.032) | 0.298 | | |
| Liver resection, S5, N (%) | 2.358 (1.532–3.021) | 0.027 | 3.687 (2.987–4.698) | 0.012 |
| Tumor size (cm), M (IQR) | 1.002 (0.986–1.063) | 0.521 | | |
| Tumor number, Multiple, N (%) | 1.021 (0.983–1.125) | 0.121 | | |
| Complications, N (%) | 4.215 (2.879–6.328) | <0.001 | 3.684 (3.489–4.398) | <0.001 |
## Factors associated with gallstones in patients with primary liver cancer
Univariate and multivariate logistic regression analyses showed that female gender (OR: 2.314, $95\%$ CI: 2.021–4.529, $$P \leq 0.037$$), BMI (OR: 2.125, $95\%$ CI: 1.546–3.356, $$P \leq 0.011$$), diabetes (OR: 1.846, $95\%$ CI: 1.259–2.136, $$P \leq 0.019$$), Child–Pugh B (OR: 2.472, $95\%$ CI: 1.261–4.025, $$P \leq 0.008$$), albumin (OR: 2.851, $95\%$ CI: 2.622–3.914, $P \leq 0.001$), liver cirrhosis (OR: 4.258, $95\%$ CI: 3.258–6.398, $P \leq 0.001$), liver resection at S5 (OR: 4.589, $95\%$ CI: 3.514–6.215, $P \leq 0.001$), postoperative complication (OR: 6.256, $95\%$ CI: 5.021–7.654, $P \leq 0.001$), and TACE after recurrence (OR: 2.691, $95\%$ CI: 2.102–3.028, $$P \leq 0.019$$) were factors related to gallstones in patients with primary liver cancer after hepatectomy (Table 3).
**Table 3**
| Variables | Univariate analysis | Univariate analysis.1 | Multivariate analysis | Multivariate analysis.1 |
| --- | --- | --- | --- | --- |
| Variables | OR (95% CI) | P | OR (95% CI) | P |
| Age (years), M (IQR) | 0.988 (0.977–1.008) | 0.258 | | |
| Female gender, N (%) | 1.587 (1.459–2.585) | 0.029 | 1.314 (1.021–1.929) | 0.037 |
| BMI (kg/m2), M (IQR) | 2.452 (1.896–3.598) | 0.009 | 2.125 (1.546–3.356) | 0.011 |
| HBsAg, N (%) | 1.895 (1.199–2.205) | 0.028 | 1.105 (0.831–1.469) | 0.125 |
| Diabetes, N (%) | 1.657 (1.451–1.982) | 0.024 | 1.846 (1.259–2.136) | 0.019 |
| Smoking, N (%) | 1.012 (0.993–1.142) | 0.241 | | |
| Drinking, N (%) | 1.029 (0.974–1.203) | 0.313 | | |
| Child–Pugh B, N (%) | 2.595 (1.377–4.889) | 0.003 | 2.472 (1.261–4.025) | 0.008 |
| ALT (U/L), M (IQR) | 0.996 (0.994–1.025) | 0.837 | | |
| AST (U/L), M (IQR) | 0.998 (0.991–1.018) | 0.785 | | |
| Albumin (g/L), M (IQR) | 3.657 (2.547–6.358) | <0.001 | 2.851 (2.622–3.914) | <0.001 |
| TBiL (μmol/L), M (IQR) | 1.254 (1.125–2.541) | 0.029 | 1.125 (0.996–1.358) | 0.125 |
| BCLC B stage | 2.241 (1.987–2.419) | 0.031 | 1.147 (0.987–1.225) | 0.134 |
| Liver cirrhosis, N (%) | 5.489 (3.532–8.102) | <0.001 | 4.258 (3.258–6.398) | <0.001 |
| Resection types, AR, N (%) | 1.084 (0.992–1.095) | 0.127 | | |
| Surgical methods, Open N (%) | 1.011 (0.998–1.048) | 0.126 | | |
| Liver resection, S5, N (%) | 3.584 (2.698–4.112) | <0.001 | 4.589 (3.514–6.215) | <0.001 |
| Tumor size (cm), M (IQR) | 1.241 (1.023–1.598) | 0.029 | 1.051 (0.874–1.918) | 0.254 |
| Tumor number, Multiple, N (%) | 1.857 (1.259–2.547) | 0.021 | 1.012 (0.798–2.024) | 0.122 |
| Complications, N (%) | 5.236 (3.034–10.752) | <0.001 | 6.256 (5.021–7.654) | <0.001 |
| Recurrence, N (%) | 1.354 (1.015–3.987) | 0.031 | 1.248 (0.751–2.145) | 0.239 |
| TACE after recurrence, N (%) | 3.521 (2.398–5.269) | 0.002 | 2.691 (2.102–3.028) | 0.019 |
| MWA after recurrence, N (%) | 1.254 (1.129–2.014) | 0.036 | 1.025 (0.869–2.069) | 0.297 |
## Typical cases of gallstone stone formation
Figure 3 shows three typical cases of gallstone formation after hepatectomy or recurrent patients receiving TACE. Case 1 displayed that the tumor was located in S5 (white triangle) and there was no gallstone in the gallbladder before surgery (Figures 3A,B). The gallstone developed in the gallbladder (arrow), and the gallbladder tightly adhered to the area of abdominal adhesions (Figure 3C). Case 2 showed that the surgical area was located in S4 and partial S5, and the surgical area presented with effusion and adhesions (arrow) (Figures 3D,E). The gallstone developed in the gallbladder ultimately (arrow) (Figure 3F). Case 3 showed that a recurrent patient after surgery whose lesion was located in S5 received TACE treatment; most areas of the lesion are necrotic (white triangle). The gallstone developed in the gallbladder ultimately and the wall of the gallbladder was significantly thickened (arrow) (Figures 3G,H).
**Figure 3:** *Three typical cases of gallstone formation after hepatectomy or recurrent patients receiving TACE. (A) Tumor was located in S5 (white triangle); (B) there was no gallstone in the gallbladder before surgery; (C) gallstone developed in the gallbladder (arrow), and the gallbladder tightly adhered to the area of abdominal adhesions; (D,E) surgical area was located in S4 and partial S5 and the surgical area presented with effusion and adhesions (arrow); (F) gallstone developed in the gallbladder ultimately (arrow); (G) recurrent patient after surgery whose lesion was located in S5 received TACE treatment; most areas of the lesion are necrotic (white triangle); (H) gallstone developed in the gallbladder ultimately and the wall of the gallbladder was significantly thickened (arrow).*
## Discussion
The traditional risk factors for gallstone disease are the four “F's: female, fat, forty, and fertile,” with many studies supporting the known risk factors for gallstone disease [10, 11]. In our study, female gender and BMI are risks factor for the formation of gallstones in patients with benign or malignant tumors after hepatectomy. BMI, which positively correlates with blood lipids, is also a well-known risk factor for gallstones [12, 13]. A cohort has confirmed a close association (in both genders, but especially pronounced in women) between increasing BMI and increased risk of symptomatic gallstone disease [14]. In addition, it was reported that insulin resistance is closely related to gallstone formation (15–17). In the study, diabetes was observed to be associated with gallstone formation in malignant tumor patients but not in patients with benign tumors.
Interestingly, in our study, the incidence of gallstones after hepatectomy was significantly higher in patients with primary liver cancer than that in patients with benign liver tumors. Generally speaking, most patients with primary liver cancer are accompanied by chronic liver disease including cirrhosis or hypoalbuminemia. It has been reported that liver cirrhosis has been identified as another risk factor for the formation of gallstones, and more severe cirrhosis and longer cirrhosis duration result in a higher incidence of gallstones (18–20). Previous studies have shown that a thickened gallbladder wall and impaired contractility have been observed when patients present with liver cirrhosis, hepatic failure, and portal hypertension (21–24). It has been reported that hypoalbuminemia will increase tissue edema, including the gallstone, which leads to impaired gallstone motility and contractility and provides a potential pathophysiologic basis for gallstone formation [24]. In addition, patients with more severe chronic liver disease are more likely to have postoperative complications. Severe surgical complication grade increased the duration of parenteral nutrition and prolonged fasting, resulting in bile concentration in the gallstone and impaired gallstone motility. Peritoneal effusion, peritoneal cavity infection, and bile leakage often indicated the existence of serious complications contributing to a severer inflammatory response in the abdominal cavity and severer abdominal organ adhesions, leading to swelling and exudation of local tissues and impairing gallstone motility.
In addition, we observed an increased risk of gallstone formation when the tumor is located in S5. Gallstone function was extremely affected when the behavior of liver resection was related to the gallbladder. On the one hand, peritoneal effusion or abdominal adhesion will inevitably occur near the gallbladder, which will impair its motility. On the other hand, when hepatectomy is performed near the gallbladder, it may be disturbed (for instance, it may be torn or squeezed), resulting in gallbladder motility and contractility.
In our study, we found that patients receiving TACE for recurrence after hepatectomy had a higher incidence of gallstones. On the one hand, as we know, the gallbladder artery arises from the right hepatic artery. When TACE is performed, the embolization agent or chemotherapy drug may reflux into the gallbladder artery, leading to a decreased blood supply to the gallbladder and mucosal necrosis of the gallstone; this is more likely when TACE is performed via the right hepatic artery. Therefore, decreased blood supply to the gallbladder leads to gallstone hypomotility and eventually to gallbladder atrophy. On the other hand, chemotherapy drugs stimulate the gallbladder mucosa for a long period, such that the gallbladder mucosa thickens and chronic cholecystitis develops.
## Conclusion
In conclusion, the retrospective case–control study suggests an association between the incidence of gallstones and the location of tumors, especially in S5. In addition, for patients with hepatocellular carcinoma (HCC), TACE further increases the risk of gallstones in patients with postoperative recurrence. Thus, the increased risk of gallstone development after hepatectomy deserves to be concerned.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found here: 10.6084/m9.figshare.21505629.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethical Committee of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
JZ and LC analyzed data and wrote the manuscript. ZZ designed the research route. BW modified the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Association of cyclophilins and cardiovascular risk factors in coronary artery
disease
authors:
- Sandra Gegunde
- Amparo Alfonso
- Rebeca Alvariño
- Nadia Pérez-Fuentes
- Jeremías Bayón-Lorenzo
- Eva Alonso
- Raymundo Ocaranza-Sánchez
- Rosa Alba Abellás-Sequeiros
- Melisa Santás-Álvarez
- Mercedes R. Vieytes
- Carlos Juanatey-González
- Luis M. Botana
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10014534
doi: 10.3389/fphys.2023.1127468
license: CC BY 4.0
---
# Association of cyclophilins and cardiovascular risk factors in coronary artery disease
## Abstract
Cyclophilins are chaperone proteins that play important roles in signal transduction. Among them, cyclophilins A, B, C, and D were widely associated with inflammation and cardiovascular diseases. Cyclophilins A and C have been proposed as coronary artery disease biomarkers. However, less is known about their relationship with cardiovascular risk factors. Therefore, this study aimed to determine the association between cyclophilin A, B, C, and D and cardiovascular risk factors in coronary artery disease. Serum levels of cyclophilins were measured in 167 subjects (subdivided according to cardiovascular risk factors presence). This study reveals that cyclophilin A and C are elevated in patients regardless of the risk factors presence. Moreover, cyclophilin B is elevated in male patients with hypertension, type 2 diabetes, or high glucose levels. In addition, cyclophilins A, B, and C were significantly correlated with cardiovascular risk factors, but only cyclophilin B was associated with type 2 diabetes. The multivariate analysis strengthens the predictive value for coronary artery disease presence of cyclophilin A (>8.2 ng/mL) and cyclophilin C (>17.5 pg/mL) along with the cardiovascular risk factors tobacco, hypertension, dyslipidemia, and high glucose and cholesterol levels. Moreover, the risk of coronary artery disease is increased in presence of cyclophilin B levels above 63.26 pg/mL and with hypertension or dyslipidemia in male patients. Consequently, cyclophilins A and C serum levels are reinforced as useful coronary artery disease biomarkers, meanwhile, cyclophilin B is a valuable biomarker in the male population when patients are also suffering from hypertension or dyslipidemia.
## 1 Introduction
Coronary artery disease (CAD) is one of the main causes of death in the world, despite the latest advances in prevention, diagnosis, and treatment (Zhou et al., 2016). CAD is an atherosclerotic cardiovascular disease (CVD) with an inflammatory component. In this sense, atherosclerosis is a chronic inflammatory disorder of the arteries promoted by lipids. Atherosclerotic plaques are mainly located in the intima of medium and large arteries. During the atherosclerosis plaque development, endothelial cells, lymphocytes, smooth muscle cells, and macrophages are involved, from the early formation of foam cells to the development and consolidation of plaques (Wolf and Ley, 2019). Moreover, modifications of low-density lipoprotein (LDL), such as oxidation or desialylation, enhance the intracellular accumulation of cholesterol or triglycerides and are implicated in the inflammatory process (Aguilar Diaz de Leon et al., 2021). When the atherosclerotic plaque grows up the lumen of arteries decreases, reducing the blood flow (Bezsonov et al., 2021). This pathology is a continuous process throughout human life, it normally begins at an early age, but it is not until years later that clinical symptoms begin to appear (Fulop et al., 2018). This progressive disorder is clinically manifested by stable or unstable angina, myocardial infarction (MI), or sudden myocardial death (Malakar et al., 2019). Hypertension (HTA), diabetes, cigarette smoking, hyperglycemia, and lipid abnormalities are the main modifiable risk factors for CAD (Wolf and Ley, 2019; Mone et al., 2021; Cui et al., 2022). Diabetes, especially type 2 diabetes (T2D), is the major risk factor involving CAD. Moreover, $75\%$ of patients with T2D die due to CVD complications, including CAD (Naito and Miyauchi, 2017). Diabetes is a metabolic chronic disease with an inflammatory component in response to high blood glucose levels (McKinley, 2019). HTA is a common comorbidity in patients with diabetes. The association of T2D, hyperglycemia, and HTA increases the possibility of cardiovascular events, being together a high cardiovascular risk. Therefore, the early diagnosis of CAD and its risk factors is critical for enhancing both prevention and treatment, especially in patients with T2D or HTA (Thomopoulos et al., 2014). In this regard, high-sensitivity C-reactive protein (hs-CRP), d-dimer, fibrinogen, and brain atrial natriuretic peptide are used as predictors of CVD events (Ndrepepa et al., 2013; Gul et al., 2016). Nevertheless, these biomarkers are not specific, and they are generally elevated in inflammatory diseases (Wang et al., 2006).
In this sense, the search for new specific biomarkers has led to the study of cyclophilins (Cyps). This family of proteins, also known as immunophilins, has peptidyl-prolyl cis/trans isomerase (PPIase). These proteins act as molecular chaperones and are involved in cell signaling and protein folding and trafficking. High levels of extracellular cyclophilins have been found in several inflammatory-based diseases, such as rheumatoid arthritis or sepsis. Moreover, among them, CypA, B, C, and D have been associated with atherosclerosis and CVD (Aumuller et al., 2010; Kumari et al., 2013; Perrucci et al., 2015). Both CypA and B are secreted upon inflammatory stimulus and oxidative stress by several cells (Satoh et al., 2010). Extracellularly, these proteins have a pathological role in human CVD. They act as cytokines and have chemoattractant activity for immune cells (Perrucci et al., 2015). CypA stimulates the adhesion, migration, and differentiation of monocytes participating in atherosclerosis progression (Ramachandran et al., 2016). Moreover, CypA mediates inflammation, ROS generation, and matrix degradation in CVD by binding to the membrane receptor CD147 (Sanchez et al., 2016; Chen et al., 2018). CypA has been described as a biomarker with a high diagnosis and prognosis value for CAD (Satoh et al., 2013; Ohtsuki et al., 2017). Furthermore, CypA levels are correlated with the severity of the disease (Xue et al., 2018). Extracellular CypB, in addition to producing immune cell chemotaxis, also induces their adhesion to damaged tissues, aggravating the inflammatory process. High CypB serum levels have been found in patients with rheumatoid arthritis and metabolic syndrome (Zhang et al., 2017). However, despite the relationship of this protein with inflammatory processes, no changes in CypB levels were found in patients with CAD (Alfonso et al., 2019; Bayon et al., 2020). Fewer is known about CypC. Nevertheless, our group has identified high CypC levels in the serum of both acute and chronic CAD patients (Alfonso et al., 2019; Bayon et al., 2020). Furthermore, after 12 months, CypC serum levels remain elevated in these patients (Bayon et al., 2021). In addition, CypC is upregulated in a rat model of brain ischemia (Shimizu et al., 2005). Although the extracellular functions of this protein are unknown, there is a clear correlation between CypC and CVD. Besides, high intracellular levels of CypA, B, and C in human T-cells were recently associated with CAD (Gegunde et al., 2021). CypD has a key role in chronic inflammation and CVD. This protein is the main regulator of the mitochondrial permeability transition pore (mPTP) opening and the mitochondrial calcium homeostasis. However, CypD has been implicated in the pathogenesis of neurodegenerative disorders, muscular dystrophy, and ischemia-reperfusion (IR) injury in the heart, brain, and kidney (Galber et al., 2020). Moreover, mitochondrial DNA mutations are implicated in several diseases, including atherosclerosis (Bezsonov et al., 2021). Nevertheless, likewise CypB, serum CypD levels remain unchanged in CAD patients (Alfonso et al., 2019; Bayon et al., 2020).
The key step in preventing major cardiovascular events is to keep the cardiovascular risk factors under control. Therefore, knowing the implications of CypA, B, C, and D in CVD, this study aimed to deeply investigate their relationship with cardiovascular risk factors in CAD patients, to better understand the disease and predict major events.
## 2.1 Population study
An observational study about serum levels of Cyps and their association with cardiovascular risk factors was conducted in patients with CAD. CAD patients were referred from the Cardiology Department of Hospital Universitario Lucus Augusti in Lugo, Spain, from January 2021 to December 2021 (Figure 1). A total of 118 patients with CAD were included in the study. CAD disease was defined as prior MI, coronary revascularization, or angiographic documentation of any significant coronary artery stenosis. Coronary angiograms were evaluated by experienced cardiologists, who were blinded to the patient data. A narrowing of the artery lumen by more than $51\%$ of the diameter was considered clinically significant for CAD. Critically or hemodynamically unstable patients, patients with valvular disease, or congestive heart failure were excluded. Also, 49 control volunteers were included in the study. Control volunteers were subjects without known atherosclerosis disease, with normal angiography findings, normal serum cardiac biomarkers, and without cardiovascular risk factors. Participants with chronic or acute inflammatory diseases, cancer, autoimmune diseases, and rheumatic disease were excluded. Initially, it was 65 subjects in the control group, but only 49 met the inclusion criteria. All participants belong to the public healthcare area of Lugo, Spain. The population to be studied was designed according to CAD prevalence in this area. The institutional and regional ethical board approved the study according to the principles outlined in the Declaration of Helsinki. Voluntary written informed consent was obtained from the participants in the present study.
**FIGURE 1:** *Flow chart of the study. CAD: coronary artery disease.*
## 2.2 Baseline measurements
Information on general vital status, significant clinical data, and medical history were obtained from all participants. Also, they respond to the personal questionnaire. HTA, T2D, dyslipidemia (DL), tobacco, age, and sex (male) were assessed as cardiovascular risk factors. The lipid-lowering, antihypertensive, hypoglycemic, and antiaggregant or antiplatelet drug consumption was considered. Hypertension was considered when blood pressure was ≥ $\frac{140}{90}$ mmHg. Diabetes was considered when fasting glucose levels were ≥ 126 mg/dL or if hypoglycemic treatment or insulin was used. DL was considered if low-density lipoprotein cholesterol (LDLc) levels were ≥ 140 mg/dL or high-density lipoprotein cholesterol (HDLc) levels were ≤ 40 mg/dL, or if an antilipidemic treatment was used. Biochemical parameters (levels of glucose, HDLc, and LDLc) were measured in the Analysis Service from Lucus Augusti Hospital using an ADVIA Clinical Chemistry System (Siemens Healthcare).
## 2.3 Blood sampling protocol
Peripheral blood samples were obtained from each patient as before described (Alfonso et al., 2019). Briefly, Blood samples were allowed to clot for 20 min at room temperature before being centrifuged at 3,000 rpm for 10 min at 4 °C. Serum supernatant fractions are collected and stored at −80°C until needed for Cyps level analysis. Samples were thawed once.
Measurement of Cyclophilin A, B, C, and D serum levels.
Levels of serum CypA (Human Cyclophilin A ELISA kit; CSB-E09920H; Cusabio), CypB (Human Cyclophilin B ELISA kit; CSB-E11218H: Cusabio), CypC (Human Cyclophilin C ELISA kit; CSB-EL018473HU; Cusabio) and CypD (Human Cyclophilin D ELISA kit; E-EL-H1936; Elabscience) were measured using ELISA regarding manufacturer’s instructions and as described before (Alfonso et al., 2019). Before carrying out the ELISA experiments for the measurement of Cyp levels, serum samples were brought to room temperature, vortexed, and centrifuged. Absorbance measurements were done in a microplate reader at 450 and 540 nm for CypA, B, and C, and 450 nm in the case of CypD). The range of determination was 3.12–200 ng/mL for CypA; 31.25–2,000 pg/mL for CypB; 23.5–1,500 pg/mL for CypC and 62.5–4,000 pg/mL for CypD. Serum levels below the lower limit of determination were undetectable and were considered as 0 pg/mL for statistical analysis. The intra and inter-assay coefficients of variation of the ELISA kits were < $10\%$. No cross-reactivity was observed between Cyp antibodies.
## 2.4 Statistical analysis
Participants were divided according to cardiovascular risk presence. Summary statistics were generated and presented as percentages for categorical variables and mean ± SEM for continuous variables. Normality was evaluated using the Kolmogorov-Smirnov test (with Lilliefors correction). Continuous variables with normal distribution were compared between groups using a t-test (including Levene´s test to assess the equality of variance). Non-parametric variables were compared using the Mann-Whitney test. Categorical variables were compared using the Pearson χ2 test. To compare the differences between the groups, ANOVA or the Kruskal-Wallis (in the case of non-parametric variables) test was used, followed by post hoc tests. Correlations between Cyps levels and cardiovascular risk factors were analyzed using bivariate analysis with Spearman rank correlation coefficient in the case of categorical variables (sex, age >50 years, active smoker, DL, HTA, T2D, glucose >100 mg/dL, TG > 200 mg/dL, total cholesterol >200 mg/dL, LDLc >100 mg/dL, and HDLc <50 mg/dL) and the principal component analysis was used for calculating the correlations between numeral variables (laboratory parameters of glucose, triglycerides; TG, total cholesterol, LDLc, and HDLc). After a significant crude correlation was found, multiple linear regression models with a subsequent backward stepwise were used to assess the association of CypA, CypB, CypC, and/or CypD serum levels with cardiovascular risk factors. All stepwise selection models used a $p \leq 0.05$ level for entry and a $p \leq 0.10$ for removal. The variables included in these models were based on the previous simple regression models. The association was measured by the odds ratio (OR) and their $95\%$ confidence interval (CI). To obtain the cut-off points of CypB levels for logistic regression, receiver-operating-characteristic curves (ROC) were used. Statistical analyses were performed using SPSS software version 25.0 (IBM Corp, NY, United States). p ≤ 0.05 was considered statistically significant.
## 3 Results
A total of 167 subjects were enrolled in the present study, as Figure 1 shows. Among all, 118 were diagnosed with CAD ($82\%$ men) and 49 belonged to the control group ($53\%$ men). The demographic, clinical, and biochemical data of the participants were collected and the most relevant data for this study were summarized in table 1. Then, the CAD population was divided according to the presence of cardiovascular risk factors sex (male), tobacco, HTA, DL, and T2D. Among the patients diagnosed with CAD, 33 are active smokers, 64 have HTA, 85 have DL, and 27 are suffering from T2D. Therefore, CypA, B, C, and D levels were first measured in the population with no associated risk factors (Figure 2). In this population, CypA serum levels were significantly high in CAD patients (6.80 ± 1.6 ng/mL) compared with controls (2.42 ± 0.48 ng/mL; $p \leq 0.001$). In the case of serum CypB levels, no statistical differences were observed when controls (114.91 ± 29.32 pg/mL) were compared with the CAD group (107.07 ± 19.94 pg/mL). Like CypA, CypC was a threefold increase in CAD subjects compared to control subjects ($p \leq 0.001$). Finally, no significant changes in CypD levels of control subjects versus CAD patients were observed.
Therefore, the next step was to evaluate Cyps levels according to cardiovascular risk factors (sex, tobacco, DL, HTA, and T2D) presence. When the total population was divided according to sex, there were no differences in CypA or CypC levels between control women and control men (Figures 3A, C). Furthermore, there were no statistical differences between the serum levels of CypA or CypC in women and men with CAD. Nevertheless, both CypA and CypC were increased in women with CAD when compared with control women (Figures 3A, C, $p \leq 0.001$). Moreover, CypA and CypC levels were higher in men with CAD than in male controls (Figures 3A, C, $p \leq 0.001$). CypB levels increased 4-fold in control women compared to control men (Figure 3B, $p \leq 0.01$). However, CypB levels were higher in men with CAD compared to women with CAD or men without CAD ($p \leq 0.001$). Serum levels of CypD remain unchanged in all groups.
**FIGURE 3:** *Cyclophilin A, B, C, and D serum levels in control subjects and patients with coronary artery disease subdivided according to sex. Serum levels of CypA (A), CypB (B), CypC (C) and CypD (D) in control female subjects (n = 23), control male (n = 26), male CAD patients (n = 97) female CAD patients (n = 21). Data are shown as mean ± SD. The values are shown as the difference between control female subjects versus female CAD patients, ***p < 0.001, or bewteen control male subjects versus male CAD patients, ###p < 0.001, or between control female versus control male, $$ p < 0.01, or between female CAD patients versus male CAD patients, ∼∼ p < 0.01 using the Mann-Whitney test. CAD: coronary artery disease; CypA; cyclophilin A; CypB: cyclophilin B; CypC: cyclophilin C; CypD: cyclophilin (D).*
There is a discrepancy in serum CypB levels between males and females. These differences between sex could be due to female hormones. In this sense, CypB has been associated with gene expression of hormone receptors in women (Fang et al., 2009). Consequently, to avoid interferences, only the male population was used in subsequent studies for CypB. Therefore, the serum CypB levels were re-evaluated in the male population with no associated risk factors. In this population, CypB serum levels were significantly elevated in CAD patients (194.31 ± 25.52 pg/mL) compared with controls (64.24 ± 23.06 pg/mL; $p \leq 0.001$; data not shown).
Then, CAD patients were divided based on smoking status (no smoker, active smoker, and ex-smoker; Figure 4). Although CypA and CypC levels were increased in CAD patients compared to controls (Figures 4A, C, $p \leq 0.001$), no differences were found between CAD groups according to smoking status. In the male population, CypB was significantly elevated in CAD patients compared to controls (Figure 4B, $p \leq 0.001$). However, no differences in CypB levels were found among CAD patients based on smoking status. Moreover, no significant variances in CypD serum levels between CAD groups or between control and CAD groups were observed (Figure 4D). Afterward, HTA was considered. In this study population, CypA and C levels were augmented in the serum of all CAD patients compared to controls, regardless of the presence of this risk factor (Figures 4E, G). Moreover, CypB levels were also augmented (in the male population) in CAD patients with or without HTA versus controls (Figure 4F; $p \leq 0.01$). Despite the CypB serum levels were increased in CAD patients with HTA than in CAD patients without HTA, this difference was not statistically significant. Nevertheless, no major differences were found regarding CypD levels in this population (Figure 4H). Next, Cyps levels were measured in CAD patients in the presence or absence of the cardiovascular risk factor T2D. Both CypA and CypC were constant in CAD patients regardless of the presence of T2D, meanwhile, these levels were statistically increased in both CAD groups compared to control levels (Figures 4I, K; $p \leq 0.001$). CypB was also augmented in the male population with CAD plus T2D and CAD patients without T2D compared with the control group (Figure 4J; $p \leq 0.001$). Nevertheless, CypD serum levels were unaltered in all groups (Figure 4L). Finally, Cyps levels were measured according to DL status in CAD patients. CypA and CypC levels were statistically elevated in CAD patients with or without DL compared to controls (Figures 4m and 4o, $p \leq 0.01$). CypB was also increased in male CAD patients regardless of DL status (Figure 4n; $p \leq 0.01$). Meanwhile, there was no change in CypD levels in DL patients relative to non-DL patients or controls (Figure 4p). Therefore, CypA and C were elevated in general in CAD patients regardless of the presence of cardiovascular risk factors. Meanwhile, CypB was raised in female controls, while in the CAD group, it was increased in men.
**FIGURE 4:** *Cyclophilin A, B, C, and D serum levels in control subjects and patients with coronary artery disease subdivided according to smoking status, HTA, T2D, or DL. Serum levels of CypA (A), CypB (in male population) (B), CypC (C), and CypD (D) in controls (n = 49, *n = 26), CAD patients no smokers (n = 50, *n = 33), smoker CAD patients (n = 29, *n = 27) and ex-smoker CAD patients (n = 39, n = 37). Serum levels of CypA (E), CypB (in male population) (F), CypC (G), and CypD (H) in controls (n = 49, *n = 26), CAD patients without HTA (n = 54, *n = 46), and CAD patients with HTA (n = 64, *n = 51). Serum levels of CypA (I), CypB (in male population) (J), CypC (K), and CypD (L) in controls (n = 49, *n = 26), CAD patients without T2D (n = 91, *n = 73), and CAD patients with T2D (n = 27, n = 24). Serum levels of CypA (N), CypB (in male population) (M), CypC (O), and CypD (P) in controls (n = 49, *n = 26), CAD patients without DL (n = 85, *n = 72), and CAD patients with DL (n = 33, *n = 25). ANOVA or the Kruskal-Wallis (in the case of non-parametric variables) test was used, followed by post hoc tests. *Studies of CypB in the male population. CAD: coronary artery disease; CypA; cyclophilin A; CypB: cyclophilin B; CypC: cyclophilin C; CypD: cyclophilin D; DL: dyslipidemia; HTA: hypertension: T2D: type 2 diabetes.*
Subsequently, the correlation between Cyps levels and cardiovascular risk factors was calculated. Bivariate correlation analysis was used to assess the correlation between Cyps and categorical variables (sex–male-, age >50 years, active smoker, HTA, DL, T2D, glucose levels >200 mg/dL, total cholesterol levels >200 mg/dL, LDLc levels >100 mg/dL and HDLc levels <50 mg/mL). The cut-off of biochemical parameters was chosen following the recommendations of the European guidelines for CVD prevention (Visseren et al., 2021). Bivariate correlation analysis shows, in table 2, a positive correlation between CypA and CypC, sex, age >50 years, active smoker, DL, and glucose >100 mg/dL ($p \leq 0.007$). CypB was associated, in the male population, with age >50 years, HTA, DL, T2D and glucose >100 mg/dL. Moreover, CypC was positively associated with CypA, age >50 years, active smoker, HTA, DL, and glucose >100 mg/dL ($p \leq 0.05$). Nevertheless, CypD was only correlated with total cholesterol >200 mg/dL.
**TABLE 2**
| Correlations | CypA | CypB* | CypC | CypD |
| --- | --- | --- | --- | --- |
| CypA | 1 | - | 0.326 (p < 0.001) | |
| CypB | - | 1 | - | - |
| CypC | 0.326 (p < 0.001) | - | 1 | |
| CypD | | - | | 1 |
| Sex (male) | 0.251 (p = 0.002) | - | | |
| Age (> 50 years) | 0.381 (p < 0.001) | 0.304 (p < 0.001) | 0.274 (p < 0.001) | |
| Active smoker | 0.220 (p = 0.006) | | 0.172 (p = 0.030) | |
| HTA | | 0.304 (p < 0.001) | 0.255 (p = 0.001) | |
| DL | 0.248 (p = 0.002) | 0.199 (p = 0.016) | 0.257 (p = 0.001) | |
| T2D | | 0.203 (p = 0.036) | | |
| Glucose (> 100 mg/dL) | 0.216 (p = 0.007) | 0.249 (p = 0.009) | 0.347 (p < 0.001) | |
| Total Cholesterol (> 200) | | | | 0.178 (p = 0.025) |
| LDLc (> 100 mg/dL) | | | | |
| HDLc (< 50 mg/dL) | | | | |
Then, the principal component analysis (PCA) was carried out to identify the grouping patterns and correlations among the laboratory parameters (glucose, TG, total cholesterol, LDLc, and HDLc) and CypA, C, and D (Figure 5A). Two principal components (PC) with eigenvalues >1 explained $49.96\%$ of the total variances. PC 1 represents the variation for total cholesterol and LDLc levels and PC2 for TG and CypC. The ordination showed pronounced differentiation between total cholesterol and LDLc with glucose, TG and CypA, and CypC. CypA and C were closer to TG. Moreover, HDLc was negatively correlated with glucose, TG, CypA, and CypC. Therefore, CypA and C are associated with glucose and TG serum levels and negatively associated with HDLc levels. Then the PCA was done in the male population to assess the correlations between CypB and the laboratory parameters (Figure 5B). Two PC represent a $60.90\%$ variation of the dataset (eigenvalues >1). PC 1 represents the variation for Total Cholesterol and LDLc, meanwhile PC 2 for TG and HDLc. The PCA showed differentiation between TG, CypB, and glucose with total cholesterol and LDLc. Furthermore, CypB was close to TG and glucose and negatively correlated with HDLc. In this sense, CypB was associated with TG and glucose and negatively associated with HDLc.
**FIGURE 5:** *Principal component analysis (PCA) plot for CypA, C and D, and laboratory parameters (A). Grouping of the variables in two principal components. PCA was performed using TG, total cholesterol, LDLc, HDLc, glucose, CypA, C, and D serum levels. The two PC are shown, and the percentage of variation accounted for PC1 is 29.87% and for PC2 is 20.09%. n = 167. PCA plot for CypB and laboratory parameters in male population (B): PCA was performed using TG, total cholesterol, LDLc, HDLc, glucose and CypB serum levels. The two PC are shown, and the percentage of variation accounted for PC1 is 37.65% and for PC2 is 23.25%. n = 123. CypA: cyclophilin A; CypB: cyclophilin B; CypC: cyclophilin (C) CypD: cyclophilin D; LDLc: low-density lipoprotein cholesterol; HDLc: high-density lipoprotein cholesterol; TC: total cholesterol; TG: triglycerides.*
Given these results, the next step was to study the relationship between risk factors and Cyps in the presence of CAD. For this purpose, the cut-off points of Cyps for CAD presence were used. Cut-off points for CypA >8.2 ng/mL and C > 17.5 pg/mL were obtained from ROC curves of our preliminary study and validated in the present study in CAD patients with or without cardiovascular risk factors associated (Alfonso et al., 2019). The cut-off for CypA > 8.2 ng/mL had a specificity of $95.5\%$ and a sensitivity of $32.5\%$ with a positive predictive value (PPV) of $86.7\%$ and a negative predictive value (NPV) of $60.9\%$. The cut-off point for CypC >17.5 pg/mL provided a specificity of $88.6\%$ and a sensitivity of $70\%$ with a PPV of $84.8\%$ and NPV of $76.5\%$ (Alfonso et al., 2019). In the case of CypB, a cut-off point >63.26 pg/mL was calculated in the present study in the male population using ROC curves (data not shown). This cut-off point has a specificity of $78.3\%$ and a sensitivity of $66.7\%$ with a PPV of $91.8\%$ and NPV of $60.86\%$. Meanwhile, from the ROC curve analysis, the cut-off point for CypD could not be obtained, because it does not significantly predict the presence of CAD. Therefore, CypD was not used in the following analyses.
Then, the cardiovascular risk factors and Cyps were combined in a single-logistic regression model. So, using the presence or absence of CAD as a state variable, univariate analyses were performed. Then, to check if Cyps cut-off points could predict the presence of CAD, a univariate analysis was done (Table 3). CypA >8.2 ng/mL were correlated with the presence of CAD with an OR of 3.58 ($$p \leq 0.024$$). Moreover, the risk of suffering from CAD was increased more than 9 times ($p \leq 0.001$) when the levels of CypC were >17.5 pg/mL. In the univariate analysis, the tobacco, HTA, DL, and high glucose levels were also correlated with the presence of CAD (with OR > 7; $p \leq 0.001$). As table 3 (analysis 1) shows when the multivariate analysis was performed with the risk factors tobacco, HTA, DL, and the biochemical parameters glucose (cut-off point >100 mg/dL) and total cholesterol (cut-off point >200 mg/dL) with CypA, this Cyp misses its correlation with the presence of CAD, meanwhile the risk factors (active smoker, HTA, DL, and glucose >100 mg/dL) were significantly associated with CAD ($p \leq 0.027$). Moreover, total cholesterol >200 mg/dL was negatively associated with the disease. When CypC was included, instead of CypA (table 3, analysis 2), a strong association of CypC >17.5 pg/mL with the presence of CAD (OR = 16.51; $$p \leq 0.002$$) together with the risk factors was observed. Furthermore, when CypA and CypC were combined with these risk factors the OR value for CypC was increased (Table 3, analysis three; OR = 18.02; $p \leq 0.001$) and was significantly associated with the presence of CAD and risk factors. The association between the active smoker, HTA, DL, and CAD when CypA and CypC are present in the multivariate analysis was in line with previous results (Alfonso et al., 2019). However, it is the first time that these Cyps were significantly correlated with serum levels of total cholesterol (>200 mg/dL) or glucose (>100 mg/dL) and CAD presence.
**TABLE 3**
| Unnamed: 0 | Univariate analysis | Univariate analysis.1 | Multivariate analysis (Zhou et al., 2016) | Multivariate analysis (Zhou et al., 2016).1 | Multivariate analysis (Wolf and Ley, 2019) | Multivariate analysis (Wolf and Ley, 2019).1 | Multivariate analysis (Aguilar Diaz de Leon et al., 2021) | Multivariate analysis (Aguilar Diaz de Leon et al., 2021).1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variable | OR [95% CI] | p-value | OR [95% CI] | p-value | OR [95% CI] | p-value | OR [95% CI] | p-value |
| Active smoker | 7.47 [3.31–16.86] | <0.001 | 36.03 [4.75–272.79] | 0.001 | 39.54 [4.81–324.63] | 0.001 | 53.22 [5.69–497.78] | <0.001 |
| HTA | 18.17 [5.35–61.72] | <0.001 | 5.98 [1.22–29.20] | 0.027 | 7.08 [1.10–45.39] | 0.039 | 7.40 [1.13–48.36] | 0.037 |
| DL | 18.46 [7.18–47.44] | <0.001 | 20.57 [5.00–84.50] | <0.001 | 24.49 [4.73–126.78] | <0.001 | 19.35 [3.59–104.28] | 0.001 |
| Glucose (> 100 mg/dL) | 35.15 [8.34-156-64] | <0.001 | 86.79 [11.99–627.94] | <0.001 | 50.45 [6.72–378.55] | <0.001 | 57.85 [6.81–490.90] | <0.001 |
| Total Cholesterol (> 200) | 0.30 [0.14–0.65] | 0.002 | 0.13 [0.02–0.72] | 0.020 | 0.08 [0.01–0.596] | 0.013 | 0.06 [0.00–0.50] | 0.010 |
| CypA (> 8.2 ng/mL) | 3.58 [1.18–10.87] | 0.024 | 4.54 [0.496–41.70] | 0.180 | | | 5.70 [0.53–60.51] | 0.148 |
| CypC (>17.5 ng/mL) | 9.57 [4.07–22,49] | <0.001 | | | 16.51 [2.90–93.80] | 0.002 | 18.02 [3.06–105.89] | 0.001 |
Finally, CypB > 63.26 pg/mL showed a significant interaction when it was combined with tobacco, DL, and glucose (<100 mg/dL) with an OR of 9.10 (table 4, analysis one; $$p \leq 0.008$$). Also, these risk factors are significantly linked to CAD ($p \leq 0.05$). Moreover, the risk factor CypB continues to have prognostic value along with tobacco, HTA, and glucose (<100 mg/dL) (Table 4, analysis two; OR = 7.29; $$p \leq 0.011$$) for CAD. Therefore, the risk of CAD is increased in presence of CypA levels above 8.2 ng/mL or CypC above 17.5 pg/mL along with other cardiovascular risk factors. Furthermore, in the presence of CypB levels above 63.26 pg/mL and risk factors along with HTA or DL the risk of CAD is also elevated. Consequently, CypA and C serum levels are reinforced as useful CAD biomarkers in the general population. Meanwhile, CypB is a valuable biomarker of CAD when patients are also suffering from HTA or DL in males.
**TABLE 4**
| Unnamed: 0 | Multivariate analysis (Zhou et al., 2016) | Multivariate analysis (Zhou et al., 2016).1 | Multivariate analysis (Wolf and Ley, 2019) | Multivariate analysis (Wolf and Ley, 2019).1 |
| --- | --- | --- | --- | --- |
| Variable | OR [95% CI] | p-value | OR [95% CI] | p-value |
| Active smoker | 13.06 [2.32–73.50] | 0.004 | 10.20 [2.12–49.05] | 0.004 |
| HTA | | | 8.24 [1.29–52.45] | 0.025 |
| DL | 8.10 [1.65–39.58] | 0.010 | | |
| Glucose (> 100 mg/dL) | 23.27 [2.67–202.56] | 0.004 | 22.43 [2.43–206.74] | 0.006 |
| CypB (> 63.26 ng/mL) | 9.10 [1.79–46.16] | 0.008 | 7.29 [1.56–33.95] | 0.011 |
## 4 Discussion
In the last 40 years, the number of CAD cases has been increasing. Nevertheless, despite advances in diagnosis and treatments, CAD is the leading cause of morbidity and mortality of CVD in western countries. This condition, which is associated with atherosclerosis, contributes to MI, heart failure, and sudden death. In addition to being an individual health burden, CVD is the costliest disease in Western Hemisphere (Slavin et al., 2021). Therefore, improving the means to identify patients at risk is necessary to enhance healthcare and maximize resource management. Circulating factors such as LDLc, hs-CRP, or atrial natriuretic peptide are established and used as serum biomarkers to evaluate the risk for adverse cardiac events. Nevertheless, as was previously mentioned, these circulating factors are frequently elevated in inflammation or in subjects without CVD (Wang et al., 2006). In this sense, in the present study, we evaluated the serum CypA, B, C, and D levels in CAD patients and their association with cardiovascular risk factors.
In the last years, Cyps were associated with CVD. In this sense, CypA mediates the progression of atherosclerosis by inducing the formation of foam cells and promoting adhesion, migration, and differentiation of monocytes, and activating endothelial cells (Ramachandran et al., 2016). Moreover, this protein mediates inflammation and contributes to the damage in IR and myocardial remodeling processes (Seizer et al., 2014). In the present study, high levels of CypA were found in the serum of patients with CAD. Furthermore, as was expected this protein was positively associated in CAD patients independently of the presence of cardiovascular risk factors. These data are consistent with previous studies, including data from our group (Satoh et al., 2013; Ramachandran et al., 2014; Bayon et al., 2020). In this sense, high levels of CypA provide prognostic information on the severity of CAD (Satoh et al., 2013; Alfonso et al., 2019). As with CypA, in the present study, CypC was also elevated in patients with CAD. Lately, our group has proposed CypC levels >17.5 pg/mL as a new biomarker of CAD and has described its correlation with cardiovascular risk factors (Alfonso et al., 2019; Bayon et al., 2020; Bayon et al., 2021). Nevertheless, this is the first time that the association of CypC with high glucose and total cholesterol serum levels has been described in CAD patients. Although little is known about the extracellular functions of CypC, it has been recurrently related to inflammation and CVD. This protein participates in the endoplasmic reticulum (ER) homeostasis and is involved in B and T-cell differentiation and macrophage activation (Paiva et al., 2021). Furthermore, increased CypC levels were found after focal cerebral ischemia (Shimizu et al., 2005). Therefore, the present data strengthen the relationship of CypC with inflammation and CVD. Moreover, both CypA and C were associated with HTA, DL, and tobacco in CAD patients with a robust OR when other cardiovascular risk factors were also included, reinforcing the predictive value of these proteins in CAD.
CypB participates in physiological processes, such as calcium and ER homeostasis, collagen folding, and prolactin signaling (Zhao et al., 2017). Nevertheless, CypB has been related to cancer, neuroinflammation, and CVD, among others. Also, this immunophilin participates in the pathogenesis of atherosclerosis and HTA by promoting vascular smooth muscle cell growth and ROS generation (Perrucci et al., 2015). In the present study, CypB serum levels were higher in females than in male controls. This sex-difference in serum Cyps levels could be due to distinct hormone and gene expression in women (Fang et al., 2009). Sex-based differences are also noted in the atherosclerosis scenario. In this sense, estrogen has a cardioprotective effect in women, however, these protective effects disappeared after menopause (Vakhtangadze et al., 2021). Therefore, CypB cannot be used as a biomarker in women. To avoid interferences, only the male population was used in the studies of CypB. In this population, CypB was elevated in patients with CAD, associating this protein with CAD for the first time. Moreover, CypB serum levels were significantly elevated in patients with CAD and T2D or HTA, being the first time to be related to diabetes. Furthermore, multivariate logistic regression analysis demonstrates that CypB >63.26 pg/mL is a risk factor for the presence of CAD along with HTA, DL, and high glucose serum levels in males. Moreover, there is a correlation between CypB serum levels and T2D. Thus, high serum levels of CypB in patients with CAD might provide prognostic information on T2D status. Moreover, CypB was also implicated in other metabolic or hormone-mediated processes. In this regard, it has been demonstrated its relationship with the atrial natriuretic peptide, prolactin, and metabolic syndrome, among others (Rycyzyn et al., 2000; Zhang et al., 2021). Therefore, the high CypB levels could have a metabolic and/or hormone course. On the other hand, CypB has been also correlated with glucose levels above 100 mg/dL. Hyperglycaemia can contribute to microvascular dysfunction and MI, increasing the risk of rehospitalization for chest pain, regardless of diabetic status (Mone et al., 2021; Mone et al., 2023). Furthermore, the management and control of glucose levels are beneficial in reducing long-term mortality after acute myocardial infarction (Cui et al., 2022).
The immunophilin CypD also plays an important function in atherosclerosis and CAD. This protein is the main regulator of mitochondrial pore opening, which upon inflammation or oxidative stress leads to mitochondrial dysfunction (Amanakis and MurphyCyclophilin, 2020). The mitochondrial pore and thus CypD, are implicated in IR injury in the heart, brain, and kidney. Therefore, this Cyp has a key role in chronic inflammation and numerous cardiovascular diseases (Perrucci et al., 2015). However, in the present study, no differences were found in serum CypD levels of patients with CAD. These data are in line with the previous data of our group (Alfonso et al., 2019; Bayon et al., 2020). This may be indicating that the role of CypD in CAD is mainly intracellular rather than extracellular. Non-etheless, further studies should be done to better understand the extracellular role of this protein and its relevance in CVD.
Former screening and therapeutic guidelines focus on cholesterol as the primary biomarker of CVD. Normally, LDLc levels above 100 mg/dL and HDLc levels below 40 mg/dL are considered cardiovascular risk factors (Vekic et al., 2022). However, in this study, LDLc levels were lower in CAD patients than in controls, due to statin treatment. Therefore, Cyps have a better prognostic for CAD presence in this study population. Additionally, Cyps levels could be used to assess the therapeutic effects of medical treatments. In this sense, treatment to control atherosclerosis risk factors also decreased CypA levels in patients with CAD (Satoh et al., 2013).
In conclusion, the present study validates the prognostic value of CypA and CypC in CAD and reinforces their relationship with cardiovascular risk factors. Furthermore, it demonstrates for the first time that CypB levels above 63.26 pg/mL have the potential as a biomarker for CAD when male patients suffer from DL or HTA. Moreover, CypB is correlated with T2D and other cardiovascular risk factors. In this sense, Cyp levels could be used for the diagnosis of CAD disease. Moreover, CypA, B, and C could serve as potential novel therapeutic strategies for CAD.
## Study limitations
Some limitations should be addressed. The study population was relatively small for T2D patients. Nevertheless, in this small population, the results were statistically significant. In this sense, this limitation did not influence the reliability of the results of the present study. Moreover, women with diabetes are more likely to have cardiovascular disease than men. In this sense, further analysis in a larger population, including more women, will clarify the importance of Cyps in CAD. In addition, there was an effect of the consumption of lipid-lowering agents on LDLc levels, since LDLc levels were lower in patients with CAD than in the control group.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by This study was reviewed and approved by the institutional and regional ethical boards according to the Declaration of Helsinki (Reference: $\frac{2016}{508}$, Approved date: 19 December 2016). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
SG: investigation, formal analysis, interpretation of data, and drafted the work; AA: conceptualization, supervision, methodology, funding acquisition, project administration, and writing review. RA: methodology and investigation. NPF: methodology and investigation. JBL, ROS, RAAS, MSA: sample collection. EA: conceptualization and methodology. MRV: validation. CJG: funding acquisition, project administration, and writing review. LMB: validation, supervision, and writing review. All authors have approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Abbreviations
CAD: coronary artery disease; CI: confidence interval; CVD: cardiovascular disease; Cyp: cyclophilin; DL: dyslipidemia; ER: endoplasmic reticulum; HDLc: high-density lipoprotein cholesterol; Hs-CRP: high-sensitivity C-reactive; HTA: hypertension; IR: ischemia-reperfusion; LDL: low-density lipoprotein; LDLc: low-density lipoprotein cholesterol; MI: myocardial infarction; mPTP: mitochondrial permeability transition pore; NPV: negative predictive value; OR: odds ratio; PC: principal component; PPIase: peptidyl-prolyl cis/trans isomerase; PPV: positive predictive value; ROC: receiver-operating-characteristic curves; T2D: type 2 diabetes; TG: triglycerides.
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|
---
title: '“From molecular to clinic”: The pivotal role of CDC42 in pathophysiology of
human papilloma virus related cancers and a correlated sensitivity of afatinib'
authors:
- Erdong Wei
- Jiahua Li
- Philipp Anand
- Lars E. French
- Adam Wattad
- Benjamin Clanner-Engelshofen
- Markus Reinholz
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10014535
doi: 10.3389/fimmu.2023.1118458
license: CC BY 4.0
---
# “From molecular to clinic”: The pivotal role of CDC42 in pathophysiology of human papilloma virus related cancers and a correlated sensitivity of afatinib
## Abstract
### Background
Human papilloma virus (HPV)-related cancers are global health challenge. Insufficient comprehension of these cancers has impeded the development of novel therapeutic interventions. Bioinformatics empowered us to investigate these cancers from new entry points.
### Methods
DNA methylation data of cervical squamous cell carcinoma (CESC) and anal squamous cell carcinoma (ASCC) were analyzed to identify the significantly altered pathways. Through analyses integrated with RNA sequencing data of genes in these pathways, genes with strongest correlation to the TNM staging of CESC was identified and their correlations with overall survival in patients were assessed. To find a potential promising drug, correlation analysis of gene expression levels and compound sensitivity was performed. In vitro experiments were conducted to validate these findings. We further performed molecular docking experiments to explain our findings.
### Results
Significantly altered pathways included immune, HPV infection, oxidative stress, ferroptosis and necroptosis. 10 hub genes in these pathways (PSMD11, RB1, SAE1, TAF15, TFDP1, CORO1C, JOSD1, CDC42, KPNA2 and NUP62) were identified, in which only CDC42 high expression was statistically significantly correlated with overall survival (Hazard Ratio: 1.6, $$P \leq 0.045$$). Afatinib was then screened out to be tested. In vitro experiments exhibited that the expression level of CDC42 was upregulated in HaCaT/A431 cells transfected with HPV E6 and E7, and the inhibitory effect of afatinib on proliferation was enhanced after transfection. CDC42-GTPase-effector interface-EGFR-afatinib was found to be a stable complex with a highest ZDOCK score of 1264.017.
### Conclusion
We identified CDC42 as a pivotal gene in the pathophysiology of HPV-related cancers. The upregulation of CDC42 could be a signal for afatinib treatment and the mechanism in which may be an increased affinity of EGFR to afatinib, inferred from a high stability in the quaternary complex of CDC42-GTPase-effector interface-EGFR-afatinib.
## Introduction
Co-evoluted with vertebrates for more than 350 million years, the epitheliotrophic papilloma virus (PV) has already well adapted to the host tissue, the squamous epithelia of skin and mucosal surfaces [1]. Despites there are numerous types of human papilloma virus (HPV) multiplying and producing progeny viruses in their hosts, most of them do not cause any detectable pathologies, if any, usually only minor and benign lesions [1].
But meanwhile, some types of HPV are responsible for approximately $30\%$ of all cancer cases caused by infectious agents [2], including cervical squamous cell carcinoma (CESC), anal squamous cell carcinoma (ASCC) and other types of carcinoma, resulting in estimated 610,000 incident cancer cases and more than 250,000 deaths worldwide annually [3].
In terms of carcinogenesis, both the etiological subgroup of HPV and the involved anatomical location are relatively restricted. Of more than 200 types of HPV, only 15 high-risk-HPV types are identified as causes of malignant neoplasms, most prevalently HPV-16 and 18 [4]. Also based on pathogenetic study in CESC, the carcinogenesis induced by HPV occurs specifically in the small, discrete cell population that localizes in the squamocolumnar junction of the cervix [5]. This similar tropism and transformation of epithelial cells are also observed in the dentate line of anal canal [6], where columnar epithelium gradually transitions to squamous epithelium, suggesting a similar carcinogenetic process in ASCC [7]. This most diagnosed histological type of malignant disease in anal canal [8], is developed from its precursor lesion — anal intraepithelial neoplasia (AIN), similar to the relation between CESC and cervical intraepithelial neoplasia (CIN).
Pathophysiological studies on carcinogenesis of HPV virus have demonstrated: the integration of HPV genoemes into the chromosomes will destabilize the genomic of the vulnerable host cells, inducing a secondary epigenetic re-programming [9]. This process features with an overexpression of E6 and E7 genes, which could stimulate the expression and activity of DNA methyltransferase I (DNMT I), triggering a consequential hypermethylation in the host cells [10, 11]. Based on these findings, it was hypothesized that, in all HPV-related cancers, the maintaining stemness-like differentiation status in epithelial cells relies on the hypermethylation induced by ongoing E6 and E7 [9], which plays an important role in the progression of cancers.
The technological developments of both profiling methods of DNA methylation and the computational approaches for processing the obtained data, have empowered us to investigate DNA methylation in different disease progressions from a global view through massive processed data, which is hard to be achieved even by dozens of “traditional” experiment-based studies [12]. However, most existing related bioinformatic studies in HPV-related cancers only focused on the DNA methylation anomalies, but ignored the downstream changes. This limits the significance of the findings in these studies to some extent. Because without an integrated analysis of the transcriptomic and other downstream data, which could not only be affected by DNA methylation, but also other intricate intracellular molecular biological processes, it is hard to achieve a deep and global comprehension to the pathophysiology of HPV-related cancers.
In this study, we tried to from a more comprehensive perspective to investigate the pathophysiology of HPV-related cancers, through muti-omic bioinformatic analyses and other methodological tools. We started with analyses of DNA methylation data, integrating downstream RNA sequencing (RNA-seq) data in HPV-related cancers, aiming to identify the hub genes in the progression of HPV-related cancers. Clinical significances of these identified hub genes were then scrutinized by conducting survival analysis based on The Cancer Genome Atlas (TCGA) database, through which we sought to validate the meaning of our findings in terms of clinical prognosis. Through the above analyses, CDC42 was identified as the pivotal gene due to its significance in both pathophysiology and clinical prognosis. To further extend the implications of our findings to the treatment of HPV-related cancers, we then picked out afatinib, a selective epidermal growth factor receptor (EGFR) inhibitor, due to its most positive sensitivity correlation with CDC42 according to analyses results from Cancer Therapeutics Response Portal (CTRP). To validate these findings, we then performed in vitro experiments to investigate EGFR, pEGFR and CDC42 expressions, together with viabilities and proliferations in HaCaT and A431 cells transfected with HPV 16 E6 and E7 and under interventions of afatinib at different concentrations. In the final step, to make our findings theoretically self-consistent, we stated a hypothesis based on a very convincing result in Computer-Aided Molecular Docking Experiment.
Through this “molecular to clinic” research work, with a spectrum from molecular docking experiments, bioinformatic analyses in molecular biology (epigenetics and transcriptome), in vitro experiments of cell biology (protein expression and cell proliferation) to clilnic-associated survival analyses, we hope to shed some new light on the disease process of HPV-related cancers, lay the foundation for further developing of precise molecular targeted therapy and provide aids for clinical decision making, to better confront the challenges posed by these cancers.
## Data collection
DNA methylation data of CESC and ASCC were collected from Gene Expression Omnibus (GEO) database GSE186859, including 121 ASCC samples, 13 adjacent AIN3 samples, 9 adjacent normal samples, 9 CESC samples, 9 CIN3 samples, 10 adjacent normal cervical samples. Single-cell RNA-seq (scRNA-seq) data were collected from GEO database GSE171894 and GSE176415 (GSM5364334, GSM5364335, GSM5364336), including 2 HPV-pos. CESC samples, 2 HPV-neg. CESC samples and 3 normal samples. Bulk RNA-seq data and paired clinical information were obtained from TCGA-CESC project, excluding samples with missing clinical information or histological types other than cervical squamous cell carcinoma, and eventually 237 samples were selected for analyses.
## Methylation profiling and data analysis
DNA methylation raw data were analyzed by the Chip Analysis Methylation Pipeline (ChAMP) R package [13]. ChAMP is a comprehensive methylation analysis package, including features of quality control, identification of Differentially Methylated Probes (DMPs), Differentially Methylated Regions (DMRs), and Differentially Methylated Blocks (DMBs). Probes with detection P-value > 0.01, probes with <3 beads in at least $5\%$ of samples per probe and probes located in sex chromosome were filtered out via champ.filter() function. Differentially designed 450K probes were normalized by function champ.norm(). Champ. DMP() function was carried out to calculate the methylation differences of p+robes. DMPs with |logFC| > 0.2 and P-value < 0.05 were picked.
## Function annotation
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to annotate the picked DMPs [14]. FerrDb [15] and GeneCard [16] database were utilized for additional annotations. Data from GeneCard with a relevance score over median value were chosen for mapping.
We next performed KEGG analysis on the 7633 hypermethylated DMPs and 1024 hypomethylated DMPs. Figure 2A showed the regulatory orientations of DMPs in the genes and the functions of these genes. Red bars represented genes with hypermethylated DMPs ($P \leq 0.05$) and blue bars represented genes with hypomethylated DMPs ($P \leq 0.05$). Most of these genes were associated with carcinogenesis and immune. In addition, HPV infection pathway ($\frac{173}{3314}$), oxidative stress ($\frac{15}{3314}$), ferroptosis ($\frac{17}{3314}$) and necroptosis ($\frac{48}{3314}$) were also enriched. Existing evidences showed that oxidative stress, ferroptosis and necroptosis processes are closely related to HPV infection and could occur in immune cells and epithelial cells (38–40). Hence, we supposed these enrichments in our study could be a result of HPV infection and subsequential HPV-induced carcinogenesis. Therefore, genes annotated with these functions were also chosen for further analyses.
**Figure 2:** *Function annotations and difference in immune infiltration of DMPs. (A) KEGG analysis of the DMPs, red bars represented the genes with hypermethylated DMPs, blue bars represented the genes with hypomethylated DMPs. (B) Immune infiltration base on the gene with DMPs, each column represented one sample, different color represented different proportions of immune cell type. (C) Immune cell types with significant differences in the enrichment of genes for DMPs. (*P < 0.05, **P < 0.01).*
Considering that KEGG is a relatively broad mapping database, there might be omissions in details. For example, only markers of genes are compared in the mapping of ferroptosis, without any inducer, promoter and driver included. After further annotation, 264 genes were identified as oxidative stress-associated, 23 genes were ferroptosis-associated and 65 genes were necroptosis-associated. CIBERSORT enrichment analysis for genes with overlapped methylations (Figure 2B) showed immune cell proportions mainly different in T-reg cells, CD4+ T cells, CD8+ T cells, dendritic cells and monocytes (Figure 2C). This result derived from CESC data due to the current absence of RNA-seq data and clinic data in ASCC.
## Immune analysis
Different immune cell proportions of genes obtained from DMPs were analyzed utilizing cell type identification by calculating relative subsets of RNA transcripts (CIBERSORT) [17]. Seurat R package was applied to identify genes related to different immune infiltration from CIBERSORT [18]. An upper bound threshold for the percentage of mitochondrial count ($5\%$) was defined, and the cells above the upper bound were filtered out [18]. Data normalization was carried out after cell filtering that use the global-scaling normalization package LogNormalize, which divides the specific feature counts of each cell by the overall counts of that cell, divides it by 104 and then performs a natural log-transformation [19]. The samples were then merged into a single data set using the merge function. The FindIntegrationAnchors function was used to find the anchors, and the Inte-grateData function was used to integrate multiple data sets [20]. Community detection algorithm was applied for clustering the cells, R tool FindCluster() and the parameter “resolution = 1” for controlling the number of clusters. Non-linear dimensional reduction technique Uniform Manifold Approximation and Projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) were performed to visualize single cell clustering in low-dimension. The cluster-specific marker genes were obtained using the Findmarkers () tool in the Seurat package with default non-parametric Wilcoxon rank sum test as well as Bonferroni correction. The characteristic cellular marker reference was obtained from R package Celldex, with which cells were automatically annotated by R package SingleR [21]. Plot1cell R package [22] was used to visualize and quantify the scRNA-seq data.
Immune differences were obtained from CIBERSORT analysis. However, the alterations of immune markers were associated with large-scale genetic alterations in immune infiltrations. In order to have a more precise understanding of immune alterations, we further obtained differential immune infiltration information by analyzing genes in DMPs via CIBERSORT. *Then* genes regarding these immune alterations were identified from scRNA-seq data.
To analyze the immune identities and functions of the cells, we first clustered and visualized the cells based on the scRNA-seq data. CESC scRNA-seq were divided into 21 clusters according to the Seurat FindCluster() function, the UMAP and tSNE algorithm from the Seurat R package (Figure 3A). 10,959 marker genes were obtained from 21 clusters based on the Findmarker() function. Top 3 marker genes of each cluster were used for heatmap visualization. The different abundances of marker gene expression were used for further analysis (Figure 3B). Reference data of cellular markers were obtained from Celldex, with which SingleR could annotate clusters automatically (Figure 3C). Clusters with cell type annotation as T-reg cells (Cluster 4, marker: FOXP3), CD4+ T cells (Clusters 5, 7, 18, marker: IL7R), CD8+ T cells (Cluster 2, marker: CD8A), dendritic cells (Cluster 11, 13, 16, marker: LYZ) and monocytes (Cluster 8, 17, marker: CXCL6) were selected as targets.
**Figure 3:** *Clusters, marker genes and annotations of CESC scRNA-seq data. (A) The dimension reduction of CESC scRNA-seq. Visualization of separate clusters based on UAMP and tSNE. (B) Heatmap of the most significant marker genes, each cluster showed top 3 marker genes. (C) Clusters annotated by immune cell markers.*
## Target genes with clinical prognosis and drug selection
An RNA matrix was constructed using immune-related genes and genes annotated with specific functions obtained from the previous steps. The “WGCNA” package was used for the weighted correlation network analysis [23]. This network can be used to identify highly synergistic genomes and identify candidate biomarker genes or therapeutic targets based on genomic endogeneity and genome-to-phenotype associations [23].
According to the TNM staging in WHO guideline for cervical cancer [24], in our study we defined TNM IA-IIA, which is mainly treated with surgery and has a good prognosis, as the “early group”, and TNM IIB-IV, which requires simultaneous radiotherapy and has a relative poor prognosis as the “advanced group”. By analyzing the correlation between RNA matrix and grouping information (early or advanced) in WGCNA network, gene clusters, which were most associated with cancer progression, were identified.
Data containing information on genes and weighted gene co-expression in the clusters were input into Cytoscape for visualization of network. Cytoscape molecular complex detection (MCODE) was used for finding the strongly interacting genes in the clusters [25], by setting degree cut off = 2, node score cut off = 0.2, K-core = 2, and maximum depth up to 100. The top 10 genes with the strongest interaction were filtered out and defined as hub genes.
Gene Expression Profiling Interactive Analysis 2021 (GEPIA 2021), a web-based program, was used to analyze the correlation between the expression level of the hub genes and the overall survival in CESC patients [26].
Cancer Therapeutics Response Portal (CTRP) V2, a data matrix contains profiles of chemical sensitivity, was then used to analyze the correlation between the hub gene expression levels and drug sensitivity for filtering out potentially promising drugs [27].
According to the selected target clusters in the previous step, 7722 immune-associated genes were obtained from scRNA-seq data, Together with genes in HPV infection pathway, oxidative stress-associated genes, ferroptosis-associated genes and necroptosis-associated genes, a new matrix including expression profiling data and clinical data was built.
The development of the WGCNA scale-free co-expression network allowed the identification of the correlations between genetic characteristics and clinical features. Co-expression network was constructed using the new matrix. We used pickSoftThreshold function to select soft threshold power β = 6 which ensured a scale-free network (Figure 4A). Then, 14 distinct gene modules were generated based on hierarchical clustering dendrogram (Figure 4B). Previously defined clinical features “Early group” and “Advanced group” were input. Pink module (r = -0.13, $$P \leq 0.04$$), purple module (r = -0.14, $$P \leq 0.04$$), salmon module (r = -0.13, $$P \leq 0.04$$), blue module (r = -0.13, $$P \leq 0.05$$) and grey module (r = -0.14, $$P \leq 0.03$$) showed significant correlations with the progression of cancer (Figure 4C). In order to assess the probable biological function of these modules, correlations between gene significance (GS) and module membership (MM) were evaluated. These correlations were shown in the form of scatter plots (Figures 4D, E), in which the modules had demonstrated a significant association between GS and MM, indicating that the genes in those modules are not only co-expressed but also positively linked to clinical features. To ensure the integrity of these results, all modules associated with clinical features were included in the study rather than just the most significant ones.
**Figure 4:** *WGCNA analysis of the correlation between gene modules and clinical features. (A) The mean connectivity and scale-free topology index for each power value between 1 to 20. Investigation of the mean connectivity (degree, Y axis) for different soft-thresholding powers (X axis). (B) Dendrogram of genes in new matrix (Associated to ferroptosis, necroptosis, oxidative stress, HPV infection pathway, T-reg cells, CD4+ T cells, CD8+ T cells, dendritic cells and monocytes) clustered based on a dissimilarity measure (1-TOM). Densely linked, highly co-expressed genes are grouped together on the dendrogram’s branches. (C) Correlations of modules and clinical feature. Each row corresponded to a module, The number in the upper left corner represents the correlation, and the number in the lower right corner represents the P-value. (D) Scatter plot of module membership (MM) vs. gene significance (GS) in blue modules. MM presents the correlation between gene expression and each module eigengene. GS represents the association between gene expression and each trait. In both modules, GS and MM have a high correlation. (E) Scatter plot of module membership (MM) vs. gene significance (GS) in grey modules.*
Cytoscape plugin MCODE was performed to find the highly interconnected regions in the network of all the nodes and edges. A node with more interconnected neighbors could achieve a higher score. In the highest region detected by MCODE, the 10 highest scoring genes were selected as hub genes (Figure 5A) (Supplementary Table 1), including PSMD11, RB1, SAE1, TAF15, TFDP1, CORO1C, JOSD1, CDC42, KPNA2 and NUP62. Next, the correlations between expressions of these gene and survival in CESC patients were analyzed via GEPIA2021 online tool (Figures 5C–L). The results showed all the hub genes manifesting correlations with cancer progression to some extent. However, only the correlation in CDC42 was statistically significant with the survival of CESC patients (Hazard Ratio: 1.6, $$P \leq 0.045$$), which implied patients with higher expression level of CDC42 had worse prognosis. Hence, we picked CDC42 as the target for downstream studies due to its significance in both pathophysiological and clinical levels. All the connections of CDC42 were listed in network (Figure 5B), green diamonds represented genes associated with ferroptosis, purple ellipses represented genes clustered in immune cell clusters of scRNA, orange rectangles represented genes related to necroptosis, and yellow triangles represented genes annotated with oxidative stress.
**Figure 5:** *Identification of hub genes. (A) Top 10 MCODE scoring genes were highlighted in the co-expression network. (B) Hub gene CDC42 with its highly co-expressed neighbors. Green diamonds represented genes associated with ferroptosis, purple ellipses represented genes clustered in immune cell clusters of scRNA, orange rectangles represented genes related to necroptosis, and yellow triangles represented genes annotated with oxidative stress. (C) KM curves (Kaplan–Meier estimator) showed the correlation between CESC overall survival and CDC42 expression. (D) KM curves showed the correlation between CESC overall survival and CORO1C expression. (E) KM curves showed the correlation between CESC overall survival and JOSD1expression. (F) KM curves showed the correlation between CESC overall survival and KPNA2 expression. (G) KM curves showed the correlation between CESC overall survival and NUP62 expression. (H) KM curves showed the correlation between CESC overall survival and PSMD11 expression. (I) KM curves showed the correlation between CESC overall survival and RB1 expression. (J) KM curves showed the correlation between CESC overall survival and SAE1 expression. (K) KM curves showed the correlation between CESC overall survival and TAF15 expression. (L) KM curves showed the correlation between CESC overall survival and TFDP1 expression.*
Before investigating the potential role of CDC42 in therapeutic development, it was necessary to understand the alteration of CDC42 during HPV infection and carcinogenesis. There were 3 DMPs in CDC42, cg08608952, cg13962372 and cg23019935. Volcano plots showed the different DNA methylation of CDC42 between CESC and normal cervical group (Figure 6A), CESC and CIN3 group (Figure 6B). Compared to the normal cervical group, hypermethylation of these 3 DMPs was significant in CESC group, while slightly in CIN3 (Figure 6C). In the RNA profiling, the expression of CDC42 was higher in CESC than normal cervical group (Figure 6D). This result could also be observed from scRNA-seq data. Compared to normal samples, CDC42 was upregulated in CESC samples (Figure 6E), implying that the upregulation of CDC42 may be driven by carcinogenesis. To figure out the impact of HPV infection on CDC42, quantifies of CDC42 were performed between HPV-pos CESC, HPV-neg CESC and normal group (Figure 6F). In the CESC scRNA-seq data, CDC42 was grouped in cluster 1 (Dendritic cells), cluster 3 (Dendritic cells) and cluster 7 (CD4+ T cells). Violin plots showed the expression of CDC42 in dendritic cells and CD4+ T cells between 3 groups. Compared with CESC, CDC42 expression was lower in normal tissues. Whereas in comparison among CESC samples, the expression was higher in the HPV-pos group than in the HPV-neg group, though not significantly. Hence, we considered the upregulation of CDC42 was not only caused by carcinogenesis, but also HPV infection.
**Figure 6:** *DNA Methylation, RNA expression, scRNA expression and chemical compounds sensitivity of CDC42. (A) Volcano plot of the DMPs between CESC and normal samples. Red plot corresponded to hypermethylated probe, green plot corresponded to hypomethylated probes. (B) Volcano plot of the DMPs between CESC and CIN3 samples. (C) β value of CDC42 DMPs (cg08608952, cg13962372 and cg23019935) in different groups. (D) RNA expression of CDC42 in CESC and normal samples. (E) Colors single cells on a dimensional reduction plot according to the expression of CDC42. (F) Quantifies of CDC42 in CD4+ T cells and dendritic cells between CESC and normal samples, HPV-pos. CESC and HPV-neg. CESC. (G) Correlation analysis of CDC42 expression level and chemical compounds sensitives, afatinib showed the significantly and positively correlation. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.*
As of now, through bioinformatic analyses, we had partly understood the underlying driving factors of CDC42 upregulation in HPV-related cancer and its impact on the prognosis of CESC patients, but the potential contribution of these findings to clinical therapeutic development remained unclear. A correlation analysis between CDC42 expression level and chemical compound sensitivities was performed via CTRP database. The result explained that the expression of CDC42 was significantly and positively correlated with 14 chemical compounds, among which EGFR inhibitor afatinib was the most significant one (Figure 6G). To validate these findings in the analyses above, we performed in vitro experiments to assess the alterations of cell proliferation, cell viability and protein expression.
## Cell culture
A431 and HaCaT were purchased from American Type Culture Collection (ATCC, Wesel, Germany). HaCaT and A431cells were cultured in DMEM (Sigma-Aldrich, Schnelldorf, Germany) media containing $10\%$ Fetal Bovine Serum (FBS) (Sigma-Aldrich, Schnelldorf, Germany) and $1\%$ penicillin-streptomycin (Sigma-Aldrich, Schnelldorf, Germany) at 37°C in a humidified incubator with $5\%$ CO2.
## Cell transfection
HPV16 E6 and E7 expressing plasmid as a bacterial stab (p1321 HPV16 E6 and E7, Addgene #8641) was gifted from Prof. Peter Howley [28]. Sterile loops were used to steak bacterial stab on the LB agar plates, grown at 37°C in a humidified incubator with $5\%$ CO2 overnight. Ampicillin-resistant colonies on LB agar plates were selected and amplified in LB/ampicillin medium overnight at 37°C in a humidified incubator with $5\%$ CO2. Plasmid DNA was recovered from the bacterial culture by ethanol precipitation.
Transfection of plasmid DNA from above steps was performed with X-tremeGENE 9 DNA transfection reagent (Roche, Mannheim, Germany). X-tremeGENE 9 DNA transfection reagent was diluted with serum free DMEM to a concentration of 3 μl reagent/100 μl DMEM for a ratio of 3:1. Then, 1 μg of DNA was mixed with 100 μl diluted X-tremeGENE 9 DNA transfection reagent, and the DNA transfection reagent complex was incubated for 20 mins at RT. In 96-well plates, 5 μl DNA transfection reagent complex was added to each well and in 10-cm dishes, 500 μl was added. The cells were incubated for 24 h before further analysis.
The untransfected groups were seeded and treated at the same time and under the same conditions. The cells were cultured with the same transfection reagent complex as the transfected group, but without the addition of DNA.
## Cell viability and proliferation assay
After 24 h transfection, cells were treated with afatinib (SML3109, Sigma-Aldrich, Schnelldorf, Germany). HaCaT and A431 cells were treated with different concentrations (0 μM, 0.1 μM, 1 μM, 10 μM) of afatinib for 24 h. Untransfected groups were treated at the same time and the same conditions. cell viability and proliferation were assessed using the Water-Soluble Tetrazolium 1 (WST-1) assay (Sigma-Aldrich, Schnelldorf, Germany). 10 µl WST-1 reagent was added to each well in 96-well plates. After incubating for 4 h at 37°C and $5\%$ CO2. The absorbance of the samples at a wavelength of 440 nm was measured via a plate reader (Spectra MR, Dynex Technologies, Chantilly, USA).
## Western blot
HaCaT/A431 cells were seeded on 10 cm dishes and incubated overnight, and grouping was the same as for cell viability and proliferation assays. Extraction of proteins were performed after cells were exposed to different concentrations (0 μM, 0.1 μM, 1 μM, 10 μM) of afatinib for 24 h. RIPA lysis and extraction buffer (89901, Thermo Fisher Scientific, Planegg, Germany) and protease inhibitors set (Roche, Mannheim, Germany) were used for protein extraction.
5 μl of protein ladder (Sigma-Aldrich, Schnelldorf, Germany) was used for determination of the molecular mass. 10 μl of cell lysate and 2 μl 6× loading buffer (Sigma-Aldrich, Schnelldorf, Germany) were added to each well of SDS-PAGE gel. Electrophoresis was conducted at 80 V for 50 mins, then 120 V until the marker proteins reached the bottom of gel.
PVDF membranes were activated by methanol for 5 mins. Filter Paper Sandwich (Thermo Fisher Scientific, Planegg, Germany) (sponge-filter paper-gel-membrane-filter paper-sponge) was mounted in the transfer tank and air bubbles were removed. It was transferred with 200 mA for 90 mins on ice. Membranes were blotted with $5\%$ skim milk in for 2 h at RT. Then, the primary antibody was applied against CDC42 (HPA069590, 1:2000, Sigma-Aldrich, Schnelldorf, Germany), EGFR (AMAB90816, 1:1000, 1 µg/ml, Sigma-Aldrich, Schnelldorf, Germany), pEGFR (07-819, 1:750, Sigma-Aldrich, Schnelldorf, Germany) and GAPDH (#2118, 1:1000, Cell Signaling Technology, USA) for overnight at 4 °C. Secondary antibodies were incubated with the membranes at room temperature for 1 h. Lastly, the protein bands were captured using ChemiDoc Imaging Systems (Bio-Rad Laboratories GmbH, Feldkirchen, Germany). ImageJ were used for analysis of western blot data [29].
Cell proliferation and cell viability assays confirmed that transfection with HPV16 E6 and E7 can make cells more sensitive to afatinib. However, the alterations in protein-level in this process remained unclear. A431 and HaCaT cells were treated as described above. Protein expressions were measured after intervention of afatinib for 24 h (sc. 48 h after transfection, Supplementary Figure 2A). Transfection with HPV16 E6 and E7 enhanced the effect of afatinib. Quantitative analysis through Image J showed that, compared to group without HPV transfection, lower expression of pEGFR in the HPV transfected group at the same afatinib concentration could be observed, accompanied by an upregulation of CDC42 (Supplementary Figure 2B). This result should be interpreted as a general trend but not a precise quantification. Because we did not expect a clear mechanistic interpretation only through Western Blot and without further experiments. We aimed only to observe the trend, so no replicate experiments were performed and the results were thus not statistically significant. For this reason, WB results are placed in the supplementary material.
## Molecular docking
Autodock Vina, a silico protein-ligand docking program, was used to examine the binding affinities and mechanisms of interaction between the drug candidate and their targets [30, 31]. The molecular structure of afatinib (PubChem 10184653) was obtained from PubChem Compound (https://pubchem.ncbi.nlm.nih.gov/) [32]. The 3D coordinates of CDC42 (PDB ID, 1AJE; Resolution: NA) [33] and EGFR (PDB ID, 6VH4; Resolution: 2.80 Å) [34] were downloaded from the PDB (http://www.rcsb.org/). All protein and molecular data were converted into PDBQT format for docking analysis, with all water molecules removed and polar hydrogen atoms applied. The grid box was positioned in the middle to allow for unrestricted molecular mobility and to cover the domain of each protein.
Rigid protein-protein docking (ZDOCK) was performed between CDC42 and EGFR to study the relationships [35]. The PDB format of the protein structural domains were the obtained from the same database, The 3D coordinates of CDC42-GTPase-effector (PDB ID,5UPL; Resolution: 3.00 Å) interface [36] and EGFR-afatinib (PDB ID,4G5J; Resolution: 2.80 Å) [37] were downloaded from PDB. The ZDOCK module was run to identify the docking sites and calculate the ZDOCK scores.
Afatinib acts as a targeted inhibitor of EGFR but has a high positive sensitivity correlation with CDC42 expression. We initially hypothesized that CDC42 also has a high affinity with afatinib and therefore performed molecular docking analysis. Using Autodock Vina, the binding poses and interactions of afatinib with CDC42 and EGFR were acquired. Binding energy was calculated for each interaction (Figures 8A, B). Results showed that afatinib bound to CDC42 and EGFR through via apparent hydrogen bonds and strong electrostatic interactions. Furthermore, afatinib successfully occupied the hydrophobic pockets of CDC42 and EGFR (Figures 8C, D).
**Figure 8:** *Molecular docking and rigid protein–protein docking. (A) Binding mode of afatinib to CDC42. CDC42 was set as ball-and-stick model with gaussian volume. The amino acids involved in the interaction are shown in a ball-and-stick model, the amino acids did not involve in the interaction were showed as cartoon. (B) Binding mode of afatinib to EGFR. (C) The Molecule of the Month feature used cartoon illustrations to demonstrate the overlay of the crystal structures of afatinib and CDC42. (D) The Molecule of the Month feature used cartoon illustrations to demonstrate the overlay of the crystal structures of afatinib and EGFR. (E) CDC42 formed hydrogen bonds with the extended GTPase-effector interface amino acid sites. (F) Extended GTPase-effector interface formed hydrogen bonds with EGFR amino acid sites. (G) Binding mode of afatinib to CDC42- Extended GTPase-effector interface -EGFR.*
Binding energy <-7.0 kcal/mol indicates strong binding activity of ligand to receptor. There are 9 binding models of afatinib to EGFR and all the binding models had low binding energy (-9.186 kcal/mol, -9.063 kcal/mol, -8.639 kcal/mol, -8.601 kcal/mol, -8.365 kcal/mol, -8.296 kcal/mol, -8.216 kcal/mol, -8.148 kcal/mol and -8.027 kcal/mol), indicating a highly stable binding between EGFR and afatinib. The only binding models of afatinib to CDC42 had a low binding energy of -81.793 kcal/mol, which means the affinity of CDC42 to afatinib even higher than EGFR. The process of ligand binding to proteins is very complex. In addition to the binding energy, the evaluation of affinity also requires the formation of two hydrogen bonds with hinge when the small molecule binds to the protein. In Figures 8A–G, the dashed line represents interaction force, and amino acids involved in the interaction are shown in a ball-and-stick model, in which afatinib could be found to have an intensive interaction with CDC42.
The affinities of afatinib to both CDC42 and EGFR were confirmed. However, it is still unclear how CDC42 acts on EGFR after binding to afatinib. We tried to simulate and calculate the CDC42-afatinib-EGFR interactions, but no interaction could be found. In the PDB database, we found that CDC42 could be bound by extended GTPase-effector interface. Hence, we tried to construct the complex of CDC42-GTPase-effector interface-EGFR-afatinib. ZDOCK provides docking of protein structures, and the higher the ZDOCK score, the stronger the docking. The top 10 best ZDOCK score of CDC42-GTPase-effector interface-EGFR-afatinib were 1264.017, 1255.494, 1219.203, 1187.345, 1156.167, 1155.194, 1154.395, 1144.608, 1134.683 and 1114.190. As shown in figure (Figure 8E), CDC42 could form hydrogen bond links with amino acid sites such as GLU 171, ASN167, LEU165, VAL 168, PHE 169 and LYS 166-GTPase-effector interface GLU1005, ARG 489, LEU 475. GTPase-effector interface formed hydrogen bond links with amino acid sites such as GLN303, ASN996-PRO 975, ALA 972 EGFR (Figure 8F). EGFR binds to afatinib through amino acids such as ASN 808, HIS 988, PRO 848 forming hydrogen bonds and strong interactions (Figure 8G).
Comprehensive analysis revealed that CDC42- GTPase-effector interface-EGFR-afatinib formed a stable docking model.
## Statistical analysis
The error bars in cell proliferation assays are presented as mean ± standard error, and statistical analyses for cell proliferation assays were performed using GraphPad Prism® 5 (GraphPad Software, San Diego, CA, USA). Statistical analysis for DNA methylation, bulk RNA-seq data and scRNA-seq data was performed using R Statistical Software, the usage and setting of all the analysis could be found in reference of R packages (v4.2.1; R Core Team 2022).
## Methylation analysis
There were 390,065 probes that passed quality control for all subsequent analyses. Principal component analysis (PCA) was performed to compare β-values for all samples (Figure 1A). As shown in Figure 1A, no intersection between normal anal group and normal cervical group were observed, which was significantly different from that between AIN3, ASCC and CESC group, in which large proportions of intersections were observed, indicating a significant similarity of methylated sites in the disease process of AIN3, ASCC, CESC, compared to that between normal cervical and anal tissues. This similarity could also be observed in the heatmap of correlation matrix (Figure 1B), in which sample clusters of AIN3, ASCC or CESC could not be distinguished. Similarly, CESC samples were mixed with ASCC samples in the visualization of sample similarity based on the top 1000 most variable probes (Supplementary Figure 1). It could be recognized there were a large number of DMPs overlapped between AIN3, ASCC and CESC samples, while CIN3 samples were similar to normal cervical samples. Hence, we tried to analyze and compare the roles of genes between ASCC and CESC group due to the observed similarity in DMPs, which suggested similar epigenetic modifications in these two HPV-related cancers, while CIN3 and AIN3 group were excluded in this step.
**Figure 1:** *Overview of DNA methylation data. (A) Individuals plot of PCA, samples were represented as follows: red, AIN3. Brown, ASCC. Green, CESC. Blue cross, CIN3. Blue square, Normal cervical. Purple, Normal anal. (B) Heatmap of Top 200 DMPs, samples were represented as: Purple, AIN3. Red, ASCC. Blue, CESC. Green, CIN3. Light blue normal anal. Brown, normal cervical. (C) Proportion of the feature of CpG-islands, orange represented for hypermethylated probes, blue represented for hypomethylated probes. (D) The overlapped DMPs between CESC and ASCC, X axis represented for different chromosome, Y axis represented for number of DMPs, black bar represented for hypomethylated DMPs, white bar represented for hypermethylated DMPs. (E) Quantities of overlapped DMPs.*
As shown in Figure 1E, a total of 44,137 ($11.31\%$) in 390,065 probes exhibited differing levels of methylation between CESC and normal cervical group and 11,440 ($2.9\%$) probes showed differing levels of methylation between ASCC and normal anal group. In CESC group, compared to normal cervical group, 25300($57.4\%$) probes were hypermethylated while 18,837 ($42.6\%$) probes were hypomethylated. In ASCC group, compared to normal anal group, 9726 ($85.0\%$) probes were hypermethylated while 1714 ($15.0\%$) probes were hypomethylated. Among these thousands of DMPs, there were only 2, that hypermethylated in CESC but hypomethylated in ASCC and only 1, that hypermethylated in ASCC but hypomethylated in CESC, indicating a very limited heterogeneity of DNA methylation in these 2 HPV-related cancers. As the final result in this step, 7633 hypermethylated DMPs and 1024 hypomethylated DMPs in both tumors were picked out for further analyses.
These DMPs varied among genomic locations, mainly enriched in open sea regions (Figure 1C). Besides, the distributions of both hypermethylated DMPs and hypomethylated DMPs were mostly enriched in Chromosome 1 (Figure 1D).
## Cell viability and proliferation
WST-1 assays were carried out on HaCaT/A431 cells with or without HPV16 E6 and E7 transfection in the presence of various concentrations (0 μM, 0.1 μM, 1 μM, and 10 μM) of afatinib in order to assess the changes in the viability and proliferation of cells. In each group, the spectrophotometric readings of cells without exposure to afatinib were used as the relative reference standard in the figure ($100\%$) (Figures 7A, C). Similarly, to compare cell proliferations between HaCaT/A431 cells with or without HPV transfection, the spectrophotometric readings of cells without transfection and exposure to afatinib were set as relative reference standard ($100\%$) (Figures 7B, D). Cell proliferations of HaCaT and A431 were increased after transfected with HPV16 E6 and E7 ($P \leq 0.05$) (Figures 7B, D). In HaCaT cells, afatinib was unable to reach the half-maximal inhibitory concentration (IC50) at 10 μM, while in HaCaT cells transfected with HPV had a lower IC50 at 1 to 10 μM (Figure 7A). Similar results could be observed in A431 cells. In A431 cells without transfection, IC50 of afatinib was between 1-10 μM and a lower IC50 was observed after transfected with HPV16 E6 and E7 (Figure 7C).
**Figure 7:** *In vitro validation of CDC42 function. (A) WST-1 cell viability assay of HaCaT cell, 0 μM afatinib groups were set as 100%. (B) WST-1 cell proliferation test of HaCaT cells transfected with HPV E6 and E7, untransfected and untreated HaCaT cells groups were set as 100%. (C) WST-1 cell viability assay of A431 cell, 0 μM afatinib groups were set as 100%. (D) WST-1 cell proliferation test of A431 cells transfected with HPV E6 and E7, untransfected and untreated A431 cells groups were set as 100%. *P<0.05, **P<0.01.*
## Discussion
Despite the global rollout of HPV vaccines, HPV-related cancers still cause huge health crisis especially in developing countries. Cervical cancer ($80\%$ histological type is CESC) remains the 4th most common malignancy in women and one of the leading causes of death in women diagnosed with cancers [41]. The overall ASCC incidence increased $2.7\%$ and incidence-based mortality increased $1.9\%$ annually from 2001 to 2015 [42], and the 5-year survival rate of patients with metastases is only $32\%$ [43]. In terms of molecular targeted treatments, as commented in an article, the very limited number of clinical trials for CESC showed “encouraging but limited” effects on survival of patients [44]. This is partially restricted by our understanding to the pathophysiology in HPV-related cancers. Phosphatidylinositide 3-kinases (PI3K) pathway is the most investigated pathway in CESC, but it has been proven difficult to design molecular targeted therapies based on this pathway [41]. For metastatic ASCC, up to now, the only molecular targeted drug entering the clinical trial is cetuximab, coincidentally, also an EGFR blockade. But the combination of cetuximab with conventional chemoradiation showed severe adverse effects, resulting in trials closures [45, 46]. For HPV-related cancers, novel molecular targeted drugs with promising efficacy and safety are still waited to be developed. In our previous study [47], we have found a resemblance of the prognostic effect of hub genes in CESC and head and neck squamous cell carcinoma (HNSCC), suggesting similar intracellular alterations in the HPV-related cancers, which we aimed to further investigate in this study. Through these studies, we hope to facilitate the development of novel molecular targeted drugs for HPV-related cancers.
The development of novel molecular targeted drugs relies on comprehension of pathophysiology in cancers. Despite considerable technological advances in the detection and analysis of DNA methylation, which allowed us to study the pathophysiology of HPV-related cancers from a new entry point, existing relevant studies have mostly focused only on epigenetic modifications but ignored the alterations in other levels, such as transcription. This limited the strength of findings, because some significant findings derived from DNA methylation data could have no similar significance in the level of transcription or protein expression.
Aiming to make our findings more significant, we conducted analyses based on integrated multi-omic data. Based on the analyses of DMPs from DNA methylation data in ASCC and CESC, we confirmed a high consistency of epigenetic modifications in these two HPV-related cancers (Figure 1). Through function annotations (Figure 2) of aberrant methylated gene, we identified the significantly altered pathways (immune, HPV infection, oxidative stress, ferroptosis and necroptosis) in HPV-related cancers. In terms of immune cell infiltration, CESC tumor tissue showed obvious immunosuppression, specifically manifested as a significant increase in T-reg cells and a significant decrease in activated dendritic cells (Figure 2C). Integrating with RNA-seq data in these pathways, we then analyzed the correlations of genes in these pathways with TNM staging of CESC through WGCNA scale-free co-expression network (Figure 4). In this step, 10 hub genes (PSMD11, RB1, SAE1, TAF15, TFDP1, CORO1C, JOSD1, CDC42, KPNA2 and NUP62) were identified, in which only the expression level of CDC42 was statistically significant in the correlation with overall survival in CESC patients (Figure 5). In the next step, we tried to further investigated the role of CDC42 in the pathophysiology of HPV-related cancer, including investigating the DMPs of CDC42 (Figure 6C), the difference of CDC42 expression in tumor and normal tissue (Figure 6D), in HPV-pos and HPV-neg samples (Figures 6E, F), respectively. In this step, we observed an upregulation of CDC42 in the CESC and HPV-pos CESC group. Based on the findings above, with various methods and from several aspects, we observed a significance of CDC42 in the pathophysiology of HPV-related cancers. Taking this as a starting point, next we tried to explore the potential implication of CDC42 in the design of molecular targeted therapy. Afatinib was picked up in this step due to its most significant positive correlation in sensitivity with the expression level of CDC42 (Figure 6G). In vitro experiments have been performed to validate these findings. Based on the results of Western blot, an upregulated expression of CDC42 was observed in A431 cells compared with HaCaT cells and in cells transfected with HPV E6 and E7 compared with those without HPV transfection (Figure 7E). Based on the results of WST-1 assays, the inhibitory effect of afatinib on proliferation and viability of A431 cells was confirmed, especially enhanced in cells transfected with HPV E6 and E7 (Figures 7A–D). Out of curiosity in the exact molecular interaction mechanism of CDC42 and afatinib, we further performed molecular docking experiments, through which an extremely stable CDC42-GTPase-effector interface-EGFR-afatinib complex was found (Figure 8), Inspired by this finding, we hypothesized that through this complex, CDC42 could increase the affinity of EGFR to afatinib, leading to a positive correlation of the expression level of CDC42 with the sensitivity of afatinib.
CDC42 is a member of the small GTPase family and plays a role in epithelial to mesenchymal transition, angiogenesis, cell cycle progression, oncogenic transformation, migration/invasion and tumor growth [48]. Similar to the results of bioinformatic analyses in our study, based on immunohistochemistry of 162 CESC samples, Ma et al. had observed an up-regulated expression of CDC42 in protein level and a correlated progression in clinical stage [49]. In the subsequent study, the same research group had reported a significantly higher expression of CDC42 in HeLa cells than control cells and an increased migration ability of HeLa cells after being transfected with CDC42 plasmids, which may be derived from an improved pseudopodia formation [50]. The finding of high CDC42 expression in CESC-derived HeLa cells is consistent with our findings in HPV-transfected skin squamous cell carcinoma-derived A431 cells, indicating a commonality of CDC42 alteration in HPV-related cancers [HPV18 transcript in HeLa cells discovered by Prof. Hausen in 1985 had made HeLa cells not only the first immortal human cell line, but also the first HPV-related cancers cell line [51]].
Unfortunately, in spite of being involved in multiple important processes in cancer progression, CDC42 is hard to be targeted with a specific inhibitor, due to its high homology within the other Rho family GTPases and in the wider Ras superfamily [52]. However, in accordance with our findings, many studies have demonstrated the associations between CDC42 and EGFR, which could be considered as an alternative pathway of action. A study reported CDC42 bound with coatomer protein complex (γCOP) could induce the accumulation of EGFR in cells. In addition, an overexpression of CDC42 could also inhibit the degradation of EGFR, inducing an increased level of EGFR, which could lead to cancer progression [48].
Afatinib is mainly used to treat cases of non-small cell lung cancer (NSCLC) that harbor mutations in the EGFR [53]. But in HPV-related cancers its role was seldom investigated and the clinical evidence is very limited. A case report showed, after administrated with afatinib as a single agent for 1 month, an EGFR-amplified metastatic CESC patient achieved a partial response (PR), with a significant lesion shrinkage observed [54]. In our study, we supposed and verified that the CDC42 upregulation can be considered as a signal for afatinib treatment in HPV-related cancers. Further efforts should be made including conducting validation in in vivo models.
There are also limitations in our study. Although we had identified the 3 DMPs in CDC42 (cg08608952, cg13962372 and cg23019935), we could not clearly interpret their roles in the regulation of transcription of CDC42. Moreover, due to the lack of relevant suitable data in other HPV-related cancers, such as HNSCC, the findings in our study are specific to ASCC or CESC, and further validation is needed in other HPV-related cancers.
In conclusion, we have identified CDC42 as a pivotal gene in the pathophysiology of HPV-related cancers. The upregulation of CDC42 could be a signal for afatinib treatment and the mechanism in which is probably an increased affinity of EGFR to afatinib, inferred from a great stability in the complex of CDC42-GTPase-effector interface-EGFR-afatinib. Through these findings, we hope to provided new insights into the disease mechanism of HPV-related cancers and lay the foundation for afatinib as a potential promising molecularly targeted drug for these cancers.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.
## Author contributions
LF, BC-E and MR conceived and designed the study. EW and JL analyzed the data and drafted the manuscript. EW and PA performed the experiments. EW prepared the figures. AW polished and revised the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1118458/full#supplementary-material
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|
---
title: EGCG identified as an autophagy inducer for rosacea therapy
authors:
- Lei Zhou
- Yun Zhong
- Yaling Wang
- Zhili Deng
- Yingxue Huang
- Qian Wang
- Hongfu Xie
- Yiya Zhang
- Ji Li
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10014537
doi: 10.3389/fphar.2023.1092473
license: CC BY 4.0
---
# EGCG identified as an autophagy inducer for rosacea therapy
## Abstract
Background: *Rosacea is* a common facial skin inflammatory disease featured by hyperactivation of mTORC1 signaling in the epidermis. Due to unclear pathogenesis, the effective treatment options for rosacea remain limited.
Methods: *Weighted* gene co-expression network analysis (WGCNA) analyzed the relationship between epidermis autophagy and mTOR pathways in rosacea, and further demonstrated it through immunofluorescence and qPCR analysis. A potential therapeutic agent for rosacea was predicted based on the key genes of the WGCNA module. In vivo and in vitro experiments were conducted to verify its therapeutic role. Drug–target prediction (TargetNet, Swiss, and Tcmsp) and molecular docking offered potential pharmacological targets.
Results: WGCNA showed that epidermis autophagy was related to the activation of mTOR pathways in rosacea. Next, autophagy was downregulated in the epidermis of rosacea, which was regulated by mTOR. In addition, the in vivo experiment demonstrated that autophagy induction could be an effective treatment strategy for rosacea. Subsequently, based on the key genes of the WGCNA module, epigallocatechin-3-gallate (EGCG) was predicted as a potential therapeutic agent for rosacea. Furthermore, the therapeutic role of EGCG on rosacea was confirmed in vivo and in vitro. Finally, drug–target prediction and molecular docking revealed that AKT1/MAPK1/MMP9 could be the pharmacological targets of EGCG in rosacea.
Conclusion: Collectively, our findings revealed the vital role of autophagy in rosacea and identified that EGCG, as a therapeutic agent for rosacea, attenuated rosacea-like inflammation via inducing autophagy in keratinocytes.
## Graphical Abstract
## Background
Rosacea is a common chronic inflammatory skin disorder with a series of features such as facial erythema, telangiectasia, papules, and pustules (Gallo et al., 2018). It significantly impacts the quality of life and affects between $5\%$ and $20\%$ of the population (Gether et al., 2018). The pathogenesis of rosacea is not well understood, but previous studies have shown that the interaction of genetics and a variety of environmental factors may lead to disorders of the skin’s immune system, particularly the abnormal production of cathelicidin LL37, leading to chronic inflammation and abnormal vascular responses of rosacea (Steinhoff et al., 2011; Ahn and Huang, 2018; Awosika and Oussedik, 2018). Due to the ambiguous pathophysiological mechanisms, there is still no effective treatment for rosacea.
The mammalian target of the rapamycin (mTOR) pathway is crucial for various biological processes including cell proliferation, apoptosis, metastasis, and angiogenesis (Deng et al., 2015; Fogel et al., 2015). Our previous work verified hyperactivated mTORC1 signaling in rosacea which promotes rosacea skin inflammation (Deng et al., 2021). Meanwhile, topical administration of rapamycin (mTOR inhibitor) ameliorated clinical lesions in rosacea patients (Deng et al., 2021). However, the underlying mechanism of mTOR signaling in rosacea still needs to be elucidated.
Autophagy is a dynamic process that maintains cellular homeostasis during environmental stress stimuli. Dysregulation of autophagy contributes to the pathogenesis of various skin diseases, including allergic contact dermatitis, atopic dermatitis, and psoriasis (Ohsumi, 2014; Cadwell, 2016). It has been reported that autophagy deficiency led to DNA damage and senescence of keratinocytes (Song et al., 2017). A recent study found that autophagy is essential for the activation of keratinocytes in wound healing (Qiang et al., 2021). In addition, autophagy plays a pivotal role in psoriasiform keratinocyte inflammation (Wang Z. et al., 2021). It is well known that mTOR is an important regulator of the autophagy process (Munson and Ganley, 2015). However, little is known about the link between autophagy and rosacea pathogenesis.
Epigallocatechin-3-gallate (EGCG), a natural polyphenol found in green tea, has many biological activities, including anti-inflammatory, antioxidant, cardioprotective, neuroprotective, and anticancer activities (Nan et al., 2019; Yi et al., 2020; Nan et al., 2021). Studies have revealed EGCG as a potential therapeutic agent for various skin inflammation conditions, including psoriasiform dermatitis (Chamcheu et al., 2018), interface dermatitis (ID) (Braegelmann et al., 2022), and atopic dermatitis (AD) (Noh et al., 2008). Although a clinical trial of four healthy volunteers demonstrated the potential anti-angiogenic effect of EGCG cream (Domingo et al., 2010), whether EGCG has a therapeutic effect on rosacea remains unknown.
Here, we revealed that the autophagy of keratinocytes was associated with the aberrant activation of mTOR signals and contributed to the progression of rosacea. Furthermore, we identified EGCG as a therapeutic agent of rosacea and found that it significantly attenuated rosacea inflammation by inducing autophagy in keratinocytes.
## Rosacea transcriptome data
*The* gene expression array of rosacea (GSE65914) was downloaded from the GEO database. Our previous epidermal transcriptome data (HRA000809) from 18 rosacea tissues and 5 normal skin tissues were downloaded for gene set variation analysis (GSVA).
## GSVA
To investigate the activation of mTOR pathways in rosacea, GSVA was performed using “GSVA” R packages.
## WGCNA
After removing the low-expressed genes (FPKM<1), the genes with the top $25\%$ largest variance were used for WGCNA with power (β) = 4 using the “WGCNA” R package as previously described (Li Y. et al., 2021). *The* genes from modules related to the mTOR pathway with GS > 0.5 were identified as hub genes and used for drug prediction.
## Drug prediction
DGIdb (https://dgidb.org/) was used for drug prediction. The predicted drugs with more than two target genes were collected for further analysis (Cotto et al., 2018; Freshour et al., 2021).
## Animals
For the experiment, 8-week-old female BALB/c mice were purchased from Shanghai SLAC Laboratory Animal Co., LTD. ( Shanghai, China). All studies and experimental procedures were approved by the Animal Ethics Committee of Xiangya Hospital of Central South University (No. 201703211). The rosacea-like mouse model was induced as previously described (Agrahari et al., 2020; Kulkarni et al., 2020). Skin inflammation of the mouse model was evaluated by the severity of erythema and edema as previously described (Deng et al., 2021). For EGCG treatment, BALB/c mice were treated with EGCG at a dose of 80 mg/kg per day for seven constitutive days. For topical bafilomycin A1 (BafA1) treatment, mice were injected intradermally with bafilomycin A1 (100 μМ) twice a day for 2 days. The rapamycin treatment was as previously described (Deng et al., 2021).
## Cell culture and treatment
HaCaT cells (Biovector Science Lab, Beijing, China) were cultured according to the manufacturer’s instructions, and the cells were then treated with different doses of EGCG with or without LL-37 (8 μM). For each experiment, 3-MA (10 μM) or BafA1 (10 nM) was added to HaCaT cells 1 h prior to the EGCG treatment. The cells treated with rapamycin, the mTOR inhibitor, were considered a positive control for this study.
## RNA extraction and real-time quantitative PCR (qPCR)
Total RNA was extracted from mouse skin tissue or cells using the Trizol reagent (Invitrogen, United States), and then, cDNA was synthesized using the Maxima H Minus First Strand cDNA Synthesis Kit with dsDNase (Thermo Fisher Scientific, United States). qPCR assay was performed with iTaqTM Universal SYBR® Green Supermix (Bio-Rad, United States) using the CFX Connect Real-Time PCR System (Bio-Rad, United States). qPCR primers are shown in Supplementary Table S1.
## Histological analysis
Skin tissues were fixed overnight with $4\%$ formaldehyde, and sections of 4 μm thickness were used for hematoxylin and eosin (H&E) staining as previously described (Xie et al., 2022). All studies and experimental procedures were approved by the Human Ethics Committee of Xiangya Hospital of the Central South University (No. 201703212).
For immunofluorescence, skin tissues were embedded in OCT and sectioned at 8 μm thickness. The sections were washed with PBS, fixed in $4\%$ frozen paraformaldehyde (PFA) for 15 min, and then blocked for 1 h in PBS containing $1\%$ BSA and $0.3\%$ Triton X-100. Primary antibodies were incubated at 4°C overnight. The sections were washed with PBS and incubated with secondary antibodies for 1 h at room temperature. The nuclei were stained with DAPI. All images were taken using a Zeiss fluorescence microscope and analyzed using Zen2 software (Germany). Anti-LC3 (1:200; Sigma-Aldrich, catalog L7543), anti-CD4 (1:100; eBioscience, catalog 12–0043-82), anti-Beclin1 (1:100; Proteintech, catalog 66665-1-Ig), and Alexa Fluor 488-conjugated goat anti-mouse IgG (H + L) cross-adsorbed secondary Ab (1:500; Invitrogen, catalog A-32723) were used.
## Cell viability assay
Cell proliferation was evaluated using a Cell Counting Kit-8 assay (Vazyme, Nanjing, China). Briefly, 1 × 103 cells/100 μl/well cells were seeded into 96-well plates. The supernatant was removed 48 h later, and 10 μl of the CCK-8 reagent and 100 μl fresh media were introduced per well and incubated for 2 h at $5\%$ CO2 and 37°C. Then, the absorbance at 450 nm was measured using the EnSight™ Multimode Plate Reader (PerkinElmer, Waltham, MA).
## Immunoblotting
The skin tissues and cells were lysed in RIPA buffer (Thermo Fisher Scientific, United States). Next, the protein was separated by SDS-PAGE and incubated with primary antibodies, including anti-LC3 (1:1,000; Sigma-Aldrich, catalog L7543), anti-GAPDH (1:5,000; Abcam, catalog ab8245), anti-p62 (1:1,000; Cell Signal Technology, catalog 88,588), anti-S6 (1:1,000; Cell Signal Technology, catalog 2317), and anti-pS6 (Ser$\frac{240}{244}$) (1:1,000; Cell Signal Technology, catalog 5364).
## Transmission electron microscopy
Cells were treated and collected by trypsinization and fixed in $2.5\%$ glutaraldehyde for 4 h and then refixed in $1\%$ osmium tetroxide for 2 h. After dehydration using a stepwise ethanol series, the cells were embedded in an embedding medium and then polymerized at 60°C for 2 days. The samples were cut on Leica EM UC6 (Leica, Wetzlar, German) at 80 nm thickness and stained with uranyl acetate and lead citrate. Images were acquired using a transmission electron microscope (Hitachi, Tokyo, Japan).
## Ad-mCherry-GFP-LC3 transfection
HaCaT cells were transfected with mCherry-GFP-LC3 adenovirus when they grew to $60\%$–$70\%$ confluence on dishes for 12 h at 37°C. Following treatment with EGCG, LL-37, or BafA1, images were taken using a confocal microscope (Leica, Germany).
## Pharmacological targets of EGCG
We used accessible online tools to predict the potential pharmacological targets of EGCG, including TargetNet, Swiss, and TCMSP (Wishart et al., 2018). Then, the candidate targets were identified using the UniProt database (Li R. et al., 2021).
## Molecular docking
The PubChem database (https://pubchem.ncbi.nlm.nih.gov/) was used to obtain the molecular structure of EGCG (CID-65064). The PDB database (https://www.rcsb.org/) was used for the protein structures of AKT1 (6HHG), MAPK1 (6DCG), and MMP9 (6ESM). Maestro software was used for molecular docking (Zhang H. et al., 2021).
## Statistical analysis
Statistical analysis was conducted with GraphPad Prism (8.0.0) (San Diego, California United States). All data were displayed as the mean ± SEM of three independent experiments. Unpaired Student’s t-test was used for the comparison of two groups, and one-way ANOVA followed by Dunnett’s test was used for multiple comparisons. The level of statistical significance was set at $p \leq 0.05.$
## WGCNA identified the keratinocyte autophagy associated with the mTOR pathway in rosacea
Our previous study identified the important role of the mTOR pathway in rosacea; however, the potential mechanism remains unknown (Deng et al., 2021). Here, based on our previous epidermis transcriptome data, GSVA identified the activation of the mTOR pathway in rosacea (Supplementary Figure S1). Next, we used WGCNA to identify the rosacea-related and mTOR pathway-related genes in the epidermis of rosacea. A total of 5,278 genes were used for WGCNA, and one abnormality (HSE_3) was removed (Figure 1A). The soft threshold β = 4 and scale-free R 2 = 0.93 are shown in Figure 1B. After merging the similar modules, 14 modules were obtained as shown in Figure 1C. The relationships between the mTOR pathway and modules are shown in Figure 1D. The black module ($r = 0.59$, $$p \leq 0.004$$) and brown module ($r = 0.74$, $$P \leq 9$$e-5) were positively associated with the mTOR pathway, while the blue module (r = -0.55, $$p \leq 0.008$$) was negatively associated with the mTOR pathway. The relationship between GS and MM in black, brown, and blue modules is shown in Figure 1E. The GO enrichment analysis demonstrated that the genes in the blue and black modules were enriched in the autophagy-related signal pathways using Metascape (http://metascape.org/) (Figure 1F). These results indicated that the activated mTOR pathway could affect keratinocytes’ autophagy in rosacea.
**FIGURE 1:** *WGCNA. (A) Sample cluster analysis associated with clinical characters. (B) Scale-free fitting index analysis and the mean connectivity for various soft-threshold powers. (C) Gene dendrogram and module colors of WGCNA. (D) Correlation analysis between modules and clinical characters. (E) Relationship between GS and MM in the blue, brown, and black modules. (F) GO analysis of genes from blue, brown, and black modules, respectively.*
## Autophagy was reduced in the keratinocytes and aggravated rosacea-like inflammation
To determine the roles of autophagy in rosacea, we analyzed the expression levels of the autophagy-related markers in rosacea lesion tissues and normal skin tissues. As shown in Figure 2A and Supplementary Figure S2, the expression of autophagy-related genes (ATG9A, ATG10, ATG12, and PIK3C3) was evidently decreased in rosacea lesions compared with normal skin tissues in the GSE65914 dataset. Immunofluorescence revealed decreased becline1 in the lesioned skin of rosacea patients (Supplementary Figure S3). These results were confirmed in LL-37-induced rosacea-like mouse models. We observed that the mRNA expressions of autophagy-related genes (Becn1, Atg5, Atg10, and Atg12) were decreased in LL-37-induced mouse skin tissue (Figure 2B). The immunofluorescence analysis also revealed that the LC3 expression was much lower in the epidermis of LL-37-induced rosacea-like lesioned skin than in control mouse skin tissues (Figure 2C).
**FIGURE 2:** *Autophagy was reduced in the keratinocytes and aggravated rosacea-like inflammation. (A) Expression of the autophagy markers, ATG9A, ATG10, ATG12, and PIK3C3, in the epidermis of rosacea patients and normal subjects. (B) mRNA expression levels of Becn1, Atg5, Atg10, and Atg12 in LL-37-induced mouse skin lesions. (C) Immunofluorescence analysis of LC3 in skin lesions from control mice and LL37-induced mice. Scale bar: 50 μm. (D) Representative images and HE straining of mice injected with BafA1 and/or LL-37 showing erythema on the ear. (E) Measurement of the mouse ear thickness. The mRNA expression levels of Il6, Tlr-2, and Tnf-α. (n = 5 for each group). All results are representative of at least three independent experiments. Data represent the mean ± SEM. One-way ANOVA with Bonferroni’s post hoc test was used for statistical analyses. *p < 0.05, **p < 0.01, and ***p < 0.001.*
Next, we investigated whether autophagy affects LL-37-induced rosacea-like inflammation. For that, 8-week-old BALB/c female mice were injected intradermally with LL-37 alone, bafilomycin A1 (autophagy inhibitor) alone, or co-injected with both LL-37 and bafilomycin A1. Enhanced ear redness and thickness were observed, accompanied by an increase in Il-6, Tlr-2, and Tnf-α (Figures 2D, E). We also found that Cxcl1, Cxcl15, Cd68, Itgam, Cma1, and Tpsab1 were increased when treated with BafA1 alone or with LL-37 + BafA1 (Supplementary Figure S4). In addition, in our previous studies, we observed that rapamycin, an agonist of autophagy, prevents the development of rosacea-like skin inflammation (Deng et al., 2021). In the present study, we found that the mRNA expression of autophagy-related genes Becn1, Atg5, Atg10, and Atg12 was significantly increased in LL37-induced rosacea lesions after topical rapamycin treatment (Supplementary Figure S5). Altogether, these results demonstrated that autophagy was reduced in keratinocytes of rosacea, and autophagy impairment/improvement aggravated/ameliorated rosacea-like skin inflammation.
## EGCG was identified as a candidate drug for rosacea
To investigate the candidate drugs for rosacea, the hub genes from black and blue modules were input into DGIdb. In total, 190 drugs targeting 23 genes from the black module and 77 drugs targeting 15 genes from the blue module were identified, and 28 drugs overlapped (Figure 3A). The Sankey diagram revealed the detailed relationship between hub genes and 28 drugs (Figure 3B). Among them, EGCG has been reported to present anti-inflammatory and immunoregulatory effects and has been increasingly recognized worldwide for its low cost, easy-to-obtain nature, low toxicity, low side effects, and high tolerance. So, EGCG was selected for further study.
**FIGURE 3:** *EGCG is a candidate drug for rosacea. (A) Overlapped drugs predicted by DGIdb. (B) Sankey diagram revealed the relationship between modules, hub genes, and drugs.*
## EGCG attenuated LL-37-induced rosacea-like dermatitis
We initially investigated the potential therapeutic effect of EGCG on rosacea in an LL-37-induced mouse model. As shown in Figure 4A, EGCG treatment significantly ameliorated the LL37-induced rosacea-like lesions. The average redness area and score were dramatically reduced in the EGCG group compared with the PBS group (Figures 4B, C). Histological analysis showed that treatment with EGCG resulted in the reduction of immune infiltration in the dermis (Figures 4A–D). Meanwhile, EGCG treatment also reduced the expression of pro-inflammatory cytokines, including Il-6, Tlr-2, and Tnf-α in LL-37-induced rosacea-like lesions (Figure 4E). Moreover, EGCG also reduced the expressions of the neutrophil-attracting chemokines (Cxcl15 and Cxcl1), macrophage markers (Cd68 and Itgam), and mast cell-related genes (Tpsab1 and Cma1) in LL-37-induced rosacea-like lesions (Supplementary Figure S6A). The infiltration of CD4+ T cells and the expression of Stat1, Stat3, and IL-17A were repressed by EGCG treatment in rosacea-like mice (Supplementary Figures S6B–D). These results demonstrated the therapeutic effect of EGCG in rosacea-like dermatitis in mice.
**FIGURE 4:** *Effect of EGCG on LL37-induced rosacea-like mice. (A) Skin manifestation of different groups. Images were taken 48 h after the first LL37 administration. Scale bar: 50 μm. The severity of inflammatory responses on the skin was assessed in the redness area (B), redness score (C), and quantitative result of HE staining for dermal cellular infiltrates (D). (E) mRNA expression levels of Il6, Tlr2, and TNF-α in skin lesions (n = 5 for each group). All results are representative of at least three independent experiments. Data represent the mean ± SEM. One-way ANOVA with Bonferroni’s post hoc test was used for statistical analyses. **p < 0.01 and ***p < 0.001.*
## EGCG decreased LL-37-induced inflammation in keratinocytes
First, we detected the role of EGCG on keratinocytes in vitro. We found that the concentrations of 80 μM EGCG repressed the viability of HaCaT cells, and drug concentrations of 10, 20, and 40 μM were chosen in the following cell experiments (Figure 5A). Next, we demonstrated that EGCG treatment reduced LL37-induced TLR-2 and CAMP, the key rosacea markers (Yamasaki et al., 2007; Yamasaki et al., 2011; Zhang J. et al., 2021), and expression in the HaCaT cells (Figure 5B). Considering the pivotal role of keratinocytes in producing excessive pro-inflammatory cytokines and chemokines in the pathogenesis of rosacea (Steinhoff et al., 2011), we demonstrated the inhibitory effects of EGCG on cytokine and chemokine expression in HaCaT cells. The expressions of pro-inflammatory cytokines and chemokines, including CXCL10, CCL20, CCL3, CCL5, CXCL12, and CXCL13, were analyzed using the qPCR assay. All these genes except CCL5 were significantly reduced by EGCG treatment (Figure 5C). Thus, we concluded that EGCG repressed LL-37-induced keratinocyte inflammation.
**FIGURE 5:** *EGCG decreased the production of cytokines and chemokines related to rosacea in keratinocytes. (A) Effect of different concentrations of EGCG on cell viability is determined by CCK-8 assay. (B) mRNA expression levels of TLR2 and CAMP. (C) mRNA expression levels of CCL3, CCL5, CCL20, CXCL10, CXCL12, and CXCL13. All results are representative of at least three independent experiments. Data represent the mean ± SEM. One-way ANOVA with Bonferroni’s post hoc test was used for statistical analyses. *p < 0.05, **p < 0.01, and ***p < 0.001. ns, no significance.*
## EGCG reduced rosacea-like inflammation by inducing keratinocyte autophagy
It has been reported that autophagy effectively protects keratinocytes against injury in inflammatory skin diseases (Hou et al., 2020; Kim et al., 2021). To confirm whether the anti-inflammatory effect of EGCG could be due to the induction of autophagy in rosacea, we detected the autophagy levels in rosacea-like mice after EGCG treatment. Here, we found that EGCG could induce keratinocyte autophagy in LL37-induced rosacea-like mice (Figure 6A). Next, we detected the role of EGCG in autophagy in LL37-treated HaCaT cells. The HaCaT cells were treated with 10, 20, and 40 μM EGCG or rapamycin (autophagy agonist) in the presence of LL-37, and subsequent autophagy events were monitored by western blotting. As shown in Figures 6A, B, LC3-I gradually transformed into LC3-II with the increase in EGCG concentration and treatment time. To determine the role of EGCG-induced autophagy in LL37-induced keratinocyte inflammation, BafA1, an autophagy inhibitor, was included in the ensuing studies. qPCR analysis showed that EGCG-repressed pro-inflammatory cytokine and chemokine expression, including CCL3, CCL5, CCL20, CXCL10, CXCL12, and CXCL15, was reversed by BafA1 treatment (Figure 6C).
**FIGURE 6:** *EGCG reduced rosacea-like inflammation by inducing keratinocyte autophagy. (A) LC3 immunofluorescence staining (green) in LL-37-induced mice treated with or without EGCG. DAPI staining (blue) indicates nuclear localization. Scale bar: 50 μm. (B) Representative immunoblot analysis for the expression of LC3 and p62 in a dose- and time-dependent manner of EGCG treatment. (C) Inhibition of autophagy impairs the anti-inflammatory role of EGCG in HaCaT cells. The mRNA expression levels of CCL3, CCL5, CCL20, CXCL10, CXCL12, and CXCL13. All results are representative of at least three independent experiments. Data represent the mean ± SEM. One-way ANOVA with Bonferroni’s post hoc test was used for statistical analyses. *p < 0.05, **p < 0.01, and ***p < 0.001. ns, no significance.*
Meanwhile, to examine whether the mechanism of EGCG on autophagy was due to an increase in the autophagy level and not due to the blocking of autophagy flux, we further analyzed p62 protein expression, which reflects the level of autophagosome clearance and negatively correlates with autophagy (Lamark et al., 2017). After EGCG treatment, there was a significant decrease in the p62 expression level (Figure 6B). EGCG significantly reduced p62 expression but induced LC3-II levels in a dose- and time-dependent manner. Cytoplasmic LC3 puncta formation is the hallmark event of autophagy (Schaaf et al., 2016). Thus, we examined EGCG induction of LC3 puncta formation after treating the HaCaT cells with EGCG by immunofluorescent staining. We observed that LC3 puncta formation was considerably augmented in the EGCG treatment group, while it was reduced in the LL-37-induced HaCaT cells compared with the untreated vehicle control (Figure 7A).
**FIGURE 7:** *EGCG-induced autophagy in LL-37-induced HaCaT cells. (A) Immunostaining of LC3 in HaCaT keratinocytes treated with LL37 and/or EGCG for 24 h. DAPI staining (blue) indicates nuclear localization. Scale bar: 20 μm. (B) Representative immunoblot analysis of autophagy marker proteins in response to various treatments. (C) Representative TEM images showing the ultrastructure of HaCaT cells incubated with EGCG with or without BafA1 in the presence of LL-37. The red arrowheads indicate the autophagic vacuoles, respectively. AP, autophagosome; ASS, autolysosome. (D) HaCaT cells were transfected with the mCherry-GFP-LC3 plasmid and then treated with EGCG and/or BafA1 in the presence of LL-37 for 24 h. Nuclei were stained with DAPI. Scale bar: 20 μm. All results are representative of at least three independent experiments.*
Furthermore, to clarify the correlation between EGCG and autophagy induction, 3-MA and bafilomycin A1 (BafA1), autophagy inhibitors, were employed in the subsequent studies. Immunoblot analysis showed that 3-MA and BafA1 blocked the EGCG-induced conversion of LC3-I to LC3-II, while p62 degradation induced by EGCG was impeded by autophagy inhibitors in LL-37-induced conditions (Figure 7B). Likewise, we found that the HaCaT cells co-treated with EGCG and LL-37 showed abundant autophagolysosomes under transmission electron microscopy. However, in contrast, the cells treated with merely LL-37 or BafA1 showed a limited number of autophagosomes and autophagolysosomes (Figure 7C). Next, tandem mCherry-GFP-LC3 fluorescence microscopy assay and transmission electron microscopy were performed to assess autophagosome and autophagolysosome formation. Our results indicated that the number of autophagosomes (green spots) and autolysosomes (yellow spots) in the EGCG treatment group was significantly increased compared to other groups, which suggested that EGCG enhanced autophagy flux in LL-37-induced HaCaT cells (Figure 7D).
Taken together, these data strongly suggested that EGCG attenuated LL-37-induced inflammation by increasing autophagy induction and autophagy flux in keratinocytes.
## ATK1, MAPK1, and MMP9 could be direct targets of EGCG in rosacea
To explore the specific molecular mechanism of EGCG-induced autophagy, three databases (TargetNet, Swiss, and Tcmsp) were used to predict the target of EGCG in rosacea, and 95 target genes were predicted in two or more databases at the same time (Figure 8A). The GO analysis revealed the enrichment of these target genes in rosacea-related, autophagy-related, and mTOR-related pathways (Figures 8B, C). Among them, ATK1, MAPK1, and MMP9 were the key molecules in these pathways. Subsequent molecular docking was used to predict the binding of EGCG to ATK1, MAPK1, and MMP9 (Figure 8D). ATK/MAPK pathways were reported as a regulator of autophagy (Yuan et al., 2022). So, we speculated that EGCG could regulate autophagy by targeting ATK1, MAPK1, and MMP9 in rosacea.
**FIGURE 8:** *Pharmacological targets of EGCG in rosacea. (A) Overlapped target genes of EGCG in TargetNet, Swiss, and Tcmsp. (B) KEGG enrichment analysis of EGCG-targeted signaling pathways. (C) Upset diagram of EGCG-targeted signaling pathways. (D) Molecular docking revealed the binding targets of EGCG.*
## Discussion
Although significant effort is devoted to revealing pathogenesis and developing new therapeutic agents, the current therapeutic strategies for rosacea are still unsatisfactory (Logger et al., 2020; Wang B. et al., 2021; Kim et al., 2022). In this study, we revealed the important role of epidermis autophagy in rosacea and demonstrated EGCG as an effective agent for rosacea treatment, which attenuated rosacea-like inflammation via inducing keratinocyte autophagy.
The mTOR pathway is a crucial signal transduction pathway implicated in various physiological and pathological processes (Deng et al., 2015; Fogel et al., 2015). Our previous work demonstrated the important role of hyperactivated mTOR signaling in rosacea (Deng et al., 2021). In this study, an upregulated mTOR pathway in the epidermis of rosacea patients was confirmed using GSVA. Subsequently, WGCNA revealed the potential regulation of mTOR signaling on autophagy in the epidermis of rosacea. Autophagy is essential for the homeostasis of keratinocytes, and dysregulation of autophagy contributes to the pathogenesis of skin diseases and has been shown to play a critical role in inflammatory skin disorders, including atopic dermatitis, psoriasis, and allergic contact dermatitis (Cadwell, 2016). In this study, we found that autophagy was decreased and contributed to the progression of rosacea. mTOR is a well-known regulator of autophagy (Munson and Ganley, 2015). It has been shown that IL-17A-activated PI3K/AKT/mTOR signaling contributed to the inflammatory response of psoriasis partly by inhibiting autophagy in keratinocytes (Varshney and Saini, 2018). Rapamycin, a well-known mTOR inhibitor, alleviated psoriasis-like dermatitis by inducing autophagy (Kim et al., 2021). Our previous study revealed the therapeutic role of rapamycin in rosacea. We also revealed the induction of autophagy by rapamycin in rosacea-like dermatitis, implying that autophagy was a novel therapeutic target for rosacea.
In recent years, natural medicinal products and plant extracts have been highly sought after for therapeutic drugs with the advantages of cost effectiveness, high bioactivity, abundant content, and safety. EGCG, a natural polyphenol found in green tea, has been reported to have many biological activities, including anti-inflammatory, antioxidant, cardioprotective, neuroprotective, and anticancer activities (Nan et al., 2019; Huang et al., 2020; Yi et al., 2020). In this study, based on the mTOR signal and autophagy-related genes, EGCG was predicted as a candidate drug for rosacea. The in vivo and in vitro experiments showed that EGCG attenuated rosacea-like inflammation by inducing keratinocyte autophagy. Consistent with our results, EGCG was proven therapeutic to various diseases by inducing cytoprotective autophagy (Wu et al., 2021). Cytoplasmic LC3 puncta formation is the hallmark of autophagy (Schaaf et al., 2016); the blocking of autophagy flux and an increase in the autophagy level lead to increased LC3-II (Lamark et al., 2017). In the present study, it was observed that EGCG promoted the formation of autophagosomes and autophagolysosomes accompanied in a dose- and time-dependent manner. Subsequently, the target prediction and molecular docking showed that ATK1, MAPK1, and MMP9 were the potential targets of EGCG. ATK/MAPK pathways were reported as a regulator of autophagy (Yuan et al., 2022).
## Conclusion
In summary, we demonstrated a contribution of impaired autophagy in rosacea pathogenesis and implied EGCG as an effective treatment strategy for rosacea, which attenuated rosacea-like inflammation via inducting autophagy in keratinocytes.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.
## Ethics statement
All studies and experimental procedures were approved by the Animal Ethics Committee of Xiangya Hospital of the Central South University (No. 201703211).
## Author contributions
JL and YZ conceived this project; LZ, YZ, and YW performed and analyzed the experiments; YZ performed bioinformatics analyses; ZD, YH, HX, and QW gave critical comments; and LZ, YZ, and JL wrote the manuscript with the approval of all other authors.
## Conflict of interest
Author QW was employed by Hunan Binsis Biotechnology Co., Ltd.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1092473/full#supplementary-material
## Abbreviations
EGCG, epigallocatechin-3-gallate; mTOR, mammalian target of rapamycin; ID, interface dermatitis; AD, atopic dermatitis; GSVA, gene set variation analysis; PFA, paraformaldehyde frozen.
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|
---
title: 'The association between arterial stiffness and cancer occurrence: Data from
Kailuan cohort study'
authors:
- Yinong Jiang
- Aijun Xing
- Tesfaldet Habtemariam Hidru
- Jiatian Li
- Xiaolei Yang
- Shuohua Chen
- Yun-Long Xia
- Shouling Wu
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10014543
doi: 10.3389/fcvm.2023.1112047
license: CC BY 4.0
---
# The association between arterial stiffness and cancer occurrence: Data from Kailuan cohort study
## Abstract
### Background
This study aimed to investigate whether increased arterial stiffness, measured by brachial-ankle pulse wave velocity (baPWV) is associated with cancer.
### Materials and methods
A total of 45,627 Chinese adults underwent a baPWV examination. The participants were followed up from 1st January 2012 to 31st December 2018. Cox proportional hazards model was used to assess the association between the baPWV values and cancer.
### Results
During a total follow-up duration of 172,775.69 person-years, there were 553 new cases of cancer. The subjects in the highest baPWV group showed an increased risk of cancer when compared with the lowest baPWV group as confirmed by multivariate-adjusted hazard ratios (HR = 1.51, $95\%$ CI = 1.14∼2.00) in the entire cohort. Compared with participants in the lowest baPWV group, the HRs ($95\%$ CI) for digestive cancer in the second and third groups were 1.55 (1.00∼2.40) and 1.99 (1.19∼3.33), respectively. The Kaplan-*Meier analysis* demonstrated a significant increase in cancer in participants with a baPWV ≥ 18 m/s ($P \leq 0.001$). Compared with the lowest baPWV group, the highest baPWV group showed an increased risk of cancer in men (HR = 1.72, $95\%$ CI = 1.22∼2.43) and those < 60 years (HR = 1.75, $95\%$ CI = 1.20∼2.55), respectively.
### Conclusion
Increased arterial stiffness measured by baPWV is associated with cancer occurrence, especially digestive cancer occurrence.
### Clinical trial registration
ClinicalTrials.gov, identifier ChiCTR-TNRC-11001489.
## 1. Introduction
The risk of arterial stiffness increases following anticancer therapies like radiotherapy or chemotherapy [1, 2]. Newer studies have suggested that the association between cardiovascular diseases and cancer extends beyond the toxicities that occur during cancer treatment [3, 4]. Recently, heart failure (HF) was identified to have potentially increased the likelihood of cancer independent of other risk factors (5–7). Overall, cancer and cardiovascular diseases are public health and economic concerns. Worldwide, an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths occurred in 2020 [8]. Globally, the prominence in the prevalence of cardiovascular diseases and cancer is partly attributed to poor adherence to a healthy lifestyle [9]. Thus, early detection or prediction of cancer is important from the perspective that cancer and cardiovascular disease are the leading cause of mortality, disability, and global healthcare costs.
Arterial stiffness describes the hardening of the arterial system and is a surrogate marker for impaired vascular function and structure. The development of arterial stiffness is a highly integrated process, connecting multiple contributors such as inflammation, immune cell dysfunction, and extracellular matrix remodeling [10, 11]. These factors are not only important in the development of arterial stiffness but also play meaningful roles in the pathogenesis of cancer.
A recent large cohort study by Harding et al. has found that hypertension is associated with an increased risk of cancer incidence and mortality, which has also been supported by several animal hypertension models [12, 13]. Further, recent research confirmed that early cardiac remodeling promoted tumor growth and metastasis [14]. However, hypertension is typically an advanced manifestation of arterial remodeling. It is important to detect whether any earlier vascular changes have already been associated with cancer. One method to assess early vascular remodeling is brachial-ankle pulse wave velocity (baPWV), a technique that measures systemic arterial stiffness [10, 15]. Whether high baPWV is associated with cancer risk in the general population remains uncovered. Therefore, the purpose of this study was to investigate the association between arterial stiffness status (measured by baPWV) and cancer occurrence.
## 2.1. Study design
This is an ancillary study of the Kailuan cohort study conducted to explore the relationship between different categories of arterial stiffness and cancer occurrence. The Kailuan study is an ongoing prospective cohort (clinical trial number ChiCTR-TNRC-11001489) that recruits adult participants from Tangshan City, China. The details regarding the Kailuan cohort study design have been reported in other studies [16, 17]. Briefly, this ancillary study recruited a total of 46,399 adult participants between January 2010 and December 2011 from 11 hospitals affiliated with the Kailuan Group. The cohort administered questionnaire assessment and underwent clinical and laboratory examinations upon enrollment every 2 years till December 2018 [17]. We measured baPWV, a simple and non-invasive examination, to determine arterial stiffness [10, 16]. The baseline baPWV was assessed from 1st January 2010 to 31st December 2011. Participants were followed up for cancer occurrence between January 2012 and December 2018 (Figure 1).
**FIGURE 1:** *The flow chart of this large cohort study. Participants who received baseline baPWV with biennial physical examinations were eligible for this study. The baseline baPWV was assessed from January 2010 to December 2011. Participants were followed up for cancer occurrence between January 2012 and December 2018. baPWV, Brachial-ankle pulse wave velocity.*
## 2.2. Population
In the current study, 46,399 Chinese participants were included after satisfying the requirements of a minimum age of 18 years and a complete physical examination with a baPWV test. Participants with the following criteria were then excluded from the study: those with a cancer diagnosis prior to baseline ($$n = 452$$), those who were underweight at baseline ($$n = 62$$), and those who had incomplete data ($$n = 258$$). A total of 45,627 participants were included in the final data analysis after exclusion criteria were applied. The study protocol was approved by the institutional review board of the Kailuan General Hospital. All procedures were performed in line with the declaration of Helsinki and its amendments, and all participants provided written informed consent to participate in this study.
## 2.3. Assessment of the arterial stiffness
Arterial stiffness was evaluated between January 2010 and December 2011 with baPWV conducted by a networked arteriosclerosis detection system, BP-203 RPE III [Omron Health Medical (China), Co., Ltd.]. All baPWV measurements were carried out by qualified physicians and nurses and the measurements were recorded from 7 AM to 9 AM following the manufacturer’s instructions. Prior to baPWV measurement, participants were prohibited from smoking and consuming caffeinated beverages or alcohol for at least 3 h and were restricted from engaging in exercise for at least 30 min. Participants were then seated in a room with temperature regulated between 22 and 25°C for at least 5 min and subsequently instructed to lie down on the examination table in a supine position with the device cuffs bound to both their arms and legs. The lower edge of the arm cuff was placed 2–3 cm above the cubital fossa transverse striation, while the lower edge of the ankle cuff was adjusted 1–2 cm above the superior edge of the medial malleolus. The electrocardiogram electrodes were attached to bilateral wrists, and a microphone was mounted on the left side of the sternum to detect heart sounds. The baPWV measurement was performed by physicians. As baPWV measurement is an operator-dependent procedure, the baPWV readings were measured twice to avoid measurement error, and the mean of the two baPWV readings was obtained and recorded as baPWV values. Additional measurements were made if there was a difference of > 2 m/s following the first two readings.
## 2.4. Blood pressure measurement
We simultaneously measured the blood pressure (BP) in four extremities in the supine position after at least 5 min bed rest. We defined inter-arm BP difference (IAD) as the absolute difference of systolic BPs measured in the right and left arms. The ankle-brachial index (ABI) was calculated as the ratio of ankle systolic BP to brachial systolic BP (a higher value) for both legs, and the lower ABI value was used in the subsequent analysis. The lower ABI and/or the larger IAD suggest the presence of peripheral artery disease (PAD) [18, 19].
## 2.5. Other measurements
Every patient performed a routine medical check-up every 2 years following baPWV measurement. Also, a structured interview was conducted at each examination to obtain data on demographic and clinical characteristics which comprised lifestyle information, family background, and the use of any medications. Blood specimens were taken (fasting > 8 h) and were biochemically analyzed for the level of C-reactive protein (CRP) and fasting plasma glucose (FPG). Standard enzymatic processes were carried out to detect the concentration of high-density lipoprotein (HDL) and serum total cholesterol (TC). All the above indexes were measured by HITACHI AUTOMATIC ANALYZER 3110 [Hitachi Instrument (Suzhou), Ltd.]. Hypertension was characterized as systolic BP (SBP) ≥ 140 mmHg and/or diastolic BP (DBP) ≥ 90 mmHg or a self-reported history of hypertension with the current use of antihypertensive drugs. Diabetes mellitus was defined as FPG ≥ 7.0 mmol/L or a self-reported history of diabetes mellitus and current diabetes treatment. Smoking status was classified into three groups: never-smoker, a former smoker, and current smoker, while physical exercise was divided into two categories: high/intense activity if participants reported exercise for ≥ 4 h per week and sedentary/moderate activity if participants reported physical activity for < 4 h per week. Hepatic dysfunction was clinically diagnosed by physicians according to the guidelines for liver diseases. Hyperbilirubinemia, an increase in serum transaminases, alkaline phosphatase (AP), and γ glutamyl-transferase (GGT), and a decrease in serum albumin and coagulation factors levels are the main laboratory parameters on which the diagnosis of hepatic dysfunction is based (20–22).
## 2.6. Outcome assessment
All cancer events were confirmed from hospital diagnosis records and were identified using the International Classification of Diseases-10 (ICD-10) codes. In this study, we analyzed several categories of cancer, which included the digestive system (C15-C26), respiratory system (C34 + C39), urogenital system (C50-C58 for females, C60-C63 for males), and other systems (all codes beginning with C). The study participants were contacted periodically during follow-up and the cancer occurrence was confirmed through hospital records.
## 2.7. Statistical analysis
Similar to previously published studies, baPWV was categorized into 3 groups: baPWV < 14.0 m/s (normal elastic artery status), 14.0 ≤ baPWV < 18.0 m/s (elasticity decreasing status), and ≥ 18.0 m/s (arterial stiffness) [10]. All continuous variables were normally distributed and expressed as mean ± SD, and categorical variables were expressed as counts and percentiles. Groups were tested for differences using the χ2 test and ANOVA for categorical and continuous data respectively.
Log-rank tests for trend and Kaplan-Meier methods were used to compare the distributions of cancer incidence and investigate the differences in survival as stratified by baPWV, respectively. For the outcomes of cancer incidence, two distinct Cox models were employed to look for possible associations with baPWV independently. The Cox proportional hazard model for our study was then employed to explore the association of 1 SD increase in baPWV (for each baPWV group), modeled as a continuous variable, with the occurrence of cancer. To account for potential confounding effects, we selected variables with $P \leq 0.05$ in the univariate Cox analyses or clinically associated with cancer occurrence were candidates for the multivariate Cox regression analysis. Thus, the multivariate Cox models were adjusted for age, gender, body mass index, mean arterial pressure, fasting plasma glucose, hepatic dysfunction, eGFR, anemia, uric acid, cigarette smoking, alcohol consumption, physical activity, C-reactive protein, TC, antihypertensive medication, HBsAg status, and family history of cancer. The proportional hazard assumption in the Cox model was tested and satisfied in all cases using the Schoenfeld residuals (Supplementary Figure 1). Furthermore, to investigate the pattern of quantitatively assessed baPWV associated with cancer occurrence, we analyzed the restricted spline curve. HRs of restricted cubic spline transformation of baPWV with 3 knots (5, 50, and $95\%$ of baPWV value) and $50\%$ of baPWV value as a reference were plotted.
The analysis was repeated using a fully adjusted model to test for the robustness of estimates in those who had no history of CVD (stroke and cardiac infarction), or those who had no sign of peripheral arterial disease (ABI ≤ 0.9 and/or IAD ≥ 15 mmHg). A P value of ≤ 0.01 was considered statistically significant. All statistical analyses were conducted using SAS 9.3 (SAS Institute; Cray, NC, USA).
## 3.1. Baseline characteristics of the participants
During a median follow-up duration of 3.35 (interquartile range: 1.66–6.07) years, there were 553 new cases of cancer. All cancer patients were different; thus 1 cancer diagnosis was referred to 1 patient. Out of these new cases, 170 had cancer of the digestive system, 138 had cancer of the respiratory system, 89 had cancer of the urogenital system, and 156 had cancer of other systems. The mean (SD) ages of participants present in the baPWV < 14 m/s ($$n = 19$$,679), baPWV 14 m/s∼18 m/s ($$n = 17$$,720), and baPWV ≥ 18 m/s ($$n = 8$$,228) group were 41.46 ± 9.72, 50.40 ± 10.86, and 61.06 ± 11.51, respectively. Participants in higher groups of baPWV had higher mean SBP, DBP, and fasting plasma glucose than in lower groups. Additionally, participants in higher groups of baPWV were more likely to have hypertension. The demographic data for the study participants are shown in Table 1. The results of the univariate and multivariate logistic analysis are presented in Supplementary Table 1.
**TABLE 1**
| Variables | baPWV < 14.0 m/s (n = 19,679) | 14.0 ≤ baPWV < 18.0 m/s (n = 17,720) | baPWV ≥ 18.0 m/s (n = 8,228) | P-value |
| --- | --- | --- | --- | --- |
| Age (year) | 41.46 ± 9.72 | 50.40 ± 10.86 | 61.06 ± 11.51 | <0.001 |
| Male, n (%) | 11600 (58.95) | 14480 (81.72) | 6767 (82.24) | <0.001 |
| SBP (mmHg) | 120.93 ± 14.60 | 135.14 ± 16.54 | 147.75 ± 19.54 | <0.001 |
| DBP (mmHg) | 77.43 ± 9.58 | 84.69 ± 10.36 | 87.13 ± 11.47 | <0.001 |
| MAP (mmHg) | 91.93 ± 10.40 | 101.50 ± 11.24 | 107.34 ± 12.30 | <0.001 |
| BMI (kg/m2) | 24.46 ± 3.19 | 25.35 ± 3.06 | 25.26 ± 3.04 | <0.001 |
| hs-CRP > 2 mg/L (%) | 4052 (8.88) | 4807 (10.54) | 2730 (5.98) | <0.001 |
| TC (mmol/L) | 4.77 ± 1.35 | 5.07 ± 1.46 | 5.14 ± 1.92 | <0.001 |
| HDL (mmol/L) | 1.47 ± 0.67 | 1.44 ± 0.70 | 1.49 ± 0.82 | <0.001 |
| FPG (mmol/L) | 5.30 ± 1.08 | 5.96 ± 1.84 | 6.80 ± 2.47 | <0.001 |
| Uric acid (mmol/L) | 303.15 ± 96.00 | 326.84 ± 99.17 | 329.05 ± 95.51 | <0.001 |
| eGFR (mL/min*1.73 m2) | 102.43 ± 23.40 | 96.86 ± 22.50 | 89.36 ± 20.65 | <0.001 |
| Current smoker, n (%) | 5329 (27.04) | 6303 (35.57) | 2388 (29.02) | <0.001 |
| Current drinker, n (%) | 654 (3.32) | 1021 (5.76) | 503 (6.11) | <0.001 |
| High/Intensive activity, n (%) | 10355 (52.62) | 9396 (53.02) | 4490 (54.57) | 0.011 |
| Hepatic dysfunction, n (%) | 1663 (8.45) | 2004 (11.31) | 746 (9.07) | <0.001 |
| Anemia, n (%) | 1315 (6.68) | 862 (4.86) | 334 (4.06) | <0.001 |
| HBsAg positive, n (%) | 188 (0.96) | 146 (0.82) | 65 (0.79) | 0.262 |
| Antihypertensive use, n (%) | 785 (3.99) | 2704 (15.26) | 2467 (29.98) | <0.001 |
| Tumor family history, n (%) | 588 (2.99) | 848 (4.79) | 666 (8.09) | <0.001 |
## 3.2. Cancer occurrence
The incidence density of cancer occurrences increased across the different values of baPWV. The incidence density in the entire cohort increased from 2.28 per 1,000-person year in patients marginalized at baPWV < 14.0 m/s to 3.17 per 1,000-person year in patients with 14.0 ≤ baPWV < 18.0 m/s, and 5.58 per 1,000-person year in patients with baPWV ≥ 18.0 m/s (Figure 2A). Similarly, the incidence density shows a progressively higher risk of digestive cancer across normal to advanced arterial stiffness status (Figure 2B). Cumulative incidence of cancer in participants by groups of baPWV is showed in Supplementary Table 2.
**FIGURE 2:** *Incidence density of cancer. Participants are categorized into three groups: baPWV < 14.0 m/s, 14.0 ≤ baPWV < 18.0 m/s, and ≥ 18.0 m/s. Incidence density of cancer in the general population, men, women, individuals < 60 years old, and individuals ≥ 60 years old. (A) Incidence density of cancer in the different systems. (B) Values are presented as per 1,000-person year. baPWV, Brachial-ankle pulse wave velocity.*
## 3.3. Relationship between the baPWV and cancer occurrence
The association between the baPWV and the risk of cancer is presented in Table 2 and Supplementary Table 3. The participants in the highest baPWV group had a significantly increased risk of cancer. Compared to participants in the lowest baPWV group, the HRs ($95\%$ CI) for cancer in the second and third groups were 1.16 (0.92∼1.46) and 1.51 (1.14∼2.00), respectively (P for trend = 0.004). With 1-SD increase in baPWV, the risk of cancer consistently increased (HR = 1.16, $95\%$ CI: 1.05∼1.27; $$P \leq 0.003$$). Similar results were found in men and participants < 60 years. The Kaplan-*Meier analysis* demonstrated a significant increase in cancer occurrence in participants with a baPWV ≥ 18 m/s ($P \leq 0.001$) (Figures 3A–C). However, women and participants ≥ 60 years had a similar risk of cancer across different categories of baPWV, and the Kaplan Meier curves were shown in Supplementary Figures 2A, B.
## 3.4. Risk of cancer at different systems based on different categories of baPWV
Participants with increased baPWV had a higher likelihood of being diagnosed with digestive cancer compared to those in the groups of lower baPWV (Table 3). The HR and $95\%$ CI for digestive cancer across moderate (baPWV = 14.0∼18.0 m/s) and high (baPWV ≥ 18 m/s) levels of arterial stiffness compared to the patients with normal elasticity were 1.55 (1.00∼2.40), and 1.99 (1.19∼3.33) (P for trend = 0.010). The risk of digestive cancer according to Kaplan-*Meir analysis* based on the baPWV is described in Figure 3D. However, participants with increased baPWV had a similar likelihood of being diagnosed as cancer of respiratory, urogenital, or other systems across different categories of baPWV, and the Kaplan Meier curves were shown in Supplementary Figures 2C–E.
**TABLE 3**
| Subgroups | baPWV (m/s) | baPWV (m/s).1 | baPWV (m/s).2 | P-value | Per 1SD increase in baPWV | P-value.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | <14.0 | 14.0–18.0 | ≥18.0 | | | |
| Digestive system (n = 170) | Ref. | 1.55 (1.00, 2.40) | 1.99 (1.19, 3.33) | 0.01 | 1.30 (1.12, 1.51) | 0.001 |
| Respiratory system (n = 138) | Ref. | 1.15 (1.72, 1.84) | 1.70 (0.97, 2.95) | 0.055 | 1.11 (0.93, 1.34) | 0.254 |
| Urogenital system (n = 89) | Ref. | 0.82 (0.46, 1.46) | 0.85 (0.42, 1.74) | 0.67 | 0.91 (0.71, 1.18) | 0.47 |
| Other systems (n = 156) | Ref. | 1.16 (0.78, 1.74) | 1.51 (0.86, 2.66) | 0.166 | 1.19 (0.97, 1.46) | 0.088 |
## 3.5. Restricted spline curve analysis
When baPWV (restricted cubic splines with 3 knots at 5, 50, and $95\%$) were computed, the observed associations with high baPWV were generally unchanged (Figure 4). Spline-curve analyses showed a linearly increasing risk, linking the severity of arterial stiffness to overall cancer risk in the entire cohort ($$P \leq 0.0104$$), men ($$P \leq 0.0102$$), and participants < 60 years old ($$P \leq 0.0010$$). There is also a significant relationship between baPWV and the risk of digestive cancer ($$P \leq 0.0015$$). However, there is no significant relationship between baPWV and the risk of respiratory ($$P \leq 0.2554$$), urogenital ($$P \leq 0.7671$$), and other malignancies ($$P \leq 0.1110$$).
**FIGURE 4:** *Risk of cancer according to restricted spline curve. Spline-curve analyses show a linearly increasing risk, linking the severity of arterial stiffness to overall cancer risk in the entire cohort (A) men (B) and participants < 60-year-old (C). Spline-curve analyses show a linearly increasing risk, linking the severity of arterial stiffness to digestive cancer risk in the entire cohort (D). Hazard ratios of restricted cubic spline transformation of baPWV with 3 knots (5, 50, and 95% of baPWV value) and 50% of baPWV value as a reference are plotted. Red lines are hazard ratios and gray lines are 95% confidence intervals. baPWV, Brachial-ankle pulse wave velocity.*
## 3.6. Sensitivity analyses
Pulse wave velocity increases with peripheral arterial diseases and severe cardiovascular disease (CVDs) conditions. As such, we performed a sensitivity analysis to control the effect of severe vascular diseases. The analysis was repeated to test for the robustness of estimates in those who had no history of CVDs ($$n = 43$$,900) or had no evidence of peripheral arterial diseases ($$n = 41$$,511). These results remained essentially unchanged after adjustment using the clinical confounder model (Figure 5).
**FIGURE 5:** *Sensitivity analyses to test the association between arterial stiffness and incident cancer in individuals without PAD/CVDs. Participants are categorized into 3 groups according to baPWV values. The sign of PAD is defined as ABI ≤ 0.9 and/or IAD ≥ 15 mmHg. The hazard ratios and 95% confidence intervals for cancer incidence remain essentially unchanged after excluding the individuals with PAD/CVDs in the entire cohort (A,E), men (B,F), individuals < 60-year-old (C,G), and individuals ≥ 60-year-old (D,H). The dashed trend lines fit HRs well and R2 show a linearly increasing risk for cancer incidence. PAD, Peripheral arterial disease; CVDs, cardiovascular diseases; baPWV, brachial-ankle pulse wave velocity.*
## 4. Discussion
The results of this large-scale observational study unveiled a positive association between arterial stiffness and cancer. The incidence density of cancer was increased with heightened arterial stiffness in men and the younger participants. Also, arterial stiffness measured by baPWV was associated with digestive cancer.
To the extent of our knowledge, this was the first study to evaluate the association between baPWV and malignant disease. In the past, several studies demonstrated an association between arterial stiffness and CVDs (23–25) during/after chemotherapy. However, there was a lack of robust knowledge on whether arterial stiffness by itself is sufficient to promote cancer severity and outcomes. The findings of the present study could be majorly attributed to: (i) the intimate relationship between CVDs and new-onset cancers, which may result from several shared biological mechanisms such as inflammation and reactive oxygen species [26].
The association of arterial stiffness and cancer events was found to differ in sex. Our findings support that high baPWV is associated with cancer incidence in men, but not in women. This discrepancy could be explained by the difference in the prevalence of hypertension between men and women, which is higher in men [27], and its associated adverse cardiovascular outcomes [28]. A previous study reported a higher risk of cancer in the hypertension group compared to non-hypertension patients [29]. Furthermore, Jessica et al. found that subjects with SBP 160∼179 mmHg or DBP 100∼10 mmHg had an $8\%$ increased risk for cancer incidence compared with the lowest grade of hypertension [12].
The baPWV was also significantly associated with the risk of cancer in participants younger than 60 years, whereas this association was weakened to a non-significant level when we analyzed data in the participants ≥ 60 years. baPWV is known to be positively associated with age [30]. Of the many risk factors associated with baPWV, age and blood pressure are by far the most important factors influencing baPWV parameters (31–33). Aging per se is a promoter of arterial stiffness, especially in the older population [15, 33, 34]. Consequently, this may mask the effect of baPWV for cancer in the age category ≥ 60 years, but not in participants < 60 years. Therefore, further follow-up studies are required to validate the potential benefit of implementing baPWV in the screening model in the middle-aged population before the development of cancer.
Despite the risk of cardiovascular disease increasing linearly with the increase in baPWV [10], the results of the present study remain essentially unchanged after excluding the effect of severe vascular diseases. The data strongly indicates the presence of an association between arterial stiffness and the progression of cancer. This implies the possibility of defining risk thresholds by baPWV in the future, specifically tailored to the contemporary concept of Onco-cardiology, to improve risk stratification with established grading algorithms.
The combination of the aforementioned commonly shared risk factors and inflammation can contribute to the pathogenesis of artery rigidity. Consequently, arterial stiffness would be accelerated. But that doesn’t fully explain how increased arterial stiffness is associated with cancer. Although it is beyond the scope of this study to investigate the biological mechanisms underlying the association between arterial stiffness and cancer events, we can speculate that both cancer [35] and arterial dysfunction [36] are characterized by inflammation. Likewise, CVD and cancer share inflammation and ROS as common biological mechanisms [26]. Moreover, the alterations in the levels of secreted factors that regulate the prognosis of CVDs and malignancy may play a vital role in the communication between the cardiovascular system and tumors in the body [37, 38].
To the extent of our knowledge, this is the first major study to recognize the prognostic implications of baPWV and the incidence of cancer. However, this study must be interpreted in the context of some significant limitations. Arterial stiffness as a marker of vascular dysfunction is an operator-dependent procedure. The unavailability of HbA1C records for many patients limited us from considering it in our analysis. Our study only registered the Chinese population living in Northern China, which may limit the generalization of our results. However, the homogeneous nature of our prospective study provided absolute control of potential confounding effects that could result from racial disparities and health service inequalities. Therefore, the enrolment of only Chinese ethnicity in this study could bolster its internal validity. Cancer itself can affect systemic vascular function, thus multiple baPWV examinations during follow-up would benefit the study. The increased cancer risk by ∼$50\%$ was not justified by the slightly increased incidence of cancer across the corresponding baPWV groups. Thus, confounding factors could have strongly influenced the results of the present study. Also, identifying the time (in days or months) of a final baPWV measurement prior to cancer diagnosis would have added significant value to the clinical research. Therefore, further research is needed to confirm whether this association exists among other ethnic groups. Of note, possible categorization of tumors might be more helpful since for example tumors on solid organs such as respiratory cancer show a trend but perhaps due to the small number is not becoming statistically significant. Moreover, the concept of vascular aging might be relevant since the prediction is better at younger ages with stiffer arteries implying early vascular aging. Hence, a possible analysis based on expected values of vascular aging in solid tumors will be a benefit in confirming the association between baPWV and cancer incidence.
## 5. Conclusion
In conclusion, the results of this study demonstrate a positive association between high baPWV and cancer occurrence, with a potentially novel correlation. The findings of our study further recommend that individuals should engage in healthy lifestyle practices to maintain a lower risk of developing arterial stiffness. Further study is required to confirm the observed associations between arterial stenosis and the occurrence of cancer.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
YJ and XY: conceptualization. TH: data curation. AX: formal analysis and investigation. Y-LX: funding acquisition and validation. TH, JL, and SC: methodology. SW: project administration. YJ: writing—original draft. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1112047/full#supplementary-material
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|
---
title: Comparative pharmacokinetics of four major compounds after oral administration
of Mori Cortex total flavonoid extract in normal and diabetic rats
authors:
- Shan Xiong
- Xiaofan Li
- Haiping Chu
- Zhipeng Deng
- Linying Sun
- Jia Liu
- Yanling Mu
- Qingqiang Yao
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10014546
doi: 10.3389/fphar.2023.1148332
license: CC BY 4.0
---
# Comparative pharmacokinetics of four major compounds after oral administration of Mori Cortex total flavonoid extract in normal and diabetic rats
## Abstract
Introduction: Mori Cortex has been used in traditional Chinese Medicine as an antidiabetic agent. The aim of this study was to establish a UPLC-MS/MS method for simultaneous determination of morin, morusin, umbelliferone and mulberroside A in rat plasma and investigate the pharmacokinetics differences between normal and diabetic rats following oral administration of Mori Cortex total flavonoid extract.
Methods: Samples were pre-treated by protein precipitation and genkwanin was used as internal standard. Chromatographic separation was performed using a Hypersil GOLD C18 column (50 mm × 2.1 mm, 3 μm). The mobile phase consisted of acetonitrile and water (containing $0.1\%$ formic acid) in gradient mode at a flow rate of 0.5 ml/min. The transitions of m/z 300.9→107.1, m/z 419.3→297.1, m/z 160.9→77.0, m/z 567.1→243.2 and m/z 283.1→268.2 were selected for morin, morusin, umbelliferone, mulberroside A and internal standard, respectively.
Results: The intra- and inter-day precision for analytes were less than $12.5\%$ and the accuracy ranged from −$8.1\%$ to $3.5\%$. The extraction recovery was >$88.5\%$ and no obvious matrix effect was observed. The AUC (0-t) and C max of morin were 501.3 ± 115.5 ng/mL*h and 127.8 ± 56.0 ng/mL in normal rats and 717.3 ± 117.4 ng/ml*h and 218.6 ± 33.5 ng/ml in diabetic rats. Meanwhile, the AUC (0-t) and C max of morusin were 116.4 ± 38.2 ng/ml*h and 16.8 ± 10.1 ng/mL in normal rats and 325.0 ± 87.6 ng/mL*h and 39.2 ± 5.9 ng/ml in diabetic rats. For umbelliferone and mulberroside A, the AUC (0-t) and C max also increased significantly in diabetic rats ($p \leq 0.05$).
Discussion: The validated method was successfully applied to the pharmacokinetic study in normal and diabetic rats.
## 1 Introduction
A large number of studies have shown that long-standing serious hyperglycemia is the main cause of metabolic disorders and autoimmune disorders (Maritim et al., 2003). Diabetes mellitus (DM) is a chronic disease caused by acquired deficiency in production of insulin by the pancreas, or by the ineffectiveness of the insulin produced (Riaz, 2009). This deficiency results in increased concentrations of glucose in the blood, which in turn leads to retinopathy, nephropathy, neuropathy, coronary heart disease, cerebrovascular disease, and peripheral vascular diseases (Gregg et al., 2016; O'Brien and Corrall, 1988). The main purpose of diabetes treatment is to prevent or delay the complications by improving blood sugar control (Kooti et al., 2016). In China, traditional Chinese medicine (TCM) has been widely used in the treatment of diabetes and its complications (Tong et al., 2012). The prevention and treatment of diabetic complications by using TCM have lots of advantage including comprehensive treatment and small toxicity and side effects (Jia et al., 2003; Wang et al., 2016; Martel et al., 2017; Aras et al., 2019).
Mori Cortex, also called “Sang-Bai-Pi” in Chinese, which is derived from the root bark of *Morus alba* L. according to the China Pharmacopeia (The Committee of China Pharmacopeia, 2020). The modern pharmacological studies have shown that Mori Cortex has the active effect of antidiabetic (Ma et al., 2018), antioxidant (Ahmad et al., 2013; Abbasa et al., 2014; Bayazid et al., 2020), anti-inflammatory (Lim et al., 2013; Bayazid et al., 2020), antimicrobial (Grienke et al., 2016) and anticarcinogenic (Lim et al., 2014). It was first recorded for the antidiabetic effect of Mori Cortex in “Compendium of Materia medica”. According to ancient prescriptions, the decoction of Cortex Mori (12 g) and Lycii Fructus (15 g) was used to control the blood glucose level for diabetic patients (Xiao et al., 2014). Recent studies have shown that Mori Cortex extract could lower the blood glucose and improve insulin resistance (Qi et al., 2016; Kim and Choe, 2017; Ma et al., 2018).
To the best of the authors’ knowledge, there is no study focusing on the simultaneous quantification of morin, morusin, umbelliferone, and mulberroside A in rat plasma. The aim of this study was to develop an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous determination of morin, morusin, umbelliferone and mulberroside A, which are the main active components of Mori Cortex total flavonoid extract with higher content, in normal and diabetic rat (Liu et al., 2019). The structures of these four target analytes and IS are shown in Figure 1.
**FIGURE 1:** *The chemical structure of morin (A), morusin (B), umbelliferone (C), mulberroside A (D) and genkwain IS, (E).*
## 2.1 Chemicals and reagents
Mori Cortex total flavonoid extract (Lot number HP20171011), morin ($98\%$ purity), morusin ($98\%$ purity), umbelliferone ($98\%$ purity) and mulberroside A ($98\%$ purity) were supplied by Baoji Herbet Bio-Tech Co. Ltd (Baoji, China). Genkwanin (internal standard (IS), $98\%$ purity) was supplied by the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China). Propylene glycol was purchased from Nanjing Weier Pharmaceutical Co. Ltd (Nanjin, China). Streptozotocin (STZ) was purchased from Sigma (Sigma-Aldrich, St Louis, MO, United States). HPLC-grade formic acid was obtained from Tianjin Kermel Chemical Reagent Co. Ltd (Tianjin, China). HPLC-grade acetonitrile and methanol were obtained from Tedia Company (Fairfield, United States). Purified water was employed by Wahaha Co. Ltd (Hangzhou, China). All of the other chemicals were analytical grade or better.
## 2.2 Animals
Male Sprague-Dawley rats (7–8 weeks old, weighing 200 ± 20 g) were purchased from Jinan Pengyue Experimental Animal Breeding Co. Ltd (Jinan, China). They were housed in a room with a $\frac{12}{12}$ h light/dark cycle and an ambient temperature of 23°C ± 3°C. All animal experiments were carried out according to the National Institute of Health Guideline for the Care and Use of Laboratory Animals, and performed by the Animal Ethics Committee of School of Pharmacy and Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences (2017036, Jinan, China).
The rats were given a single intraperitoneal injection with a freshly prepared solution of STZ (60 mg/kg) in 0.1 mol/L citrate buffer (pH = 4.4) to induce diabetes (Seke Etet et al., 2017; Zhang et al., 2017). After 8 weeks, the rats with fasting blood glucose (FBG) for 5–6 h exceeding 16.7 mmol/L were considered successful diabetes models. The FBG was measured from the tail vein using a One-Touch Ultra® Blood Glucose Meter (LifeScan Inc., Milpitas, United States).
## 2.3 Instrumentation and UPLC-MS/MS conditions
A Shimadzu Prominence UPLC (Shimadzu, United States) system coupled to an AB SCIEX™ 5500 Q-Trap® mass spectrometer (Applied Bio-systems, United States) equipped with an electrospray ionization interface operated in negative multiple reaction monitoring (MRM) mode were applied to analysis. The analytes separation was achieved via gradient elution of $0.1\%$ formic acid in water (A) and acetonitrile (B) at a flow rate of 0.5 mL/min on a Thermo Hypersil Gold C18 column (50 mm × 2.1 mm, 3 μm; Thermo Scientific, New York, United States). The run time was 4.0 min for each analysis. The gradient elution program was used as follows: $5\%$ B→$10\%$ B at 0–0.2 min, $10\%$ B→$70\%$ B at 0.2–2.0 min, $70\%$ B at 2.0–2.5 min, $70\%$ B→$5\%$ B at 2.50–2.51 min; $5\%$ B at 2.51–4.0 min. The temperatures of autosampler and column were set at 15°C and 40°C, respectively. The supernatant injection volume was 2 μl. The MRM conditions (source-dependent mass parameters) were defined as follows: Ion Spray Voltage, −4500 V; temperature, 550°C; curtain gas, 35.0 psi; collision gas, medium; Gas 1, 55.0 psi; Gas 2, 55.0 psi. The transitions of m/z 300.9→107.1, m/z 419.3→297.1, m/z 160.9→77.0, m/z 567.1→243.2 and m/z 283.1→268.2 were selected for morin, morusin, umbelliferone, mulberroside A and IS, respectively. The system control and data analysis were performed using AB SCIEX Analyst software (version 1.6.3).
## 2.4 Preparation of standard solutions, calibration standards and quality control samples
The analytes were accurately weighted and separately dissolved in methanol to yield the stock solutions with a concentration of 5 mg/mL. The stock solutions were stored at −80°C until analyzed. The stock solutions were stepwise diluted with acetonitrile to make a series of mixed working solutions at concentration levels of 20–20000 ng/ml for target analytes. In addition, the IS was dissolved in methanol and then diluted with acetonitrile to obtain a working solution of 50 ng/ml. The solution of IS was maintained at 4°C.
Calibration standards were prepared by adding 10 μl of the mixed working solutions to 190 μl blank plasma to obtain final concentrations in the range 1–1000 ng/mL for target analytes. The quality control (QC) samples were prepared separately by the same method at four concentration levels of 1 (lower limit of quantification, LLOQ), 3 (LQC), 80 (MQC) and 800 ng/ml (HQC) for the four analytes.
## 2.5 Sample preparation
50 μl of rat plasma was mixed with 50 μl of IS solution (50 ng/ml) in a 1.5 ml polypropylene tube and then 100 μl acetonitrile was added to precipitate protein. The mixture was vortexed for 5 min, then centrifuged at 13000 rpm for 5 min at 4°C. The supernatant was collected in an autosampler vial and 2 μl of aliquot was injected into the UPLC-MS/MS system for analysis.
## 2.6 Validation of UPLC-MS/MS analytical method
The selectivity, linearity range, carryover, inter- and intra-day precision and accuracy, matrix effect, recovery and stability under different storage conditions were evaluated according to the FDA Guidance for Industry Bioanalytical Method Validation (Xiong et al., 2017; Food and Drug Administration, 2018; Li et al., 2019).
The selectivity was evaluated by comparing the chromatogram of blank rat plasma from six different matrices with standard plasma samples spiked with analytes and IS. The purpose was to explore whether there was interference from endogenous substances.
The linearity for morin, morusin, umbelliferone and mulberroside A were evaluated by plotting the peak area ratios of the analytes/IS versus the concentration values of the standard plasma samples on three consecutive days (a weighted 1/X 2 least squares linear regression). The LLOQ was determined by spiking the lowest concentration on the calibration curve with acceptable precision of less than $20\%$ of the relative standard deviation (RSD, %) and relative error (RE, %) of ±$20\%$. The carryover was assessed by analyzing the response of the blank plasma following the upper limits of quantification (ULOQ).
The intra- and inter-day accuracy and precision were determined by analyzing five replicates on three consecutive days. The intra-day assessment was investigated in the 1 day and the inter-day assessment was investigated for three consecutive days. The precision was calculated in terms of RSD (%), while the accuracy was expressed as the RE (%). The RSD should be within $15\%$ and accuracy was required to not be exceed ±$15\%$ at four QC levels.
The recovery of four target analytes were evaluated at three QC levels (LQC, MQC, and HQC) by comparing the mean peak areas of QC samples in six replicates with that of the pre-extraction blank plasma spiked with the corresponding working standard solution. The matrix effect was investigated by comparing the peak area of analytes resolved in pre-extraction matrix of blank plasma with those in the water-substituted samples. The recovery and matrix effect of the IS were assessed in the same way.
The stability of analytes in rat plasma was assessed by analyzing QC samples at low, middle and high levels ($$n = 5$$) under different storage condition including the short-term stability at room temperature for 6 h, the post-treatment stability at 15°C in autosampler for 12 h, three freeze-thaw stability, and the long-term stability at −80°C for 7 days.
## 2.7 Preparation of the oral solutions
The Mori Cortex total flavonoid extract (8 g) was stirred with propylene glycol (20 ml) by ultrasonic in a 50 ml volumetric flask, and then added purified water to 50 ml slowly. The contents of morin, morusin, umbelliferone and mulberroside A in the Mori Cortex total flavonoid extract were $0.1093\%$, $1.0050\%$, $0.1128\%$ and $0.0764\%$, respectively.
## 2.8 Pharmacokinetic study
Ten male Sprague-Dawley rats were enrolled in the pharmacokinetic study: five normal rats and five diabetic rats. All of the rats were fasted for at least 12 h and had free access to drinking water before the experimental. The two groups of rats were administered Mori Cortex total flavonoid extract at the doses of 2 g/kg by oral after overnight fasting. Blood samples (150 μl) were obtained from retroorbital plexus into a heparinized tube at 0 (pre-dose), 0.083, 0.167, 0.5, 0.75, 1, 1.5, 2, 3, 5, 8, 12 and 24 h after oral administration. The collected blood samples were immediately centrifuged at 3500 rpm at 4°C for 15 min to obtain plasma fraction. The samples were frozen at −80°C until analyzed.
## 3.1 Method development
Acetonitrile/methanol-water (containing 0, $0.1\%$ formic acid and 10 mM ammonium acetate with $0.1\%$ formic acid) being the mobile phase with gradient elution was used to evaluate UPLC separation and sensitivity in MS detection in order to obtain the good peak shape and optimal response for all the analytes. Finally, the mobile phase containing acetonitrile and $0.1\%$ formic acid in water was selected for the separation of the four analytes and IS, and the excellent peak shape and lower background noise were also obtained.
The ESI source was operated in negative ion mode to achieve maximum response and the deprotonated precursor molecular ions [M−H]− were chosen to be monitored. The m/z 300.9→107.1 as the quantitative ion for morin was selected due to the better peak shape of LLOQ samples. For morusin, the mass transition of m/z 419.3→89.0 was not chosen for quantitative analysis because it showed interference of endogenous substances in blank plasma samples. The MS/MS spectrogram of morin, morusin, umbelliferone, mulberroside A and IS are shown in Supplementary Figure S1. The optimal mass spectrometry parameters and transitions for analytes are listed in Table 1.
**TABLE 1**
| Analytes | Precursor ion (m/z) | Quantitative ion (m/z) | Qualitative ion (m/z) | Dp (V) | Ep (V) | Cxp (V) | CE (V) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Morin | 300.9 | 107.1 | 125.0 | −20 | −15 | −15 | −35 |
| Morusin | 419.3 | 297.1 | 89.0 | −60 | −15 | −43 | −20 |
| umbelliferone | 160.9 | 77.0 | 105.0 | −100 | −15 | −5 | −34 |
| mulberroside A | 567.1 | 243.2 | 405.2 | −200 | −10 | −5 | −39 |
| IS | 283.1 | 268.2 | 239.9 | −65 | −15 | −50 | −32 |
## 3.2 Method validation
As shown in Figure 2, the retention times were 1.47, 2.62, 1.27, 0.95 and 2.05 min for morin, morusin, umbelliferone, mulberroside A and IS, respectively. No unacceptable interference was observed at the retention times.
**FIGURE 2:** *Typical multiple reaction monitoring (MRM) chromatograms of morin, morusin, umbelliferone and mulberroside A from rat plasma: (A) blank plasma sample; (B) blank plasma spiked with analytes; (C) rat plasma sample after oral administration of Mori Cortex total flavonoid extract (1: morin, 2: morusin, 3: umbelliferone, 4: mulberroside A; (a) diabetic rats, (b) normal rats).*
The eight-point calibration curve was found to be linear over the concentration range of 1–1000 ng/mL for four analytes. Correlation coefficients were ≥0.99 for all analytes. The calibration curve results for four analytes are summarized in Table 2. The LLOQ values for analytes were 1 ng/mL with RSD <$12.5\%$ and RE varied from −$5.6\%$ to $3.5\%$ (Table 3), indicating LLOQs of all analytes met the requirements. There was no carryover effect after the injection of ULOQ samples.
The intra- and inter-day precision and accuracy within the acceptance limit for the analytes are summarized in Table 3. The precision was ≤$10.4\%$ and the accuracy was within ± $8.1\%$ for four analytes. The data suggested that the analytical method was reliable and accurate.
The mean extraction recovery and matrix effect of the analytes and IS are summarized in Supplementary Table S1. The results suggested that the assay obtained high recovery and no obvious matrix effect of each analyte and IS was observed.
The stability data of four analytes are shown in Supplementary Table S2. The results proved that the storage conditions, disposal, intermittent analysis and analysis techniques were valid and reliable for the analytes in rat plasma.
## 3.3 Application to a pharmacokinetic comparison
The established UPLC-MS/MS method was successfully applied to the determination of morin, morusin, umbelliferone and mulberroside A in rat plasma samples collected from normal rats and diabetic rats after oral administration of Mori Cortex total flavonoid extract. The mean plasma concentration versus time plots for morin, morusin, umbelliferone and mulberroside A are shown in Figure 3. In addition, the pharmacokinetic parameters of the normal and diabetic rats were calculated by DAS software version 2.0 and compared using the independent samples t-test (a value of $p \leq 0.05$ was considered statistically significant). Compared with normal rats, some main pharmacokinetic parameters of all target analytes obtained from diabetic rats changed. The AUC (0-t) and C max of morin were 501.3 ± 115.5 ng/ml*h and 127.8 ± 56.0 ng/mL in normal rats and 717.3 ± 117.4 ng/ml*h and 218.6 ± 33.5 ng/mL in diabetic rats. Additionally, the AUC (0-t) and C max of morusin were 116.4 ± 38.2 ng/ml*h and 16.8 ± 10.1 ng/mL in normal rats and 325.0 ± 87.6 ng/mL*h and 39.2 ± 5.9 ng/mL in diabetic rats. For umbelliferone and mulberroside A, the AUC (0-t) were 16.4 ± 5.1 and 36.8 ± 14.6 ng/ml*h in normal rats, and 28.4 ± 3.6 and 75.6 ± 11.1 ng/ml*h in diabetic rats. Meanwhile, the C max were 7.6 ± 0.7 and 13.1 ± 6.5 ng/mL in normal rats, and 16.8 ± 6.6 and 28.5 ± 12.3 ng/ml in diabetic rats. The changes were significant ($p \leq 0.05$). The main pharmacokinetic parameters are presented in Table 4.
**FIGURE 3:** **Mean plasma* concentration-time curves of morin (A), morusin (B), umbelliferone (C) and mulberroside A (D) after oral administration of Mori Cortex total flavonoid extract ($$n = 5$$).* TABLE_PLACEHOLDER:TABLE 4
## 4 Discussion
The current cognition regarding the effects of diabetes mellitus on the pharmacokinetics and pharmacodynamics of antidiabetic drugs remains unclear. There are only substantially less data about the effects of diabetes mellitus on these properties of drugs for human, so the data obtained from the experimental animal models has extremely referential and practical values.
In this study, as shown in Table 4, the AUC (0-t) of morin, morusin, umbelliferone and mulberroside A in diabetic rats were 717.3 ± 117.4, 325.0 ± 87.6, 28.4 ± 3.6 and 75.6 ± 11.1 ng/mL*h, compared with 501.3 ± 115.5, 116.4 ± 38.2, 16.4 ± 5.1 and 36.8 ± 14.6 ng/mL*h in normal rats ($p \leq 0.05$). Meanwhile, the AUC (0-∞) of the target analytes obtained from diabetic rats were also significant increased ($p \leq 0.05$), compared with those obtained from the normal rats. Significant differences in maximum concentration (C max; 127.8 ± 56.0 vs 218.6 ± 33.5 ng/ml for morin, 16.8 ± 10.1 vs 39.2 ± 5.9 ng/mL for morusin, 7.6 ± 0.7 vs 16.8 ± 6.6 ng/mL for umbelliferone and 13.1 ± 6.5 vs 28.5 ± 12.3 ng/ml for mulberroside A) were observed between normal and diabetic rats ($p \leq 0.05$). Compared with normal rats, the MRT increased by $56.5\%$ ($p \leq 0.05$) in the diabetic rats for mulberroside A. In addition, the CLz/F were decreased by $33.7\%$, $40.2\%$, $53.2\%$ and $34.0\%$ for morin, morusin, umbelliferone and mulberroside A in the diabetic rats, respectively. In addition, the bimodal phenomenon appeared in the mean plasma concentration-time curves of morusin in diabetic rats. Many factors would lead to the results such as the changes of gastrointestinal tract, liver and kidney function, local blood flow rate caused by diabetes. The results indicated that the pharmacokinetics of four target analytes in diabetic rats was significantly changed and the bioavailability was enhanced.
Diabetes mellitus is one of the most grievous problems threatening public health. Not only are antidiabetic drugs more widely used, but the pharmacokinetics of these antidiabetic drugs may also be changed due to the disease itself (Gwiltet al., 1991). Diabetes affects the metabolism of the three major nutrients, including protein, lipid and carbohydrate, and the systems that regulate biotransformation pathways of these nutrients also participates in the regulation of drug metabolism in vivo in many cases. The researches show diabetes could influence all processes of drugs in the body, such as the absorption, distribution, metabolism and excretion of drugs (Zini et al., 1990; Okabe and Hashizume, 1994; Cashion et al., 2004). For absorption, the significantly reduced in digestive tract blood flow caused by diabetes could be associated with the change of gastric pH, the prolongation of gastric emptying time and slowing of intestinal peristalsis. The differences in the absorption rate and bioavailability between normal and diabetic rats might depend on the above factors (Horowitz and Fraser, 1994; Horowitz et al., 1996). The higher level of circulating glucose in the blood would lead to non-enzymatic glycation of several proteins including albumin, which could affect the plasma protein binding rate of drugs (Day et al., 1979; Cohen et al., 2006). In addition, diabetes could affect drug metabolism due to the abnormal hepatic function caused by diabetes including non-alcohol steatohepatitis, macrovesicular steatosis, liver fibrosis/cirrhosis and focal fatty liver. It was also one of the important reasons for the changes in pharmacokinetics of the target analytes from Mori Cortex total flavonoid extract in vivo (Petrides et al., 1994; Wang et al., 2000). Diabetes nephropathy occurred frequently and it would influence the glomerular filtration, tubular secretion and tubular reabsorption, so the influence of excretion on the pharmacokinetics of drugs was also worthy of attention (Raine, 1995; Suzuki and Arakawa, 1995).
There were lots of factors that lead to pharmacokinetic changes in diabetes and further investigations would be required to reveal the underlying mechanisms for pharmacokinetics and pharmacodynamics of four target analytes in Mori Cortex total flavonoid extract in diabetic rats.
## 5 Conclusion
In summary, we have developed and validated a reliable, accurate and rapid UPLC-MS/MS method for simultaneous determination of morin, morusin, umbelliferone and mulberroside A in rat plasma. Moreover, this method was successfully applied for pharmacokinetic comparisons in normal and diabetic rats. The results would provide the pharmacokinetic rationale for the pharmacology and toxicology research of Mori Cortex.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by Animal Ethics Committee of School of Pharmacy and Pharmaceutical Sciences & Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences (Jinan, China).
## Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1148332/full#supplementary-material
## Abbreviations
UPLC-MS/MS, ultra-performance liquid chromatography-tandem mass spectrometry; DM, diabetes mellitus; TCM, traditional Chinese Medicine; STZ, streptozotocin; FBG, fasting blood glucose; ESI, electrospray ionization source; MRM, multiple-reaction monitoring; QC, quality control; LLOQ, the lower limit of quantification; ULOQ, the upper limits of quantification; RSD, relative standard deviation; RE, relative error.
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|
---
title: Effects of anti-inflammatory therapies on glycemic control in type 2 diabetes
mellitus
authors:
- Dandan Li
- Jiaxin Zhong
- Qirui Zhang
- Jingjing Zhang
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10014557
doi: 10.3389/fimmu.2023.1125116
license: CC BY 4.0
---
# Effects of anti-inflammatory therapies on glycemic control in type 2 diabetes mellitus
## Abstract
### Background
The overall evidence base of anti-inflammatory therapies in patients with type 2 diabetes mellitus (T2DM) has not been systematically evaluated. The purpose of this study was to assess the effects of anti-inflammatory therapies on glycemic control in patients with T2DM.
### Methods
PubMed, Embase, Web of Science, and Cochrane Library were searched up to 21 September 2022 for randomized controlled trials (RCTs) with anti-inflammatory therapies targeting the proinflammatory cytokines, cytokine receptors, and inflammation-associated nuclear transcription factors in the pathogenic processes of diabetes, such as interleukin-1β (IL-1β), interleukin-1β receptor (IL-1βR), tumor necrosis factor-α (TNF-α), and nuclear factor-κB (NF-κB). We synthesized data using mean difference (MD) and $95\%$ confidence interval (CI). Heterogeneity between studies was assessed by I2 tests. Sensitivity and subgroup analyses were also conducted.
### Results
We included 16 RCTs comprising 3729 subjects in the meta-analyses. Anti-inflammatory therapies can significantly reduce the level of fasting plasma glucose (FPG) (MD = - 10.04; $95\%$ CI: -17.69, - 2.40; $$P \leq 0.01$$), glycated haemoglobin (HbA1c) (MD = - 0.37; $95\%$ CI: - 0.51, - 0.23; $P \leq 0.00001$), and C-reactive protein (CRP) (MD = - 1.05; $95\%$ CI: - 1.50, - 0.60; $P \leq 0.00001$) compared with control, and therapies targeting IL-1β in combination with TNF-α have better effects on T2DM than targeting IL-1β or TNF-α alone. Subgroup analyses suggested that patients with short duration of T2DM may benefit more from anti-inflammatory therapies.
### Conclusion
Our meta-analyses indicate that anti-inflammatory therapies targeting the pathogenic processes of diabetes can significantly reduce the level of FPG, HbA1c, and CRP in patients with T2DM.
## Introduction
Obesity and type 2 diabetes mellitus (T2DM) are associated with decreased physical activity and unhealthy high-calorie diets. Obesity is related to insulin resistance and is a crucial risk factor for the development of T2DM [1]. Chronic low-grade inflammation plays an important role in the pathogenesis of diabetes and the development of diabetic complications [2, 3]. Inflammation has been seen in the pancreatic islets, liver, muscle, adipose tissue, and the sites of diabetic complications [4]. Long-term inflammation that occurs in adipose tissue can lead to systemic inflammation and contribute to insulin resistance. In the presence of insulin resistance, β cells secrete more insulin to maintain normal glucose control. Inflammation impairs β cell function and induces β cell apoptosis, and T2DM happens when insulin production fails to reach the insulin needs [5].
Many proinflammatory cytokines and inflammation-associated nuclear transcription factors are related to impaired insulin secretion and contribute to the pathogenesis of T2DM, including interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and nuclear factor-κB (NF-κB) etc. ( 4, 6–8). High concentration glucose can induce IL-1β production and secretion from human pancreatic β cells, and IL-1β was observed in β cells in diabetic patients [9]. IL-1β is involved in β cell apoptosis and partially dependent on the activation of NF-κB [10]. Obesity can activate the NF-κB signaling pathway, which plays an important role in the development of insulin resistance [8]. TNF-α is also involved in β cell apoptosis [11], and more TNF-α expression was found in adipose tissue in obese than lean people, and the plasma level of TNF-α was elevated in patients with T2DM [6, 7].
Anti-inflammatory treatments can improve insulin sensitivity and β cell function in patients with insulin resistance or T2DM [12]. Treatments of diabetes focused on inflammation can benefit many inflammatory tissues at the same time, which is less likely to induce hypoglycemia [13]. Small molecules or antibody-based molecules targeting inflammatory cytokines, cytokine receptors, or inflammation-associated nuclear transcription factors, such as IL-1β, interleukin-1β receptor (IL-1βR), NF-κB, and TNF-α, can improve metabolism [13, 14]. But the effects of anti-inflammatory therapies on glycemic control in patients with T2DM were controversial (15–19). Previous meta-analyses have assessed the effects of anti-IL-1 therapies on T2DM [20, 21]. However, the totality of the evidence base of the anti-inflammatory therapies on T2DM has not been systematically assessed. We conducted the meta-analyses to clarify the effects of anti-inflammatory therapies on glycemic control in patients with T2DM.
## Methods
The meta-analyses were performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22].
## Search strategy
We searched randomized controlled trials (RCTs) from PubMed, Embase, Web of Science and Cochrane Library from database inception up to 21 September 2022. Search terms include Medical Subject Headings (MeSH), keywords and free-text terms related to anti-inflammatory therapies, type 2 diabetes mellitus, T2DM, fasting plasma glucose, FPG, glycated haemoglobin, HbA1c, C-reactive protein, CRP, anakinra, canakinumab, diacerein, gevokizumab, LY2189102, tocilizumab, salsalate, salicylate, etanercept, remicade, infliximab, adalimumab, enbrel, and dapansutrile. The detailed search strategy is available in Table S1. Following the search and removal of duplicates, D Li and J Zhong screened titles and abstracts to identify relevant studies.
## Study selection
Studies were eligible for inclusion if they met the following criteria [1]: Participants: patients with T2DM; [2] Interventions: at least one of the following treatments was used, anakinra, canakinumab, diacerein, gevokizumab, LY2189102, tocilizumab, salsalate, salicylate, etanercept, remicade, infliximab, adalimumab, enbrel, or dapansutrile; [3] Controls: placebo with or without approved antidiabetic medications, such as metformin, sulfonylureas, and insulin etc.; [ 4] Outcomes: at least one of the following outcomes was reported, FPG, HbA1c, or CRP; [5] Studies: RCTs. Trials without accessible data or full text were excluded.
## Data extraction
Data extraction and analyses from included studies were performed by two authors independently, and conflicts were resolved by a third author. The following information was extracted: first author, publication year, agent, dosage and frequency, follow-up duration, number of participants, patient baseline information (mean age, sex distribution, diabetes duration, baseline BMI, and HbA1c) and outcomes of interest (follow-up FPG, HbA1c, and CRP).
## Risk of bias assessment
Risk of bias assessment of the included RCTs was carried out by two authors (D Li and Q Zhang) independently according to the Cochrane Collaboration’s Risk of Bias Tool, which including random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias.
## Data analyses
Continuous variables were expressed as mean difference (MD) with $95\%$ confidence interval (CI). When mean and SD were not available, we calculated from SEM, sample size, median, range, or interquartile range (IQR) using methodology from the Cochrane Library Handbook or the article written by Wan et al. [ 23, 24]. Several studies had more than one intervention groups with different dosages, and for these studies, we chose only one comparable dosage as motioned in Table 1. Statistical heterogeneity among studies was assessed with the I 2 statistic, considering the I 2 value of 50 - $75\%$ was moderate heterogeneity and above $75\%$ was high heterogeneity [25]. We performed subgroup analyses based on the targets of interventions, names of the medication, diabetes duration, follow-up duration, and drug administration regimen. Leave-one-out studies were performed for sensitivity analyses to examine the effect of each trial on the overall analyses. Funnel plot and Egger’s test were used to assess the publication bias and tested for statistical significance. All statistical analyses were performed using Review manager 5.3 and Stata 12.0. A value of p ≤ 0.05 was considered statistically significant.
**Table 1**
| First author, year | Agent | Target and mechanism of action | Dosage, frequency | Study duration | Patients randomized, n | Age, years | Male sex, % | Duration of diabetes, year | Baseline BMI | Baseline HbA1c, % |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Cardoso CRL 201726 | diacerein | TNF-α antagonismin combinationwith IL-1βRblockade | 100 mg/day | 48 weeks | 84 | 63.7 | 20 | 9 | 31.3 | 8.2 |
| Cavelti-Weder C 201227 | gevokizumab | IL-1β-specificantibodies | a single dose of 0.03 or 0.1 mg/kg | 3 months | 48 | 50.0 | 82 | 9.7 | 31 | 9.1 |
| Choudhury RP 201617 | canakinumab | IL-1β-specificantibodies | 150 mg monthly | 12 months | 189 | 61.9 | 80 | – | 30.3 | 6.85 |
| Dominguez H 200519 | etanercept | TNF-α inhibition | 25 mg twice weekly | 4 weeks | 19 | 55.0 | 55.6 | – | 32 | 7.6 |
| Everett BM 201828 | canakinumab | IL-1β-specificantibodies | 150 mg once every 3 months | 48 months | 2303 | 61.0 | 77 | – | 29.1 | 7.1 |
| Faghihimani E 201329 | salsalate | NF-κBinhibition | 3 g/day | 12 weeks | 60 | 50.8 | – | within 2 months b | 29.2 | 5.9 |
| Goldfine AB 201030 | salsalate | NF-κBinhibition | 3 g/day | 14 weeks | 54 | 55.9 | 55.6 | 5.1 | 34 | 7.8 |
| Goldfine AB 201331 | salsalate | NF-κBinhibition | 3.5 g/day | 48 weeks | 286 | 55.8 | 52.9 | 4.9 | 33.2 | 7.7 |
| Jangsiripornpakorn J 202216 | diacerein | TNF-α antagonismin combinationwith IL-1βRblockade | 50 mg/day | 12 weeks | 35 | 52.0 | 47.1 | 11.4 | 29.5 | 8.5 |
| Larsen CM 200718 | anakinra | IL-1 receptorantagonism | 100 mg/day | 13 weeks | 69 | 60.3 | 77.1 | 11 | 31.8 | 8.2 |
| Noe A 201432 | canakinumab | IL-1β-specificantibodies | a single dose of 10 mg/kg | 24 weeks | 86 | 57.5 | 68.9 | 5.1 | 30.8 | 7.8 |
| Piovesan F 201733 | diacerein | TNF-α antagonismin combinationwith IL-1βRblockade | 50 mg twice daily | 90 days | 72 | 62.5 | 23 | 13.4 | – | 8.9 |
| Ramos-Zavala MG 201115 | diacerein | TNF-α antagonismin combinationwith IL-1βRblockade | 50 mg once or twice daily | 2 months | 40 | 47.8 | 40 | within 6 months c | 30.8 | 7.9 |
| Ridker PM 201234 | canakinumab | IL-1β-specificantibodies | 150 mg/month | 4 months | 271 | 54.3 | 59 | 4 | 29.3 | 7.4 |
| Sloan-Lancaster J 201335 | LY2189102 | IL-1β-specificantibodies | 180 mg/week | 24 weeks | 42 | 52.9 | 25.9 | 8 | 32.5 | 7.9 |
| Tres GS 201836 | diacerein | TNF-α antagonismin combinationwith IL-1βRblockade | 50 mg twice daily | 12 weeks | 71 | 59.0 | 75 | 14.8 | 31.3 | 8.6 |
## Included studies and baseline characteristics
Figure 1 shows the details of the literature search and selection process. Of 1271 reports identified, 241 reports were excluded due to duplication, and 981 were excluded based on titles and abstracts. Of 49 reports reviewed in full, 33 were excluded based on eligibility criteria. A total of 16 reports involving 3729 participants with T2DM were included in the final analyses (15–19, 26–36). Table 1 shows the baseline characteristics of the 16 RCTs. Trials included were published between 2005 and 2022. The follow-up duration was between 1 and 48 months. Trails reported by Everett et al. [ 28] had the longest follow-up duration (48 months), which was not comparable with all the others, and a more comparable time point (6 months) was used in the subsequent analyses. Among the 16 trails, 4 trails were for canakinumab [17, 28, 32, 34], 5 trails for diacerein [15, 16, 26, 33, 36], 3 trails for salsalate (29–31), and the rest were for anakinra [18], gevokizumab [27], LY2189102 [35], and etanercept [19]. The dosage and frequency of the treatments are shown in Table 1.
**Figure 1:** *Flowchart diagram of study selection process.*
## Risk of bias of individual studies
The quality of the included trials was assessed according to the criteria of the Cochrane Handbook. A detailed evaluation of the risk of bias for each clinical trial and risk of bias summary are presented in Figure S1. Among the 16 RCTs, only 1 was judged to be at high risk of bias as an open-label randomized trial [19], 6 were judged to be at low risk of bias and 9 as being at unclear risk of bias. Unclear risks were related to selection bias, reporting bias, and other bias.
## FPG
Figure 2 shows anti-inflammatory therapies can significantly decrease the level of FPG ($$n = 12$$; MD = - 10.04; $95\%$ CI: - 17.69, - 2.40; $$P \leq 0.01$$) compared with control, and there was statistically significant heterogeneity among studies (I2 = $77\%$; $P \leq 0.00001$) (Figure 2A). We did a series of subgroup analyses of FPG based on the targets of interventions, diabetes duration, and follow-up duration. Subgroup analyses based on the targets of interventions show that drugs targeting IL-1β plus TNF-α (diacerein) ($$n = 5$$; MD = - 13.52; $95\%$ CI: - 23.77, - 3.27; $$P \leq 0.01$$) or NF-κB alone (salsalate) ($$n = 3$$; MD = - 22.03; $95\%$ CI: - 34.59, - 9.47; $$P \leq 0.0006$$) can significantly decrease the level of FPG compared with control, whereas drugs targeting IL-1β (canakinumab) or TNF-α (etanercept) alone had no significant effect on the change of FPG (Figure 2B). Patients with T2DM less than 3 years since diagnosis ($$n = 2$$, MD = - 20.64; $95\%$ CI: - 32.03, - 9.25; $$P \leq 0.0004$$) seem to benefit more from anti-inflammatory therapies than those between 3 and 10 years ($$n = 3$$, MD = - 14.79; $95\%$ CI: - 28.69, - 0.89; $$P \leq 0.04$$), and no significant effect was found in those more than 10 years ($$n = 4$$, MD = - 7.94; $95\%$ CI: - 20.17, 4.3; $$P \leq 0.2$$) (Figure S2A). Anti-inflammatory therapies can decrease the level of FPG in patients whose follow-up duration was less than or equal to 3 months ($$n = 6$$, MD = - 19.01; $95\%$ CI: - 28.57, -9.45; $P \leq 0.0001$), but no significant effect was found in patients with longer follow-up duration (Figure S2B).
**Figure 2:** *Forest plot of pooled mean difference in change in FPG (mg/dL). (A) Meta-analyses of the effects of anti-inflammatory therapies on FPG in patients with T2DM; (B) The forest plot of FPG in subgroup analyses defined by the targets of interventions. fasting plasma glucose, FPG; CI, confidence interval; IV, inverse variance; SD, standard deviation.*
## HbA1c
The change in HbA1c was assessed in all studies. Figure 3A shows anti-inflammatory therapies can significantly decrease the level of HbA1c ($$n = 16$$; MD = - 0.37; $95\%$ CI: - 0.51, - 0.23; $P \leq 0.00001$) with moderate heterogeneity among studies (I 2 = $69\%$; $P \leq 0.0001$) (Figure 3A). The sensitivity analyses of HbA1c indicated the stability of the results (Figure S3). Subgroup analyses based on the targets of the interventions show that drugs targeting IL-1β plus TNF-α (diacerein) ($$n = 5$$; MD = - 0.63; $95\%$ CI: - 1.08, - 0.19; $$P \leq 0.005$$) can reduce the level of HbA1c better than targeting IL-1β (gevokizumab, canakinumab, anakinra, or LY2189102) ($$n = 7$$; MD = - 0.25; $95\%$ CI: - 0.42, - 0.08; $$P \leq 0.004$$) or TNF-α (etanercept) ($$n = 1$$; MD = 0.00; $95\%$ CI: - 0.88, 0.88; $$P \leq 1.00$$) alone (Figure 3B). Anti-inflammatory therapies targeting NF-κB (salsalate) ($$n = 3$$; MD = - 0.40; $95\%$ CI: - 0.59, - 0.20; $P \leq 0.0001$) can significantly decrease the level of HbA1c compared with control, and there was no heterogeneity among studies (I 2 = $27\%$; $$P \leq 0.25$$). Subgroup analyses according to the name of the medications show in Figure S4A, gevokizumab ($$n = 1$$; MD = - 0.85; $95\%$ CI: - 1.60, - 0.10; $$P \leq 0.03$$) can reduce the level of HbA1c more than diacerein ($$n = 5$$; MD = - 0.63; $95\%$ CI: - 1.08, - 0.19; $$P \leq 0.005$$), anakinra ($$n = 1$$; MD = - 0.46; $95\%$ CI: - 0.61, - 0.31; $P \leq 0.00001$), salsalate ($$n = 3$$; MD = - 0.40; $95\%$ CI: - 0.59, - 0.20; $P \leq 0.0001$), and canakinumab ($$n = 4$$; MD = - 0.11; $95\%$ CI: - 0.21, - 0.02; $$P \leq 0.02$$). LY2189102 and etanercept had no significant effect on HbA1c compared with the control. Subgroup analyses based on diabetes duration show that more reduction of HbA1c was seen in patients with T2DM less than 3 years since diagnosis ($$n = 2$$, MD = -1.54; $95\%$ CI: - 2.04, - 1.04; $P \leq 0.00001$) than those between 3 and 10 years ($$n = 6$$, MD = - 0.32; $95\%$ CI: - 0.43, - 0.21; $P \leq 0.00001$), and those more han 10 years ($$n = 5$$, MD = - 0.44; $95\%$ CI: - 0.56, - 0.31; $P \leq 0.00001$) (Figure S4B). Anti-inflammatory therapies were more effective in patients whose follow-up duration was less than or equal to 3 months ($$n = 7$$, MD = - 0.71; $95\%$ CI: - 1.16, - 0.26; $$P \leq 0.002$$) (Figure S4C). Repeated drug administration regimen ($$n = 14$$, MD = - 0.37; $95\%$ CI: - 0.52, -0.21; $P \leq 0.00001$) and single dosing ($$n = 2$$, MD = - 0.45; $95\%$ CI: - 1.01, 0.10; $$P \leq 0.11$$) had similar effects on HbA1c (Figure S4D).
**Figure 3:** *Forest plot of pooled mean difference in change in HbA1c (%). (A) Meta-analyses of the effects of anti-inflammatory therapies on HbA1c in patients with T2DM; (B) The forest plot of HbA1c in subgroup analyses defined by the targets of interventions. glycated haemoglobin, HbA1c; CI, confidence interval; IV, inverse variance; SD, standard deviation.*
## CRP
Figure 4 shows anti-inflammatory therapies can decrease the level of CRP compared with control ($$n = 6$$; MD = - 1.05; $95\%$ CI: - 1.50, - 0.60; $P \leq 0.00001$), and there was high heterogeneity among studies (I 2 = $77\%$; $$P \leq 0.0007$$) (Figure 4A). Subgroup analyses based on the targets of interventions show that drugs targeting IL-1β (canakinumab) can significantly reduce the level of CRP ($$n = 3$$; MD = - 1.31; $95\%$ CI: - 1.63, - 0.99; $P \leq 0.00001$), whereas no significant effect was found in drugs targeting IL-1β plus TNF-α (diacerein) ($$n = 2$$; MD = - 1.95; $95\%$ CI: - 4.39, 0.49; $$P \leq 0.12$$) or NF-κB (salsalate) ($$n = 1$$; MD = - 0.24; $95\%$ CI: - 0.80, 0.32; $$P \leq 0.40$$) (Figure 4B).
**Figure 4:** *Forest plot of pooled mean difference in change in CRP (mg/L). (A) Meta-analyses of the effects of anti-inflammatory therapies on CRP in patients with T2DM; (B) The forest plot of CRP in subgroup analyses defined by the targets of interventions. C-reactive protein, CRP; CI, confidence interval; IV, inverse variance; SD, standard deviation.*
## Publication bias
Egger’s test for HbA1c suggested significant publication bias ($$p \leq 0.003$$) (Figure S5). However, the effect was the same as the original effect after using Duval and Tweedie’s trim and fill, and the result showed that no trimming was performed, and the data stayed unchanged.
## Discussion
Our meta-analyses of 16 RCTs published between 2005 and 2022 examined the effects of anti-inflammatory therapies on glycemic control in patients with T2DM. Two previous meta-analyses published in 2018 and 2019, concluded that anti-IL-1 therapies can significantly decrease the level of HbA1c and CRP, and have mild hypoglycaemic effect on patients with T2DM [20, 21]. However, the effects of anti-inflammatory therapies targeting other inflammatory molecules and the overall effects of anti-inflammatory therapies on T2DM remain to be discovered. Therefore, we performed further analyses of anti-inflammatory therapies based on different inflammatory targets, including IL-1β, IL-1βR, TNF-α, and NF-κB. Our results show that anti-inflammatory therapies, including anti-IL-1 therapies, can significantly decrease the level of FPG, HbA1c and CRP in patients with T2DM. Our findings indicate the clinical efficacy of treating T2DM based on the pathogenesis of diabetes and give suggestions for the future anti-inflammatory clinical trials.
Chronic low-grade inflammation was found in diabetic islets, with increased innate immune cell infiltration and cytokine secretion [37]. Immune cell infiltration and cytokine release directly impairs β cell mass and function [38]. IL-1β was the first described proinflammatory cytokine in the islets of patients with T2DM [39]. IL-1β impairs β cell function and induces the apoptosis of β cells [40]. Block IL-1β signaling pathway by antagonists or antibodies had beneficial effects on β cell function and glycemic control in patients with T2DM [41, 42]. Anakinra, a recombinant human IL-1βR antagonist, can significantly reduce the level of HbA1c and may improve glycemic control by increasing insulin secretion [18]. Canakinumab, gevokizumab and LY2189102 are recombinant human engineered monoclonal antibodies, which can neutralize the activity of IL-1β by forming a complex with circulating IL-1β. Canakinumab can also reduce the blood levels of IL-6 and CRP [17]. All the anti-IL-1β therapies mentioned above had significant effect on glucose control as reflected by reductions in HbA1c, which was also reported by previous meta-analyses [20, 21]. However, some of the beneficial effects were only detected by certain treatment periods, not the whole follow-up periods [28, 35]. As shown in our subgroup analyses, anti-inflammatory therapies may work better in patients with short follow-up duration (less than or equal to 3 months). LY2189102 can improve blood glucose control for 12 weeks, but the effect was attenuated over time and there was no difference at 24 weeks [35]. The study reported by Everett BM et al. showed that canakinumab can reduce HbA1c during the first 6 to 9 months of treatment, but no significant effect was found by the end of the follow-up period at 48 months [28]. The exact reason for this attenuation is unclear, but the availability of other antidiabetic therapies and lifestyle interventions may contribute to this phenomenon [28].
TNF-α can diminish glucose-dependent insulin secretion and impair the function of β cells both in vitro and in vivo [43, 44]. But etanercept, a TNF-α inhibitor, has no significant effect on FPG or HbA1c [19]. Etanercept can improve the glucose tolerance of some individuals, but no significant effect was found in the whole group [19]. It was difficult to say whether etanercept has a positive effect on β cells since no more than 20 individuals was included in this clinical trial, and studies with a larger number of patients with T2DM are needed to elucidate this issue.
Diacerein is both an IL-1βR blocker and a TNF antagonist. It can inhibit the synthesis and activity of IL-1 and TNF-α by its active metabolite rhein [45]. Diacerein can reduce the HbA1c level without affecting the homeostasis model assessment-insulin resistance (HOMA-IR), indicating that it may play a role in insulin secretion [36]. And a higher dosage of diacerein (100 mg/day) may be more effective in improving the glycemic outcome [16]. Our results show that interventions targeting IL-1β plus TNF-α can reduce the level of HbA1c better than targeting IL-1β or TNFα alone in patients with T2DM. Diacerein had no significant effect on CRP in patients with T2DM, though reduced TNF-α was observed [26, 36]. Those studies were carried out in patients with longer duration of diabetes, and most participants were undergoing treatment with metformin, statins, sulfonylureas, or renin-angiotensin system blockers, which have potential roles in anti-inflammation, and might attenuate the anti-inflammatory effect of diacerein [13, 26, 36].
Salsalate, a prodrug form of salicylate, shows anti-inflammatory effects by inhibiting the IKKb/NF-κB and JNK signaling pathways [46, 47]. Salsalate can improve glycemic control by affecting cellular kinases nonspecifically and increasing insulin secretion of β cells [48]. After 1 year treatment, salsalate still had effects on HbA1c and FPG in patients with T2DM [31]. Salsalate can decrease the level of inflammatory mediators, such as leukocytes, neutrophils, and lymphocytes, but had little effect on CRP in patients with T2DM [31]. T2DM seems to result from a long-term process of inflammation, even years before diagnosis [35]. Greater benefits of salsalate might be seen in patients with newly diagnosed T2DM or longer treatment duration.
Our results show that patients with newly diagnosed T2DM may benefit more from anti-inflammatory therapies. However, Kataria Y et al. reported that the effects of anti-IL-1β therapies depend on the baseline dysmetabolic status, and patients with a more metabolic imbalance at baseline may benefit more after treatment [21]. The differences between our studies may come from the different types of medications analyzed, as we included lots of anti-inflammatory medications, not just IL-1β antibodies and IL-1βR antagonists. Since no newly diagnosed T2DM patients were included in the studies of anti-IL-1β therapies, the effects of anti-IL-1β therapies on those patients remain to be seen.
There are some limitations in our study. First, lifestyle modification and antidiabetic medications were allowed in most of the included trials, which may affect or attenuate the efficacy of anti-inflammatory therapies. Second, most of the follow-up duration varied from 1 to 12 months, and longer clinical trials are needed since medication efficacy may change over time. Finally, publication bias exists in the meta-analyses, but the results stay the same after a trim and fill analysis.
## Conclusions
This study helps us better understand the possibility and efficiency of anti-inflammatory therapies for T2DM based on the pathogenetic processes of the disease. The present analyses demonstrated that targeting cytokines, cytokine receptors, and inflammation-associated nuclear transcription factors, such as IL-1β, IL-1βR, TNF-α, and NF-κB, alone or in combination can significantly reduce the level of FPG, HbA1c, and CRP in patients with T2DM. In addition, patients with a short duration of T2DM may benefit more from anti-inflammatory therapies. Since anti-inflammatory medications can reduce inflammation throughout the body, these medications may be used to treat diseases with similar pathologies, such as cardiovascular disease, chronic kidney disease, and rheumatic arthritis with or without T2DM.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Author contributions
DL and JinZ conceived and designed the study. DL and JiaZ did the scientific literature search and data extraction of the included studies. DL and QZ did the quality assessment and carried out the analyses. DL wrote the first draft of the present manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1125116/full#supplementary-material
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|
---
title: Intestine microbiota and SCFAs response in naturally Cryptosporidium-infected
plateau yaks
authors:
- Hailong Dong
- Xiushuang Chen
- Xiaoxiao Zhao
- Chenxi Zhao
- Khalid Mehmood
- Muhammad Fakhar-e-Alam Kulyar
- Zeeshan Ahmad Bhutta
- Jiangyong Zeng
- Shah Nawaz
- Qingxia Wu
- Kun Li
journal: Frontiers in Cellular and Infection Microbiology
year: 2023
pmcid: PMC10014559
doi: 10.3389/fcimb.2023.1105126
license: CC BY 4.0
---
# Intestine microbiota and SCFAs response in naturally Cryptosporidium-infected plateau yaks
## Abstract
Diarrhea is a severe bovine disease, globally prevalent in farm animals with a decrease in milk production and a low fertility rate. Cryptosporidium spp. are important zoonotic agents of bovine diarrhea. However, little is known about microbiota and short-chain fatty acids (SCFAs) changes in yaks infected with Cryptosporidium spp. Therefore, we performed 16S rRNA sequencing and detected the concentrations of SCFAs in Cryptosporidium-infected yaks. Results showed that over 80,000 raw and 70,000 filtered sequences were prevalent in yak samples. Shannon ($p \leq 0.01$) and Simpson ($p \leq 0.01$) were both significantly higher in Cryptosporidium-infected yaks. A total of 1072 amplicon sequence variants were shared in healthy and infected yaks. There were 11 phyla and 58 genera that differ significantly between the two yak groups. A total of 235 enzymes with a significant difference in abundance ($p \leq 0.001$) were found between healthy and infected yaks. KEGG L3 analysis discovered that the abundance of 43 pathways was significantly higher, while 49 pathways were significantly lower in Cryptosporidium-infected yaks. The concentration of acetic acid ($p \leq 0.05$), propionic acid ($p \leq 0.05$), isobutyric acid ($p \leq 0.05$), butyric acid ($p \leq 0.05$), and isovaleric acid was noticeably lower in infected yaks, respectively. The findings of the study revealed that Cryptosporidium infection causes gut dysbiosis and results in a significant drop in the SCFAs concentrations in yaks with severe diarrhea, which may give new insights regarding the prevention and treatment of diarrhea in livestock.
## Introduction
The long-haired ruminant yak is a plateau bovine species living in the 3000-5000 m high-altitude regions and is mostly found on the Qinghai Tibet plateau (Li et al., 2022a). Diarrhea is a serious bovine problem detected globally in livestock farms associated with a decrease in fertility rate and milk production, especially neonatal diarrhea is usually found with high morbidity and mortality (Han et al., 2017; Li et al., 2019a; Lan et al., 2021) Previously, studies revealed that diarrhea contributed to more than $50\%$ of calf mortality in Canada (Smith et al., 2014), and affected $19\%$ of the cattle population in the USA (Smulski et al., 2020), which indeed was the cause of huge economic detriment. Like other bovine animals, diarrhea has been commonly reported in yaks (Diao et al., 2020; Cui et al., 2022; Li et al., 2022a). There have been many biological factors which are associated for diarrhea and leading cause of death in calves (Kim et al., 2021). Many pathogens like bovine viral diarrhea virus, Noroviruses, Escherichia coli, Salmonella spp., and Cryptosporidium spp. have been commonly observed in infected cattle (Meganck et al., 2014; Cui et al., 2022). Among others, Cryptosporidium spp. are important zoonotic protozoa infecting various animal species (Li et al., 2019b; Kandeel et al., 2022), and are also generally recognized as the primary agent of cattle diarrhea (Li et al., 2019a; Li et al., 2019b). A previous study reported that the infection of Cryptosporidium spp. was an important issue in UK and Scotland (Smith et al., 2014). As yaks and cattle species are economically important for native herdsmen in China (Cheng et al., 2022), infectious diseases like those caused by Cryptosporidium spp. may not only affect animal health but are also potential threats leading to public health concerns.
Intestine microbiota is composed of millions of complex and diverse microorganisms, which contribute greatly to host health, nutrition absorption, host metabolism, and immunological development (Zeineldin et al., 2018). Previous studies demonstrated that this bacteria was related to various diseases like Type 2 diabetes (Martinez-Lopez et al., 2022), acute pancreatitis (Mei et al., 2022), obesity (Salazar et al., 2022), and diarrhea (Han et al., 2017; Zeineldin et al., 2018; Li et al., 2022b). Short-chain fatty acids are metabolic products of microbiota, which contribute to the cellular metabolism of the host (Bachem et al., 2019), regulating immune function and suppressing inflammatory reactions (Abdalkareem Jasim et al., 2022). In our previous study, we observed prominent changes in intestinal microbiota in a horse infected with Cryptosporidium spp. ( Wang et al., 2022). However, scarce information is available about microbiota and SCFAs changes in plateau yaks infected with Cryptosporidium spp. Therefore, this study was conducted to explore intestinal microbiota and SCFAs response to natural Cryptosporidium infection in plateau yaks.
## Samples
Fecal samples ($$n = 40$$) were collected from free-ranged yaks in Xining, Qinghai (North latitude 31˚36´-39˚19´, east longitude 89˚35´-103˚04´) and examined for Cryptosporidium spp. by employing nested PCR (Chen et al., 2022) and positive samples were saved for further analysis. In this study, all the Cryptosporidium spp. positive samples ($$n = 4$$) with equal number of negative samples ($$n = 4$$) were sequenced and divided into infected (INF) and healthy (H) groups, respectively.
## DNA extraction and PCR amplification
The extraction of total genomic DNA was performed by utilizing a commercial TIANamp Stool DNA Kit (Tiangen Biotech (Beijing) Co., Ltd, China) according to the product’s specifications. Fecal DNA concentration, purification, and quality examination were performed through NanoDrop 2000 UV-Vis spectrophotometer (Thermo Scientific, USA) and $1.2\%$ agarose gel electrophoresis, respectively. Then the hypervariable regions of bacterial 16S rRNA gene (V3-V4) were amplified using primers 338F and 806R as described in a previous study (Wang et al., 2019). All PCR products were individually subjected to agarose gel electrophoresis, gel extraction, and purification using the PureLink™ PCR Purification kit (Invitrogen™, USA). Finally, the purified DNA products were quantified by piloting QuantiFluor™-ST as guided by the instruction manual (Promega, USA).
## Library construction, Illumina miSeq sequencing, and bioinformatics analysis
Library construction was carried out by employing commercial Hieff NGS® OnePot II DNA Library Prep Kit for Illumina® (Yeasen, China) according to the product’s instructions, and sequenced through the Illumina NovaSeq platform (Illumina, San Diego, USA). Quality control of sequencing data was performed by employing QIIME2 (https://docs.qiime2.org/2019.1/) to generate amplicon sequence variant (ASV) (Callahan et al., 2016) and taxonomy table (Bokulich et al., 2018). Analysis of variance was performed using ANCOM (Analysis of Composition of Microbiomes), One-way ANOVA, Kruskal Wallis, LEfSe (LDA (Linear Discriminant Analysis) score >2), DEseq2 ($p \leq 0.05$ and log2 (FoldChange) > 2), clustering heatmap (with Z-score > 0.5 or < -0.5) and evolutionary tree ($p \leq 0.05$) methods to reveal differences in bacterial abundance among yak samples (Segata et al., 2011; Love et al., 2014; Mandal et al., 2015). Microbial alpha diversities analyses were performed through QIIME2 by calculating indices including observed OTUs, Chao1, Shannon, and Faith’s. Microbial beta diversities of principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS) (Vazquez-Baeza et al., 2013), and partial least squares discriminant analysis (PLS-DA) were carried out to explore the structural variation of microbial communities across yak samples. The evolutionary relation tree was constructed by using ggtree in R package.
## Function analysis
The potential KEGG Ortholog (KO) functional profiles of yak microbiota was predicted with PICRUSt (Langille et al., 2013) by annotating with MetaCyc and ENZYME database. One-way ANOVA was used to analyze the data, while Duncan test was used as post-hoc test to measure the individual differences in microbial function between the yak groups with a $p \leq 0.05$ as statistically significant.
## SCFAs detection
The concentrations of SCFAs in fecal samples were detected by employing GC-MS (Hsu et al., 2019; Zhang et al., 2019), and the differences between yak groups were explored via t-test.
## Statistical analysis
The differences between different yak groups were calculated by the chi-square test piloting IBM SPSS Statistics (SPSS 22.0). P values < 0.05 were considered as statistically significant.
## Analysis of 16S rDNA sequencing data
In the current study, over 80,000 raw and 70,000 filtered sequences were obtained in yak samples. The non-chimeric sequences ranged from 62,133 to 73,453 in healthy yaks, and 68,173 to 74,350 in infected yaks (Table 1). There were a total of 1072 shared ASVs between the healthy (group H) and infected (group INF) groups. ( Figure 1A). Alpha diversity index analysis showed that there was no significant difference in chao1, faith, and observed features between group H and INF, respectively. Shannon ($p \leq 0.01$) and Simpson ($p \leq 0.01$) were both significantly higher in group INF than in group H (Figure 1B).
## Grouping of yak microbiota in different taxa
The sequence percentage in different taxa of group H and INF is shown in Figure 2A. At the phylum level, the dominant phyla were Firmicutes ($69.61\%$), Proteobacteria ($8.97\%$), and Actinobacteria ($8.72\%$) in group H, while Firmicutes ($56.38\%$) and Bacteroidetes ($29.83\%$) were the main phyla in group INF (Figure 2B). At the class level, Clostridia ($51.13\%$) and Bacilli ($17.28\%$) were the primary classes in healthy yaks, while Clostridia ($51.13\%$) and Bacteroidia ($29.83\%$) were the major classes in infected yaks (Figure 2C). At the order level, Clostridiales ($51.13\%$), Lactobacillales ($8.20\%$), and Bacillales ($8.10\%$) were the primary orders in healthy yaks, while Clostridiales ($51.04\%$) and Bacteroides ($29.83\%$) were the main orders in infected yaks (Figure 2D). At the family level, the main families were unclassified, Ruminococcaceae and Lachnospiraceae in groups H and INF (Figure 2E). At the genus level, unclassified ($52.06\%$), Pseudomonadaceae Pseudomonas ($6.13\%$), and Lactobacillus ($6.00\%$) were the dominating genera in healthy yaks, while unclassified ($69.25\%$), Prevotellaceae Prevotella ($5.13\%$) and Arthrobacter ($2.45\%$) were the main genera in infected yaks (Figure 2F). At the species level, the main bacteria in group H were unclassified ($87.85\%$), Veronii ($6.11\%$), and Alactolyticus ($1.66\%$), while unclassified ($95.15\%$), Flavefaciens ($1.50\%$) and Veronii ($1.12\%$) were the main bacteria in group INF (Figure 2G).
**Figure 2:** *Statistical analysis of yak microbiota in different taxa. (A) Sequence percentages in different taxa, (B) Phylum, (C) Class, (D) Order, (E) Family, (F) Genus, (G) Species.*
## Shifts of yak microbiota infected by Cryptosporidium
To reveal the microbiota difference between healthy and infected yaks, beta diversity analysis was carried out through NMDS, PCoA, Qiime 2β, and PCA analysis. The results showed a huge difference in composition and structure between samples from group H and group INF animals (Figure 3). To explore the microbiota changes caused by Cryptosporidium in different taxa, a clustering heatmap (top 20 abundance) and evolutionary tree (top 50 abundance) with heat map analysis were plotted. The results revealed that at the order level, infected yaks showed an abundance of Bacteroidia and Deltaproteobacteria, while healthy animals showed abundance of Bacilli, Erysipelotrichi, Betaproteobacteria, Alphaproteobacteria, and Nitriliruptoria as expressed in the clustering heatmap. The evolutionary tree also showed an obvious abundance difference in Betaproteobacteria, Fibrobacteria, SJA_176, 4C0d_2, Nitriliruptoria, Clostridia, and Bacilli between groups H and INF (Figure 4A). At the order level, the clustering heatmap revealed significant differences in the abundance of Bacteroidales, Lactobacillales, Burkholderiales, Erysipelotrichales, YS2, Turicibacterales and Enterobacteriales between healthy and infected animals. Evolutionary tree detected remarkable differences in the abundance of Oceanospirillales, Burkholderiales, Enterobacteriales, Fibrobacterales, Turicibacterales, RB046, YS2, Nitriliruptorales, Clostridiales and Lactobacillales between healthy and infected animals (Figure 4B). At the family level, there was a noteworthy difference of Clostridiaceae, Prevotellaceae, Lactobacillaceae, Peptostreptococcaceae, BS11, Christensenellaceae, Oxalobacteraceae, Paraprevotellaceae, Streptococcaceae and Erysipelotrichaceae between groups H and INF as revealed by the clustering heatmap. Evolutionary tree analysis showed a clear difference of Halomonadaceae, Oxalobacteraceae, Enterobacteriaceae, Streptococcaceae, Peptostreptococcaceae, Turicibacteraceae, Dietziaceae, Sanguibacteraceae, Nitriliruptoraceae, Christensenellaceae, Clostridiaceae and Lactobacillaceae between healthy and infected yaks (Figure 4C). At the genus level, interesting difference of Lactobacillus, Prevotellaceae_Prevotella, Ralstonia, Streptococcus, SMB53, Turicibacter, and Adlercreutzia was found between the two yak groups. Evolutionary tree analysis demonstrated that the abundance of Halomonadaceae, Oxalobacteraceae, Streptococcaceae, Clostridiaceae, Turicibacteraceae, Planococcaceae, Erysipelotrichaceae, Sanguibacteraceae, Coriobacteriaceae, Paraprevotellaceae, Ruminococcaceae, Lachnospiraceae, Clostridiaceae, Lactobacillaceae and Lachnospiraceae were significantly different between the two yak groups (Figure 4D). At the species level, the abundance of alactolyticus, celatum, reuteri, butyricum, ruminicola, prausnitzii, biforme, p_1630_c5, and aerofaciens were noticeably different in groups H and INF. Evolutionary tree analysis uncovered that the abundance of alactolyticus, ruminis, p_1630_c5, biforme, umbonata, aerofaciens, prausnitzii, butyricum, celatum and reuteri were significantly different between healthy and infected animals (Figure 4E).
**Figure 3:** *Beta diversity analysis between yak groups. (A) NMDS, (B) PCoA, (C) Qiime 2β, (D) PCA.* **Figure 4:** *Clustering heatmap and evolutionary tree with heat map analysis of yak microbiota in different taxa. (A) Class, (B) Order, (C) Family, (D) Genus, (E) Species.*
To further uncover the marker bacteria between healthy and Cryptosporidium-infected yaks, we performed one-way ANOVA and Kruskal Wallis tests to determine the significance of the difference and depicted results by DESeq 2 volcano diagram and LEfSe chart, respectively. Results showed that at the phylum level, the abundance of SR1 ($p \leq 0.0001$), Bacteroidetes ($p \leq 0.0001$), Armatimonadetes ($p \leq 0.0001$), Fibrobacteres ($p \leq 0.01$), and Synergistetes ($p \leq 0.01$) were visibly higher in infected yaks, while Cyanobacteria ($p \leq 0.0001$), Proteobacteria ($p \leq 0.0001$), Armatimonadetes ($p \leq 0.0001$), Euryarchaeota ($p \leq 0.0001$), Actinobacteria ($p \leq 0.01$), Firmicutes ($p \leq 0.01$), and Elusimicrobia ($p \leq 0.05$) were significantly lower (Figure 5A). At the genus level, the abundance of YRC22 ($p \leq 0.0001$), Prevotellaceae_Prevotella ($p \leq 0.0001$), CF231 ($p \leq 0.0001$), L7A_E11 ($p \leq 0.0001$), BF311 ($p \leq 0.0001$), Desulfovibrio ($p \leq 0.0001$), Succiniclasticum ($p \leq 0.0001$), Desemzia ($p \leq 0.0001$), Anaerovorax ($p \leq 0.0001$), Pseudobutyrivibrio ($p \leq 0.0001$), Acinetobacter ($p \leq 0.0001$), Fibrobacter ($p \leq 0.0001$), Ruminococcaceae_Ruminococcus ($p \leq 0.0001$), Anaerorhabdus ($p \leq 0.0001$), Treponema ($p \leq 0.0001$), Selenomonas ($p \leq 0.001$), Clostridium ($p \leq 0.001$), Shuttleworthia ($p \leq 0.001$), Dehalobacterium ($p \leq 0.001$), TG5 ($p \leq 0.01$), unclassified ($p \leq 0.01$), Anaerostipes ($p \leq 0.01$), Syntrophomonas ($p \leq 0.01$), Brachymonas ($p \leq 0.01$), Pyramidobacter ($p \leq 0.01$), SHD_231 ($p \leq 0.05$), Butyrivibrio ($p \leq 0.05$), Desulfobulbus ($p \leq 0.05$), RFN20 ($p \leq 0.05$), and Anaerofustis ($p \leq 0.05$) were significantly higher in infected yaks, while Turicibacter ($p \leq 0.0001$), Lactobacillus ($p \leq 0.0001$), Sporosarcina ($p \leq 0.0001$), Ralstonia ($p \leq 0.0001$), Akkermansia ($p \leq 0.001$), Streptococcus ($p \leq 0.001$), Methylobacterium ($p \leq 0.01$), Adlercreutzia ($p \leq 0.01$), Faecalibacterium ($p \leq 0.01$), Roseburia ($p \leq 0.01$), Paenibacillus ($p \leq 0.01$), Methanosphaera ($p \leq 0.01$), Pseudomonadaceae_Pseudomonas ($p \leq 0.01$), Peptostreptococcaceae_Clostridium ($p \leq 0.01$), Slackia ($p \leq 0.01$), Cupriavidus ($p \leq 0.01$), Halomonas ($p \leq 0.01$), Gemmiger ($p \leq 0.01$), Dietzia ($p \leq 0.01$), Blautia ($p \leq 0.05$), Agrobacterium ($p \leq 0.05$), Nesterenkonia ($p \leq 0.05$), Sanguibacter ($p \leq 0.05$), Phascolarctobacterium ($p \leq 0.05$), Actinomycetospora ($p \leq 0.05$), Bifidobacterium ($p \leq 0.05$), SMB53 ($p \leq 0.05$), and Dorea ($p \leq 0.05$) were significantly lower in infected animals (Figure 5B).
**Figure 5:** *Cryptosporidium infection changes microbiota in different taxa through DESeq 2 volcano plot and LEFSe analysis. (A) Phylum, (B) Genus.*
## Cryptosporidium infection potentially affected the microbiota function of yaks
The prediction of yaks’ microbiota function was carried out by PICRUSt2, and the functional difference between yaks was explored by using one-way ANOVA and Duncan test through R language as previously reported (Zhai et al., 2020). A total of 235 enzymes with a significant difference in abundance ($p \leq 0.001$) were found between healthy and infected yaks, with 119 higher and 116 lower abundance enzymes in INF yaks (Figure 6A). Only one different MetaCys pathway of pentose phosphate pathway (non-oxidative branch) was found between the two yak groups (Figure 6B). KEGG L1 analysis found that the abundance of genetic information processing was prominently higher in infected yaks, while cellular processes and environmental information processing were significantly lower (Figure 7A). KEGG L2 analysis revealed that the abundance of biosynthesis of other secondary metabolites, glycan biosynthesis, and metabolism, metabolism of cofactors and vitamins, and nucleotide metabolism were remarkably higher in INF yaks, while amino acid metabolism, chemical structure transformation maps, lipid metabolism, metabolism of other amino acids, xenobiotics biodegradation, and metabolism were conspicuously lower (Figure 7B). KEGG L3 analysis discovered that the abundance of 43 pathways was significantly higher in INF yaks, while 49 pathways were significantly lower (Figure 7C).
**Figure 6:** *Cryptosporidium infection affected enzyme and MetaCys pathway abundance of yaks. (A) Enzyme (p<0.001), (B) MetaCys (p<0.05). "a, b" are showing significance relation.* **Figure 7:** *Cryptosporidium infection potentially affected the microbiota function of yaks. (A) KEGG L1 (p<0.05), (B) KEGG L2 (p<0.05), (C) KEGG L3 (p<0.05). "a, b" are showing significance relation.*
## Cryptosporidium infection decreased the concentration of SCFAs in yaks
The concentration of acetic acid ($p \leq 0.05$), propionic acid ($p \leq 0.05$), isobutyric acid ($p \leq 0.05$), butyric acid ($p \leq 0.05$) and isovaleric acid was significantly lower in infected yaks, respectively, while there was no significant difference of valeric acid and caproic acid between H and INF groups (Figure 8).
**Figure 8:** *Concentration of SCFAs in yaks. Significance is presented as *p < 0.05; data are presented as the mean ± SEM (n = 4).*
## Discussion
Cattle diarrhea is still an important worldwide issue on farms, despite observing advanced preventive measures such as herd management, animal facilities and care, feeding and nutrition, and timely medication (Wei et al., 2021). The infectious Cryptosporidium was one of the main causative agents of diarrhea with limited available effective treatments (Li et al., 2019a). The harsh climatic conditions with heavy snowfall in the long frigid season (from October to May, with average temperature −15 to −5°C) didn’t permit collection of many samples in the Plateau region. Also, very few positive samples ($$n = 4$$) were observed out of total collected samples ($$n = 40$$) in the present study. However, a prevalence as low as $1.3\%$ of Cryptosporidium spp. positive samples has been reported in yaks in China region (Li et al., 2020). Moreover, despite the harsh climatic conditions and the low number of positive samples available for analysis, this number was above the minimum required for high throughput sequencing, and validation of changes of the microbiota (Ray et al., 2019). In the current study, we performed 16S rDNA sequencing of fecal samples collected from healthy and Cryptosporidium-infected yaks. Results showed that Cryptosporidium infection increased the alpha diversity index of Shannon ($p \leq 0.01$) and Simpson ($p \leq 0.01$) (Figure 1B), which demonstrated the increased microbiota complexity of infected animals. The current results are in line with our previous results found in Cryptosporidium-infected horses (Wang et al., 2022). Beta diversity analysis through NMDS, PCoA, Qiime 2β, and PCA analysis revealed microbiota differences between healthy and infected yaks (Figure 3), which were confirmed by comparing the dominating gut microbiota in different taxa (Figures 2, 4). Then we explored the significantly different bacteria between the H and INF groups through DESeq 2 volcano diagram and LEfSe chart analysis. The results showed that a total of 11 phyla and 58 genera were significantly different (Figure 5), which is in accordance with the previously reported results in a study conducted on infected people and horses (Chappell et al., 2016; Wang et al., 2022). The increased genera in yaks were in line with previous studies that found a higher abundance of Desulfovibrio and Butyrivibrio in colitis patients (Berry and Reinisch, 2013; Gryaznova et al., 2021), Prevotellaceae_Prevotella in diarrheic pigs (Yang et al., 2017), Anaerovorax in slow growth performers in nursery pigs (Zhai et al., 2020), Succiniclasticum in LPS induced dual-flow continuous culture system (Dai et al., 2019), Pseudobutyrivibrio in chronic kidney people (Wu et al., 2020), Anaerorhabdus in pulmonary fibrosis persons (Tong et al., 2019), Selenomonas in gastric cancer patients (Zhang et al., 2021), Anaerostipes in diabetic nephropathy patients (Du et al., 2021), Pyramidobacter in endoscopic sphincterotomy surgery gallstone patients (Shen et al., 2021), and Anaerofustis in Alzheimer people (Hou et al., 2021). The genus of *Acinetobacter is* an underrated food-borne pathogen (Amorim and Nascimento, 2017). A previous study found Acinetobacter in acute diarrhea of children (Polanco and Manzi, 2008). The genus of *Treponema is* the main pathogen in bovine dermatitis (Mamuad et al., 2020), Clostridia are clinical species and some of them may cause severe infections like colitis (Sanchez Ramos and Rodloff, 2018). Those increased genera may have contributed greatly to diarrhea caused by Cryptosporidium. The lower abundance of genera in yaks was in accordance with the results revealing Turicibacter and Lactobacillus in Salmonella-infected pigs (Garrido et al., 2021), Akkermansia and Roseburiain in colitis in mouse (Bu et al., 2021; Li et al., 2021), Adlercreutzia in influenza virus-infected mouse (Lu et al., 2021), Faecalibacterium in pre-eclampsia people (Chen et al., 2020), Methanosphaera in sheep without treatment of anthelmintic (Moon et al., 2021), Slackia in Vogt-Koyanagi-Harada patients (Li et al., 2022a; Li et al., 2022b), Gemmiger in immune-mediated inflammatory people (Forbes et al., 2018), and Dorea in HIV patients (Xu et al., 2021). Those deficient genera in Cryptosporidium-infected animals may be the reason for diarrhea in yaks. The genus of Cupriavidus was related to mycotoxin biodegradation (AL-Nussairawi et al., 2020), and the dropped Cupriavidus in yaks may affect mycotoxin metabolism in yaks. The previous study uncovered probiotics of Dietzia as a new therapy for Crohn’s disease (Click, 2015), and Blautia, Phascolarctobacterium, and Bifidobacterium are probiotic genera (Papizadeh et al., 2017; Chen et al., 2021; Liu et al., 2021), which demonstrated that Cryptosporidium led diarrhea may be due to the decrease of probiotics in the microbiota.
The shifted intestine microflora also changed their functions, as 235 significantly different enzymes were found between healthy and infected yaks ($p \leq 0.001$) (Figure 6A). Only one obvious different MetaCys pathway of pentose phosphate pathway (non-oxidative branch) was found between the two yak groups (Figure 6B). Also, KEGG L3 analysis discovered that the abundance of 92 pathways was significantly different between healthy and infected animals (Figure 7C). Those results may infer that Cryptosporidium broke the balance of gut microbiota, which affected the microbiota function and caused diarrhea in yaks.
In the present study, significantly lower concentrations of SCFAs were found in Cryptosporidium-infected animals (Figure 8), consistent with yak diarrhea (Li et al., 2022a), LPS-induced piglets (Yang et al., 2021), and dextran sulfate sodium-induced colitis in mouse (Xu et al., 2020). SCFAs play very important roles in host physiology and energy homeostasis (Chambers et al., 2018). Among them, acetate and propionate can provide energy to peripheral tissues (den Besten et al., 2013). A previous study reported that acetate was responsible for maintaining intestine barrier integrity by inhibiting pathogens infection (Skonieczna-Żydecka et al., 2018). In a recent study, it was found that acetate could regulate IgA reactivity (Takeuchi et al., 2021), and propionate contributed to intestinal epithelial turnover and repair (Bilotta et al., 2021). Butyrate is highly related to intestine structure, energy providing to epithelial cells, and regulates immune function (Abdalkareem Jasim et al., 2022). Isobutyric acid and isovaleric acid may be related to mucosal and inflammation responses (Li et al., 2022a). Therefore, the decreased SCFAs in Cryptosporidium-infected yaks might have affected the intestinal barrier and immunity of the host (Aho et al., 2021), which potentially caused diarrhea in plateau yaks.
In conclusion, *Cryptosporidium is* an important zoonotic protozoon causing severe diarrhea in young animals; however, limited treatment measures are available. Here we reveal that Cryptosporidium infection causes dysbiosis and results in reduced SCFAs in yaks with severe diarrhea, which may give new insights regarding the prevention and treatment of diarrhea in livestock. The low sample size remains the limitation of our study.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA880359.
## Ethics statement
The animal study was reviewed and approved by ethics committee of Nanjing Agricultural University.
## Author contributions
KL and QW, research idea and methodology. HD, XC, XZ, and CZ, reagents, materials, and analysis tools. KL, writing-original draft and preparation. KM, MF-E-A, ZB, QW, JZ, SN, and KL, writing-review and editing. KL, JZ, and QW, visualization and supervision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Using deep leaning models to detect ophthalmic diseases: A comparative study'
authors:
- Zhixi Li
- Xinxing Guo
- Jian Zhang
- Xing Liu
- Robert Chang
- Mingguang He
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10014566
doi: 10.3389/fmed.2023.1115032
license: CC BY 4.0
---
# Using deep leaning models to detect ophthalmic diseases: A comparative study
## Abstract
### Purpose
The aim of this study was to prospectively quantify the level of agreement among the deep learning system, non-physician graders, and general ophthalmologists with different levels of clinical experience in detecting referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy.
### Methods
Deep learning systems for diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy classification, with accuracy proven through internal and external validation, were established using 210,473 fundus photographs. Five trained non-physician graders and 47 general ophthalmologists from China were chosen randomly and included in the analysis. A test set of 300 fundus photographs were randomly identified from an independent dataset of 42,388 gradable images. The grading outcomes of five retinal and five glaucoma specialists were used as the reference standard that was considered achieved when ≥$50\%$ of gradings were consistent among the included specialists. The area under receiver operator characteristic curve of different groups in relation to the reference standard was used to compare agreement for referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy.
### Results
The test set included 45 images ($15.0\%$) with referable diabetic retinopathy, 46 ($15.3\%$) with age-related macular degeneration, 46 ($15.3\%$) with glaucomatous optic neuropathy, and 163 ($55.4\%$) without these diseases. The area under receiver operator characteristic curve for non-physician graders, ophthalmologists with 3–5 years of clinical practice, ophthalmologists with 5–10 years of clinical practice, ophthalmologists with >10 years of clinical practice, and the deep learning system for referable diabetic retinopathy were 0.984, 0.964, 0.965, 0.954, and 0.990 ($$p \leq 0.415$$), respectively. The results for referable age-related macular degeneration were 0.912, 0.933, 0.946, 0.958, and 0.945, respectively, ($$p \leq 0.145$$), and 0.675, 0.862, 0.894, 0.976, and 0.994 for referable glaucomatous optic neuropathy, respectively ($p \leq 0.001$).
### Conclusion
The findings of this study suggest that the accuracy of this deep learning system is comparable to that of trained non-physician graders and general ophthalmologists for referable diabetic retinopathy and age-related macular degeneration, but the deep learning system performance is better than that of trained non-physician graders for the detection of referable glaucomatous optic neuropathy.
## Introduction
Diabetic retinopathy (DR), glaucomatous optic neuropathy (GON), and age-related macular degeneration (AMD) are responsible for more than $18\%$ of visual impairment and blindness cases globally (1–6). While it is estimated that $80\%$ of vision loss is avoidable through early detection and intervention (7–9), approximately $50\%$ of cases remain undiagnosed [10, 11]. High rates of undiagnosed disease can be attributed to these conditions being asymptomatic in their early stages, coupled with a disproportionately low availability of eye care services, particularly within developing countries and under-served populations [12].
Previous research has demonstrated that color fundus photography is an effective tool for the diagnosis of AMD, GON, and DR (13–15). Despite this, accurate interpretation of the optic nerve and retina is highly dependent on clinical experts, limiting the utility in low recourse settings. Deep learning represents an advancement of artificial neural networks that permits improved predictions from raw image data [16]. Recently, several studies have investigated the application of deep learning algorithms for the automated classification of common ophthalmic disorders (17–21), with promising results for disease classification (sensitivity and specificity range = 80–$95\%$). Thereby, these systems offer great promise to improve the accessibility and cost-effectiveness of ocular disease screening in developing countries.
Despite this, most previous systems could only detect a single ocular disorder, thus would omit severe blinding eye diseases. In addition, previous studies have evaluated on retrospective datasets, and there is a paucity of data directly comparing the performance of deep learning system (DLS) capable to detect common blindness diseases to that of general ophthalmologists or non-physician graders. Given the fact that in real world screening programs, human graders or general ophthalmologists may also make mistakes, a robust study to directly compare DLS and general ophthalmologists or non-physician graders is of paramount importance for healthcare decision makers and patients to make informed decisions relating to the deployment of these systems.
Therefore, in the present study, we investigated the diagnostic agreement between ophthalmologists with varying levels of experience, non-physician graders, and validated deep learning models [22] for DR, GON, and AMD on an independent dataset in China.
## Methods
This study was approved by the Institutional Review Board of the Zhongshan Ophthalmic Center, China (2017KYPJ049) and conducted in accordance with the Declaration of Helsinki. All graders and ophthalmologists have been informed that their data will be compared with the DLS. Informed consent for the use of fundus photographs was not required as images were acquired retrospectively and were fully anonymized.
## Test set development, reference standard, and definitions
A total of 300 fundus photographs were randomly selected from a subset of 42,388 independent gradable images from the online LabelMe dataset (http://www.labelme.org, Guangzhou, China) [22, 23]. The LabelMe dataset includes images from 36 hospital ophthalmology departments, optometry clinics, and screening settings in China that include various kinds of eye diseases, such as DR, glaucoma, and AMD. The data will be available upon request. Retinal photographs were captured using a variety of common conventional desktop retinal cameras, including Topcon, Canon, Heidelberg, and Digital Retinography System. The LabelMe dataset was graded for DR, GON, and AMD by 21 ophthalmologists who previously achieved an unweighted kappa of ≥0.70 (substantial) on a test set of images. Images were randomly assigned to a single ophthalmologist for grading and were returned to the pooled dataset until three consistent grading outcomes were achieved. Once an image was given a reference standard label it was removed from the grading dataset. This process has been described in detail elsewhere [22, 23].
Stratified random sampling was used to select 50 images of each disease category and an additional 150 images classified as normal or a disease other than DR, AMD, and GON. Poor quality images (defined as ≥$50\%$ of the fundus photograph area obscured) were excluded. Images that were included in the training and internal validation datasets of the deep learning models were not eligible for inclusion. Following the selection of images, experienced retinal ($$n = 5$$) specialists independently labeled all 300 images to establish a reference standard for DR and AMD. Similarly, glaucoma specialists ($$n = 5$$) independently graded all images to determine the GON reference standard. Specialists were blinded to any previous medical history or retinal diagnosis for the included images. Once all images were graded, they were converted to a two-level classification for each disease: non-referable and referable. Each image was only assigned a conclusive label if more than $50\%$ of the specialists reported a consistent grading outcome.
A website1 was developed to allow human graders to log in and interpret images. Diabetic retinopathy severity was classified as none, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR using the International Clinical Diabetic Retinopathy scale [24]. Diabetic macular edema (DME) was defined as any hard exudates within one-disk diameter of the fovea or an area of hard exudates in the macular area at least $50\%$ of the disk area [25]. Referable DR was defined as moderate NPDR or worse with or without the presence of DME. The severity of AMD was graded according to the clinical classification of AMD, which has been described elsewhere [26]. For the purpose of this study, referable AMD was defined as late wet AMD as it was the only subtype of AMD that could be managed with effective therapy currently. Glaucomatous optic neuropathy was classified as absent or referable GON according to definitions utilized by previous population-based studies (27–29). The definition of referable GON included the presence of any of the following: vertical cup to disk ratio (VCDR) ≥0.7; rim width ≤0.1 disk diameter; localized notches; and presence of retinal nerve fiber layer (RNFL) defect and/or disk hemorrhage.
## Development of the deep learning system
The development and validation of the DR, GON, and AMD models have been described in detail elsewhere (22, 30–32). In brief, referable GON, DR, and AMD deep learning algorithms were developed using a total of 210,473 fundus photographs (referable DR, 106,244; referable GON, 48,116; referable AMD 56,113). Several pre-processing steps were performed for normalization to control for variations in image size and resolution. This included augmentation to enlarge heterogeneity, applying local space average color for color constancy and downsizing image resolution to 299 × 299 pixels [33]. Finally, eight convolutional neural networks were contained within the DLS (Version 20,171,024), all adopting Inception-v3 architecture [34]. The development of the networks was described in our previous studies [22, 23, 32]. Briefly, the networks were downsized to 299 × 299, and local space average color and data augmentation were adopted. These networks were trained from scratch and included [1] classification for referable DR, [2] classification of DME, [3] classification of AMD, [4] classification of GON, and [5] assessment of the availability of the macular region and rejection of non-retinal photographs.
## Graders and ophthalmologists identification and recruitment
Five trained non-physician graders, who also previously received training for DR, AMD, and GON classification, usually graded images from 50 to 100 participants for common blindness diseases every workday and underwent tests per quarter, from Zhongshan Ophthalmic Center Image Grading Center with National Health Screening (NHS) DR grader certification were recruited to grade all these images.
We also invited general ophthalmologists from four provincial hospitals and five county hospitals in seven provinces in China (Guangdong, Guangxi, Fujian, Jiang Su, Yunnan, Xinjiang, and Inner Mongolia province). General ophthalmologists who had at least 3 years clinical practice including residency were eligible to participate.
Selected ophthalmologists were sent an invitation to participate via email or mobile phone text message. Those who did not respond were followed up with a telephone call. The clinical practice characteristics of invited ophthalmologists were obtained from publicly available resources or personally via telephone.
Of the 330 ophthalmologists who were eligible to participate, 66 ($20\%$) were randomly selected and subsequently invited to participate in the study. Nineteen ophthalmologists ($28.8\%$) declined or did not respond and 47 ophthalmologists ($71.2\%$) agreed to participate. A flow chart outlining the recruitment of ophthalmologists is shown in Figure 1.
**Figure 1:** *Recruitment, workflow, and grading of ophthalmologists and non-physician graders.*
## Test set implementation
Participants independently reviewed all 300 images in a random order. They were blinded to the reference standard and the grades assigned by other participants. Due to the variability in existing classification criteria for GON, a standardized grading criteria was provided to all participants. Participants were not provided with details of the comprehensive grading criterion utilized for the grading of DR and AMD, as it was assumed that the participants’ experience would be sufficient to enable them to classify these disorders into the specific categories (DR: mild, moderate, severe NPDR and proliferative DR; AMD: early or moderate AMD, late dry AMD, and late wet AMD). There was no time limit for the interpretation of each image. All grading results were converted to a two-level classification for each disease (referable and non-referable disorders) and then compared against the reference standard. The eight deep learning models were also tested using the same images.
In order to characterize the features of misclassified images by DLS and human graders, an experienced ophthalmologist (Z.X.L.) reviewed misclassified fundus photographs and classified them into categories arbitrarily developed by a consensus meeting by investigators.
## Statistical analysis
The area under the receiver operating characteristic curve (AUC), rate of agreement and unweighted kappa were calculated. Agreement was defined as the proportion of images that were correctly classified by participants or the DLS models using the gold standard label as a reference standard. Firstly, data from all participants were used and in this situation, the CIs accounting for within and between subject variability by estimating the variance using the form; {var.(parameterp) + [avg(parameterp) × (1−avg(parameterp))]/nc}/np, where avg.(parameterp) denotes the average corresponding parameter (AUC, agreement rate or kappa) among participants, var.(parameterp) denotes the sample variance of parameter among participants, nc denotes the number of images interpreted by each participant, and np denotes the number of participants.
Then, a representative grading result for graders and ophthalmologists was made when more than $50\%$ of group members achieved consistent grading outcomes. As the DLS can generate a continuous probability between 0 and 1 for referable disorders, AUC for DLS was calculated using these continuous probabilities to compared with reference standard, whereas the agreement rate and unweighted kappa were dichotomized by assigning a certain probability when reaching the highest accuracy. The AUCs of graders, ophthalmologists, and DLS were calculated by comparing with reference standard for two-level classification (referable and non-referable).
We investigated the extent to which the clinical experience of ophthalmologists was associated with agreement. Logistic regression models of ophthalmologist agreement that simultaneously incorporated several ophthalmologist characteristics (hospital level, academic affiliation, clinical practice years, and clinical expertise) were modeled. Non-physician graders were not included in this analysis due to the relatively small sample size ($$n = 5$$).
Sensitivity analyses was used to explore whether the grading results would change by using an alternate reference standard instead of the specialist-derived standard. Firstly, cases where the reference standard was different from the most frequent (≥$80.0\%$) grading result of the participants were identified (8 of 300 images). Then, the results were reanalyzed by substituting the most frequent grading outcome of participants as the reference standard for the eight images, or just excluding the eight images. A p value of less than 0.05 was regarded as statistically significant. Stata statistical software (version 14; College Station, Texas, United States) was used.
## Reference dataset
Of the 300 images included in the dataset, the total number of images labeled as referable DR, AMD, and GON according to the final specialist grading were 45 ($15.0\%$), 46 ($15.3\%$), and 46 ($15.3\%$), respectively. The remaining 163 ($54.4\%$) images were classified as normal or a disease other than DR, AMD, and GON.
## Graders and ophthalmologists characteristics
The five trained non-physician graders were all females with a mean age of 30.4 ± 2.2 years (range, 27–34 years) and an average of 3.6 ± 0.6 years (range, 2–5 years) of grading experience in DR screening support and research image grading. There were 6, 23, 12, and 6 general ophthalmologists aged <30, 30–40, 40–50, and ≥50 years, respectively. Among these ophthalmologists, there were 22 males and 25 females. Twenty-seven were from affiliated hospitals and the other were from nonaffiliated hospitals. Their lengths of clinical practice were 5 years ($$n = 13$$), 5–10 years ($$n = 16$$), and ≥10 years ($$n = 18$$).
## Diagnostic agreement among deep learning models, trained non-physician graders, and ophthalmologists
Table 1 displays the agreement distribution by individual grading outcomes of specialists performing initial reference standard grading compared to the final reference standard. The overall agreement rate of the initial independent specialist diagnoses was $96.5\%$ for referable DR, $98.1\%$ for referable AMD, and $92.8\%$ for referable GON.
**Table 1**
| Unnamed: 0 | Specialist ophthalmologists independent gradings | Specialist ophthalmologists independent gradings.1 | Specialist ophthalmologists independent gradings.2 | Specialist ophthalmologists independent gradings.3 |
| --- | --- | --- | --- | --- |
| Final reference standard | Absent | Present | Missing | Total |
| Referable DRb | | | | |
| Absent | 1269 | 6 | 0 | 1275 |
| Present | 45 | 178 | 2 | 225 |
| Total | 1314 | 184 | 2 | 1500 |
| Late wet AMDc | | | | |
| Absent | 1258 | 12 | 0 | 1270 |
| Present | 16 | 214 | 0 | 230 |
| Total | 1274 | 226 | 0 | 1500 |
| Referable GONd | | | | |
| Absent | 1176 | 94 | 0 | 1270 |
| Present | 14 | 216 | 0 | 230 |
| Total | 1190 | 310 | 0 | 1500 |
Table 2 provides a comparison between the DLS and general ophthalmologists. The sensitivity and specificity of the DLS for referable DR were $97.8\%$ ($\frac{44}{45}$) and $92.5\%$ ($\frac{236}{255}$), respectively. The results for general ophthalmologists for referable DR were $91.1\%$ ($\frac{41}{45}$) and $99.6\%$ ($\frac{254}{255}$), respectively.
**Table 2**
| Unnamed: 0 | Reference standard | Deep learning system | Deep learning system.1 | Deep learning system.2 | Ophthalmologists | Ophthalmologists.1 | Ophthalmologists.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | Reference standard | Agreement (%) | Misclassification (%) | Total | Agreement (%) | Misclassification (%) | Total |
| Diabetic | Referable | 44 (97.8) | 1 (2.2) | 45 | 41 (91.1) | 4 (8.9) | 45 |
| Retinopathy | Non-referable | 236 (92.5) | 19 (7.5) | 255 | 254 (99.6) | 1 (0.4) | 255 |
| Age related macular degeneration | Referable | 39 (83.0) | 8 (7.0) | 47 | 43 (91.5) | 4 (8.5) | 47 |
| Age related macular degeneration | Non-referable | 245 (96.8) | 8 (3.2) | 253 | 248 (98.0) | 5 (2.0) | 253 |
| Glaucomatous optic neuropathy | Referable | 45 (97.8) | 1 (2.2) | 46 | 42 (91.3) | 4 (8.7) | 46 |
| Glaucomatous optic neuropathy | Non-referable | 252 (99.2) | 2 (0.8) | 254 | 249 (98.0) | 5 (2.0) | 254 |
Table 3 compares the grading agreement of trained non-physician graders, ophthalmologists, and the DLS versus the reference standard. There were no significant differences in the AUC of non-physician graders, general ophthalmologists with different levels of clinical experience, and the DLS for the interpretation of referable DR ($$p \leq 0.415$$, compared with expert consensus reference diagnosis) and referable AMD ($$p \leq 0.145$$, compared with expert consensus reference diagnosis). For the classification of GON, the DLS achieved a superior AUC result compared to non-physician graders ($p \leq 0.001$).
**Table 3**
| Unnamed: 0 | Trained non-physician graders (95% CI) | Ophthalmologists (95% CI) | Ophthalmologists (95% CI).1 | Ophthalmologists (95% CI).2 | Ophthalmologists (95% CI).3 | Deep learning systema (95% CI) | p value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Clinical experience 3–5 years | Clinical experience 5–10 years | Clinical experience >10 years | Total | | |
| Referable DR | | | | | | | |
| Model 1 | | | | | | | |
| AUC | 0.984 (0.960–1.000) | 0.964 (0.926–1.000) | 0.965 (0.927–1.000) | 0.954 (0.911–0.996) | 0.954 (0.911–0.995) | 0.990 (0.982–0.999) | 0.415 |
| Kappa | 0.959 (0.845–1.000) | 0.946 (0.832–1.000) | 0.947 (0.834–1.000) | 0.933 (0.820–1.000) | 0.933 (0.820–1.000) | 0.775 (0.665–0.886) | |
| Agreement rate | 0.989 (0.971–0.998) | 0.983 (0.961–0.996) | 0.987 (0.966–0.996) | 0.983 (0.961–0.995) | 0.983 (0.962–0.995) | 0.933 (0.899–0.959) | |
| Referable AMD | | | | | | | |
| Model 1 | | | | | | | |
| AUC | 0.912 (0.859–0.964) | 0.933 (0.887–0.979) | 0.946 (0.904–0.987) | 0.958 (0.922–0.995) | 0.948 (0.906–0.989) | 0.945 (0.903–0.986) | 0.145 |
| Kappa | 0.823 (0.710–0.936) | 0.851 (0.738–0.964) | 0.876 (0.762–0.989) | 0.901 (0.788–1.000) | 0.887 (0.774–1.000) | 0.798 (0.685–0.911) | |
| Agreement rate | 0.953 (0.923–0.974) | 0.960 (0.931–0.979) | 0.967 (0.940–0.983) | 0.973 (0.948–0.988) | 0.970 (0.944–0.986) | 0.947 (0.915–0.969) | |
| Referable GON | | | | | | | |
| Model 1 | | | | | | | |
| AUC | 0.675 (0.604–0.746) | 0.862 (0.797–0.926) | 0.894 (0.836–0.953) | 0.976 (0.946–1.000) | 0.953 (0.911–0.994) | 0.994 (0.988–0.999) | <0.001 |
| Kappa | 0.445 (0.341–0.549) | 0.779 (0.666–0.891) | 0.825 (0.712–0.938) | 0.961 (0.848–1.000) | 0.922 (0.809–1.00) | 0.926 (0.813–1.00) | |
| Agreement rate | 0.887 (0.845–0.920) | 0.947 (0.914–0.969) | 0.957 (0.927–0.977) | 0.990 (0.971–0.998) | 0.980 (0.957–0.993) | 0.980 (0.956–0.993) | |
## Ophthalmologist characteristics related with image interpretation agreement
The agreement between general ophthalmologists’ image grading and the reference standard is shown in Table 4. Table 4 shows that the overall agreement was higher for referable DR in ophthalmologists with greater clinical experience ($$p \leq 0.009$$) and those who were specialists ($$p \leq 0.040$$). Agreement was significantly higher for referable AMD in ophthalmologists from provincial level hospitals ($$p \leq 0.017$$), adjunct academic affiliations ($$p \leq 0.002$$), ophthalmologists with more years of clinical practice ($$p \leq 0.009$$), and those who were glaucoma or retinal specialist ophthalmologists ($$p \leq 0.006$$). Similarly, the level of agreement for referable GON was greater among ophthalmologists from provincial level hospitals ($p \leq 0.001$), those from adjunct academic affiliations ($p \leq 0.001$), those with more years of clinical experience ($p \leq 0.001$) and those who were glaucoma or retinal specialist ophthalmologists ($p \leq 0.001$).
**Table 4**
| Characteristics | Referable diabetic retinopathy | Referable diabetic retinopathy.1 | Referable diabetic retinopathy.2 | Referable diabetic retinopathy.3 | Referable age-related macular degeneration | Referable age-related macular degeneration.1 | Referable age-related macular degeneration.2 | Referable age-related macular degeneration.3 | Referable glaucomatous optic neuropathy | Referable glaucomatous optic neuropathy.1 | Referable glaucomatous optic neuropathy.2 | Referable glaucomatous optic neuropathy.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | n | AUC (95% CI) | Agreement rate (95% CI) | p | n | AUC (95% CI) | Agreement rate (95% CI) | p | n | AUC (95% CI) | Agreement rate (95% CI) | p |
| Hospital | | | | | | | | | | | | |
| County level (n = 20) | 5794 | 0.929 (0.929–0.930) | 0.955 (0.955–0.956) | | 5878 | 0.871 (0.871–0.872) | 0.929 (0.929 0.930) | | 5868 | 0.818 (0.818–0.820) | 0.903 (0.902–0.903) | |
| Provincial level (n = 27) | 7894 | 0.932 (0.931–0.932) | 0.956 (0.956–0.957) | | 7971 | 0.872 (0.871–0.872) | 0.929 (0.929–0.930) | | 8030 | 0.875 (0.875–0.876) | 0.933 (0.932–0.933) | |
| | | | | 0.808a | | | | 0.017a | | | | <0.001a |
| Academic affiliation | | | | | | | | | | | | |
| None (n = 18) | 5196 | 0.926 (0.925–0.926) | 0.954 (0.954–0.955) | | 5281 | 0.867 (0.867–0.868) | 0.926 (0.926–0.927) | | 5269 | 0.810 (0.810–0.811) | 0.897 (0.897–0.898) | |
| Adjunct affiliation (n = 29) | 8492 | 0.934 (0.934–0.935) | 0.957 (0.957–0.958) | | 8568 | 0.891 (0.891–0.892) | 0.941 (0.941–0.942) | | 8629 | 0.877 (0.876–0.878) | 0.934 (0.934–0.935) | |
| | | | | 0.343b | | | | 0.002b | | | | <0.001b |
| Clinical practice (yrs) | | | | | | | | | | | | |
| ≤5 (n = 13) | 3718 | 0.925 (0.924–0.925) | 0.951 (0.950–0.951) | | 3780 | 0.875 (0.875–0.876) | 0.928 (0.927–0.928) | | 3782 | 0.806 (0.805–0.807) | 0.569 (0.892–0.893) | |
| 5–10 (n = 16) | 4637 | 0.929 (0.928–0.929) | 0.953 (0.953–0.954) | | 4703 | 0.876 (0.876–0.877) | 0.934 (0.934–0.935) | | 4743 | 0.848 (0.847–0.849) | 0.919 (0.919–0.920) | |
| >10 (n = 18) | 5333 | 0.937 (0.937–0.938) | 0.839 (0.838–0.840) | | 5366 | 0.892 (0.891–0.892) | 0.942 (0.942–0.943) | | 5373 | 0.887 (0.886–0.888) | 0.941 (0.940–0.041) | |
| | | | | 0.009c | | | | 0.009c | | | | <0.001c |
| Expertise in ophthalmology | | | | | | | | | | | | |
| Nonexpert (n = 27) | 7797 | 0.929 (0.929–0.930) | 0.953 (0.953–0.954) | | 7919 | 0.873 (0.873–0.874) | 0.930 (0.930–0.931) | | 7934 | 0.817 (0.816–0.817) | 0.902 (0.901–0.902) | |
| Expert (n = 20) | 5891 | 0.933 (0.933–0.934) | 0.960 (0.960–0.961) | | 5930 | 0.894 (0.894–0.895) | 0.942 (0.942–0.943) | | 5964 | 0.898 (0.898–0.899) | 0.944 (0.944–0.945) | |
| | | | | 0.040d | | | | 0.006d | | | | <0.001d |
## Image disagreement characteristics
The interpretations of non-physician graders, ophthalmologists, and the DLS compared with the reference standard for each of the 300 fundus photographs for diabetic retinopathy are shown in Figure 2. This figure also demonstrates that several images caused mistakes common to nonphysician graders, ophthalmologists, and the DLS; for example, images #1 and #87 triggered consistent false positives. In the same way, images #71, #97, #140, #181, #232, and #239 displayed consistent false negatives. These images are shown in Figure 3. *The* general features of images that were misclassified by human participants (trained non-physician graders and ophthalmologists) are summarized in Table 5.The primary reason for false negative of referable DR was the presence of DME ($$n = 10$$, $58.9\%$), while two cases ($100.0\%$) with microaneurysm/s and artifacts resulted in false positive by human participants. For referable AMD, false negative cases were mostly related to the presence of subtle subretinal hemorrhage ($$n = 6$$, $50.0\%$). False positives resulted from misclassification of earlier forms of AMD ($$n = 9$$, $75.1\%$). Among human participants, the most common reason for false negative of referable GON were those images with borderline VCDR ($$n = 8$$, $27.7\%$), while false positives occurred in those images which displayed physiological cupping ($$n = 14$$, $93.3\%$).
**Figure 2:** *The interpretations of graders, ophthalmologists, and artificial intelligence compared with the reference standards for each of the 300 fundus photographs for diabetic retinopathy.* **Figure 3:** *Sample images consistently misclassified by human participants. (A,B) Images with only microaneurysm misclassified as referable diabetic retinopathy. (C) Images of diabetic macular edema misclassified as non-referable diabetic retinopathy. (D) Microaneurysm and dot hemorrhage misclassified as non-referable diabetic retinopathy.* TABLE_PLACEHOLDER:Table 5 *One fundus* image demonstrated coexisting intraretinal microvascular abnormality and DME that were not identified by the DLS. The most common reason for false positives by the DLS was the presence of microaneurysm/s only ($$n = 10$$, $55.5\%$; Table 6). For referable AMD, the presence of subretinal hemorrhage ($$n = 5$$, $71.4\%$) was the primary reason for false negative and other diseases ($$n = 7$$, $87.5\%$) including DR or GON. For referable GON, the DLS under-interpreted one image with VCDR less than 0.7, while two images with physiological large cupping ($$n = 2$$, $40\%$) and three images with other diseases ($$n = 3$$, $60\%$) were incorrectly classified as positive.
**Table 6**
| Reason | No. | Proportion (%) |
| --- | --- | --- |
| Referable DR | | |
| False negative | | |
| MA, IRMA, DME | 1.0 | 100.0 |
| Sub-total | 1.0 | 100.0 |
| False positive | | |
| MA only | 10.0 | 55.5 |
| Other diseases | | |
| Late wet AMD | 4.0 | 22.2 |
| Retinal degeneration | 3.0 | 16.7 |
| RVO | 1.0 | 5.6 |
| Subtotal | 18.0 | 100.0 |
| Referable AMD | | |
| False negative | | |
| Subretinal hemorrhage | 5.0 | 71.4 |
| Serous detachment of the sensory retina or RPE | 2.0 | 28.6 |
| Subtotal | 7.0 | 100.0 |
| False positive | | |
| Other diseases | | |
| DR | 7.0 | 87.5 |
| GON | 1.0 | 12.5 |
| Subtotal | 8.0 | 100.0 |
| Referable GON | | |
| False negative | | |
| VCDR < 0.7 with notch | 1.0 | 100.0 |
| Sub-total | 1.0 | 100.0 |
| False positive | | |
| Physiologic large cupping (0.5 ≤ VCDR < 0.7) | 2.0 | 40.0 |
| Other diseases | | |
| AMD | 2.0 | 40.0 |
| Juxtapapillary capillary hemangioma | 1.0 | 20.0 |
| Subtotal | 5.0 | 100.0 |
## Discussion
In this study, we prospectively compared the diagnostic agreement of trained non-physician graders and ophthalmologists using three validated deep learning models for the detection of referable DR, late wet AMD, and GON from color fundus photographs. Our results suggest that the performance of the deep learning models for referable DR and AMD are comparable to non-physician graders and ophthalmologists. As for referable GON, the DLS outperformed non-physician graders.
There was no difference among the non-physician graders, ophthalmologists with different years of clinical practice, and the DLS for the diagnostic accuracy of referable DR. The non-physician graders included in this study all had grader certification from the NHS DR screening program, underwent regular assessments every month, and routinely interpreted fundus photographs of diabetic patients from nationwide screening programs, which may explain their relatively high agreement compared to the gold standard. While the DLS also exhibited comparably good performance when compared with non-physician graders and general ophthalmologists.
Comparison of the DLS with general ophthalmologists found that the DLS had higher sensitivity (97.8 vs. $91.1\%$) and lower specificity (92.5 vs. $99.6\%$) for the classification of referable DR. However, nearly half of the false positive cases identified by the DLS included ($$n = 8$$, $44.5\%$) other disorders, for example, late wet AMD and retinal degeneration. The remaining false positive images ($$n = 10$$, $55.5\%$) had mild NPDR. Those images identified as false positive by the DLS would receive a referral and be identified during confirmatory examination conducted by a specialist.
Previous studies have shown that the majority of referral cases for DR ($73\%$) are as a result of DME [35]. There are 100 million patients with DR worldwide which corresponds to 7.6 million DME patients [36]. However, our results showed that images that were characterized as DME ($$n = 10$$, $58.9\%$) were under interpreted by human graders more often than other DR lesions. DR changes related to DME displayed considerable variation among graders and ophthalmologists, with an overall agreement rate of $71\%$ when compared with the reference standard. Therefore, the importance of not overlooking the diagnosis of DME among graders and ophthalmologists should be emphasized.
The DLS outperformed non-physician graders in the classification of referable GON in this study. The variability in inter-assessor agreement among non-physician graders and ophthalmologists for the classification of ocular disorders is well known, especially glaucoma [37, 38]. The Glaucomatous optic neuropathy evaluation (GONE) project previously reported that ophthalmology trainees underestimated glaucoma likelihood in $22.1\%$ of optic disks and overestimated $13.0\%$ of included optic disks. This has been similar in our study where general ophthalmologists underestimated $23.8\%$ and underestimated $8.9\%$ of included optic disks [37]. Furthermore, Breusegem et al. [ 38] reported that non-expert ophthalmologists had significantly lower accuracy compared with experts in the diagnosis of glaucoma. Our results are in agreement with previous studies and showed that ophthalmologists with more clinical experience and specialist training in ophthalmology achieve higher inter-assessor agreement. The experience and knowledge obtained through years of clinical practice is likely to play a significant role in interpretation and performance accuracy. In contrast, the DLS is easily able to adopt labels from experienced ophthalmologists to learn the most representative characteristics of GON. Fundus photography is an important method to evaluate GON, however, the diagnosis of glaucoma requires the results of visual field analysis, optical coherence tomography, and intra ocular pressure measurements to make an accurate diagnosis. Thus, further studies to compare DLS with ophthalmologists using multi-modality clinical data is warranted.
The main strength of our study was to prospectively compare the performance of a DLS for the detection of three common blinding eye diseases to non-physician graders and ophthalmologists of varying levels of experience and with different specialties. Our study is also distinctly different from previous reports (19, 39–42). First, we evaluated three ocular diseases at the same time. Second, no prospective comparison of ophthalmologists with varying levels of clinical experience and trained non-physician graders with a DLS for common ocular disorders has been reported. Previous authors have compared the performance of the DLS with that of graders or specialists; this is often considered the gold standard for the development of the DLS [39, 41, 43]. Non-physician graders and ophthalmologists are susceptible to making diagnostic mistakes. Our study included independent graders and ophthalmologists to evaluate the performance of the DLS. Therefore, the current study will provide information on the accuracy of the DLS, as well as a more comprehensive understanding and acceptance of how AI systems might work or contribute.
There are several limitations of this study which warrant further consideration. On one hand, human participants included in this study were recruited from China. This has the potential to affect the generalizability of these results to other human graders, especially those in developed countries. In the future, similar studies should be attempted in other countries with different physician or specialist training system. On the other hand, the use of single-field, non-stereoscopic fundus photographs without the inclusion of optical coherence tomography may lead to a reduced sensitivity for DR and particularly DME detection for human participants and the DLS.
In conclusion, our DLS demonstrated sufficient agreement with non-physician graders and general ophthalmologists when compared to the reference standard diagnosis agreement for referable DR and AMD. The DLS performance was better than non-physician graders and ophthalmologists with ≤10 years of clinical experience for referable GON. Further investigation is required to validate the performance in real-world, clinical settings which display the full spectrum and distribution of lesions and manifestations encountered in clinical practice.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Review Board of the Zhongshan Ophthalmic Center, China. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
ZL and MH were involved in the concept, design, and development of the deep learning algorithm. ZL, XG, JZ, XL, RC, and MH contributed to the acquisition, analysis, and interpretation of data. ZL wrote the manuscript. All authors revised and edited the manuscript. MH is the guarantor of this work and as such has full access to all the data in the study and takes responsibility for data integrity and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by National Key R&D Program of China (2018YFC0116500), the Fundamental Research Funds of the State Key Laboratory in Ophthalmology, National Natural Science Foundation of China [81420108008], and Science and Technology Planning Project of Guangdong Province (2013B20400003).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'The effect of carbon nanoparticles staining on lymph node tracking in colorectal
cancer: A propensity score matching analysis'
authors:
- Fei Liu
- Dong Peng
- Xiao-Yu Liu
- Xu-Rui Liu
- Zi-Wei Li
- Zheng-Qiang Wei
- Chun-Yi Wang
journal: Frontiers in Surgery
year: 2023
pmcid: PMC10014567
doi: 10.3389/fsurg.2023.1113659
license: CC BY 4.0
---
# The effect of carbon nanoparticles staining on lymph node tracking in colorectal cancer: A propensity score matching analysis
## Abstract
### Purpose
The aim of this study was to evaluate the effect of carbon nanoparticles staining (CNS) on colorectal cancer (CRC) surgery, lymph node tracing and postoperative complications using propensity score matching (PSM).
### Method
Patients who were diagnosed with CRC and underwent surgery were retrospectively collected from a single clinical center from Jan 2011 to Dec 2021. Baseline characteristics, surgical information and postoperative information were compared between the CNS group and the non-CNS group. PSM was used to eliminate bias.
### Results
A total of 6,886 patients were enrolled for retrospective analysis. There were 2,078 ($30.2\%$) patients in the CNS group and 4,808 ($69.8\%$) patients in the non-CNS group. After using 1: 1 ratio PSM to eliminate bias, there were 2,045 patients left in each group. Meanwhile, all of their baseline characteristics were well matched and there was no statistical significance between the two groups ($P \leq 0.05$). In terms of surgical information and short-term outcomes, the CNS group had less intraoperative blood loss ($P \leq 0.01$), shorter operation time ($P \leq 0.01$), shorter postoperative hospital stay ($P \leq 0.01$), less metastatic lymph nodes ($$P \leq 0.013$$), more total retrieved lymph nodes ($P \leq 0.01$), more lymphatic fistula ($$P \leq 0.011$$) and less postoperative overall complications ($P \leq 0.01$) than the non-CNS group before PSM. After PSM, the CNS group had less intraoperative blood loss ($$P \leq 0.004$$), shorter postoperative hospital stay ($P \leq 0.01$) and more total retrieved lymph nodes ($P \leq 0.01$) than the non-CNS group. No statistical difference was found in other outcomes ($P \leq 0.05$).
### Conclusion
Preoperative CNS could help the surgeons detect more lymph nodes, thus better determining the patient's N stage. Furthermore, it could reduce intraoperative blood loss and reduce the hospital stay.
## Introduction
Colorectal cancer (CRC) is one of the most common malignancies among both men and women and the second leading cause of cancer-related death in the world (1–3). Currently, there are about 185 million CRC patients worldwide [4]. CRC increases the burden on world health, especially on the elderly [5]. Although the treatment has developed, radical CRC surgery is still the main treatment at present [6, 7]. The current mainstream surgical approach is usually performed laparoscopically or robotically assisted, and robotic assistance shows advantages [8]. In recent years, carbon nanoparticles staining (CNS) technology has been widely used to improve surgical outcomes [9].
The applications of carbon nanoparticles were developing rapidly [10, 11]. It was proved that carbon nanoparticles were safe and reliable tracers for CRC [9]. Carbon nanoparticles could selectively penetrate lymphatic vessels rather than capillaries. When they entered the lymphatic vessels, they could be phagocytized by macrophages, and then stained the lymph nodes black [12]. Thus, it facilitated the detection of lymph nodes during pathological examination. Meanwhile, a sufficient number of lymph nodes was crucial for accuracy of a patient's cancer stage [13]. Studies reported that the number of lymph nodes dissected should be ≥ 12 for more accurate staging of the patient's N stage [14]. Accurate cancer staging could guide post-operative treatment, furthermore, optimiz patients' short-term outcomes and improve patients' survival rates [14].
At present, CNS has been widely used in the localization of CRC and lymph node tracking (14–17). However, the number of metastatic lymph nodes detected remained controversial. The CNS group was considered to have more metastatic lymph nodes compared with the non-CNS group [5, 12]. Other studies reported that there was no difference in the rate of metastatic lymph nodes between the two groups (18–20). Therefore, the aim of this study was to evaluate the effect of CNS on lymph node tracing and postoperative complications for CRC surgery.
## Patients
Patients who were diagnosed with CRC and underwent surgery were collected from a single clinical center from Jan 2011 to Dec 2021, retrospectively. A total of 6,886 patients were enrolled. This study was approved by the Institutional Ethics Committee of our hospital (2021–536), and informed consents were obtained from all patients.
A total of 6,886 patients were included in this study according to the inclusion and exclusion criteria, and there were 2,078 ($30.2\%$) patients in the CNS group and 4,808 ($69.8\%$) patients in the non-CNS group. After 1:1 ratio PSM, there were 2,045 patients in each group (Figure 1). The mean age of the enrolled patients was 62.7 ± 12.4 years old. Meanwhile, 4,044 ($58.7\%$) were males and 2,042 ($41.3\%$) were females. In addition, more baseline characteristics including BMI, smoking, drinking, hypertension, T2DM, CHD, surgical history, open surgery, tumor location, TNM stage, tumor size, CNS, operation time, intraoperative blood loss and short-term outcomes were shown in Table 1.
**Table 1**
| Characteristics | No. 6886 |
| --- | --- |
| Age (mean ± SD), year | 62.7 ± 12.4 |
| Sex | Sex |
| Male | 4,044 (58.7%) |
| Female | 2,842 (41.3%) |
| BMI (mean ± SD), kg/m2 | 22.6 ± 3.2 |
| Smoking | 2,574 (37.4%) |
| Drinking | 2,089 (30.3%) |
| Hypertension | 1,685 (24.5%) |
| T2DM | 746 (10.8%) |
| CHD | 267 (3.9%) |
| Surgical history | 1,717 (24.9%) |
| Open surgery | 1,195 (17.4%) |
| Tumor location | Tumor location |
| Colon | 3,239 (47.0%) |
| Rectum | 3,647 (53.0%) |
| TNM stage | TNM stage |
| I | 1,238 (18.0%) |
| II | 2,785 (40.4%) |
| III | 2,546 (37.0%) |
| IV | 317 (4.6%) |
| Tumor size | Tumor size |
| < 5cm | 3,922 (57.0%) |
| ≥ 5cm | 2,964 (43.0%) |
| CNS | 2,078 (30.2%) |
| Operation time, min | 232.2 ± 83.6 |
| Intraoperative blood loss | 118.7 ± 186.7 |
| Metastatic lymph nodes | 1.5 ± 3.2 |
| Total retrieved lymph nodes | 14.3 ± 7.7 |
| Post-operative hospital stay, day | 11.3 ± 7.4 |
| Post-operative major complications | 176 (6.1%) |
| Post-operative overall complications | 1,550 (22.5%) |
## Inclusion and exclusion criteria
We included CRC patients who underwent radical surgery in a single clinical center ($$n = 8152$$). The exclusion criteria were as follows: 1, non-R0 resection ($$n = 22$$); 2, recurrent CRC surgery ($$n = 47$$); 3, incomplete baseline information ($$n = 148$$); and 4, incomplete information of carbon nanoparticles staining ($$n = 1049$$). Ultimately, a total of 6,886 patients were enrolled (Figure 1).
**Figure 1:** *Flow chart of patient selection.Abbreviations: CNS, carbon nanoparticles staining.*
## Data collection
The baseline characteristics, operation information and short-term outcomes were collected from electronic medical records. The baseline characteristics were as follows: age, sex, body mass index (BMI), smoking, drinking, hypertension, type 2 diabetes mellitus (T2DM), coronary heart disease (CHD), surgical history, open surgery, tumor location, tumor nodes metastasis (TNM) stage, tumor size and CNS. The operation information included: intraoperative blood loss and operation time. The postoperative information included: postoperative hospital stay, metastatic lymph nodes, total retrieved lymph nodes, anastomotic fistula, lymphatic fistula, postoperative major complications and postoperative overall complications.
## PSM
PSM was used to reduce the intergroup bias in this study. We conducted PSM method including age, sex, BMI, smoking, drinking, hypertension, T2DM, CHD, surgical history, open surgery, tumor location, TNM stage and tumor size. The CNS group was matched to the non-CNS group by using the nearest neighbor matching at a 1:1 ratio, and within a caliper of 0.01.
## Procedures
Patients in the CNS group were injected carbon nanoparticles (1 ml; 50 mg, Chongqing lummy Co.) into the submucosal layer before surgery by electronic colonoscopy. The carbon nanoparticles entered the lymphatic vessels rather than the blood vessels. Then, the tumor and lymph nodes would be stained black (Figure 2).
**Figure 2:** *(A), nanocarbon stained tumour; (B), tumour without nanocarbon staining; (C), nanocarbon stained lymph nodes; (D), lymph nodes without nanocarbon staining.*
## Definition
The tumor node metastasis stage was diagnosed according to the AJCC 8th Edition [21]. The complications were graded according to the Clavien-Dindo classification [22], and major complications were defined as ≥ III classification complications. The CNS group was defined as the patients who underwent injection of carbon nanoparticles before surgery, while the non-CNS group was defined as the patients who did not receive injection of carbon nanoparticles before surgery. R0 resection was defined as a negative margin on pathological examination.
## Statistical analysis
Continuous variables were expressed as mean ± standard deviation (SD). Categorical variables were expressed as n (%). The Chi-square test was used to analyze categorical variables and the t-test was used to analyze continuous variables between the CNS group and the non-CNS group. Statistical analysis was performed by the SPSS (version 22.0) software. P-value of < 0.05 was considered statistically significant.
## Baseline characteristics
The baseline characteristics before and after PSM were shown in Table 2. Before PSM, we found that the CNS group had a higher BMI ($P \leq 0.01$), a higher proportion of smoking ($$P \leq 0.037$$) and a higher proportion of open surgery ($P \leq 0.01$) than the non-CNS group. Meanwhile, significant difference was found in tumor location ($P \leq 0.01$) and TNM stage ($P \leq 0.01$). There was no difference in age, sex, drinking, hypertension, T2DM, CHD, surgical history or tumor size ($P \leq 0.05$). After PSM, all of these baseline characteristics were well matched and there was no statistical significance ($P \leq 0.05$).
**Table 2**
| Characteristics | Before PSM | Before PSM.1 | Before PSM.2 | After PSM | After PSM.1 | After PSM.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Characteristics | CNS (2078) | Non-CNS (4808) | P value | CNS (2045) | Non-CNS (2045) | P value |
| Age, year | 62.5 ± 11.9 | 62.7 ± 12.7 | 0.589 | 62.6 ± 11.9 | 62.5 ± 12.4 | 0.729 |
| Sex | | | 0.607 | | | 0.874 |
| Male | 1,230 (59.2%) | 2,814 (58.5%) | | 1,201 (58.7%) | 1,206 (59.0%) | |
| Female | 848 (40.8%) | 1,994 (41.5%) | | 844 (41.3%) | 839 (41.0%) | |
| BMI, kg/m2 | 22.8 ± 3.2 | 22.4 ± 3.2 | <0.01* | 22.8 ± 3.2 | 22.7 ± 3.2 | 0.428 |
| Smoking | 667 (32.1%) | 1,422 (29.6%) | 0.037* | 776 (37.9%) | 802 (39.2%) | 0.404 |
| Drinking | 55 (2.6%) | 33 (0.7%) | 0.898 | 641 (31.3%) | 638 (31.2%) | 0.919 |
| Hypertension | 524 (25.2%) | 1,161 (24.1%) | 0.343 | 511 (25.0%) | 515 (25.2%) | 0.885 |
| T2DM | 235 (11.3%) | 511 (10.6%) | 0.404 | 231 (11.3%) | 225 (11.0%) | 0.766 |
| CHD | 84 (4.0%) | 183 (3.8%) | 0.641 | 83 (4.0%) | 89 (4.3%) | 0.640 |
| Surgical history | 506 (24.4%) | 1,211 (25.2%) | 0.461 | 498 (24.4%) | 477 (23.3%) | 0.441 |
| Open surgery | 145 (7.0%) | 1,050 (21.8%) | <0.01* | 145 (7.1%) | 144 (7.0%) | 0.951 |
| Tumor location | | | <0.01* | | | 0.706 |
| Colon | 1,149 (55.3%) | 2,090 (43.5%) | | 1,116 (54.6%) | 1,128 (55.2%) | |
| Rectum | 929 (44.7%) | 2,718 (56.5%) | 0.668 | 929 (45.4%) | 917 (44.8%) | 0.668 |
| Tumor size | | | 0.613 | | | 0.570 |
| < 5cm | 1,174 (56.5%) | 2,748 (57.2%) | | 1,157 (56.6%) | 1,175 (57.5%) | |
| ≥ 5cm | 904 (43.5%) | 2,060 (42.8%) | | 888 (43.4%) | 870 (42.5%) | |
| TNM stage | | | <0.01* | | | 0.671 |
| I | 419 (20.2%) | 819 (17.0%) | | 398 (19.5%) | 370 (18.1%) | |
| II | 880 (42.3%) | 1,905 (39.6%) | | 868 (42.4%) | 881 (43.1%) | |
| III | 702 (33.8%) | 1,844 (38.4%) | | 702 (34.3%) | 709 (34.7%) | |
| IV | 77 (3.7%) | 240 (5.0%) | | 77 (3.8%) | 85 (4.2%) | |
## Surgical and postoperative characteristics
The operation and postoperative characteristics of the two groups were compared before and after PSM, and the outcomes were shown in Table 3. The operation information included intraoperative blood loss and operation time. The postoperative information included postoperative hospital stay, metastatic lymph nodes, total retrieved lymph nodes, anastomotic fistula, lymphatic fistula, postoperative overall complications and postoperative major complications. Before PSM, intraoperative blood loss ($P \leq 0.01$), operation time ($P \leq 0.01$), postoperative hospital stay ($P \leq 0.01$), metastatic lymph nodes ($$P \leq 0.013$$), total retrieved lymph nodes ($P \leq 0.01$), lymphatic fistula ($$P \leq 0.011$$) and postoperative overall complications ($P \leq 0.01$) had significant differences in the two groups.
**Table 3**
| Unnamed: 0 | Before PSM | Before PSM.1 | Before PSM.2 | Before PSM.3 | After PSM | After PSM.1 | After PSM.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | CNS (2078) | Non-CNS (4808) | Non-CNS (4808) | P value | CNS (2045) | Non-CNS (2045) | P value |
| Operative information | Operative information | Operative information | Operative information | Operative information | Operative information | Operative information | Operative information |
| Intraoperative blood loss, ml | 96.1 ± 151.6 | 96.1 ± 151.6 | 128.5 ± 199.2 | <0.01* | 95.7 ± 151.4 | 109.5 ± 151.2 | 0.004* |
| Operation time, min | 226.2 ± 85.3 | 226.2 ± 85.3 | 234.9 ± 82.7 | <0.01* | 226.0 ± 84.6 | 230.9 ± 85.3 | 0.065 |
| Postoperative information | Postoperative information | Postoperative information | Postoperative information | Postoperative information | Postoperative information | Postoperative information | Postoperative information |
| Post-operative hospital stay, day | 10.6 ± 6.9 | 10.6 ± 6.9 | 12.5 ± 9.1 | <0.01* | 10.7 ± 6.9 | 11.7 ± 9.0 | <0.01* |
| Metastatic lymph nodes | 1.3 ± 3.6 | 1.3 ± 3.6 | 1.5 ± 2.9 | 0.013* | 1.3 ± 3.6 | 1.3 ± 2.8 | 0.885 |
| Total retrieved lymph nodes | 17.1 ± 8.0 | 17.1 ± 8.0 | 13.0 ± 7.2 | <0.01* | 17.2 ± 8.0 | 13.8 ± 7.7 | <0.01* |
| Anastomotic fistula | 51 (2.5%) | 51 (2.5%) | 115 (2.4%) | 0.877 | 51 (2.5%) | 42 (2.1%) | 0.345 |
| Lymphatic fistula | 15 (0.7%) | 15 (0.7%) | 14 (0.3%) | 0.011* | 15 (0.7%) | 7 (0.3%) | 0.087 |
| Post-operative major complications | 47 (2.3%) | 47 (2.3%) | 129 (2.7%) | 0.309 | 46 (2.2%) | 45 (2.2%) | 0.916 |
| Post-operative overall complications | 406 (19.5%) | 406 (19.5%) | 1,144 (23.8%) | <0.01* | 401 (19.6%) | 448 (21.9%) | 0.070 |
After PSM, the CNS group also had less intraoperative blood loss ($$P \leq 0.004$$), shorter postoperative hospital stay ($P \leq 0.01$) and more total retrieved lymph nodes than the Non-CNS group ($P \leq 0.01$).
## Discussion
There was a large sample size of 6,886 CRC patients in this study. After 1:1 ratio PSM, there were 2,045 patients left in each group. Before and after PSM, intraoperative blood loss, postoperative hospital stay and total retrieved lymph nodes were statistically significant. This suggested that CNS before surgery could help surgeons retrieve more lymph nodes, reduce intraoperative blood loss and reduce hospital stay. In term of total retrieved lymph nodes, we found that the CNS group retrieved more lymph nodes than the non-CNS group.
The guidelines of the European Society for Medical Oncology and the American Society of Clinical Oncology consider inadequate lymph nodes harvested to be one of the risk factors for stage II CRC [23, 24]. Lymph nodes metastasis is an independent prognostic factor after radical resection for T1-2 CRC [25]. Accurate TNM staging could guide postoperative chemoradiotherapy and improve the prognosis of CRC patients. However, it was required that the total number of lymph nodes detected exceeded twelve [26]. Moreover, in order to accurately determine the pathological staging of patients with adenocarcinoma of esophagogastric junction, Zheng J et al. proposed that no less than 11 LNs must be resected in patients with stage T1-2 and no less than 16 LNs must be resected in patients with stage T3-4 [27]. Meanwhile, it was found that the number of removed lymph nodes was positively correlated with the number of metastatic lymph nodes [28]. Resection of more lymph nodes could improve the accuracy of postoperative pathological analysis. Lelin Pan et al. showed that the more lymph nodes were removed, the more accurate N-stage we obtained [18]. However, a 2010 study indicated that lymph node detection rates for colorectal cancer remain low [29]. A variety of lymph node staining were beginning to be used in clinical practice. Cawthorn et al. used xylene alcohol clearance technique to facilitate the identification of lymph nodes [30]. Quadros et al. performed lymphoscintigraphy using technetium-99 m-phytate and patent blue to detect lymph nodes of rectal adenocarcinoma patients [31]. However, these techniques are not widely used in clinical practice because they are time-consuming, labour-intensive and toxic to doctors [12]. In this study, we used carbon nanoparticles for lymph node tracking and more total number of dissected lymph nodes in the CNS group were found than the non-CNS group. This could help surgeons obtain the accurate N stage of patients.
Although, the use of carbon nanoparticles increased the total number of lymph nodes detected, the number of metastatic lymph nodes detected remained controversial. Some studies reported that the rate of lymph node metastasis detected in the CNS group was higher than the non-CNS groups [5, 12]. Other studies reported that there was no difference in the rate of metastatic lymph nodes between the two groups (18–20). The mechanism was not clear, but there were possible reasons as follows: first, CNS allowed for better anatomical clarity, making it more convenient for the operator to clear the lymph nodes. Second, it was found that there were many other factors that affected metastasis lymph node dissection, such as the patient's sex, age, tumor stage, type of surgery, and neoadjuvant chemotherapy (32–35). Although the information underlying the two groups was not statistically significant after PSM, we were still unable to determine their combined effect on metastatic lymph node dissection. We searched nine similar articles and listed some information in Table 4 (5, 12, 14, 18–20, 36–38). The main outcomes reported in these articles were the total or mean number of lymph nodes detected. Dissections of metastatic lymph nodes were reported in five articles (5, 12, 18–20). Two articles also reported that the dissections of microscopic lymph nodes in the CNS group was more than the non-CNS group [5, 12]. Wang Q et al. reported the effect of CNS on intraoperative information [14]. Complications and long-term survival without difference were reported by Wang LY et al. [ 20].
**Table 4**
| Author | Year | Country | Sample size | CNS | Non-CNS | Others | Outcomes |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Hong-Ke Cai | 2012 | China | 80 | 20 | 20 | 20 | There were no statistically significant were observed among the tree groups in age, gender, tumor location, tumor diameter, T-stage, degree of differentiation, postoperative complications and peritoneal drainage retention time. The mean number of detected lymph nodes per patient was significantly higher in CNS group than in Non-CNS group. |
| Qingxuan Wang | 2016 | China | 54 | 27 | 27 | – | The time for detecting the tumor, operation time, and blood loss during the operation were lower in the CNS group than in the Non-CNS group. Average numbers of dissected lymph nodes in the CNS group exceeded those in the Non-CNS group, and the rate of dissected lymph nodes 12 was higher in the CNS group than in the Non-CNS group. |
| Xing-Mao Zhang | 2016 | China | 87 | 35 | 52 | – | The mean number of lymph nodes removed in CNS group was higher than that in Non-CNS group. And the mean number of lymph nodes smaller than 5 mm in diameter in CNS group was more than Non-CNS group. |
| Li-yu Wang | 2017 | China | 444 | 327 | 117 | 26 | The number of positive lymph nodes was higher and the prevalence of blood loss was lower in the CNS group than in the control group. There were no significant differences in the operative time, number of lymph nodes detected, or the prevalence of postoperative complications, survival, metastasis, or recurrence between the two groups at 3 years. |
| Jie Sun | 2018 | China | 80 | 40 | 40 | – | There were no statistically significant differences in the metastasis rate and lymph node metastasis rate between the two groups. The total number of lymph nodes and the number of lymph nodes with micrometastases (<2 mm) in the observation group were larger than those in the control group; the ratio of fewer than 12 lymph nodes in the observation group was lower than that in the control group. |
| L. Tang | 2018 | China | 80 | 40 | 39 | 1 | The average number of lymph nodes harvested from each patients was markedly more in the CNS group than in the Non-CNS group, and the average number of lymph nodes less than 5 mm in greatest dimension was significantly more in the CNS group than in the Non-CNS group. |
| Lelin Pan | 2018 | China | 99 | 52 | 47 | – | The number of total harvested LNs and the number of positive patients in the CNS group increased significantly compared with the Non-CNS group. |
| Renjie Wang | 2019 | China | 239 | 123 | 116 | – | All the patients characteristics between two groups did not achieve statistical significance. Patients in CNS group were more likely to be associated with more lymph nodes retrieved totally compared with Non-CNS group. The number of lymph nodes retrieved in CNS group were more likely to be 12 than that in the Non-CNS group. |
| Wei Ge | 2021 | China | 132 | 60 | 72 | – | The mean number of lymph nodes harvested from patients in CNS group was higher than that in Non-CNS group. And the mean number of positive lymph nodes got from patients in CNS group was also higher than Non-CNS group. |
So far, CNS has been widely used. Studies reported that CNS could avoid aggressive axillary treatment of breast cancer [39]. CNS was also reported to improve the outcomes of surgery for thyroid papillary carcinoma [40]. In addition, CNS had a positive impact on the surgical results of early gastric cancer [41, 42]. It was also reported that CNS played an essential role in lymph node dissection for non-small cell lung cancer [43]. Fortunately, no significant adverse effects of CNS on patients have been identified at present. Van Tongeren MJ et al. did not demonstrate mutagenic or carcinogenic effects of carbon nanoparticles [44]. Meanwhile, Magrez A et al. proved that CNS had no adverse effects on the central serious system, cardiovascular system and respiratory system [45]. Expectantly, more systematic reactions to carbon nanoparticles are looking forward to be discovered. In the future, CNS may be widely used to improve patients' survival.
The application of CNS could also help surgeon obtain more convenience. Previous study found that CNS could help surgeons distinguish tissue structure [9]. This finding could lead to less intraoperative blood loss and shorter postoperative hospital stay. Expectedly, it was found that the CNS group had less intraoperative blood loss and shorter postoperative hospital stay than the non-CNS group in this study. More benefits of CNS are waiting to be discovered by researchers.
To our knowledge, this was the first study to use PSM to analyze CNS on lymph node dissection, operation information and short-term outcomes for CRC. However, there were some limitations in this study. First, our data in this study came from a single clinical center. Second, we did not analyze the influence of other factors on lymph node dissection in detail. Third, there was no agreement on when patients should be injected carbon nanoparticles. Forth, lack of long-term patient outcomes. Therefore, prospective studies with a larger sample size were needed.
## Conclusion
Preoperative CNS could help the surgeons detect more lymph nodes, thus better determining the patient's N stage. Furthermore, it could reduce intraoperative loss and reduce the hospital stay.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
This study was approved by the Institutional Ethics Committee of our hospital (2021–536), and informed consents were obtained from all patients.
## Author contributions
All authors contributed to data collection. All the authors have agreed on the manuscript that will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Improved fully convolutional neuron networks on small retinal vessel segmentation
using local phase as attention
authors:
- Xihe Kuang
- Xiayu Xu
- Leyuan Fang
- Ehsan Kozegar
- Huachao Chen
- Yue Sun
- Fan Huang
- Tao Tan
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10014569
doi: 10.3389/fmed.2023.1038534
license: CC BY 4.0
---
# Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention
## Abstract
Retinal images have been proven significant in diagnosing multiple diseases such as diabetes, glaucoma, and hypertension. Retinal vessel segmentation is crucial for the quantitative analysis of retinal images. However, current methods mainly concentrate on the segmentation performance of overall retinal vessel structures. The small vessels do not receive enough attention due to their small percentage in the full retinal images. Small retinal vessels are much more sensitive to the blood circulation system and have great significance in the early diagnosis and warning of various diseases. This paper combined two unsupervised methods, local phase congruency (LPC) and orientation scores (OS), with a deep learning network based on the U-Net as attention. And we proposed the U-Net using local phase congruency and orientation scores (UN-LPCOS), which showed a remarkable ability to identify and segment small retinal vessels. A new metric called sensitivity on a small ship (Sesv) was also proposed to evaluate the methods’ performance on the small vessel segmentation. Our strategy was validated on both the DRIVE dataset and the data from Maastricht Study and achieved outstanding segmentation performance on both the overall vessel structure and small vessels.
## 1. Introduction
In recent years, many research works have revealed that retinal fundus images can provide much helpful information, which is related to multiple diseases, such as Age-related Macular Degeneration (AMD), Glaucoma, Diabetic Retinopathy (DR) arteriosclerosis, and hypertension [1]. Therefore, retinal image analysis is increasingly essential for computer-aided diagnosis [2].
Retinal vessels, as a part of the blood circulation system, have been proven to contain many essential biomarkers [1]. The small retinal vessels (defined as vessels with a width less than 65 μM in this paper), including small-artery and small-vein, are much more sensitive to the blood circulation system lesion and have great significance in the early diagnosis and warning of diseases [1]. Thus, the accurate segmentation of retinal vessel structures, tiny vessels, is a crucial part of the quantitative analysis of retinal images. Considering the complexity of retinal vascular trees, automatic segmentation is necessary to eliminate the cumbersome and time-consuming manual label processing. However, due to the low contrast between retinal vessels and background, the variation of vessel width, the complex geometric structure of small boats, and the severe background noise problem [1], that is a challenging task.
Most retinal vessel segmentation methods were focused on the performance of the overall retinal vessel structures and evaluated via global performance metrics like accuracy (Acc), sensitivity (Se), specificity (Sp), and area under the ROC curve (AUC). At the same time, the segmentation performance on the small vessels needed to receive more attention due to their small percentage in retinal images. A segmentation method with high sensitivity on small plates was required to identify and segment the small vessels from retinal images.
The recent works about retinal vessel segmentation can be classified into two categories, unsupervised and supervised methods.
Unsupervised retinal vessel segmentation methods, usually based on prior knowledge about the vessel structures in retinal images, include kernel-based methods, vessel tracking methods, model-based methods, etc. Kernel-based methods depend on the different specially designed filter kernels to detect the vessel structures. Azzopardi et al. [ 3] proposed an approach based on the combination of shifted filter response (COSFIRE) to see the bar-shaped facilities and achieved rotation invariability. Zhang et al. [ 4, 5] designed the left-invariant derivative (LID) filter on orientation scores (LID-OS) and achieved superior performance on the segmentation of crossings and bifurcations. Vessel tracking methods detect vessel structures by tracing the ridges of retinal vessels. Bekkers et al. [ 6] described two vessel tracking approaches, edge tracking in orientation scores (ETOS) and multi-scale vessel center line tracking in orientation scores (CTOS). De et al. [ 7] proposed a two-step tracing approach and gave a top way to address the problem of tracing with crossover. Model-based methods apply deformable models to identify vessel structures. Al-Diri et al. [ 8] and Zhao et al. [ 9] designed an infinite active contour model for the vessel segmentation task. With much simpler processes, unsupervised methods are usually faster than supervised ones. Besides, manually labeled ground truths are unnecessary for unsupervised methods. Thus the most troublesome step can be avoided. However, due to the complexity of vessel structures, one unsupervised way is often only able to cope with some possible vessel structures. Therefore unsupervised methods usually have poorer performance.
Supervised methods usually have better segmentation performance. Generally, a supervised learning algorithm produces a model which contains specific knowledge derived from the input images and manually labeled ground truths for the segmentation of retinal vessels. Fraz et al. [ 10] proposed the ensemble classifier of boosted and bagged decision trees and achieved Acc, Se, Sp, and AUC for 0.7406, 0.9807, 0.9480, and 0.9747 on the DRIVE dataset. Convolutional Neural Network (CNN) has dominated many computer vision tasks. With the ability to extract hierarchical features and take advantage of contextual information, CNN performs remarkably in medical image segmentation tasks. Ronneberger et al. [ 11] proposed the U-Net, a CNN specialized for biomedical image segmentation tasks. The U-Net conducts the convolution operator to extract features from original images and gets segmented photos directly via the up-sample operator. Wang et al. [ 12] used the U-Net in the retinal segmentation task and achieved an AUC of 0.9790 on the DRIVE dataset. The best vessel segmentation results were achieved in supervised ways [13]. However, because of the reliance on manually labeled ground truths, laborious work is inevitable for supervised methods. Besides, for most deep learning approaches, massive labeled retinal images are required in network training, which makes things even trickier.
Notably, most of the proposed works mentioned above neglected the tiny vessels in retinal images, and none involved specific reports about the segmentation performance on small retinal vessels. No segmentation method or performance evaluation method for small plates was proposed.
This paper proposed a novel method called the U-Net using local phase congruency and orientation scores (UN-LPCOS). We combined the unsupervised methods of local phase congruency (LPC) and orientation scores (OS) with the deep learning network modified from the U-Net. The LPC, proposed by Kovesi et al. [ 14], showed a superior ability to enhance small vessels in retinal images. The LPC was invariable to the image contrast through analyzing images in the frequency domain and had high sensitivity to the small plates. The OS method was also adopted. With outstanding performance on complex vessel structures, such as crossings and bifurcations, the OS method can improve the robustness of our method. The retinal vessels were enhanced by LPC and OS, respectively. Then the original retinal images and two vessel-enhanced images were combined and served as the input of a U-Net-based deep learning network. The network produced the vessel probability score of each pixel. After thresholding, the binary images of vessel segmentation were obtained.
The proposed UN-LPCOS was validated on the DRIVE dataset [15] and the data from Maastricht Study [16]. Some commonly used metrics, such as sensitivity (Se), specificity (Sp), accuracy (Acc), and area under the ROC curve (AUC), were calculated and compared with other proposed methods.
A new evaluation metric, called sensitivity on a small vessel (Sesv), was defined to describe different methods’ abilities on small vessel segmentation. We also discussed the effect of different unsupervised vessel enhancement results in our practice and revealed the significance of LPC and OS in small vessel segmentation.
The main contributions in this paper were summarized as follows: The rest of this paper was organized as follows. In section 2, some crucial methodologies involved in our method were introduced. The details of the experiments to validate our approach are displayed in section 3. And the experiment results are shown in section 4. Finally, further discussion was involved in section 5.
## 2. Methodology
The overall process of the proposed UN-LPCOS was presented in Figure 1, which contained three significant parts, image preprocessing, vessel enhancement, and vessel segmentation. The local luminosity normalization method was adopted in the image preprocessing to deal with retinal images’ local luminosity and contrast variation problem. Then the unsupervised vessel enhancement was conducted to highlight the vessels, especially the tiny and complex vessel structures in retinal images. In this paper, the LPC and OS were applied for vessel enhancement, and they significantly improved the ability of our method on small vessel detection and identification. The vessel-enhanced images combined with the preprocessed retinal photos served as the input of the deep learning network based on the U-Net. The network produced the vessel probability score for each pixel. After the thresholding, the binary vessel segmentation results were derived.
**Figure 1:** *The overall process of UN-LPCOS, including unsupervised vessel enhancement and vessel segmentation.*
This section introduces some essential principles and methods adopted in the UN-LPCOS. The local luminosity normalization for the image preprocessing was presented first. Then we introduced two unsupervised vessel enhancement methods, local phase congruency and left-invariant derivative filter on orientation scores. Finally, the architecture of the deep learning network was illustrated.
## 2.1. Local luminosity normalization
The local luminosity and contrast variation problem are the significant interferences in retinal images due to the irregular retinal surface and non-uniform illumination. To overcome this issue, the local luminosity normalization method proposed by Foracchia et al. [ 17] was adopted for image preprocessing. It can be denoted below, where the N(x,y) represents the normalized pixel intensity at the position (x,y), and the numerator is the original pixel value. The denominator is the arithmetic mean of the n×n neighbors pixels’ intensity around (x,y), respectively [17].
## 2.2. Local phase congruency
Local phase congruency, proposed by Kovesi et al. [ 14], was a frequency domain image processing method with high sensitivity on small vessel structures. Unlike gradient-based methods, the LPC defined features based on the similarity of the local phase angles of different frequency components in images. Therefore, a dimensionless measurement of parts, invariable to the image contrast, was produced.
In this paper, the LPC of images was calculated through wavelets transformation. The original idea was denoted as I, while Mn,θo and Mn,θe represented the even and odd wavelet filters with the scale n and orientation θ, respectively. The image was convolved with the wavelet filters, and the filter responses were marked as Fn,θ and Hn,θ, which were written as [14], Based on that, the local spectrum amplitude An,θ, and local phase angle ϕn,θ of the image with corresponding scale and orientation were calculated as [14], And the weighted mean of the local phase angle with the orientation θ was defined as [14]: Then the modified local energy of the image, which achieved more localization accuracy, was derived as:
and the local energy was calculated in each orientation [14].
Noise interference was one of the most intractable problems in the LPC. Thus, the noise threshold was introduced to suppress the noise in the LPC results. Since the noise spectrum was flat, the smallest wavelet filter, with the most considerable bandwidth, obtained the most energy from the noise, and the noise threshold was derived from it. The magnitude of the most negligible wavelet response followed a Rayleigh distribution. Its median was the expectation (denoted as μmin) of the noise distribution. The noise responses of filters with other scales and the response of the smallest filter were proportional to the bandwidth. Therefore, the noise threshold Tθ was given as follows: where μn,θ, and σn,θ represented the estimation of the expectation and variance of the noise response of the filter with the scale of n and the orientation of θ, respectively [14].
Due to the smoothing operation, different frequency components in the image had extra significance. We applied the frequency weighting function to describe this difference. It was written as: where c was the cut-off value of the filter response spread and g was a gain factor that controlled the cut-off’s sharpness [14].
Finally, we obtained the expression of the LPC. It was notable that, since the features would present in any direction of images, we calculated and combined the results of different orientations. Thus, the LPC was denoted as, where the small constant 𝜀 was provided to cope with the situation where the ∑θ∑nAn,θ was tiny, and the ()+ meant that if the local energy were smaller than the noise threshold, the difference would be 0 [14].
The LPC orientation was also derived for the following vessel segmentation process to distinguish vessels from noise in the LPC results. LPC orientation was defined as the orientation with the maximum LPC value at each pixel, which was written as, Figure 2 shows one LPC vessel enhancement result of the DRIVE dataset. The small retinal vessels with low contrast were highlighted well and had similar feature intensity to the main retinal vessels. A low noise threshold was adopted to present as many small structures as possible, which leads to more noise in the LPC result. Fortunately, it would not influence the final vessel segmentation.
**Figure 2:** *The example of LPC vessel enhancement result. (A) The original retinal image from the DRIVE dataset. (B) The LPC vessel enhanced the result.*
## 2.3. Left-invariant derivative filter on orientation scores
Due to the extremely complex structure, many segmentation methods lost their stability on retinal vessels. Many vessel segmentation approaches, based on the detection of tubular structures, failed on the bifurcation points and crossovers of retinal vessels. To overcome this problem and improve the robustness of the proposed UN-LPCOS, orientation scores (OS) was adopted to support vessel segmentation in this paper. More specifically, the left-invariant derivative filter on orientation scores (LID-OS), proposed by Zhang et al. [ 4], was involved.
The basic idea of the OS and LID-OS filters is described as follows. The original 2D image was disentangled into several orientation channels via anisotropic wavelets transformation, which was given as: Where Rθ(ψ) represented a set of anisotropic filters, and f represented the retinal image. The orientation scores of the image were denoted as Uf(x,y), which was derived through the convolving of, and f [4].
Then a set of unique filters called left-invariant derivative (LID) filters were applied to the orientation scores to enhance the tubular vessel structures. The LID-OS filters were constructed on the LID frame to ensure the Euclidean invariance, which was defined as, Based on the LID frame, the multi-scale rotating LID-OS filters were derived from the second-order Gaussian derivatives on orientation scores, and they were defined as: where μ was a normalization factor, which was given by μ=σo/σs. The physical unit 1/ length kept the convolution results dimensionless and truly scale-invariant [4].
Finally, the 2D vessel enhanced image was reconstructed by taking maximum filter response in the orientation scores, and the final reconstruction output was written as: One LID-OS vessel enhancement result of the DRIVE dataset was displayed in Figure 3. The bifurcations were enhanced with high quality. The enhancement result achieved outstanding vessel connectivity, and the noise was suppressed well [4].
**Figure 3:** *The example of LID-OS vessel enhancement result. (A) The original retinal image from the DRIVE dataset. (B) The LID-OS vessel enhanced the result.*
## 2.4. Modified u-net
The deep learning method was adopted in this paper to produce the final pixel-wise vessel probability image. And the preprocessed retinal photos combined with the vessel-enhanced results served as the input of the deep learning network. The basic architecture of our network was modified from the U-Net (shown in Figure 4A), consisting of two major parts, the encoder, and the decoder. The encoder extracted features from the input image through the hierarchical convolution operation. At the same time, the decoder did the de-convolution and up-sample procedure and produced the vessel probability image via the final softmax active function. The vessel probability image had the same size as the manual labeled ground truth and the input image. In each expansive operation of the decoder, the corresponding feature images from the encoder were concatenated with the feature images to be up-sampled. The deep learning model could be trained end-to-end with very little training data. It was crucial since only a few labeled retinal images were available [11].
**Figure 4:** *The basic architecture of (A) our deep learning network modified from the U-Net and (B) the modified convolution operation set.*
Two significant modifications were introduced in our deep learning network. First, we modified the convolution operation sets in the original U-Net, which contained two repeated 3×3 convolutional layers and each followed by a rectified linear unit (ReLU). We introduced the identity short-cut, which was inspired by the ResNet, and the detailed structure of modified convolution operation sets was presented in Figure 4B. Besides, to reduce the loss of the local image feature of the small vessel structure, we replaced all max-pooling layers in the original U-Net with convolutional layers with two strides for the down-sampling. These modifications could accelerate the training process of the deep learning model and enable the model to better identify the small vessel’s features.
## 3.1. Dataset
In this paper, the proposed method was validated on three datasets, the DRIVE dataset, the data from the Maastricht Study and the UoA-DR [18] database. The DRIVE dataset consists of 40 retinal images, divided into two parts, a training dataset and a test dataset, and each piece has 20 photos. Every picture has a resolution of 565*584 pixels. The vessel structures of each image are annotated by two human investigators separately. Our deep learning model was trained on the training dataset and evaluated on the test datasets.
The data from Maastricht Study contains 600 retinal images, which are taken through the NIDEK AF230 with the resolution of 3,744 * 3744 and resized to 1024*1024 with small structures preserved. The population of the Maastricht Study contains 1,363 healthy subjects (NGM), 366 prediabetes subjects (preDM), and 610 type two diabetes subjects (T2DM). Experienced ophthalmologists from the ophthalmology department in the Maastricht Medical Center, Maastricht, Netherlands label the ground truth of the blood vessels for each image. And the dataset is divided into two parts, a training dataset with 400 photos and a test dataset with 200 illustrations. The Eindhoven University of Technology IRB exempted the study from IRB approval.
The UoA-DR database consists of 200 high-quality images captured using a Zeiss VISUCAM 500 fundus camera with a FOV of 45 and a resolution of 2,124 × 2056 pixels in JPEG format. The optic nerve head center, optic nerve head, macula, fovea, and the retinal vessels of all the 200 images in this database were manually segmented by a specialist ophthalmologist who acted as the first observer and by an optometrist as the second observer. The manually segmented features, such as retinal vessels, OD and fovea, for the 200 retinal images may be used to benchmark the performance of new ARIA methods in the future. This database can be downloaded for free with certain access rights mentioned in Ref. Like the MESSIDOR database, not all the images marked as high quality are of MSRI quality.
## 3.2. Evaluation measurements
Based on the manually labeled ground truths, the pixels of final vessel segmentation results were divided into four categories: The true positive (TP) represented the pixels that were labeled as vessels in ground truths and correctly identified in model outputs. The actual negative (TN) referred to the pixels labeled as non-vessels in ground truths and denoted as non-vessels in output images. The human investigator considered the false positive (FP) as the pixels labeled as non-vessels but classified as vessels in segmentation results. And the false negative (FN) represented the pixels that were denoted as vessels in ground truths but not identified in segmented images.
For comparison, we adopted some commonly used evaluation metrics, Sensitivity (Se), Specificity (Sp), and Accuracy (Acc), to evaluate the global performance of the proposed method on vessel segmentation. These metrics were given by: Besides, the receiving operator characteristics (ROC) curve and the area under the ROC curve (AUC) were also recorded to evaluate our method’s performance on vessel enhancement.
The evaluation metric, called sensitivity on small vessels (Sesv), was defined to describe different methods’ abilities in segmenting small retinal vessels. The pixels of small vessels were separated from the ground truth first, and they made up the ground truth of small vessels, which determined the range of pixels we cared about in the small vessel segmentation. In this paper, vessels with a width under 65 μM were defined as small vessels, which can be separated from the segmentation result and ground truth, respectively, using morphological opening operation. The kernel used for morphological opening can be round with a diameter of 65 μM. Two new pixel categories were proposed: true positive on small vessels (TPsv) and false negative on small vessels (FNsv). They represented the pixels labeled as vessels in the ground truths of small vessels and identified as vessels or non-vessels, respectively. The 𝑆𝑒𝑠𝑣 was defined as: The ROC curve on a small vessel (ROCsv) was derived from the *Sesv versus* the 1 − Sp concerning the varying threshold value Th. The area under the ROCsv (AUCsv) was also calculated to evaluate the model’s performance on the minor vessel enhancement.
## 3.3.1. Local phase congruency configuration
The LPC analysis in retinal images was conducted via 2D wavelet transform. With six different scales, a set of anisotropic Gabor wavelets were adopted to calculate the embodiment’s local amplitude and phase. The minimum wavelet length was 3, and each following wavelet length was multiplied by 2. There were 12 wavelets with different orientations for every size to detect the features in any image direction. The coefficient of standard deviations k for the noise threshold calculation was set as 3. And in the frequency weighting function, the cut-off value c and the gain factor g were set as 0.4 and 10, respectively. Besides, in the LPC calculation, the feature orientation at every pixel was also recorded, which was the direction where the maximum LPC value was obtained.
## 3.3.2. Left-invariant derivative filter on orientation scores configuration
To apply the LID-OS in the vessel enhancement, retinal images were transformed into orientation scores. In this paper, the construction of orientation scores was implemented by convolving images with a set of rotated filters, precisely, the cake wavelets proposed by Duits et al. [ 19] They could be regarded as a set of quadrature filters, and the fundamental part represented the locally symmetric structures like ridges/lines. In contrast, the imaginary part responded to the antisymmetric structures like edges. Eight filter directions were uniformly selected. Then the second-order Gaussian derivatives in the LID frame were applied directly to the orientation scores of retinal images, and blood vessel structures were highlighted in each orientation layer. Therefore, the complex vessel structures, such as crossings and bifurcations, were enhanced well. The final vessel-enhanced results were reconstructed by obtaining the maximum filter response on all orientation layers.
## 3.3.3. Training of deep learning network
Data augmentation was conducted first to generate enough training data. In this paper, the method of cutting was adopted. Small patches, of dimension 64*64, on both the DRIVE dataset and the Maastricht Study data, were sampled randomly from the original training images and corresponding ground truths. To give our network the ability to discriminate the edge of the field of view (FOV) from vessel structures, the patches partially or entirely outside the FOV were also selected. For each training epoch, 9,000 patches were obtained by randomly extracting 450 patches in each of the 20 DRIVE training images. The first $90\%$ of patches (8,100 patches) were used for training, while the last $10\%$ (900 patches) were used for validation. And for the data from Maastricht Study, 100 patches were extracted randomly from each training image, and we got a total of 40,000 patches from all 400 images. Similarly, the first $90\%$ of patches were training data, and the other $10\%$ were validation data. For both datasets, all patches were reselected in each training epoch.
The Categorical Cross Entropy (CCE) served as the loss function. The Stochastic Gradient Descent (SGD) was performed as the optimizer, with a learning rate of 0.01. And the training strategy of the mini-batch with the size of 32 patches was used on both the DRIVE dataset and the data from Maastricht Study.
## 4.1. Deep learning network training process
Our UN-LPCOS had a faster and more efficient deep-learning training process than the original U-Net. Our method’s network needed fewer epochs to complete training and achieve better performance. In Figure 5, the AUC values of each way were calculated with different training epochs. Then the AUC epoch − curves were derived, which showed the superior performance of our method on vessel enhancement. When the training was completed, the AUC values of our approach were higher than the original U-Net. With the modification of the network architecture, the deep learning model of our method was much easier to train. On the DRIVE dataset, our model needed 70 epochs to complete training, and on the data from Maastricht Study, we needed no more than 10 epochs. For the original U-Net, 100 and 20 epochs were needed on these two datasets, respectively.
**Figure 5:** *The AUC values of proposed UN-LPCOS and original U-Net concerning different training epochs on (A) the DRIVE dataset and (B) data from the Maastricht study.*
## 4.2. Vessel segmentation result
In Figure 6, we presented the ROC curves of our UN-LPCOS on the DRIVE dataset, the data from the Maastricht Study and the UoA-DR dataset. For comparison, the performance of other proposed methods was also depicted. On the Maastricht Study data, our method’s ROC curve was compared with the original U-Net. The evaluation metrics of Se, Sp, Acc, and AUC values were presented in Table 1. These metrics, especially Acc and AUC value, proved the superior performance of our UN-LPCOS. The segmentation performance on small vessels is shown in Table 2. It demonstrated that our method outperformed both datasets’ original U-Net in small vessel segmentation.
**Figure 6:** *The ROC curves of our UN-LPCOS on (A) DRIVE dataset, (B) data from the Maastricht study, and (C) UoA-DR dataset, compared with the second human observer; the method by Yin et al. (20), Zhang et al. (5), Wang et al. (12), and original U-Net. The dotted box was the critical area which was enlarged and displayed in the figure.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2
## 4.3. Results on small vessels
This section highlights the superior performance of the proposed UN-LPCOS in the small vessel segmentation. Figure 7 shows examples of small vessel segmentation produced by the UN-LPCOS and compared with the results derived from the original U-Net. Our method could better segment when faced with minor and low-contrast vessel structures. The example patches were extracted from the DRIVE dataset and the data from Maastricht Study.
**Figure 7:** *Small vessel segmentation results of our UN-LPCOS and original U-Net. (A) Original patches. (B) Normalized patches. (C) Segmentation results of original U-Net. (D) Segmentation results of our UN-LPCOS.*
Besides the effect of different input combinations was also discussed. Figure 8 presents the ROCsv curves of the UN-LPCOS and original U-Net. With the changing segmentation threshold, our method consistently achieved higher Sesv than the original U-Net on the same Sp level. And the ROCsv curves of the original U-Net were surrounded by ours on both two datasets. In Figure 9, we analyzed the effect of different unsupervised methods on the segmentation performance in small vessels. Three different input combinations were involved: 1) only normalized retinal images (NOR), 2) normalized retinal images and LPC enhanced retinal images (NOR+LPC), 3) normalized retinal images, LPC and LID-OS enhanced retinal images (NOR+LPC + LID). And we calculated the ROCsv curves for each model.
**Figure 8:** *The ROCsv curves of proposed UN-LPCOS and original U-Net derived from the Sesv versus the 1 − Sp with respect to the varying threshold value Th, on (A) DRIVE dataset, (B) data from the Maastricht study, and (C) UoA-DR dataset. The dotted box was the critical area which was enlarged and displayed in the figure.* **Figure 9:** *ROCsv curves of proposed UN-LPCOS with different input combinations, which included (1) only normalized retinal images, (2) normalized retinal images and LPC results, (3) normalized retinal images, LPC and LID-OS enhancement results, on (A) DRIVE dataset and (B) data from Maastricht Study. The dotted box was the critical area which was enlarged and displayed in the figure.*
On the DRIVE dataset, we could see the significant effect of LPC on small vessel segmentation. Besides, adopting LID-OS also impacted the model’s performance, but it was not so obvious compared with LPC. The ROCsv curves of the NOR+LPC (green curves) and the NOR+LPC + LID (red curves) were almost coincident on the DRIVE dataset. On the data from Maastricht Study, however, both LPC and LID-OS obviously influenced model performance. The ROCsv curve of NOR (blue curve) was surrounded by the NOR+LPC one (green curve), which was surrounded by NOR+LPC + LID (red curve).
## 4.4. Result summary
The ROC curve of our UN-LPCOS on the DRIVE dataset is presented in Figure 6A. It showed that our method outperformed other vessel segmentation methods listed in this paper. Compared with the original U-Net, our method also had a superior performance on the data from Maastricht Study and the data from UoA-DR (shown in Figures 6B,C. A more detailed evaluation is presented in Table 1. On the DRIVE dataset, compared with the Se and Sp of some of the best-unsupervised methods (0.7246 and 0.9790 for Yin et al. [ 20], 0.7743 and 0.9725 for Zhang et al. [ 5], 0.7653 and 0.9735 for Chalakkal et al. [ 22]), our method obtained better performance (0.8117 and 0.9841). Besides, we got the highest Acc and AUC value than other supervised methods (0.9658 and 0.9830) on the DRIVE dataset. Compared with the original U-Net, the performance of our modified version achieved significant improvement, with 0.0307 higher in Se and 0.0024 higher in Sp on the DRIVE dataset; 0.0836 higher in Se at the same specificity level on the data of the Maastricht Study;0.0214 higher in Se and 0.0017 higher in Sp on the UoA-DR dataset.
Our method obtained remarkable performance on the small vessel segmentation. We achieved 0.0205, 0.2024 and 0.0211 higher Sesv on the DRIVE dataset, the data from Maastricht Study and UoA-DR dataset than the original U-Net with the same specificity level. Besides, our method produced 0.0163, 0.0208 and 0.0061 higher AUCsv values than the original U-Net on the 3 datasets involved. The comparison of different input combinations was presented in Figure 9, and we could see the effect of other unsupervised methods on the model’s performance of small vessel segmentation. The introduction of LPC significantly improved the AUCsv value on the DRIVE dataset (0.9598 for the input of NOR and 0.9664 for the NOR+LPC) and the data from the Maastricht Study (0.9557 for the information of NOR and 0.9710 for the NOR+LPC). The LID-OS also had a positive effect, but it was much more limited (0.9664 for the NOR+LPC and 0.9665 for the NOR+LPC + LID on the DRIVE dataset 0.9710 for the NOR+LPC and 0.9774 for the NOR+LPC + LID on the data from Maastricht Study).
## 5.1. Global segmentation performance analysis
The global vessel segmentation performance of different methods is presented in Table 1, and the ROC curves of our proposed UN-LPCOS were showed in Figure 6. The global evaluation metrics and ROC curves demonstrated the outstanding performance of our method. More specifically, compared with other proposed methods, our UN-LPCOS achieved higher Acc and AUC values on both the DRIVE dataset and the data from Maastricht Study. Besides, the ROC curve of our plan was above all other methods involved in the DRIVE dataset, and on the data from Maastricht Study, our method’s ROC curve surrounded the original U-Net’s.
The UN-LPCOS modified the network structure of the state of art method and adopted the unsupervised methods of LPC and LID-OS for the enhancement of small and complex vessel structures. Therefore, we maintained the excellent results of primary retinal vessel segmentation and improved the ability of small vessel segmentation in the meantime.
However, the small vessels accounted for only a tiny proportion of the retinal image pixels, and the advance of their segmentation did not impact the global result a lot. Thus, the improvement of global performance seemed very limited, especially for the AUC value (0.9791 for Wang et al. [ 12] and0.9830 for proposed UN-LPCOS). Since the retinal image from the Maastricht Study contained more small vessels, the improvement on it was more significant.
## 5.2. Small vessel segmentation performance analysis
The segmentation of small and complex vessel structures is a challenging task. Small retinal vessels are usually a few pixels wide and have low contrast with the background. Therefore, they usually melt in the background noise and are hard to be detected. Besides, the small vessel structure is only a tiny part of the retinal images, which brings more difficulty to training a deep learning network with high sensitivity on small vessels. We applied the LPC method to the retinal images to deal with these problems, and the small vessel structures were significantly highlighted. To present as many details of vessel structures as possible, a low noise threshold and small Gaussian kernels were used, even though they led to more noise in the LPC results.
To avoid the influence of LPC noise on the vessel segmentation, in addition to the LPC value, we also inputted the LPC orientation of each pixel into the deep learning network. The LPC orientations of a vessel segment were the exact or continuous change, while the orientations of LPC noise were the mess. Based on the contextual LPC orientation information, our deep learning network could distinguish between vessels and noise, and the harsh noise in LPC results would not affect the final segmentation. With the support of the LPC, our method was proved to have higher sensitivity in small vessel segmentation.
Moreover, we modified the original network structure of U-Net by replacing the max-pooling layer with the convolutional layer to reduce the loss of image features of small vessels and thus improve the model’s ability to identify small vessels.
## 5.3. Limitation
There are still some limitations of the proposed UN-LPCOS, which need to be solved in future work. Although our method achieved exciting performance on the small vessel segmentation, accurate identification of vessel boundaries remained challenging. Since the boundaries of small vessels were usually blurred in retinal images, it took much work to determine the range of vessel much more accurately. To achieve higher sensitivity on small vessels, our method was more inclined to regard a pixel as the vessel during the vessel enhancement; thus, compared with ground truths, our approach tended to expand the range of small vessels. And the vessel width measured through our method was often more significant than that based on manual segmentation. Besides, connectivity is another weakness of our approach. Since we split one retinal image into many patches, conducted vessel segmentation separately, and stitched together, the vessel connectivity at the suture was reduced. In this paper, only two unsupervised methods were considered to improve the ability of deep learning networks on retinal vessel segmentation. And some other methods shall be introduced, and their effect on the segmentation results will be observed in future work.
## 6. Conclusion
In this paper, we proposed a novel retinal vessel segmentation method named UN-LPCOS. It incorporated a modified U-Net structure and adopted two unsupervised vessel enhancement methods, local phase congruency (LPC) and orientation scores (OS), for attention. The LPC is a frequency domain image analysis method, which is sensitive to the small blood vessels even with low contrast in the retinal images. The OS is a multi-orientation image analysis approach and performs well on complex vessel structures. Adopting these two unsupervised methods boosts our method’s outstanding ability in the segmentation of small and complex vessel structures. An evaluation metric, called sensitivity on small vessels (Sesv), was proposed to describe the method’s performance on the small vessel segmentation. Our plan was validated on the DRIVE dataset and the data from Maastricht Study and achieved superior performance compared to all other methods. Besides, our approach showed outstanding ability in detecting small vessels and outperformed the original U-Net on small retinal vessel segmentation.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
XK: methodology, software, and writing—original draft. XX, LF, EK, HC, and YS: writing—review and editing. FH: software, writing—review, and editing. TT: conceptualization, supervision, project administration, and writing—review and editing. All authors contributed to the article and approved the submitted version.
## Funding
This study was funded by Macao Polytechnic University (grant no. RP/FCA-$\frac{05}{2022}$).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Expansion of ventral foregut is linked to changes in the enhancer landscape
for organ-specific differentiation
authors:
- Yan Fung Wong
- Yatendra Kumar
- Martin Proks
- Jose Alejandro Romero Herrera
- Michaela Mrugala Rothová
- Rita S. Monteiro
- Sara Pozzi
- Rachel E. Jennings
- Neil A. Hanley
- Wendy A. Bickmore
- Joshua M. Brickman
journal: Nature Cell Biology
year: 2023
pmcid: PMC10014581
doi: 10.1038/s41556-022-01075-8
license: CC BY 4.0
---
# Expansion of ventral foregut is linked to changes in the enhancer landscape for organ-specific differentiation
## Abstract
Cell proliferation is fundamental for almost all stages of development and differentiation that require an increase in cell number. Although cell cycle phase has been associated with differentiation, the actual process of proliferation has not been considered as having a specific role. Here we exploit human embryonic stem cell-derived endodermal progenitors that we find are an in vitro model for the ventral foregut. These cells exhibit expansion-dependent increases in differentiation efficiency to pancreatic progenitors that are linked to organ-specific enhancer priming at the level of chromatin accessibility and the decommissioning of lineage-inappropriate enhancers. Our findings suggest that cell proliferation in embryonic development is about more than tissue expansion; it is required to ensure equilibration of gene regulatory networks allowing cells to become primed for future differentiation. Expansion of lineage-specific intermediates may therefore be an important step in achieving high-fidelity in vitro differentiation.
Wong et al. report that expanding human embryonic stem cell-derived endodermal progenitors serve as a ventral foregut model, through which they identify a link between progenitor expansion and tissue-specific changes in the enhancer landscape.
## Main
The regulation of gene expression during differentiation is considered a linear process involving the action of signalling and transcription factors (TFs). Cell proliferation is regarded as peripheral to differentiation, although it has a clear function in the selection of specific cell types. While cell cycle phase has been linked to differentiation1,2, here we explore the notion that differentiation requires progenitor proliferation itself to enhance the processing of lineage-promoting information.
The visceral organs are formed during embryonic development from the endoderm germ layer3. These cells are initially specified during gastrulation and undergo extensive proliferation as they prepare to differentiate into distinct organ primordia4. In particular, the liver and pancreas are derived from the anterior definitive endoderm (ADE). ADE is formed as a result of the anterior migration of cells from the anterior region of the primitive streak at the beginning of gastrulation. The anterior-most definitive endoderm (DE) will then migrate ventrally to form the ventral foregut, containing a bipotent precursor of liver and ventral pancreas5,6, a population that has recently been shown to expand and retain potency for both lineages in vivo over a period of several days of mouse development7.
Pluripotent embryonic stem cells (ESCs) can be differentiated in vitro to form all embryonic germ layers including endoderm8,9. As a result, directed linear ESC differentiation is used to produce organ-specific cell types such as pancreatic beta cells10–12 and hepatocytes13,14. An alternative to directed differentiation is the use of ESC-derived expandable endodermal progenitors (EPs) as a staging platform for further differentiation15–17, and the expansion of endodermal cells from human ESCs (hESCs) promotes the generation of more mature pancreatic beta cells15.
In this Article, we find that the in vivo identity of human EP (hEP) cells is ventral foregut and that continued proliferation of these cells results in lineage priming that is correlated with organ-specific enhancer accessibility. Lineage priming is not accompanied by large changes in transcription of organ-specific genes, but instead prepares appropriate enhancers for their activation and decommissions enhancers normally present in other lineages. Our findings suggest that the extensive cell proliferation that characterizes normal embryonic development is not merely required for tissue expansion, but ensures equilibration of gene regulatory networks for future high-fidelity differentiation.
## Expanding endoderm progenitors mimic ventral foregut in vitro
To characterize the impact of expansion on endodermal differentiation, we focused on 3D hEP culture15. This protocol expands endoderm in the presence of FGF2, BMP4, VEGF and EGF15,17, cytokines known to act in the ventral foregut region. We quantitated gene expression during expansion by single-cell RNA sequencing (RNA-seq) and found that transient ADE cells comprised two subpopulations (ADE.1 and ADE.2) while EP culture was homogeneous (Extended Data Fig. 1a, left). In human development, ventral foregut endoderm has been described at Carnegie stages 8 and 9 (ref. 18), and we compared hESC-derived endoderm with single-cell RNA-seq from these stages of human embryos with our cluster alignment tool (CAT)19. For this analysis, we used a recently published dataset containing human embryonic foregut (hFG.1-4), the lip formed from ventral foregut—referred to as the lip of the anterior intestinal portal (hAL)—midgut (hMG.1-3) and hindgut (hHG.1-2) (ref. 20) (Extended Data Fig. 1a, right). We found that ADE aligns to the foregut hFG.2 and midgut hMG1 clusters (Fig. 1a). In contrast, EP cells align with hAL and hMG1, a population of midgut cells located adjacent to the hAL20. As EP cells align to both these clusters, we assessed gene expression specifically enriched in RNA-seq from H9-derived EP cells (Extended Data Fig. 1b) and asked whether this set contained genes with differential expression between hAL and hMG.1 clusters. With a few exceptions, genes expressed at higher levels in hAL were also elevated in EP cells (Fig. 1b). The hAL or ventral foregut identity of EP cells was confirmed by immunohistochemistry of the hAL markers HHEX20 and TBX3 (ref. 15) (Extended Data Fig. 1c).Fig. 1Expanding endoderm progenitors as an in vitro model for ventral foregut.a, Visualization of the CAT alignments between in vitro clusters (ADE.1, ADE.2 and EP) from this study and in vivo endodermal clusters from the Li et al. dataset20. Only significant CAT alignments between clusters are shown. b, Heat map showing expression of hAL and hMG marker genes in ESC, ADE and EP cells (bulk RNA-seq dataset, scaled normalized expression, $$n = 3$$ independent experiments). Only markers expressed significantly different between ADE and EP are shown (log2FC > 1.5, adjusted $P \leq 0.05$). c, Cumulative growth curves showing EP cell counts at different passages of expansion for control and HHEX KD (EPs were derived from H9 (circle) or HUES4 (triangle) ESCs). Data are represented as mean ± s.e.m.; $$n = 6$$ independent experiments. ** $P \leq 0.01$, ****$P \leq 0.0001$ (one-way ANOVA Tukey’s multiple comparison test was applied to analyse differences at day 8; only significant comparisons are shown). d, Dot plots showing percentage of G1, S and G2M cycling cells assayed by flow cytometry with EdU and DAPI staining in control and HHEX KD EP expansion. Data are represented as mean ± s.e.m.;$$n = 6$$ independent experiments. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ (one-way ANOVA Tukey’s multiple comparison test; only significant comparisons are shown). e, Representative images (from three independent experiments) of control (top row) and HHEX shRNA (bottom row) EP cells stained with EdU, FOXA2, HHEX and DAPI. Scale bars, 50 µm. f, Top: representative immunostaining of PDX1 and SOX9, including DAPI, of VFG-derived pancreatic spheroids at passage 5. Bottom: representative immunostaining of AFP and ALB, including DAPI, of VFG-derived hepatic organoids at passage 5. Images represent three independent experiments. Scale bars, 50 µm. DIC, differential interferance contrast. Source data As murine ventral foregut endoderm is actively cycling21, we measured the proliferation rate of hEP cultures and found it increased with time in culture (p6, p8, p12 and p15) (Fig. 1c). In mouse, HHEX is known to support ventral foregut expansion and morphogenesis21. To further confirm the identity of hEP, we knocked down HHEX by short hairpin RNA (shRNA) and observed a reduction in growth without induction of apoptosis (Extended Data Fig. 1d,e). We measured actively proliferating cells in ADE, EP cells and HHEX knockdown (KD) EP cells by 5-ethynyl-2′-deoxyuridine (EdU) labelling followed by cell-cycle analysis based on 4′,6-diamidino-2-phenylindole (DAPI) staining (Extended Data Fig. 1f). The percentage of S-phase cells increased with expansion in an HHEX-dependent fashion, while the fraction in G2M was reduced (Fig. 1d,e). On the basis of the expression of ventral foregut markers, the cytokines used in these cultures and the function of HHEX in proliferation, we conclude hEP cells are an in vitro model for human ventral foregut and refer to them hereafter as ventral foregut progenitor cells (VFGs).
To probe VFG differentiation efficiency, we established VFG cultures from an hESC line containing a pancreatic reporter (PDX1-eGFP)22 and determined the minimal cytokine set required to transform VFG spheres into proliferating pancreatic spheroids or hepatic organoids (Extended Data Fig. 2a). Removal of BMP4 from VFG culture resulted in negligible PDX1 reporter expression (<$2\%$ GFP+), no PDX1 protein and no dramatic transcriptional change at single-cell level (Extended Data Fig. 2b–d). Subsequent addition of FGF7 and FGF10, and to a lesser extent FGF2, stimulated PDX1-eGFP expression and induced robust transcriptional change (Extended Data Fig. 2d–f). In response to initial cytokine treatment, we could separate PDX1+ and PDX1− cells, and expand PDX1+ cells as pancreatic spheroids, or PDX1− cells as hepatic organoids (Fig. 1f and Extended Data Fig. 2g–i) in defined media23,24. These observations indicate that human VFG culture is poised to generate expanding hepatic and pancreatic endoderm.
## Expansion enhances pancreatic differentiation of VFG cells
To compare the differentiation efficiency of expanding VFGs with standard differentiation, we employed aspects of three established protocols for the derivation of pancreatic endoderm (PE) from ESCs10,12,22 (Extended Data Fig. 3a). In two of these protocols12,22 we observed relatively inefficient differentiation (<$20\%$ PDX1+) (Fig. 2a and Extended Data Fig. 3b). However, a protocol coupling BMP inhibition, FGF and WNT activation10 resulted in >$80\%$ PDX1+ induction, suggesting that VFG cultures are adapted to protocols harnessing signals regulating ventral pancreatic specification. VFG-derived PE expressed pancreatic markers including PDX1 and NKX6-2, Glycoprotein 2 (GP2) (refs. 22,25) and the ventral pancreatic marker Roundabout2 (ROBO2) (ref. 26) (Extended Data Fig. 3c). Consistent with the observation that ventral pancreatic bud expands more than the dorsal bud18, cells differentiated via this third protocol, and not the other two, proliferate (Fig. 2b,c).Fig. 2Expansion enhances pancreatic differentiation of VFG cells.a,b, Bar plots showing percentage of PDX1-eGFP+ (a) or EdU+ (b) cells from flow cytometry analysis in VFG cells and PE generated from VFG cells on the basis of different differentiation protocols. Data are represented as mean ± s.e.m.; $$n = 3$$ independent experiments. * $P \leq 0.05$, ****$P \leq 0.0001$ (one-way ANOVA Dunnett’s multiple comparison test compared with VFG cells). c, Representative immunostaining (from three independent experiments) of VFG cells and PE generated using conditions from Nostro et al. ( ref. 10), stained with PDX1, EdU and DAPI. Scale bar, 50 µm. d, Left: schematic of PE differentiation using conditions from Nostro et al. ( ref. 10). from ADE and VFG at p3, p6 and p12. Right: bar plot showing percentage GFP+-positive cells generated for the indicated conditions. Data are represented as mean ± s.e.m.; $$n = 3$$ independent experiments. Statistical analysis was performed for differentiation of each indicated cell type (**$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$, unpaired two-tailed t-test), as well as comparisons between different differentiations (***$P \leq 0.001$, ****$P \leq 0.0001$, one-way ANOVA Tukey’s multiple comparison test; only significant comparisons are shown). e, Bar plots showing percentage of INS+ cells generated from VFGp3 or VFGp6 cultures derived from HUES4 (triangles) and H9 (circles) ESCs. Data are represented as mean ± s.e.m.; $$n = 4$$ independent experiments. Statistical analysis was performed for differentiation of each indicated cell type (**$P \leq 0.01$, ****$P \leq 0.0001$, unpaired two-tailed t-test), as well as comparisons between different differentiations (****$P \leq 0.0001$, unpaired two-tailed t-test; only significant comparisons are shown). f, Representative immunostaining (from three independent experiments) of VFGp6-derived β-like cells, stained with PDX1, INS and DAPI. Scale bar, 50 µm. g, *Expression analysis* of ESC-derived VFG cultures at different passages: RT–qPCR of the indicated genes in transient ADE and VFGs. Expression is normalized with ACTB. Data are represented as mean ± s.e.m.; $$n = 6$$ independent experiments. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ (one-way ANOVA Dunnett’s multiple comparison test compared with ADE; only significant comparisons shown). * $P \leq 0.05$ (one-way ANOVA Dunnett’s multiple comparison test compared with VFGp3-4; only significant comparisons shown).*Source data* The efficiency of pancreatic differentiation increased with time in expansion and was maintained at a similar level following six passages (Fig. 2d and Extended Data Fig. 3d). Later passage VFG cells re-introduced into differentiation also produced more insulin-positive (INS+) endocrine cells (Fig. 2e,f). Similarly, extended VFG expansion produced enhanced hepatic, but not intestinal, differentiation (Extended Data Fig. 3e,f). Expression of primitive-streak and early endoderm genes, GSC, GATA6 and CER1, decreased upon expansion (Fig. 2g). General endoderm markers expressed in the ventral foregut, such as FOXA2, HHEX and SOX17, were expressed throughout expansion at levels comparable to those in transient ADE cells. Expression of the foregut marker HNF1B27 and the ventral foregut markers TBX3, ID2 and GATA3 (refs. 15,28,29) were elevated in early passaged (p3 and p4) VFG cells and maintained during expansion. The pancreatic progenitor marker PDX1 was never detected during VFG expansion.
## Chromatin accessibility is fine-tuned in VFG expansion
Principal component analysis (PCA) of VFG RNA-seq data at multiple passages showed that VFG cells form a cluster separated from ADE, PE and ESC (Fig. 3a). Different passages of VFGs, cultured with and without BMP4, cluster together and separate in the first principal component from PE. Comparison between VFG passage (p)3 and VFGp6 cells shows a small set of genes (21 upregulated and 102 downregulated) with significant changes in expression (log2 fold change (FC) > 2, $P \leq 0.05$), including downregulated primitive-streak markers (GSC, CER1 and LEFTY1) (Extended Data Fig. 4a and Supplementary Table 1a). The only Gene Ontology terms for gene set enrichment with expansion were associated with chromatin modification and cell-cycle transition (Extended Data Fig. 4b). We used assay for transposase-accessible chromatin using sequencing (ATAC-seq) to map chromatin accessibility during the progression of hESCs to pancreatic progenitors, at five defined stages of differentiation and expansion: hESC, ADE, VFGp3, VFGp6 and PE. Unlike the transcriptome of different passage VFG cultures that cluster together by PCA, we observed considerable change in the ATAC-seq profile as a function of time in culture, with the higher-passage VFGs moving towards PE (Fig. 3b).Fig. 3Dynamic chromatin accessibility and gene expression during VFG expansion and pancreatic differentiation.a, PCA based on top 2,000 differentially expressed genes in bulk RNA-seq dataset (from three or two (VFGp18) independent experiments) of ESC, transient ADE and VFG cells (at p3, p6 and p18), VFG cells cultured without BMP4 (at p6) and PE cells generated from VFGp6 cells. b, PCA of ATAC-seq dataset (from two independent experiments) for ESC, transient ADE and VFG cells (at p3 and p6). c, Left: heat maps of the normalized ATAC-seq signal for the dynamic clusters identified by fuzzy clustering. DHS is defined as a peak of Tn5 insertions in ATAC-seq. Right: Time-course sequencing (TC-seq) trajectories for each cluster. Membership score reflects how well a given enhancer follows the pattern identified in time-course analysis. d,e, Left: representative UCSC Genome Browser screenshot (from two independent experiments) at the GLIS3 (d) and TBX3 (e) locus showing ATAC-seq data from ESC, ADE, VFGp3, VFGp6 and PE. Genome coordinates (bp) are from the hg19 assembly of the human genome. The PEPRIMED regulatory element (peak246749) (d) and VFGTR element (peak60307) (e) are shown with a black bar. Approximate distance between the element and the respective TSS is indicated by a broken dashed line in each panel. Right: RNA-seq data (normalized read count) for GLIS3 (d) and TBX3 (e) across the same conditions as the ATAC tracks. RNA-seq data are represented as mean ± s.e.m.; $$n = 3$$ independent experiments. f,g, Bar plot showing enrichment scores (log2 observed/expected) of ATAC peak sets found within a 200 kb window from genes upregulated (f) or downregulated (g) between PE and VFGp6 across the defined ATAC peak clusters. Genes considered here had a base mean expression >1,000, log2FC > 1.5 and adjusted $P \leq 0.05.$ For annotation, see Supplementary Table 1d–g. Analysis using lower base mean [100] or reduced genomic window sizes (25 kb) are shown in Extended Data Fig. 3g–j. All data shown are significant using chi-squared analysis. Source data We used general linear modelling30 to define the dynamic changes in chromatin accessibility at promoter-distal ATAC-seq peaks (putative enhancers) across these five stages of differentiation. This resulted in a dynamic set of 57,803 sites (Extended Data Fig. 4c) showing chromatin opening or closing in at least one stage of differentiation. Temporal patterns of chromatin accessibility were defined using c-means clustering, producing eight clusters corresponding to six distinct groups of putative enhancers (Fig. 3c and Supplementary Table 1b). The largest group of sites are where chromatin accessibility is reduced at the start of differentiation and remains closed for the duration through to PE. The VFGOFF cluster contains sites that become accessible during ESC to ADE differentiation, but that then lose accessibility during VFG differentiation or expansion, so that they are inaccessible in PE. The PEOFF cluster also appears at ADE and then loses accessibility but only after VFG expansion. The PEON cluster encompasses regions that only open up during differentiation to PE. We defined two VFG clusters, VFG transient (VFGTR) and PEPRIMED clusters.
Chromatin accessibility for the PEPRIMED cluster increases gradually during VFG expansion and is most accessible in PE. An element located ~5 kb upstream of the GLIS3 transcriptional start site (TSS) (Fig. 3d, left, and Extended Data Fig. 4d) is an example of this. In vivo Glis3 is expressed in pancreatic endocrine progenitors and then beta cells31. RNA-seq shows that GLIS3 is not expressed until PE differentiation from expanded VFGs (Fig. 3d, right). We also observed increases in accessibility in the conserved enhancer regions (area IV) of PDX1 (refs. 32,33) (Extended Data Fig. 4e). The VFGTR cluster contains regions where chromatin accessibility increases during VFG expansion and is then shut down during differentiation to PE. The putative enhancers located ~7 kb upstream of the TBX3 TSS (Fig. 3e, left, and Extended Data Fig. 4f) are an example of this. TBX3 is expressed in the developing human posterior foregut (FG) and liver bud progenitors20,34 and is expressed specifically in VFGs, but then silenced during the differentiation to PE (Fig. 3e, right).
To link these enhancer clusters to changes in gene expression, we defined significantly changing genes in the transition from expansion into further differentiation (log2FC > 1.5, $P \leq 0.05$) (Supplementary Table 1c). To pair enhancers with specific genes, we considered enhancers located either within 25 kb or 200 kb of the single nearest gene’s TSS and we excluded low level changes in basal gene expression (Supplementary Table 1d,e). While filtering out gene expression noise that occurs with passaging reduces the size of the gene set, we were able to define enhancers located within either 25 kb or 200 kb of upregulated PE genes. Regardless of which enhancer set used, we observed significant enrichment of both PEPRIMED and PEON enhancer classes with upregulated PE genes (Fig. 3f and Extended Data Fig. 4g,h), although the enrichment is greater for enhancers located closest to the genes they regulate. We also identified enhancers at the same distance from genes downregulated in differentiation (Supplementary Table 1f,g). These downregulated PE gene sets were associated with the PEOFF and VFGTR enhancer categories (Fig. 3g and Extended Data Fig. 4i,j). Taken together, this suggests that VFG expansion primes some pancreatic enhancers for later target gene induction while decommissioning enhancers driving gene expression inappropriate for the PE lineage.
## Differentiation imperfectly realizes the VFG enhancer landscape
To understand the extent to which the enhancer network induced during expansion is normally exploited in directed differentiation, we compared our data with a previous study that profiled chromatin accessibility by ATAC-seq during the differentiation of hESC through DE and posterior FG stages to pancreatic progenitors (PP1) (ref. 35). On the basis of this analysis we could define a common set of putative enhancers activated in either VFGs or FG35 and that then remain accessible in later differentiation (PE or PP1), respectively (PE-PP1 common); and a class of element that is not induced in the absence of expansion (PE-not-PP1) (Extended Data Fig. 5a,b and Supplementary Table 2a). Many of the peaks that closed down during or after VFG expansion (VFGOFF-in-DE-PP1 and VFGTR-in-PP1) remain accessible in the FG or PP1 stages (Extended Data Fig. 5a,c). Together, these data suggest that VFG expansion allows for the commissioning of enhancers relevant to pancreatic differentiation and the decommissioning of enhancers for alternative lineages. This process appears bypassed in directed differentiation.
Mapping of these enhancer elements to potential target loci (located within 200 kb) (Supplementary Table 2b,c) reveals an enrichment for the two pancreatic endoderm enhancer clusters, PE-PP1 common and PE-not-PP1, in the vicinity of genes upregulated in VFG-derived PE (the same gene set used for Fig. 3f,g) (Extended Data Fig. 5d). However, elements induced in directed differentiation, but not active in VFG-derived PE (VFGOFF-in-DE-PP1 or VFGTR-in-PP1), do not correlate with our PE upregulated gene set. Moreover, the PE downregulated gene set correlates with VFGTR-in-PP1 elements. These observations suggest that expansion is required for appropriate enhancer decommissioning.
In embryogenesis, the pancreas is derived from two buds that originate in different regions of the posterior FG, dorsal and ventral6. As the ventral pancreas is derived from the ventral foregut, we assessed the expression of markers thought to distinguish the dorsal pancreatic lineages36. Extended Data Fig. 6a shows the increase in expression of these markers in directed differentiation as foregut-like cells give rise to PP1 and suggests that directed differentiation has more of a dorsal identity.
To explore global correlations between genes differentially regulated in pancreatic endoderm derived from VFGs and directed differentiation, we plotted gene expression from both protocols (Extended Data Fig. 6b) and focused on the two classes of expansion dependent elements, PE-not-PP1 and VFGTR-in-PP1 (Extended Data Fig. 6c,d, left). Genes in the vicinity of PE-not-PP1 elements are better induced in VFG-derived PE than directed differentiation, whereas genes mapped to elements decommissioned as a result of expansion—VFGTR-in-PP1—are more extensively downregulated when PE is differentiated from expanding VFGs. Examples of expansion-dependent upregulation include FRMD6 and FGFR2 and for those ectopically expressed in directed differentiation, IHH and EPHA4 (Extended Data Fig. 6c,d, right). These analyses suggest that there are differences in messenger RNA expression related to expansion dependent changes in enhancer accessibility.
## VFG expansion captures human foetal organ-specific enhancers
To determine how the enhancer landscape captured during VFG expansion and PE differentiation in vitro corresponds with pancreatic development in vivo, we compared our ATAC-seq data with H3K27ac data obtained from micro-dissected endodermal (pancreatic, liver, lung and stomach), mesodermal (adrenal and heart) and ectodermal (retinal pigment epithelium (RPE) and brain) tissues collected from Carnegie stages 15–22 human embryos37 (Supplementary Table 3). Consistent with the VFG identity of our cultures, the PEPRIMED class of element is enriched for both liver and pancreatic enhancers, while the PEON class overlaps more extensively with pancreatic elements (Extended Data Fig. 7a and Supplementary Table 4a). Enhancer clusters that shut down as expanded VFGs differentiate to PE (VFGTR and PEOFF) are most enriched for enhancers active in the developing liver, consistent with their role in non-pancreatic VFG differentiation. Elements decommissioned in early differentiation or expansion (ADEOFF or VFGOFF) are non-VFG enhancers, including elements spanning the ectodermal and mesodermal lineages (Extended Data Fig. 7b and Supplementary Table 4a,b).
We assessed how the enhancers classes that differ between in vitro VFG expansion and direct differentiation from pluripotent cells compare with human organogenesis. Not surprisingly, the PE-PP1 common class of element was enriched in enhancers accessible in the ventral-foregut-derived pancreas and liver, while expansion-dependent PE-not-PP1 enhancers were more enriched in pancreatic elements (Extended Data Fig. 7c,d). Moreover, the set of enhancers accessible in directed differentiation, but decommissioned as consequence of expansion (VFGOFF-in-DE-PP1) or VFG differentiation to PE (VFGTR-in-PP1), did not contain meaningful numbers of pancreatic elements.
## Enhancers explicitly correlating with VFG proliferation
Although differentiation efficiency increased with time in VFG culture, we wished to exclude alterations to enhancer accessibility that could result from the shift to VFG culture and variations in pancreatic differentiation arising between the dorsal and ventral lineages. We therefore defined a restricted set of enhancers specifically regulated between passages 3 and 6, correlating with enhanced pancreatic and hepatic, but not intestinal, differentiation. We segregated defined enhancers activated or inactivated for the first time at passage 3 (VFGp3OPEN and VFGp3CLOSE) and those responding to increased passaging (VFGp6OPEN and VFGp6CLOSE) (Fig. 4a). While the chromatin accessibility of VFGp3OPEN and VFGP3CLOSE enhancer elements also respond to expansion, the influence of passaging is difficult to resolve from an initial response to the change in culture medium. Fig. 4VFG proliferation-dependent enhancers are associated active histone marks and correlate with later gene expression.a, Enhancer classification relative to VFG expansion time (from two independent experiments). Top: heat maps of normalized ATAC-seq signal in enhancers that open (VFGp3OPEN and VFGp6OPEN) or close (VFGp3CLOSE and VFGp6CLOSE) at VFGp3 or p6. VFGp3CLOSE group comprises ADE enhancers that are shut down during VFG expansion at passage 3. Bottom: average ATAC-seq signal in 10 bp bins for these enhancers in same stages. b,c, H3K4me1 (b) and H3K27ac (c) enrichment by ChIP–qPCR for ADE, VFGp3 and p6 culture at VFGp6OPEN enhancer regions: SFRP5 (peak32665), HNF1B (peak97567) and FGFR2 (peak35254); and at VFGp6CLOSE enhancer regions of LGR5 (peak56279), ANGPT1 (peak242621) and SOX1 (peak70345). Circles and triangles mark cells derived from H9 and HUES4 WT ESCs, respectively. Data are represented as mean ± s.e.m.; $$n = 4$$ independent experiments. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ (one-way ANOVA Tukey’s multiple comparison test; only significant comparisons shown). d,e, Bar plot showing the prevalence (log2 observed/expected) of ATAC peaks within a 200 kb window from genes upregulated between PE and VFGp6 (d) and from genes downregulated between ADE and VFGp6 (e) across the ATAC peak clusters (defined in a). Genes considered here had a base mean expression >1,000, absolute log2FC > 1.5 and adjusted $P \leq 0.05.$ *All data* shown are significant using chi-squared analysis. Source data To investigate whether there was a change in chromatin state of enhancers specifically responding to expansion, we performed chromatin immunoprecipitation (ChIP)–quantitative polymerase chain reaction (qPCR) for H3K27 acetylation (H3K27ac) and H3K4 monomethylation (H3K4me1) for multiple expansion-regulated elements (Fig. 4b,c). There were robust changes in H3K27ac deposition at these elements between VFGp3 and VFGp6, while changes in H3K4me1 were more subtle. We also paired these explicitly expansion-dependent enhancers to specific genes (within 200 kb of the single nearest gene’s TSS) (Supplementary Table 5a). We identified 480 enhancers explicitly correlated with expansion and located them within 200 kb of PE upregulated genes (the same gene set being used in Fig. 3f) (Supplementary Table 5b). Chromatin accessibility at both VFGp3OPEN and VFGp6OPEN enhancers correlated with gene expression (Fig. 4d). Similarly, for genes downregulated during VFG expansion (log2FC < −1.5, $P \leq 0.05$), we observed good correlation with decommissioning (Supplementary Table 5c–e), where in this instance, only the expansion-specific VFGp6OFF correlates well with gene expression (Fig. 4e) here.
To ask whether enhancers correlating directly with expansion are also related to ventral foregut specific differentiation, we compared these enhancers with the in vivo regulatory landscape in foetal organ development (Supplementary Table 6). Consistent with the interpretation that extended VFG culture lays the groundwork for further differentiation, both the VFGp3OPEN and the expansion-specific VFGp6OPEN clusters overlap with active enhancer sets from the foetal pancreas and liver, but not stomach, lung or other non-endodermal organs (Fig. 5a,b). Both sets of VFGOPEN enhancers are enriched in the endoderm lineage, while the VFGCLOSED enhancers contain more mesodermal and ectodermal elements (Fig. 5c). Finally, we compared expansion clusters with directed-differentiation clusters (Fig. 5d). Both VFGOPEN enhancer clusters that are not regulated in directed differentiation (VFGp3OPEN and VFGp6OPEN-not-PP1) overlap with foetal pancreas and liver enhancers sets, while VFG decommissioned enhancers that remain accessible in directed differentiation (VFGp3CLOSED and VFGp6CLOSED-in-PP1) have little in common with pancreatic and hepatic elements. Fig. 5VFG expansion captures enhancers that are active during human ventral foregut-derived organogenesis.a, Enrichment of tissue-specific H3K27ac enhancers from human embryos (from two independent experiments for most tissue types, except for stomach where only one sample was available) in different ATAC clusters defined in Fig. 4a displayed by enrichment score (observed/expected) in radar charts. b, Representative UCSC Genome Browser screenshot (from two independent experiments) at the HNF1B locus showing ATAC-seq data from this study (ESC, ADE, VFGp3, VFGp6 and PE) and H3K27ac ChIP–seq data37 from multiple human embryonic tissues (pancreas, liver, lung, stomach, brain, RPE, adrenal and heart). Genome coordinates (bp) are from the hg19 assembly of the human genome. VFGp6OPEN (peak97567) element overlapping with pancreatic-specific H3K27ac enhancer is shown at the bottom, and the approximate distance between the elements and the HNF1B TSS is indicated. c, Enrichment of lineage-specific H3K27ac enhancers (endoderm, ectoderm and mesoderm) from human embryos37 in the different VFG expansion-specific ATAC clusters defined in Fig. 4a by enrichment score (observed/expected). d, Enrichment of tissue-specific H3K27ac enhancers from human embryos across different VFGOPEN and VFGCLOSE clusters (defined in Fig. 4a) that are not regulated in directed differentiation were displayed by enrichment score (observed/expected) in radar charts. P, pancreas; Lv, liver; H, heart; A, adrenal; B, brain; R, RPE; Ln, lung; S, stomach. Source data
## TFs FOXA and HHEX in pancreatic priming
To determine factors responsible for VFG enhancer priming, we assessed TF motifs in different enhancer classes (Fig. 6a and Supplementary Table 7), focusing on those linked directly to expansion, and regulated between P3 and P6. TF motifs in VFGp6OPEN enhancers included FOXA factors and, to a lesser extent, a number of unrelated endodermal/hepatic factors broadly classed as hepatic nuclear factors (HNFs)38 and TEAD1. In contrast, motifs in VFGp6CLOSED elements included early endoderm and mesendoderm factors such as GATA4,6 and EOMES. To further refine the association of specific TFs with these enhancer classes, we used k-means clustering to define patterns of mRNA expression associated with enhancers that are upregulated or downregulated during VFG expansion (Extended Data Fig. 8a,b) and selected clusters that correlated with differentiation. In those enhancers related to clusters of upregulated gene expression in pancreatic differentiation, we identified motifs for TF classes relevant to human pancreatic and liver development36,39, such as FOXA, HNF1B, TEAD and the architectural factor CTCF. For those enhancers mapping to downregulated clusters, we observed no motifs linked to pancreatic differentiation or function (Extended Data Fig. 8c).Fig. 6FOXA proteins are required for VFG enhancer priming towards pancreatic differentiation.a, TF motif enrichment in VFGp6OPEN ($$n = 1$$,804) and VFGp6CLOSE ($$n = 7$$,421) ATAC clusters; n, number of peaks analysed. P values were derived from hypergeometric enrichment using HOMER default background. Candidate factors with $P \leq 1$ × 10−10 for both clusters were not included in the plot. Gene expression of the candidate factors that upregulated (red) or downregulated (green) from VFGp3 to VFGp6 (log2FC > 0.5, $P \leq 0.05$) are labelled. b, Top: schematic of FOXA1 and FOXA2 shRNA KD VFG cells and their PE differentiation. Bottom: histogram for proliferation assay (cell counts) for FOXA1 and FOXA2 shRNA KD and scrambled shRNA control VFG cells. Data are represented as mean ± s.e.m.; $$n = 4$$ independent experiments. Statistical analysis was performed between KDs and control VFG cells (**$P \leq 0.01$, unpaired two-tailed t-test; only significant comparisons are shown). c, Differentiation of FOXA1 and FOXA2 shRNA KD and scrambled control VFG cells to PE, with legend shown in b. Relative FC in mRNA of pancreatic genes (PDX1, GLIS3, SOX9 and NKX6-2) was assayed by RT–qPCR. Expression is normalized to ACTB. Data are represented as mean ± s.e.m.; $$n = 4$$ independent experiments. * $P \leq 0.05$, ***$P \leq 0.001$, ****$P \leq 0.0001$ (one-way ANOVA Dunnett’s multiple comparison test compared with control). d–f, FOXA1 binding (d), H3K4me1 (e) and H3K27ac (f) enrichment by ChIP–qPCR at enhancer regions of PDX1 (area IV), GLIS3 (peak246749) and TBX3 (peak60307) in FOXA1 shRNA KD VFG and scrambled control cell lines. An intragenic region of NCAPD2 served as a non-bound (n.b.) control. Data are represented as mean ± s.e.m.; $$n = 4$$ independent experiments. Statistical analysis was performed between the KD and control VFG cells. * $P \leq 0.05$, ***$P \leq 0.001$, ****$P \leq 0.0001$, (unpaired one-tailed t-test; only significant comparisons are shown).*Source data* Of TFs known to recognize FOX DNA binding motifs, embryonic expression patterns and phenotypes in mouse development suggest that FOXA1 and FOXA2 could be relevant to VFG-mediated enhancer priming33,40. FOXA2 is required for pancreas development and differentiation in both mouse33 and human ESCs35, and a requirement for FOXA1 in pancreas development is observed in the context of FOXA$\frac{1}{2}$ double mutants. FOXA factors are known ‘pioneer TFs’ that access regulatory regions and prepare them for later activation41. However, FOXA1 mutant ESCs undergo apparently normal directed pancreatic endoderm differentiation35. To assess their function in pancreatic priming during human VFG expansion, we knocked down FOXA1 and FOXA2 by shRNA during VFG expansion (Fig. 6b and Extended Data Fig. 9a). Neither factor produced a significant reduction in VFG marker expression (Extended Data Fig. 9b,c), although FOXA2, but not FOXA1, KD impaired VFG expansion. When VFG cells knocked down for either FOXA1 or FOXA2 were challenged in pancreatic differentiation, expression of pancreatic markers were significantly reduced (Fig. 6c). We confirmed FOXA1 binding, using ChIP–qPCR, at the PDX1 enhancer area IV, the PEPRIMED enhancers of GLIS3, and VFGp6OPEN enhancer of SFRP5, but not in the VFGTR element of TBX3. Binding was reduced in the stable FOXA1 KD VFG lines (Fig. 6d). KD of FOXA1 led to a significant reduction in H3K4me1 and H3K27ac at primed enhancers associated with PDX1 and SFRP5 (Fig. 6e,f), but not the enhancer associated with TBX3.
HHEX is suggested to be an essential transcriptional regulator of directed ESC differentiation to pancreatic endoderm42. We therefore asked whether HHEX was required for expansion-linked pancreatic enhancer regulation. KD of FOXA1 or HHEX produced similar defects in pancreatic differentiation, and the double KD had a combinatorial effect on PDX1 induction (Extended Data Fig. 10a,b) consistent with the specific influence they have on each other’s binding at the PDX1 enhancer (Extended Data Fig. 10c,d). At VFG-linked enhancer elements, HHEX has a particularly pronounced effect on H3K27ac (Extended Data Fig. 10e,f). To gain insight into the relation of HHEX binding to our enhancer dataset, we aligned HHEX ChIP–seq data from directed differentiation42 to the enhancer regions from the different classes defined here (Extended Data Fig. 10g). HHEX binding at both direct differentiation stages (FG and PP1) was detected at PE-PP1 common enhancers, but was depleted at the expansion-dependent PE-not-PP1 class of elements. Moreover, the VFG decommissioned enhancers VFGOFF-in-DE-PP1 and VFGTR-in-PP1 elements, which are incompletely silenced in directed differentiation, were still occupied by HHEX during these stages of directed differentiation. As a result, it appears that all enhancer classes defined here and represented in the directed differentiation dataset are occupied by HHEX, including those normally decommissioned during VFG expansion. Perhaps the binding of HHEX at these elements in directed differentiation prevents their decommissioning during rapid directed differentiation.
## Discussion
A portion of the pancreas comprising the uncinate process, in addition to the liver and gall bladder, is derived from the ventral foregut region of the developing embryo beginning at embryonic day 8.5 in mouse or at Carnegie stage 10 (25–27 days post coitum) in human18,43. On the basis of gene expression and differentiation competence, hESC-derived EP cells were found to recapitulate ventral foregut. While prior studies have shown that VFG expansion can produce functional pancreatic endocrine cells15, here we demonstrate that this is a direct consequence of time in VFG culture. In vivo, pancreas development begins from two locations, the dorsal and ventral foregut, promoting organ development via distinct signalling. Dorsal pancreas is induced by factors derived from the notochord and dorsal aorta (retinoic acid (RA), activin and FGF2) (ref. 44), while the ventral pancreas differentiates in the absence of signals driving hepatic specification (FGF2 produced by the cardiac mesoderm and BMP4 originating in the septum transversum)45. Ventral foregut embryonic explants therefore default to pancreatic differentiation in the absence of exogenous signalling5. However, in vivo, VFG progenitors and their descendants retain multipotency up to E11.5 in mouse where the cell cycle time has been estimated to be between 17.3 h and 26.6 h (ref. 7). As these progenitor cells are located close to both the cardiac mesoderm (FGF source) and septum transversum mesenchyme (BMP source), both components in VFG culture medium, these founder populations may persist via self-renewing cell division in vivo exploiting their proliferation to ensure efficient onwards differentiation.
An increasing set of TFs have the ability to bind DNA in chromatin and to destabilize nucleosomes. These pioneer factors include the FOXA proteins identified here as important for VFG priming. FOXA proteins are associated with enhancer priming during foregut development45 and associate with mitotic chromatin46. Yet, FOXA1 is not required for directed differentiation to pancreatic endoderm in vitro. While we have not shown a direct relationship between the cell cycle and enhancer priming by FOXA proteins, the major variable in our experiments is the amount of time in VFG culture and we cannot formerly exclude the influence of prolonged culture in these conditions on the enhancer network. However, it is possible that FOXA1 pioneer activity in VFG culture depends on proliferation, leading to a progressive equilibration of the enhancer network, involving both commissioning and decommissioning. Although pioneer factors are known to recognize their sites in chromatin, they may have an enhanced ability to bind their sites before the full restoration of heterochromatic marks following replication and then remain at these positions through mitosis. The hESC-directed differentiation protocol that comes closest to reproducing the proliferative nature of early ventral foregut is the one instance where a role for FOXA1 was previously suggested47. HHEX is also associated with enhancer priming in VFGs and can influence the stability of FOXA1 binding at the PDX1 enhancer. As HHEX physically interacts with FOXA1 in both gut tube and pancreatic progenitor stages of directed hESC differentiation42, it is possible that HHEX could act together with FOXA1 to enhance the stability of binding to targets in mitotic chromatin.
While we are not aware of many progenitor culture systems where the impact of proliferation on differentiation has been explored, the transition into expanded primed pluripotent cells alters the type of endoderm induced by the same cytokines48. It is intriguing to hypothesize that the reconfiguration of the enhancer network during the transition from naïve to primed pluripotency49 may also involve proliferation as cells at gastrulation stages proliferate rapidly with a cell cycle as short as 5 h as measured in rodents50,51. Moreover, in both naïve and primed pluripotency, the binding of pluripotency TFs to differentiation specific genes determines how these enhancers will respond to signalling and whether differentiating cells retain plasticity52,53, suggesting that TFs function to set the enhancer network for lineage specific progenitors to respond to signalling. In addition to preparing enhancers for later activation, we also found that enhancer decommissioning exploits expansion, perhaps as a result of going through multiple rounds of replication in the absence of specific TFs that protect these enhancers from nucleosome occlusion following replication. In VFGs, these decommissioned elements contain motifs for GATA factors, with GATA4 and GATA6 being downregulated in the early stages of VFG culture. While FOXA1 can bind mitotic chromatin, GATA factors are only partially retained54, suggesting that expansion could provide FOXA proteins with a competitive advantage. In this way, expansion not only primes differentiation, but shields the later developing endoderm from the lingering action of early endoderm enhancers.
We observe that the proliferation or expansion of lineage-restricted progenitors may be essential for high-efficiency later differentiation. Proliferation is therefore not just about producing sufficient numbers of cells, but fine-tuning the response of these cells to upcoming differentiation cues. Progenitor cell expansion can also equalize the differentiation efficiency of poorly performing hESCs16,55,56, suggesting that the lineage potential of different pluripotent cell lines may be determined by the extent they proliferate in differentiation. Moreover, as proliferation and growth are a hallmark of later foetal development, additional expansion steps could enhance the efficiency with which more mature organ-specific cell types can be obtained from human pluripotent cells.
## Maintenance of hESC
Undifferentiated hESCs H9 (WA09, WiCell) were maintained on tissue culture plates pre-coated with $0.1\%$ gelatine with irradiated C57BL6 mouse embryonic fibroblast feeder cells (MEFs) (25,000 cells cm−2) in H9 ESC medium: Dulbecco’s modified *Eagle medium* (DMEM)/F12 GlutaMAX medium (Thermo Fisher Scientific, 10565018) supplemented with KnockOut Serum Replacement (Thermo Fisher Scientific, 10828010), MEM Non-Essential Amino Acids (Thermo Fisher Scientific, 11140050), β-mercaptoethanol (Thermo Fisher Scientific, 21985023) and 10 ng ml−1 FGF2 (Peprotech, 100-18B). Cells were passaged as clusters with collagenase IV (Thermo Fisher Scientific, 17104019) when reaching approximately $70\%$ confluence and maintained in $20\%$ O2/$5\%$ CO$\frac{2}{37}$ °C. Undifferentiated ESC HUES4 wild-type (WT) and PDXeG clone 170-3 (ref. 22) were adapted and maintained in Defined Culture System (DEF-CS) (Takara, Y30017). When reaching approximately $80\%$ confluence, cells were dissociated with TrypLE (Thermo Fisher Scientific, 12604013) and counted with the automated NucleoCounter NC-200 cell counter (Chemometec). Cells were re-plated at a density of 40,000 cells cm−2 and maintained in $20\%$ O2/$5\%$ CO$\frac{2}{37}$ °C. All hESC lines were routinely screened for mycoplasma, and all were negative. All cell lines were approved for use in this project by De Videnskabsetiske Komiteer, Region Hovedstadenunder number H-4-2013-057 and H-21043866.
## Transient differentiation of ADE cells
Transient ADE cells were generated from WT H9 and HUES4 ESCs, as well as HUES4 PDX1-eGFP reporter (PDXeG clone 170-3) ESC cell line22 as described in Cheng et al.15. In brief, ESC cells at 70–$80\%$ confluence were collected with Accutase (Thermo Fisher Scientific, 00455556), re-plated at a density of 50,000 cells cm−2 on polystyrene cell culture plates (Corning, 353047) pre-coated with undiluted growth factor reduced (GFR) Matrigel (Corning, 354230), cultured in either H9 ESC or DEF-CS medium for 48 h with 10 µM ROCK inhibitor Y-27632 (STEMCELL Technologies, 72302) for the first 24 h and maintained in $20\%$ O2/$5\%$ CO$\frac{2}{37}$ °C. The ESC clusters were used to generate transient ADE cells in three-dimensional differentiation under hypoxic conditions ($5\%$ O2/$5\%$ CO$\frac{2}{37}$ °C) for 5 days. On day 1, the cell clusters were cultured in RPMI 1640 GlutaMAX (Thermo Fisher Scientific, 61870036) with $10\%$ Serum-Free Differentiation (SFD) medium57 supplemented with Activin A (100 ng ml−1) (Peprotech, 120-14 P), CHIR99021 (3 µM) (Tocris, 4423) and 4.5 × 10−4 M monothioglycerol (Sigma-Aldrich, M6145). On day 2, the medium was changed to RPMI 1640 GlutaMAX supplemented with Activin A (100 ng ml−1), BMP4 (0.5 ng ml−1) (Peprotech, 120-05ET), FGF2 (10 ng ml−1), VEGF (10 ng ml−1) (Peprotech, 100-20), 0.5 mM ascorbic acid (Sigma-Aldrich, A92902) and 4.5 × 10−4 M monothioglycerol. The same medium was applied at day 3. At day 4, differentiation medium was changed to SFD medium supplemented with Activin A (100 ng ml−1), BMP4 (0.5 ng ml−1), FGF2 (10 ng ml−1), VEGF (10 ng ml−1), 0.5 mM ascorbic acid and 4.5 × 10−4 M monothioglycerol.
## Generation and expansion of VFG
EP/VFG expansion was performed as described15 with minor modifications. In brief, day-5 transient ADE clusters were dissociated with 1 volume of trypsin–EDTA ($0.25\%$) (Thermo Fisher Scientific, 25200056) for 5 min at 37 °C and the enzyme then inactivated with 0.5 volume of foetal bovine serum (FBS) (Sigma-Aldrich, F4135). Single-cell suspensions were obtained by repeatedly washing with 10 volumes of ice-cold washing buffer, which contains $3\%$ FBS in phosphate-buffered saline without calcium and magnesium (PBS−/−) (Thermo Fisher Scientific, 10010023). Single cells were incubated with 1:100 CD184-PEcy7 (BD Biosciences, 560669) and CD117-APC (BD Biosciences, 561118) for 45 min at 4 °C and stained with DAPI (Thermo Fisher Scientific, D3571) to exclude dead cells. CD184-CD117 double-positive cells were sorted into SFD medium with 1:100 penicillin–streptomycin (Thermo Fisher Scientific, 15140122) by fluorescence-activated cell sorting (FACS) on an SH800 (SONY SH800 Software). Sorted cells were re-plated at a density of 20,000–30,000 cells cm−2 on polystyrene cell culture plates pre-coated with GFR-Matrigel and pre-seeded with low-density (8,000 cells cm−2) irradiated DR4 MEFs (ATCC, SCRC-1045). Cells were cultured in complete EP/VFG medium (SFD medium supplemented with BMP4 (50 ng ml−1), FGF2 (10 ng ml−1), VEGF (10 ng ml−1), EGF (10 ng ml−1) (Peprotech, AF-100-15), 0.5 mM ascorbic acid and 4.5 × 10−4 M monothioglycerol) and maintained under hypoxic conditions ($5\%$ O2/$5\%$ CO$\frac{2}{37}$ °C).
Medium was changed every other day until cells reached confluence, at 80,000–120,000 cells cm−2. When VFG cells reached approximately 100 μm in diameter, they were passaged by dissociation using 1 volume of trypsin–EDTA ($0.25\%$) for 5 min at 37 °C, detached from the plate using a cell scraper and then supplemented with 0.5 volume of FBS for enzyme inactivation. Single-cell suspension was obtained by repeatedly washing with 10 volumes of ice-cold washing buffer. VFG single cells were re-plated on the pre-coated GFR-Matrigel with feeders at 15,000–20,000 cells cm−2. Antibody information is listed in Supplementary Table 8.
## Single-cell preparation for RNA-seq and index sorting
Dissociated ADE and VFG single cells with treatments (mock, BMP4 withdrawal and BMP4 withdrawal plus FGF2 stimulation) were incubated with 1:100 CD184-PEcy7 and CD117-APC for 45 mi at 4 °C, and cells were stained with DAPI to exclude dead cells. The single cells from BMP4 withdrawal plus FGF2 stimulated VFG culture were incubated only with 1:100 CD117-APC in a similar condition to that described above. Cells were sorted using a BD FACS Aria III (FACSDiva) with a 100 µm nozzle and 20 psi sheath pressure. Forward scatter (FSC) and side scatter (SSC) were used to define a homogeneous population. FSC-H/FSC-W gates were used to exclude doublets, and dead cells were excluded on the basis of DAPI inclusion. The boundary between positive and negative populations was set on the basis of a negative population of unstained cells. Sorting speed was kept at 100–300 events s−1 to eliminate sorting two or more cells into one well. Single-cell sorting was verified colourimetrically on the basis of a previously described protocol58. Cells were sorted directly into lysis buffer containing the first RT primer and RNase inhibitor, immediately frozen and later processed by the MARS-seq1 protocol as described previously59. All single-cell RNA-seq libraries were sequenced using Illumina NextSeq 500 at a median sequencing depth of 225,000 reads per single cell. Antibody information is listed in Supplementary Table 8.
## Immuno-histochemical analysis
Medium was removed completely, and Matrigel-dome-containing 3D clusters were gently mixed with fresh undiluted Matrigel 1:1 and transferred to eight-well μ-slides (Ibidi, 80826) wells (20 µl cm−2 well) for whole-mount immunostaining. When the Matrigel was solidified at 37 °C, room-temperature $4\%$ paraformaldehyde (Sigma-Aldrich, 158127) was added and cultures were fixed at room temperature for 10 min, blocked and permeabilized with $2\%$ donkey serum (Jackson Immuno Research, 017-000-121), 0.3 % Triton X-100 (Sigma-Aldrich, X100) and 0.1 % BSA (Sigma-Aldrich, A7906) in PBS−/− for 1 h at room temperature. Primary antibodies were incubated with $3\%$ FBS in PBS−/− overnight at 4 °C, subsequently incubated with the appropriate secondary antibody (Alexa Fluor, Molecular Probes) and DAPI at room temperature for 1 h. Antibody information is listed in Supplementary Table 8. Brightfield and fluorescent imaging were done using a Leica SP8 confocal microscope with Las X software (3.5.7.23225) and processed in Imaris 9.6.
## EdU labelling and apoptosis assay
Cells were incubated with 10 µM EdU (Click-iT EdU) (Thermo Fisher Scientific, C10634) in medium for 4 h at $5\%$ O2/$5\%$ CO$\frac{2}{37}$ °C. The 3D clusters were prepared for whole-mount immunostaining as described above. Dissociated cells were collected for flow cytometry as described above. Permeabilization, blocking and Click-iT reaction for EdU detection were performed according to the manufacturer’s instructions. Immunostaining of EdU-labelled 3D clusters were performed with antibodies supplied with the kit and with DAPI (1 μg ml−1) for nuclear staining. Flow cytometry of EdU-labelled dissociated cells was performed with DAPI (10 μg ml−1) staining cells for DNA content. Cell apoptosis was measured by Annexin V Conjugates for Apoptosis Detection kit (Thermo Fisher Scientific, A13202) according to the manufacturer’s instructions.
## Flow cytometry
For surface marker staining, dissociated cells were incubated with conjugated antibodies for 1 h at 4 °C and were stained with DAPI (1 μg ml−1) to exclude dead cells. For intracellular staining, cells were stained with Ghost Dye 450 (TONBO Biosciences, 13-0868) before $4\%$ paraformaldehyde fixation to stain dead cells. Fixed cells were permeabilized in PBS with $5\%$ donkey serum and $0.3\%$ Triton X-100 for 30 min at room temperature. Cells were incubated with primary antibodies in 1× PBS−/− with $5\%$ donkey serum and $0.1\%$ Triton X-100 overnight at 4 °C. The following day, cells were washed twice in 1× PBS and unconjugated antibodies were further incubated with secondary antibodies (Alexa Fluor conjugates) for 2 h. Antibody sources and concentrations are indicated in Supplementary Table 8. Cells were analysed using an LSR Fortessa (BD Bioscience) or FACS sorted by SH800 (SONY SH800 Software). All data were analysed with FCS Express 6 software (BD Biosciences). Antibody information is listed in Supplementary Table 8.
## Generation of PDX1-eGFP-positive and PDX1-eGFP-negative cells with minimal cytokine sets for pancreatic spheroid and hepatic organoid expansion
PDX1-eGFP reporter VFG cells passage 6 was plated at 25,000 cells cm−2 on polystyrene cell culture plates pre-coated with undiluted GFR-Matrigel and pre-seeded with 8 × 103 cells cm−2 MEFs. The cells were cultured in BMP4 withdrawal medium (SFD medium supplemented with FGF2 (10 ng ml−1), VEGF (10 ng ml−1), EGF (10 ng ml−1), 0.5 mM ascorbic acid and 4.5 × 10−4 M monothioglycerol) and maintained under hypoxic conditions ($5\%$ O2/$5\%$ CO$\frac{2}{37}$ °C) for 5 days with medium changing every other day. *For* generating PDX1-eGFP-positive and PDX1-eGFP-negative fractions, cells were further differentiated in DMEM high-glucose GlutaMAX Supplement (Thermo Fisher Scientific, 10566016) with $1\%$ vol/vol B27 supplement (Thermo Fisher Scientific, 17504044), 50 ng ml−1 FGF2, FGF7 (Peprotech, 100-19) or FGF10 (Peprotech, 100-26) for 5 days with medium changed every day. Both BMP4 withdrawal and FGF stimulation were performed under hypoxic conditions ($5\%$ O2/$5\%$ CO$\frac{2}{37}$ °C).
The single PDX1-eGFP-positive and PDX1-eGFP-negative cells generated from the BMP4 withdrawal and FGF10-stimulated VFG culture were sorted by FACS using a SH800. GFP+ cells were expanded as pancreatic spheroids and GFP– cells as hepatic organoids according to the described protocols23,24, except that the cultures were maintained under hypoxic conditions ($5\%$ O2/$5\%$ CO$\frac{2}{37}$ °C).
## Pancreatic differentiation
VFG cells at passages 6–8 were plated at 25,000 cells cm−2 on polystyrene cell culture plates pre-coated with undiluted GFR-Matrigel and pre-seeded with 8,000 cells cm−2 MEFs in the VFG medium. Day-5 expanding VFG cells were used for pancreatic differentiations under hypoxic conditions ($5\%$ O2/$5\%$ CO$\frac{2}{37}$ °C) according to protocols described as below: For the protocol adapted from Ameri et al.22, day-5 expanding VFG cells were treated with DMEM high-glucose GlutaMAX Supplement with $1\%$ vol/vol B27 supplement as basal medium throughout the differentiation and were supplemented with 2 µM RA (Sigma-Aldrich, R2625) for 3 days; then with 64 ng ml−1 FGF2 and 50 ng ml−1 hNOGGIN (R&D Systems, 6057-NG-100/CF) for 3 days; and finally with 64 ng ml−1 FGF2, 50 ng ml−1 hNOGGIN and 0.5 μM TPB (PKC activator) (Merck Millipore, 565740) for 3 days, with the medium changed every day.
For the protocol adapted from Rezania et al.12, day-5 expanding VFG cells were exposed to MCDB 131 basal medium (Thermo Fisher Scientific, 10372019) throughout differentiation and supplemented with 1.5 g l−1 sodium bicarbonate (Thermo Fisher Scientific, 25080094), 1× Glutamax Supplement (Thermo Fisher Scientific, 35050061), 10 mM d-(+)-glucose (Thermo Fisher Scientific, G8270) $0.5\%$ BSA, 0.25 mM ascorbic acid and 50 ng ml−1 FGF7 for 2 days; and then with 2.5 g l−1 sodium bicarbonate, 1× Glutamax Supplement, 10 mM glucose, $2\%$ BSA, 0.25 mM ascorbic acid, 1:200 insulin–transferrin–selenium–ethanolamine (ITS-X) (Thermo Fisher Scientific, 51500056), 50 ng ml−1 FGF7, 1 µM RA, 0.25 µM SANT-1 (Sigma-Aldrich, S4572), 100 nM LDN193189 (Tocris, 6053) and 80 nM TPB (EMD Millipore) for 2 days; and finally with 2.5 g l−1 sodium bicarbonate, 1× Glutamax, 10 mM glucose, $2\%$ BSA, 0.25 mM ascorbic acid, 1:200 ITS-X, 2 ng ml−1 FGF7, 0.1 µM RA, 0.25 µM SANT-1, 200 nM LDN193189 and 40 nM TPB for 3 days.
For the protocol adapted from Nostro et al.10, day-5 expanding VFG cells were fed SFD medium supplemented with 50 ng ml−1 of FGF10, 3 ng ml−1 mouse WNT3A (R&D Systems, 1324-WN-010/CF) and 0.75 μM dorsomorphin (Sigma-Aldrich, P5499) for 3 days with the medium changed every day. Medium was then changed to DMEM high-glucose GlutaMAX Supplement with $1\%$ vol/vol B27 supplement, 50 ng ml−1 FGF10, 50 ng ml−1 hNOGGIN, 50 μg ml−1 ascorbic acid and 2 µM RA, with 0.25 μM KAAD-cyclopamine (Sigma-Aldrich, 239804) for 1 day. Finally, medium was changed to DMEM high-glucose GlutaMAX Supplement with $1\%$ vol/vol B27 supplement, 50 ng ml−1 hNOGGIN, 50 ng ml−1 EGF, 10 mM nicotinamide (Sigma-Aldrich, N0636) and 50 μg ml−1 ascorbic acid for 4 days with the medium changed every day.
The protocol adapted from Nostro et al.10 was used to assess efficiency of pancreatic differentiation in a directed protocol from ADE cells, VFGp3, VFGp6 and VFGp12 cells generated from the PDX-eGFP reporter. Day-5 transient ADE cells were generated as described previously and directly used for differentiation. Differentiation of WT H9 and HUES4 VFGp3 and VFGp6 cells to pancreatic beta-like cells were performed as reported15,55 with modifications during endocrine differentiation. In brief, day -13 differentiating VFG cells were re-aggregated following treatment with 1 ml Corning Cell Recovery Solution (Sigma-Aldrich, CLS354270) and cultured on the membrane surface of Millicell insert (Millipore, PICM03050) in the same medium described in Tiya et al.55.
## Hepatic and intestinal differentiations
Hepatic and intestinal differentiations were started from day-5 expanding VFG cells according to the protocols described in Cheng et al.15.
## Total mRNA purification, reverse transcription and qPCR analysis
Two hundred thousand cells were washed in 1× PBS twice, lysed in RLT buffer (RNeasy Micro kit) (Qiagen, 74004) containing $1\%$ β-mercaptoethanol (Sigma-Aldrich, M6250) and stored at −80 °C until processing. Total mRNA was isolated using the RNeasy Micro kit according to the manufacturers’ instructions and digested with RNase-free DNase I, (Qiagen, 79254) to remove genomic DNA. First-strand complementary DNA synthesis was performed with SuperScript III First-Strand Synthesis System (Thermo Fisher Scientific, 18080051) using random hexamers (Thermo Fisher Scientific, N8080127) and amplified using SYBR Green PCR Master Mix (Thermo Fisher Scientific, 4309155). PCR primers were designed using Primer3Plus60 and validated for efficiency ranging between $95\%$ and $100\%$. Primer sequences used in quantitative reverse transcription PCR (RT–qPCR) are listed in Supplementary Table 9. StepOnePLUS Real-Time PCR System (Thermo Fisher Scientific) was used for RT–qPCR in 96-well plate format. Expression values for each gene were normalized against ACTB, using the delta–delta CT method.
## Sample preparation for bulk RNA-seq
Total mRNA amount and RNA integrity were assessed using a Fragment Analyzer (AATI). Ribosomal RNA was removed from samples using the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB, E7490L). Sequencing libraries were prepared from 100 ng of purified total mRNA using NEBNext Ultra II RNA Library Prep Kit for Illumina (NEB, E7770L) according to the manufacturer’s instructions. RNA-seq libraries were sequenced for 75 cycles in single-end mode on NextSeq 500 platform (Illumina, FC-404-2005).
## Sample preparation for ATAC-seq
Dissociated single cells were washed with ice-cold PBS−/− and pelleted at 500g for 10 min at 4 °C. Fifty-thousand cells were taken from a diluted stock in PBS buffer to prepare ATAC-seq libraries as described in Buenrostro et al.61 with slight modifications. Nuclei were prepared by resuspending the cells in 100 µl ice-cold ATAC lysis buffer (10 mM Tris–HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2 and $0.1\%$ NP40) followed by incubation on ice for 15 min while mixing every 5 min. Nuclei were then collected by centrifuging at 1,000g for 10 min at 4 °C, and the pellet was resuspended in 50 µl transposition buffer (10 mM Tris pH 8, 5 mM MgCl2 and $10\%$ dimethylformamide). Tagmentation was performed by adding 2.5 µl Tn5 transposase (Illumina, 20034197) and incubating at 37 °C while shaking in a thermomixer set at 1,000 rpm. Tagmentation reactions were stopped and purified with MinElute PCR Purification Kit (Qiagen, 28004) and tagmented DNA eluted in 10 µl elution buffer (10 mM Tris pH 8.0). A 50 µl PCR reaction was assembled containing 10 µl of tagmented DNA, 25 µl NEBNext High-Fidelity PCR Mix (NEB, M0541S), 5 µl of SYBR Green (Invitrogen, S7563) and index primers at 2 µM concentration. Ten microlitres of each PCR reaction was used to decide the optimum number of PCR cycles required with following conditions: 5 min at 72 °C; 30 s at 98 °C; and 20 cycles of 10 s at 98 °C, 30 s at 63 °C and 60 s at 72 °C. The reaction was monitored in a LightCycler-480 qPCR (Roche), and the number of cycles required was deduced from the amplification curve. The remaining PCR reaction was then subjected to this number of PCR cycles. The PCR reaction was purified with an equal volume of AMPure XP beads (Beckman, A63880) following manufacturer’s protocol and was eluted in 20 µl Tris pH 7.8. Libraries were quantified with Qubit dsDNA High-sensitivity Assay (Invitrogen, Q32851), and fragment profiles were checked using Bioanalyzer High Sensitivity assay (Agilent) or Fragment Analyzer (AATI). Samples that showed nucleosomal bands were sequenced for 75–150 cycles in paired-end mode on an Illumina HiSeq-2000 platform or NextSeq 500.
## Generation of shRNA KD VFG cell lines
shRNAs targeting HHEX, FOXA1 and FOXA2 transcripts were designed using RNAi consortium (TRC) GPP Web Portal (Broad Institute) (https://portals.broadinstitute.org/gpp/public) (for HHEX, FOXA1 and FOXA2 shRNA sequences, see Supplementary Table 9). A vector delivering a scrambled sequence was used as control (for scrambled shRNA sequence, see Supplementary Table 9). All shRNA sequences were cloned into a lentiviral vector (pL-U6-sgRNA-SFFV-Puro-P2A-EGFP), a gift from Kristian Helin (Addgene, 175037) (ref. 62), using BsmBI sites. HEK293FT packaging cells were co-transfected with the pL-U6-sgRNA-SFFV-Puro-P2A-EGFP carrying individual shRNAs and pAX8 and pCMV-VSV using Lipofectamine 2000 supplemented with polyethylenimine (Sigma-Aldrich, 408727) according to standard protocols. SFD medium carrying lentivirus produced from HEK293FT cells (48 h post-transduction) was applied 1:1 with fresh VFG expansion medium to one 12-well plate of day 2 VFG cell culture (passaged at 25,000 cells cm−2 at day 0). Transduction was performed in presence of 1:1,000 polybrene infection/transfection reagent (Merck Millipore, TR-1003-G) at 8 µg ml−1. Forty-eight hours after transduction with the sgRNA-encoding lentiviral plasmids, the VFG cells were selected and maintained at 0.25 μg ml−1 puromycin in standard VFG condition.
## ChIP–qPCR
ChIP was carried out using the True MicroChIP kit (Diagenode, C01010132) with modifications. One-hundred-thousand sorted CD184-CD117 double-positive cells ADE, VFGp3 and VFGp6 cells; or shRNAs (scrambled, FOXA1, FOXA2 or HHEX) KD VFGp6 cells were fixed in $1\%$ formaldehyde (Thermo Fisher Scientific, 28906) in ADE or VFG medium for 10 min at room temperature followed by a 5 min quench with glycine (in True MicroChIP kit, Diagenode) at room temperature. Cells were lysed and immunoprecipitation performed using the True MicroChIP kit (Diagenode, AB-002-0016) with the following modifications. Up to 100,000 cells were sonicated in one lysate and split into 50,000 equivalents after sonication. Samples were lysed using 50 µl of buffer tL1 and incubated for 5 min on ice. One-hundred-fifty microlitres of Hank’s buffered salt solution with 1× protease inhibitor cocktail (in True MicroChIP kit, Diagenode) was added, and the lysate was sonicated in 0.65 ml Bioruptor Pico Microtubes (Diagenode, C30010020). Chromatin was sheared using a Bioruptor Pico (Diagenode) with ten cycles (30 s on, 30 s off). Sonicate was aliquoted in 100 µl (for 50,000 cells), and an equivalent volume of complete ChIP buffer tC1 was added. For immunoprecipitation, the following antibodies and amounts of antibody were used for the 50,000-cell ChIP: 2 µg of FOXA1 (1:50) (Abcam, ab170933), 2 µg of H3K4me1 (1:50) (Abcam, ab8895), 2 µg of H3K27ac (1:50) (Abcam, ab4279) and 2 µg of HHEX (1:100) (R&D, MAB83771). Immunoprecipitation and washes were as described in the True MicroChIP protocol, then purified by phenol chloroform extraction and ethanol precipitation. The pull-down DNA was eluted in 100 µl elution buffer and qPCR was performed as described in the True MicroChIP protocol for different genomic loci. Enrichment was calculated as percentage of input. Antibody information is listed in Supplementary Table 8. The primer sequences used in ChIP–PCR are listed in Supplementary Table 9.
## In vitro scRNA-seq analysis
Sequences were mapped to the hg38 assembly of the human genome, de-multiplexed and filtered as previously described59,63 extracting a set of unique molecular identifiers (UMIs) that define distinct transcripts in single cells for further processing. We estimated the level of spurious UMIs in the data using statistics on empty MARS-seq wells as previously described59. Mapping of reads was done using HISAT (version 0.1.6) (ref. 64). Reads with multiple mapping positions were excluded. Reads were associated with genes if they mapped to an exon. Raw counts were further analysed using Seurat (4.0.1) (ref. 65) (https://satijalab.org/seurat/). Cells were filtered with the following thresholds (lower bound: 2,000 UMIs; 550 genes and upper bound: 35,000 UMIs; 4,950 genes). Additionally, cells with more than $20\%$ of mitochondria content were removed. In Extended Data Fig. 1a, we subset ADE and VFG cells (505 cells). Raw counts were further normalized, log-transformed and scaled using NormalizeData and ScaleData, respectively. PCA was computed on 2,000 highly variable genes without cell cycle regression. The dataset was clustered using Louvain with 0.7 resolution followed by uniform manifold approximation and projection dimension reduction on top 20 PCs. In Extended Data Fig. 2d, we subset for treated and withdrawal cells (562 cells). We follow the same steps above adjusting only clustering resolution set to 0.5. Detailed analyses can be found at https://github.com/brickmanlab/wong-et-al-$\frac{2022}{.}$
## In vivo scRNA-seq re-analysis
The Li et al.20 dataset HRA000280 was downloaded from Genome Sequence Archive. Cells with low quality and mitochondrial content higher than $20\%$ were filtered out (lower bound: 3,000 genes and upper bound: 9,000 genes; 400,000 UMIs). Additionally, cells labelled as ‘poor quality’ were also discarded. We followed the same pre-processing steps as mentioned above without clustering. We subsetted the final dataset for hMG, hHG, hFG and hAL population.
## CAT
We used CAT to determine similarity between clusters from in vivo and in vitro studies. CAT calculates mean gene expression of randomly sampled cells with replacement for each cluster 1,000 times. Euclidian distance is measured between all pairs of clusters. A small distance represents high similarity. A detailed explanation of the method can be found in Rothova et al.19.
## Analysis of bulk RNA-seq data
Fastq files from bulk RNA-seq samples were aligned to the hg38/GRCh38 genome using STAR v2.5.3a66. Transcript expression levels were estimated with the quantMode GeneCounts option and GRCh38p10.v27 annotations. FastQC v0.11.7 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) was used for quality control metrics and multiqc v1.7 (ref. 67) for reporting. Data analysis was then performed with R/Bioconductor68 (https://www.R-project.org). Normalization was performed with DEseq2 (v1.24.0) (ref. 69). The Lee et al.35 dataset was retrieved from NCBI GEO (GSE114102) and analysed as above. *Differential* gene expression was assessed using DESeq2 (R package version 1.32.0). Z-scoring was calculated as previously described for each dataset separately. Gene set enrichment analysis was performed by Webgestalt (http://www.webgestalt.org) (log2FC between VFGp3 and VFGp6) for Gene Ontology Biological Process (GO-BP) with false discovery rate <0.05.
## Processing of ATAC-seq datasets
The quality of the sequencing reads was assessed with FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) followed by trimming of poor-quality base calls and adaptor sequences with cutadapt70. Read pairs were then aligned to the hg19 reference genome using bowtie2 (ref. 71) with the following parameters: bowtie2–no-discordant–no-mixed–no-unal–very-sensitive -X 2000. Samtools72 was used for sorting alignments and format conversions. Alignments from PCR duplicates were removed using Picard (http://broadinstitute.github.io/picard/). Alignments were then converted into BED format using bedtools73. The 5′ ends of the reads were offset by +4 bases for the reads on Watson strand and by −5 bases for the reads on Crick strand, to reflect the exact location of Tn5 insertion site. Single-base genome-wide coverage was computed using a 30 bp fragment centred at the Tn5 insertion site in BigWig format. We called peaks using Macs2 (ref. 74) with the following parameters: macs2 callpeak–nomodel–extsize 150–shift -75 -g ‘hs’ -p 0.01. For each condition, data from two biological replicates were used to create a set of highly reproducible peaks using irreproducible discovery rate (≤0.05, ref. 75). Deeptools76 was employed to compute Pearson’s correlation among the conditions/replicates and for PCA plots. Bedtools intersect command was used to find overlapping or unique (with parameter ‘-v’) enhancer positions (bed format) between two conditions in question (Fig. 3b).
## Detection of differential chromatin accessibility and temporal dynamics of enhancers from ATAC-seq data
A consensus set of ATAC-seq peaks was created using reproducible peaks from all five stages of differentiation. Next, we computed normalized read coverage (RPKM) for the consensus peak set in all stages. General linear modelling was applied to the normalized counts from the step above to detect changes in chromatin accessibility across the stages and in both directions. We used the following parameters for differential accessibility: log2FC > 2 or log2FC < 2 at adjusted P value <0.005 (time-course sequencing (TC-seq), ref. 30). We then defined stage-specific peaks using c-means clustering of the dynamic peak-set from the step above. We called eight clusters that gave a functionally relevant pattern along the timeline of differentiation. Some clusters were merged, as they were too similar to be dealt with separately. This led to formation of the six groups of dynamic enhancers (Fig. 3c, right). RPKM-normalized BigWig tracks from merged replicates were used to plot heat maps in deeptools76. For locus-specific visualizations, we used the UCSC Genome Browser (http://genome.ucsc.edu, ref. 77) to load BigWig tracks.
## Enrichment scoring of defined ATAC-clusters from the mapped gene sets that are up- or downregulated at the PE stage compared with VFGp6
ATAC-seq peaks were assigned to genes using GREAT78 with the setting of single nearest gene within 25 or 200 kb (Supplementary Table 1b). The enrichment of gene-annotated ATAC clusters in differential expression gene sets was calculated by log2 ratio between number of observed overlaps and number of expected overlaps from the dataset. We compared the impact of very low levels of background gene expression noise (those genes not reaching more than 100 or 1,000 reads in a particular sample, baseMean 100 or 1,000) on these gene sets (Supplementary Table 1d–g). While filtering out gene expression noise reduces the size of the gene set, it can be expanded by considering enhancers located within 200 kb of a target gene.
## Motif analysis from ATAC-clusters
Enrichment of known and de novo TF binding motifs was calculated with the HOMER v4.11.1 suite79 using the findMotifsGenome function with default parameters.
## Hierarchical k-means clustering of expression patterns of genes annotated to ATAC-peaks clusters
Bulk RNA-seq gene expression levels were normalized using DESeq2 R package version 1.32.0 (ref. 69). The mean of normalized expression was calculated for each condition and transformed into z-scores. Gene expression levels were then separated into the different annotated ATAC-peaks clusters. Finally, gene expression patterns were grouped using hierarchical clustering ($k = 10$) based on Euclidian distances.
## Mapping and analysis of H3K27ac data from human embryo samples
Pre-processing and alignment of ChIP–seq reads was as described in Gerrard et al.37. Single-end reads were aligned to hg19 genome assembly with bowtie 1.0.0 (parameters: -m1 –n 2 –l 28, uniquely mapped reads only). These alignments were received in compressed BAM format from European Genome-Phenome Archive (https://ega-archive.org/) under accession numbers EGAS00001003163 and EGAS0001004335. We converted the alignments to BED format and called peaks with HOMER (parameters: findPeaks -style histone) against a pooled input sample. We then used bedtools-2.30 (ref. 73) to select the peaks present in both replicates (bedtools intersect –f 0.50 –r –u -a rep1.bed –b rep2.bed) of most tissue types except for stomach.
Lineage-specific sets of H3K27ac regions were generated by concatenating peaks from relevant tissues as follows: ectoderm (RPE and brain), endoderm (pancreas, liver, lung and stomach) and mesoderm (heart and adrenal). To identify unique regions for each germ layer, we use bedtools intersect command, followed by sorting regions using sort option and finally merging smaller regions that are subsets of larger regions using bedtools merge command. This process ensures a unique count of peaks even if a given peak is part of a larger regulatory region. Similarly, we identified regions unique to tissue types. To map different ATAC clusters to the H3K27ac regions described above, we took regions in different enhancer classes and intersected these with different classes of H3K27ac regions from human foetal samples with bedtools intersect command. These overlaps were used in generating over-representation scores defined as observed/expected.
## Enrichment scoring of dynamic ATAC-seq clusters with H3K27Ac regions from human embryonic tissues
The enrichment of ATAC clusters in different lineage- and tissue-specific H3K27ac groups was calculated on the basis of the ratio between number of observed overlapped regions (between ATAC and H3K27ac peaks) and number of expected overlapped regions from the datasets.
## Analysis of HHEX ChIP–seq dataset
We aligned HHEX ChIP–seq data from Yang et al.42 to hg19 assembly using bowtie-1.3.1 (ref. 80) with default parameters and converted the alignments to HOMER tag-directory format. We created depth-normalized bigwig files using the HOMER79 makeUCSCfile program. ComputeMatrix (deeptools suite76) was used to plot the coverage centred at the midpoint of enhancer regions in different classes (Extended Data Fig. 10g). This dataset can be found on NCBI GEO under accession number GSE181480.
## Statistical analyses and reproducibility
No statistical methods were used to pre-determine sample size. Data distribution was assumed to be normal, but this was not formally tested. The experiments were not randomized. Data collection and analysis were not performed blind to the conditions of the experiments. No data points were excluded from the analyses. Data collection was performed using Microsoft Office Excel (16.16.2). Data representation and statistical analyses were performed using GraphPad Prism. Unless mentioned otherwise, data are shown as mean ± standard error of the mean (s.e.m.) and N numbers refer to biologically independent replicates. Statistical significance ($P \leq 0.05$) was determined as indicated in figure legends using one-way analysis of variance (ANOVA) Tukey’s multiple comparison test (Figs. 1c,d, 2d and 4b,c and Extended Data Fig. 3d–f), one-way ANOVA Dunnett’s multiple comparison test (Figs. 2a,b and 6b,c and Extended Data Figs. 2h,i, 9a,c and 10a,b), unpaired two-tailed t-test (Fig. 2d,e), unpaired one-tailed t-test (Fig. 6d–f and Extended Data Fig. 10c–f) and chi-squared test (Figs. 3f,g and 4d,e and Extended Data Fig. 5d).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41556-022-01075-8.
## Supplementary information
Reporting Summary Supplementary Table 1Summary of dynamic ATAC peaks and their annotated genes classified by differential expression between VFGs and PE samples. Supplementary Table 2Summary of VFG-specific ATAC peaks and their annotated genes classified by differential expression between VFG and PE samples. Supplementary Table 3H3K27ac datasets derived from different human embryonic tissues. Supplementary Table 4Dynamic enhancers defined by ATAC-seq clusters mapped to H3K27ac datasets from human embryonic tissues. Supplementary Table 5Summary of VFG expansion-specific ATAC peaks and their annotated genes classified by differential expression between VFGs and PE samples. Supplementary Table 6VFG expansion-specific enhancers defined by ATAC-seq clusters mapped to H3K27ac datasets from human embryonic tissues. Supplementary Table 7Motif enrichment results for dynamic and VFG expansion-specific enhancers defined by ATAC-seq clusters. Supplementary Table 8Antibodies used in this study. Supplementary Table 9Oligonucleotide sequences used in this study.
The online version contains supplementary material available at 10.1038/s41556-022-01075-8.
## Extended data
Extended Data Fig. 1Ventral foregut identity of expanding endodermal progenitors.a, Left: UMAP visualization of single cells from the transient ADE (ADE.1 and ADE.2) and EP passage 6 samples. Right: UMAP visualization of single cells from different endodermal populations from early human embryos reported in Li et al.20. b, Heatmap illustrating gene expression in H9-derived ESC, ADE, and EP cells ($$n = 3$$ independent experiments) from bulk RNA-seq dataset. Scaled normalized expression of the top 20 differentially expressed genes for each condition (ADE vs EP) is shown. c, Representative immunostaining of hAL markers, HHEX and TBX3, in EP passage 6 cells derived from H9 ESC cells. Images represent three independent experiments. Scale bar = 50 µm. d, *Expression analysis* in HHEX shRNA KD cells (set 1 and set 2) and scrambled shRNA control by RT-qPCR. Relative fold change in mRNA of HHEX gene in KDs and control EP/VFG cells was assayed by RT-qPCR. Expression is normalized to ACTB. Circles and triangles mark cells derived from H9 and HUES4 WT ESCs respectively. Data are represented as mean ± SEM ($$n = 4$$ independent experiments). Statistical analysis (**$P \leq 0.01$, unpaired two-tailed t-test) was performed between KD and control EP/VFG cells. Comparisons without an indicated P value are not significant. e, Apoptosis assay in HHEX shRNA (set2) KD and scrambled shRNA control EP/VFG. Bar plot showing percentage of Annexin V + cells for each assay. Circles and triangles mark cells derived from H9 and HUES4 WT ESCs respectively. Data are represented as mean ± SEM ($$n = 4$$ independent experiments). No statistical difference (unpaired two-tailed t-test) was found between HHEX shRNA KD and scrambled shRNA control. f, Representative flow cytometry plots used to analyze the cell cycle in transient ADE, early EP/VFG (p3-4), expanding EP/VFG (p6-8) and HHEX depleted EP/VFG (p6-8). Cells were stained with EdU and DAPI. Cells in G1 (red), S (blue), and G2M (green) were gated and percentages of each fraction shown. Flow Cytometry plots represent three independent experiments. Source data Extended Data Fig. 2Human VFG cultures can be readily transformed to either pancreatic or hepatic lineages.a, Schematic representation showing the conversion of VFG culture to pancreatic and hepatic expansion. The figure illustrates the generation of PDX1−eGFP positive (PDX1+) and negative (PDX1−) cells from VFG culture after BMP4 withdrawal and subsequent stimulation with FGF. b-c, Flow cytometry of eGFP expression (b) or intracellular PDX1 (c) for HUES4 wild type (grey), PDX1−eGFP reporter (purple) in VFG culture, and the reporter following BMP4 withdrawal (green). Fractions of eGFP+ or PDX1+ were gated and percentages are shown. Flow Cytometry plot represents three independent experiments. d, Left: UMAP visualization of 526 cells isolated from mock-treated VFG (blue), VFG cells grown in the absence of BMP4 (red), and transient pancreatic induction by FGF2 simulation (green). Right: UMAP visualization of Seurat clustering from the samples described on the left. e, Representative bright-field (top) and fluorescent (bottom) images for the PDX1-eGFP reporter VFGs (left) or following BMP4 withdrawal (right), and then treated with FGF2, FGF7, or FGF10. Images represent three independent experiments. Scale bar = 50 μm. f, Flow cytometry of eGFP expression for the conditions described in (e), including mock-treated cells. Percentages of PDX1+ cells were shown in the rectangle boxes of each histogram. Flow Cytometry plots represent three independent experiments. g, PDX1+ cells form 3D spheres and expand as pancreatic spheroids (Top). PDX1−cells form 2D clusters and expand as hepatic organoids (bottom). Images represent three independent experiments. Scale bar = 50 µm. h-i, Relative fold change in mRNA of pancreatic markers (PDX1, SOX9, and ONTCUT1) (h) and hepatic markers (AFP, ALB, and SERPINA1) (i) in the VFGs and VFG-derived cell types (as described in a and g). Expression is normalized with ACTB. Data are represented as mean ± SEM ($$n = 3$$ independent experiments). ** $P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ (one-way ANOVA Dunnett’s multiple comparison test compared with VFG). Source data Extended Data Fig. 3in vitro differentiation of VFG culture towards pancreatic, hepatic, and intestinal endoderm.a, Schematic diagram for stepwise pancreatic differentiation with protocols from Ameri et al.22, Rezania et al.12 and Nostro et al.10 from established VFG culture. b, Representative bright-field (left) and fluorescent (right) images for the PDX1-eGFP reporter VFGs differentiated with protocols indicated. Images represent three independent experiments. Scale bar = 50 μm. c, Representative immunostaining of PDX1 (green) and NKX6-2 (red) in the top row; ROBO2 (green) GP2 (red) in the bottom row, including DAPI (blue) for p6 VFG cells differentiated with protocol from Nostro et al.10. Images represent three independent experiments. Scale bar = 50 μm. d, Bar plot showing relative fold change in mRNA of pancreatic markers PDX1, SOX9, ROBO2, and NKX6-2 in pancreatic differentiation from ADE and VFG at p3 and p6 cells. Data are represented as mean ± SEM ($$n = 3$$ independent experiments). ** $P \leq 0.01$, (one-way ANOVA Tukey’s multiple comparison test, only significant comparisons are shown). e, Bar plot showing relative fold change in mRNA of hepatic markers HNF4A, ALB, CYP3A7, and CYP3A4 in hepatic differentiation from ADE and VFG at p3 and p6 cells. Data are represented as mean ± SEM ($$n = 3$$ independent experiments). ** $P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ (one-way ANOVA Tukey’s multiple comparison test, only significant comparisons are shown). f, Bar plot showing relative fold change in mRNA of intestinal markers CDX2, LGR5, KLF5, and HNF4A in hepatic differentiation from ADE and VFG at p3 and p6 cells. Data are represented as mean ± SEM ($$n = 3$$ independent experiments). * $P \leq 0.05$ (one-way ANOVA Tukey’s multiple comparison test, only significant comparisons are shown). Comparisons without an indicated P value are not significant. Source data Extended Data Fig. 4The dynamic chromatin landscape and gene expression in VFG expansion and further differentiation.a, MA-plot representing differential expression in VFGp6 versus VFGp3 culture (Log2 fold change > 2, $P \leq 0.05$) ($$n = 3$$ independent experiments). b, GSEA for GO-BP of VFGp6 compared to VFGp3 cells. Normalized Enrichment Score for significant terms for VFGp6 are shown as positive value, and VFGp3 as negative value (FDR < 0.05). c, Pie-chart showing distribution of dynamic ATAC-peaks ($$n = 57803$$) with percentage and numbers of peak indicated per cluster in Fig. 3c. d, Representative UCSC Genome Browser screen shot (from two independent experiments) at the GLIS3 locus showing ATAC-seq data from ESC, ADE, VFGp3, VFGp6, and PE. Genome coordinates (bp) are from the hg19 assembly of the human genome. PEPRIMED elements (peaks 246735, 246749, and 246752) are shown at the bottom and the corresponding regions are highlighted in yellow. e, Left: Representative UCSC Genome Browser screen shot as in d. The region of the area IV enhancer is highlighted in yellow. Approximate distance between the region and PDX1 TSS is indicated by a broken dashed line. Right: bar plots for expression (normalized RNA-seq counts, $$n = 3$$ independent experiments) for PDX1 RNA across the same samples as ATAC-seq. f, Representative UCSC Genome Browser screen shot at the TBX3 locus as in d. VFGTR elements (peaks 60300, 60307, and 60310) are shown at the bottom and the corresponding regions are highlighted in yellow. g-h, Mapping dynamic enhancer classes to gene expression (up-regulated genes). Left: Number of mapped ATAC peaks in each cluster defined in Fig. 3c located within 25 Kb (g) or 200 Kb (h) of the single nearest gene’s TSS from the PE up-regulated gene set with baseMean >100 (grey) or >1000 (green). Right: Enrichment (log2 observed/expected) of the PE up-regulated gene set with baseMean > 100 (grey) or >1000 (green), in proximity (within a 25 or 200 Kb window) to ATAC-clusters defined in Fig. 3c. i-j, Mapping dynamic enhancer classes to gene expression (down-regulated genes) for elements located within 25 Kb (i) or 200 Kb (j) of the single nearest gene’s TSS from the PE down-regulated gene set, analysis and labels as in g. All data shown are significant by chi-square analysis. Source data
Extended Data Fig. 5VFG expansion enables consolidation of an enhancer landscape that is imperfectly realized during directed differentiation.a, A comparison of chromatin accessibility of enhancers charted in this study (heatmap, left) with the Lee et al. dataset35 (heatmap, right). Enhancers in the group “PE-PP1 common” are the pancreatic endoderm enhancers that are activated independent of VFG expansion ($37.46\%$, $$n = 7504$$). Enhancers in the group PE-not-PP1 are PE enhancers that are activated only if PE is differentiated from expanding VFGs ($21.27\%$, $$n = 4260$$). Enhancers in the “VFGOFF-in-DE-PP1” group, represent a subset of ADE enhancers that are inactivated during VFG expansion ($14.85\%$, $$n = 2974$$). The “VFGTR-in-PP1” enhancer group at the bottom of the heatmap ($26.42\%$, $$n = 5293$$) are inactivated in PE derived from expanding VFGs, but not in directed differentiation. b-c, Representative UCSC Genome Browser screen shot (from two independent experiments) showing examples of a PE-not-PP1 enhancer (peak35254), in an intron of the FGFR2 locus (b) and a VFGOFF-in-DE-PP1 group (peak192828) contained within an intron of the MEF2C locus (c). Approximate distance between elements and TSS is indicated by a broken dashed line in each panel. d, Bar plot showing the prevalence (log2 observed/expected) of ATAC peaks within a 200 Kb window from genes up-regulated (green) or down-regulated (red) between PE and VFGp6 across the defined ATAC peak clusters. Genes considered have a base mean expression > 1000, log2 fold change > 1.5 and $P \leq 0.05.$ *All data* shown are significant in chi-square analysis. Source data Extended Data Fig. 6Characterization of global transcriptional changes between PE/VFG and PP1/FG cells.a, Bar plot showing differential expression of 13 dorsal pancreas markers (log2 fold change) in PE/VFGp6 and PP1/FG from RNA-seq dataset ($$n = 3$$ independent experiments). DHS is defined as a peak of Tn5 insertions in ATAC-seq. b, Scatter plot of differential expression for gene regulated in PE/VFGp6 (horizontal axis) and in PP1/FG (vertical axis) ($$n = 3$$ independent experiments). c-d, Left: Scatter plot of differential expression for genes up-regulated (c) or down-regulated (d) in PE vs VFG (as defined in Extended Data Fig. 5d), and within 200Kb of minimum one ATAC peak in the PE-not-PP1 (c) or VFGTR-in-PP1 (d) clusters respectively, vs their expression after directed differentiation (PP1/FG). The diagonal line indicates where there is no difference in differential expression between two comparisons (datasets). Right: normalized z-score expression of representative candidates (c: FRMD6 and FGFR2 and d: IHH and EPHA4). Normalized z-score expression for each candidate was plotted for the ADE, VFG (p6), and PE conditions (green in c and red in d), and the DE, FG, and PP1 conditions (grey) ($$n = 3$$ independent experiments).
Extended Data Fig. 7VFG expansion insures higher fidelity regulation of enhancers normally exploited in fetal organogenesis.a-b, Enrichment of tissue-specific (a) and lineage-specific (b) H3K27ac enhancers from human embryos (from two independent experiments for most tissue types, except for stomach where only one sample was available) in different ATAC clusters defined in Fig. 3c were displayed by enrichment score (observed/expected) in radar charts (a) and in bar plot (b). c-d, Enrichment of tissue-specific (c) and lineage-specific (d) H3K27ac enhancers from human embryos (from two independent experiments for most tissue types, except for stomach where only one sample was available) across different VFG-specific ATAC clusters defined in Extended Data Fig. 4a by enrichment score (observed/expected) in a radar chart (c) and bar plot (d). P: pancreas, Lv: liver, H: heart, A: adrenal, B: brain, R: RPE, Ln: lung, S: stomach. Source data Extended Data Fig. 8k-means clustering and motif analysis for VFG expansion dependent ATAC-clusters.a-b, k-means clustering of genes within 200Kb of peaks in ATAC clusters as defined in Fig. 4a (VFGp3OPEN, VFGp6OPEN, VFGp3CLOSE, and VFGp3CLOSE). Z-scored log10 normalized gene expression of ADE, VFGp6, and PE samples (a); and of ADE, VFGp3, and VFGp6 samples (b) were plotted for VFGOPEN and VFGCLOSE clustered genes respectively ($$n = 10$$). c, De novo motif search was made using Homer findMotifsGenome and searched within ±200 bp of peak center for genes mapped to the vicinity of VFGp3OPEN (k-means clusters 1 and 9, $$n = 1330$$), VFGp6OPEN (k-means clusters 5 and 6, $$n = 376$$), VFGp3CLOSE (k-means clusters 1 and 8, $$n = 1006$$), and VFGp6CLOSE (k-means clusters 1 and 2, $$n = 524$$).
Extended Data Fig. 9Characterization of FOXA1 and FOXA2 shRNA KD VFG cells.a, *Expression analysis* in FOXA1 and FOXA2 shRNA KD cells (described in Fig. 6b) by RT-qPCR. Expression of FOXA1 (left) and FOXA2 (right) in the KD cells was normalized relative to the expression in scrambled shRNA controls. Triangles and circles mark cells derived from HUES4 and H9 ESCs respectively. Data are represented as mean ± SEM ($$n = 4$$ independent experiments). * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ (unpaired two-tailed t-test). Comparisons without an indicated P value are not significant. b, Representative flow cytometry density plots showing CD184 (CXCR4) and CD117 (KIT) expression in scrambled shRNA control, FOXA1, and FOXA2 shRNA KD VFG cells. Bottom left quadrant indicates gating based on isotype staining controls in scrambled shRNA control VFG cells. Flow Cytometry plots represent three independent experiments. c, *Expression analysis* in FOXA1 and FOXA2 shRNA KD cells (described in Fig. 6b) by RT-qPCR. Expression of VFG markers TBX3, GATA3, ID2, and ISL1 in the KD cells was normalized relative to that in scrambled shRNA controls. Triangles and circles mark cells derived from HUES4 and H9 ESCs respectively. Data are represented as mean ± SEM ($$n = 4$$ independent experiments). No statistical difference (unpaired two-tailed t-test) was found in the comparisons. Source data Extended Data Fig. 10HHEX is also required alongside FOXA1 for enhancer priming in VFGs.a, RT-qPCR of HHEX (left) and FOXA1 (right) in the HHEX or FOXA1 KD cells. Expression was normalized relative to the scrambled shRNA controls. Triangles and circles mark cells derived from HUES4 and H9 ESCs respectively. Data are represented as mean ± SEM ($$n = 4$$ independent experiments). ** $P \leq 0.01$ (unpaired two-tailed t-test, only significant comparisons are shown). b, Differentiation of scrambled control, HHEX KD, FOXA1 KD and HHEX/FOXA1 double KDs VFG cells to PE. Relative fold change of pancreatic genes (PDX1, GLIS3, SOX9 and NKX6-2) was assayed by RT-qPCR and normalized relative to the scrambled shRNA controls. Data are represented as mean ± SEM ($$n = 3$$ independent experiments). * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ (one-way ANOVA Dunnett’s multiple comparison test compared with scramble control and HHEX/FOXA1 double KDs). c-d, FOXA1 (c) and HHEX (d) binding enrichment by ChIP-qPCR at enhancer regions of PDX1 (area IV), SFRP5 (peak32665), GLIS3 (peak246749), and TBX3 (peak60307) in HHEX and FOXA1 shRNA KD VFG and scrambled control cell lines. An intragenic region of NCAPD2 served as non-bound control. Data are represented as mean ± SEM ($$n = 3$$ independent experiments). Statistical analysis was performed between KD and control VFG cells (*$P \leq 0.05$, **$P \leq 0.01$, unpaired one-tailed t-test, only significant comparisons are shown). e-f, H3K4me1 (e) and H3K27ac (f) enrichment by ChIP-qPCR at enhancer regions of PDX1 (area IV), SFRP5 (peak32665), GLIS3 (peak246749), and TBX3 (peak60307) in HHEX shRNA KD VFG and scrambled control cell lines. An intragenic region of NCAPD2 served as a non-bound control. Data are represented as mean ± SEM ($$n = 3$$ independent experiments). Statistical analysis was performed between the KD and control VFG cells (*$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$, unpaired one-tailed t-test, only significant comparisons are shown). g, HHEX signal plotted on VFG-specific enhancer classes (PE-PP1 common, PE-not-PP, VFGOFF-in-DE-PP1, and VFGTR-in-PP1) at the FG and PP1 stages of directed differentiation (ChIP-seq dataset42) (from two independent experiments). Source data is available for this paper at 10.1038/s41556-022-01075-8.
## Source data
Source Data Fig. 1Source data for Fig. 1c,d. Source Data Fig. 2Source data for Fig. 2a,b,d,e,g. Source Data Fig. 3Source data for Fig. 3d–g. Source Data Fig. 4Source data for Fig. 4b–e. Source Data Fig. 5Source data for Fig. 5a,c,d. Source Data Fig. 6Source data for Fig. 6b–f. Source Data Extended Data Fig.1Source data for Extended Data Fig. 1d,e. Source Data Extended Data Fig.2Source data for Extended Data Fig. 2h,i. Source Data Extended Data Fig.3Source data for Extended Data Fig. 3d–f. Source Data Extended Data Fig.4Source data for Extended Data Fig. 4e. Source Data Extended Data Fig.5Source data for Extended Data Fig. 5d. Source Data Extended Data Fig.7Source data for Extended Data Fig. 7a–d. Source Data Extended Data Fig.9Source data for Extended Data Fig. 9a,c. Source Data Extended Data Fig.10Source data for Fig. Extended Data 10a–f.
## Peer review information
Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work.
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|
---
title: Engineered exosomes derived from miR-132-overexpresssing adipose stem cells
promoted diabetic wound healing and skin reconstruction
authors:
- Lifeng Ge
- Kangyan Wang
- Hang Lin
- Endong Tao
- Weijie Xia
- Fulin Wang
- Cong Mao
- Yongzeng Feng
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC10014603
doi: 10.3389/fbioe.2023.1129538
license: CC BY 4.0
---
# Engineered exosomes derived from miR-132-overexpresssing adipose stem cells promoted diabetic wound healing and skin reconstruction
## Abstract
The tissue reconstruction of diabetic wounds mainly depends on the proliferation and remodelling of cutaneous cells around wounds and the transplantation of random skin flaps, however, the proliferation of cells or survival of skin flaps are difficult due to the severe inflammation and other problems caused by diabetes. The stem cell-derived exosomes loaded with miRNA can be an effective therapeutic strategy for promoting diabetic wound healing. Therefore, in this study, the engineered exosomes derived from miR-132-overexpressing adipose stem cells (miR-132-exo) was obtained for promoting the healing of diabetic wounds and skin flaps. In vitro, the miR-132-exo promoted the proliferation and migration of human umbilical vein endothelial cells (HUVECs). In vivo, streptozotocin (STZ) induced diabetic mice were used to create full-thickness skin wounds and random skin flaps to further investigate the healing effect of miR-132-exo. The results showed miR-132-exo evidently enhanced the survival of skin flaps and promote diabetic wound healing, through reducing local inflammation, promoting angiogenesis and stimulating M2-macrophages polarization mediated by NF-κB signaling pathway. These novel findings demonstrated that engineered miR-132-exo can be a potent therapeutic for treating diabetic wounds and inflammatory-related disease.
## 1 Introduction
The repair of diabetic wounds remains a major concern in clinic (Zhang et al., 2022). The healing of skin wound is mainly depending on the proliferation and migration of several kinds of cells surrounding wounds and/or the survival of transplanted skin random flaps (Park and Park, 2022). However, due to diabetes, patients usually suffer from metabolic disorders, impaired immune functions, and slow nerve activity that causes excessive inflammation, cellular dysfunction, and other problems (Thomas et al., 2020). On one hand, excessive and continuous inflammation inhibits cell proliferation and migration that is necessary for wound repair; on the other hand, the dysfunction of vascular endothelial cells and microcirculation disorders causes the difficulty of vascularization of the wounds or the transplanted skin flaps (Vu et al., 2021). In that case, these grafted skin flaps are at high risk of ischemia and necrosis (Mao et al., 2019), and it’s difficult to regenerate skin tissue due to cell dysfunction and inadequate blood supply. Therefore, diabetic wounds are often difficult to heal on their own.
In recent years, many studies have shown the stem cells may exert their functions through the paracrine mechanism, particularly through releasing extracellular vesicles to regulate the function and signal of target cells (Gentile and Garcovich, 2019). Exosomes (Exo) secreted by various cells are active vesicles with a size of about 40–150 nm (Kalluri and LeBleu, 2020). Exosomes are rich in bioactive substances, including mRNAs, miRNAs, and proteins, which can be received by target cells through the process of endocytosis (Hu et al., 2020), which also shows the importance of exosomes for cell-to-cell communication (Pitt et al., 2016). Previous studies have shown that exosomes derived from adipose stem cells can promote the healing process through accelerating cell proliferation and tissue vascularization in wounds (Li et al., 2018). Another exciting point of exosomes is that due to their physicochemical stability in vivo and the characteristics of multidimensional packaging, the miRNA loaded by exosomes is not easy to be degraded and can be modified and customized by primitive cells (Desdín-Micó and Mittelbrunn, 2017). Therefore, the effective decoration of exosomes derived from ADSCs, such as the loading the functional miRNA, has a good prospect for diabetic wound healing.
Wound repair is a dynamic and complex process that mainly includes inflammation, proliferation, and remodeling phases (Wilkinson and Hardman, 2020). The inflammatory environment of the normal wound can help resist the invasion of bacteria, and can secrete cytokines that are conducive to wound repair. However, diabetic wound inflammation can cause more severe inflammation and further damage the wound (Li et al., 2015; Davis et al., 2018; Hodge et al., 2022a; Hodge et al., 2022b). Li et al. reported that the level of miR-132 was dramatically decreased in diabetic wounds and the local use of miR-132 can accelerate the diabetic wound healing by alleviating the inflammation through NF-κB signaling pathways (Li et al., 2017; Liu et al., 2017). Another study by Essandoh et al. found that miR-132 can switch M1 macrophages polarization into M2 state by targeting multiple transcription factors and adaptor proteins (Essandoh et al., 2016), which can be beneficial for anti-inflammation and further healing. The hyperglycemic environment of diabetic patients impairs the conversion of macrophages from a M1 pro-inflammatory state to an M2 anti-inflammatory state and leads to hyperinflammation and vascular circulation problems in diabetic wounds (Du et al., 2018; Aitcheson et al., 2021). In addition, miR-132 is a specific pro-angiogenic miRNA (Cannavicci et al., 2022) that has been proved to enhance angiogenesis in ischemic related diseases (Che et al., 2018; Ma et al., 2018). Shruti (Rawal et al., 2017) et al. also performed function loss experiments by using anti-miR-132 to inhibit the activity of miR-132 in HUVECs cultured with high glucose and discovered that downregulation of miR-132 was the leading cause of vascular endothelial dysfunction. Hence, the use of miR-132 can be effective for diabetic wound healing due to its anti-inflammation ability and angiogenic effect. However, bare microRNAs are easily hydrolyzed in wound micro-environment, and only a small number of microRNAs can be endocytosed by tissue cells to play a role in actual treatment process (Qian et al., 2020). Therefore, we use an engineering method to effectively obtain exosomes overexpressing miR-132 for promoting diabetic wound healing, which can protect miR-132 from degradation and increase the intracellular uptake of miR-132 to target cells (Lv et al., 2020).
In this study, engineered exosomes derived from miR-132-overexpressing adipose stem cells (miR-132-exo) were obtained through lentiviral transduction and ultracentrifuge method for promoting diabetic wound healing. In vitro, the effect of miR-132-exo on the proliferation and migration of HUVECs was investigated. In vivo, a diabetic wound model and a diabetic random skin flap model were established to study the healing effect of miR-132-exo. The former animal model mimicked the hyperactivated and persistent chronic inflammatory environment in diabetic wound remodelling. The latter simulated the situation of the impairment of endothelial function and microcirculation caused in diabetes mellitus, which leads to the difficulty of revascularization after flap transplantation. Further, the related mechanism about miR-132-exo inducing M2 macrophage polarization was studied. The results of this study will provide a new direction for the application of ADSC-exo as bioactive carriers by engineering miRNAs or other molecules into exosomes, and also provide new strategies for the clinical treatment of diabetic wound healing and improving the survival rate of skin flap.
## 2.1 Cell culture and lentivirus transduction of adipose stem cells
The mouse adipose mesenchymal stem cells used in this study were purchased from OriCell® (MUBMD-01001) and cultivated in Dulbecco’s modified Eagle’s medium (DMEM) with $10\%$ fetal bovine serum (FBS) and low glucose. The lentivirus used in this experiment was constructed by Shanghai Jikai Technology Co., LTD. The lentivirus carrying the green fluorescent protein (GFP) tag of murine miR-132 was named as LV-MMU-miR-132. LV-MMU-miR-132 was then transfected into ADSCs to obtain ADSCs overexpressing miR-132 (miR-132-ADSCs), and untreated ADSCs were used as control. For transduction, ADSCs were cultured in medium (DMEM) and incubated with lentivirus for 24 h at a multiplicity of infection (MOI) of $90\%$. After that, the medium was renewed, and these ADSCs will be used for the following experiments.
## 2.2 Extraction and characterization of exosomes
The cells were spread in medium (DMEM) with $10\%$ FBS and cultured to a density of approximately $70\%$. The medium was then replaced by $5\%$ exosome-depleted FBS, and ADSCs were cultured for another 48 h. The medium was then collected and centrifuged at 4°C at 1,500 g for 10 min, followed by another 10 min at 2000 g, to remove residual cell debris. The supernatant was transferred to a centrifuge tube and centrifuged at 4°C at 10,000 g for 30 min. The supernatant obtained from the above process was transferred to a sterile centrifuge tube of the same specification, and then centrifuged at 4°C at 100,000 g for 70 min. The supernatant was removed and the obtained precipitation was re-suspended with PBS. In order to further purify exosomes, the suspension was filtered by a 0.22 μm filter membrane, then centrifuged at 4°C at 100,000 g for 60 min. After removing the supernatant, the exosomes were obtained and suspended in 100 μL PBS for further use. Transmission electron microscopy (TEM) (JEM-1400) was used to analyze the ultrastructure and morphology of exosomes, while nanoparticle tracking analysis was used to evaluate size distribution and nanoparticle concentration (NTA, PMX 110, particle matrix). The concentration of the extracted exosome solution was determined by a BCA kit and then diluted to a uniform concentration using the special solvent in the kit. Proteins from exosomes were extracted using an Exosome Protein extraction kit (Invitrogen) according to the manufacturer’s instructions, and the rest of exosomes were stored at −80°C for following experiments.
## 2.3 Quantitative real-time polymerase chain reaction
The expression levels of miR-132 in miR-132-exo and exo were evaluated by the RT-qPCR. Briefly, total RNA was extracted from exosome samples using RNA Extraction (Servicebio) and quantified by Nanodrop 2000 (Thermo Scientific). Then mRNA was then reverse transcribed into cDNA by SweScript RT I First Strand cDNA Synthesis Kit (Servicebio G3330) for miR-132 expression analysis, and cDNA amplification was performed using a Real-Time PCR System (Bio-Rad CFX Connect). The amplification conditions were as follows: Stage 1: Pre-denaturation 95°C, 30 s; Stage 2 (40 cycles): Denaturation 95°C, 15 s; Annealing 60°C, 30 s; Stage 3: 65°C, 5 min. The relative level of miRNA expression was analyzed by the 2−ΔΔCT method. Each assay was performed in triplicate. The primer used are as follows (Table 1).
**TABLE 1**
| Gene | Primer sequences |
| --- | --- |
| mmu-miR-132-RT | 5′-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGCGACCATG-3′ |
| mmu-miR-132 | Forward:5′-ACACTCCAGCTGGGTAACAGTCTACAGCCA-3′ |
| mmu-miR-132 | Reverse:5′-TGGTGTCGTGGAGTCG-3′ |
| U6 | Forward:5′-CTCGCTTCGGCAGCACA-3′ |
| U6 | Reverse:5′-AACGCTTCACGAATTTGCGT-3′ |
## 2.4 In Vitro cell responses of HUVECs to miR132-Exo
The cell proliferation induced by miR132-exo was assessed by EdU analysis using an EDU-594 kit. In brief, HUVECs were seeded in a 6-well plate at a concentration of 105/mL. After cell adhesion, equal volumes of miR-132-exo or Exo were added at a concentration of 2 μg/μl. The culture medium was changed 24 h later and co-cultured with EdU working solution (1:1,000) at 37°C for 3 h. The Click reaction solution was then added to each well and incubated at room temperature in the dark for 30 min. After washing, all cores were Hoechst stained. Images were captured by fluorescence microscopy and quantified by ImageJ. The specific methods are as follows: Firstly, the nuclei were selected and the number of nuclei in each field (Hoechst fluorescence image) was calculated by ImageJ software, which represented the number of all cells in the field and was named At. The number of green fluorescent cells in each field (EdU fluorescence image) was also calculated by mage J software and named A.The ratio of EdU positive cells was calculated as follows: EdU positive cell ratio (%) = A/At × $100\%$, and the cell proliferation ability was compared.
To assay the in vitro wound healing ability of miR-132-exo, cell scratch assay was performed. HUVECs were planted in 24-well plates (5 × 105 cells/well) for 24 h. Next, cell surface was scratched in a straight line with a 200 μL pipette tip to generate wounds in each well, followed by PBS washing to remove cell debris. The miR-132-exo, exo or PBS were added to the wells for 24 h co-incubation at a concentration of 2 μg/μl. Subsequently, the scratched region was then photographed using a Leica microscope at the given time intervals (0, 24, 48 h). The scratch closure was analyzed by the ImageJ soft-ware. The percentage of wound closure was calculated as follows: migration area (%)=(B0–Bn)/B0 × $100\%$, where B0 represents the initial wound area at 0 h and Bn represents the wound area at the time of measurement (24 and 48 h).
The cell migration was also assessed by a transwell migration assay. The HUVECs were cultured in the upper chamber, and miR-132-exo, Exo (2 μg/μl), or PBS were added to the lower chamber. After incubation at 37°C for 12 h, the membrane with migrated cells were fixed in $4\%$ paraformaldehyde for 15 min and stained by $1\%$ crystal violet in the dark for 30 min. The non-migrated side was carefully removed with a wet cotton swab, and the stained migrated cells were counted under a microscope.
## 2.5 Animal experiments
Animal Care and Use Committee at Wenzhou Medical University approved all animal procedures. Male C57BL/6 mice aged 6 weeks were obtained from Beijing Weitonglihua Laboratory Animal Technology Co., LTD. Prior to any experimental treatments, all C57BL/6 mice were kept under regular conditions and given food and water for 1 week. Mice were given a single intraperitoneal injection of 110 mg/kg streptozotocin (STZ, Sigma Aldridge) in citrate buffer to create a diabetic model (pH 4.5). At the end of the first 2 weeks, the weight and blood glucose levels of the mice were checked. Mice were diagnosed with diabetes when their glucose levels continuously surpassed 16.8 mmol/L, in conjunction with weight loss and polyuria symptoms. In order to determine the effective injection concentration, we did a preliminary experiment using 6 mice in each animal model and in the preliminary experimental design, we determined the injection concentration (2 μg/ml) for the experiment according to references (Shi et al., 2020) and it showed effective healing in both animal models. All diabetic mice were operated to create full-thickness skin wounds or free skin flaps on their back. In brief, mice were anesthetized with $1\%$ sodium pentobarbital (50 mg/kg), and their shaved backs were sterilized with iodophor solution. Two round full-thickness wounds (1 cm in diameter) were made with a skin punch on the back of mice. Total thirty mice were used and randomly divided into three groups: control (normal saline), Exo, and miR-132-exo group. On day 0, 3, and 7, four injection points around the wound in each group were injected subcutaneously. The miR-132-exo group was injected with exosomes transfected with miR-132 (2 μg/μl, 25 μl for each injection point), and the Exo group was injected with pure exosomes (2 μg/μl, 25 μl for each injection point). The mice in control group were injected with equal volume of saline (25 μl for each injection point). At day 0, 3, 7, and 14, wound pictures were taken and wound area was further calculated by ImageJ software. The wound area percentage was determined as follows: (At/A0) × $100\%$, whereas A0 and At meant the wound area on day 0 and day 3, 7, and 14, respectively. A laser Doppler flowmeter was also used to scan the blood flow from wounds during healing and the results were quantified by MoorLDI Review software (ver.6.1; Moor Instruments). The collected tissue sections at certain time points were then used for H&E staining, immunohistochemistry (IHC) or immunofluorescence analysis.
In addition, another 30 mice were used to created random skin flaps (1.5 × 4.5 cm) on the back of mice and the bilateral subcutaneous trophoblast arteries providing blood supply of the flap pedicles were excised. Mice were also randomly divided into three groups: control, Exo, and miR-132-exo group. At day 0 and 3, the miR-132-exo, Exo or saline were injected subcutaneously at 8 injection points on each flap with the same dose and concentration as above. Then, the effect of relevant treatments on angiogenesis and flap regeneration was evaluated, and the flap tissue was further examined by histology experiments. High-quality random flap images were taken on the 0, 3, and 7 days respectively. According to the above images, ImageJ software was used to calculate the area of survival and area of necrosis. The necrotic area of the flap was defined by color change (appearance of a dull color), eschar, and non-epithelialized area. Survivable area percentage: survivable area × $100\%$/total area. A laser doppler flowmeter was used to assess the blood flow of the flap during the healing process, and the tissue of flap area II (at the junction of necrosis) was extracted for further study. The water content of specific flap tissue was manipulated as follows: on day 7, three flap tissues were taken from each group and weighed to calculate the “wet weight.” Next, these flaps were placed in an autoclave (50°C) and weighed daily until the weight remained stable for more than 2.days, at which point the recorded weight was considered “dry weight.” *Tissue edema* was calculated by the formula: ([wet weight—dry weight]/wet weight) × $100\%$.
## 2.6 Histological analysis, immunohistochemical staining and immunofluorescence staining
The collected wound or flap samples were fixed with $4\%$ PFA for 3 days and then dehydrated in graded ethanol solutions ($75\%$, $85\%$, $95\%$, $95\%$, $100\%$). The samples were then embedded in paraffin and cross-sectioned into 4-μm thickness slices. Standard H&E staining and Masson’s trichrome were performed. After sealing, the samples were observed and analyzed by a Nikon microscopy. The collagen-positive area and total tissue area of tissue sections were calculated by the statistical method of ImageJ software. Collagen deposition fraction = (collagen positive blue area/total tissue area) × $100\%$.
As for immunohistochemical staining, the rehydrated samples were immersed in $3\%$ H2O2 for 10 min to block the endogenous peroxidase activity and then heated in 10.2 mM sodium citrate buffer (20 min, 95°C) for antigen retrieval. After blocking with $10\%$ (w/v) bovine serum albumin (BSA)/PBS for 1 h, samples were then incubated with collagen I (1:200, Proteintech), collagen III (1:200, Proteintech), TNF-α (1: 200, Proteintech) or IL-6 (1: 200, Proteintech) primary antibodies at 4°C overnight. Later, the slides were incubated with secondary antibodies for 1 h at room temperature. The binding sites were then visualized with a DAB detection kit. All the samples were viewed under a microscope. The number of new vessels was determined by observing three random portions from each group. In addition, the IPP software measured the integrated optical density (IOD) of each field’s positive protein.
For immunofluorescence staining, the deparaffinized samples were rehydrated, heated and permeabilized with $0.1\%$ (v/v) PBS/Triton X-100 for 20 min and blocked in BSA/PBS for 1 h. The samples were then incubated with α-SMA (1:100, Proteintech), Fibr (1:100, Proteintech), a mixture of α-SMA (1:100, Proteintech) and ZO-1 (1:100, Proteintech), a mixture of CD86 (1:100, Proteintech) and CD206 (1:100, Santa Cruz), a mixture of INOS (1:1,000, Abcam) and ARG-1 (1:100, Proteintech) or a mixture of α-SMA (1:100, Proteintech) and CD31 (1:100, Servicebio). After being rinsed with PBS, slices were incubated with Alexa Fluor 488- or Alexa Fluor 594-conjugated secondary antibodies (1:200, Abcam) for 30 min at room temperature. The nuclei were stained with mounting solution containing DAPI. The slides were examined using ImageJ software using a Nikon confocal laser microscope (A1 PLUS; Nikon, Tokyo, Japan).
## 2.7 Western blotting
Exosome protein extraction kit (Invitrogen) and BCA protein assay kit (Beyotime, Shanghai, China) were used to measure the concentration of exosomes and tissue protein. The protein samples (40 μg) were initially separated using SDS-PAGE before being transferred to a PVDF membrane (Bio-Rad, United States). After incubating with skim milk at a concentration of $5\%$ for 2 hours to suppress the non-specific protein, the sample was then washed three times with TBST. The membranes were then incubated with primary antibodies against IKB Alpha (1:2000, Proteintech), phosph-IKB Alpha (1:2000, Affinity), NF-kb p65 (1:2000, Proteintech), phosph NF-kb p65 (1:2000, Affinity), TSG101 (1:1,000, Abcam), CD63 (1:1,000, Abcam)、CD81 (1:1,000, Abcam) overnight at 4°C, followed by incubation with the corresponding secondary antibody for 2 h. After rinsing, the binding site was treated with Electrochemiluminescence Plus reagent (Invitrogen), and image lab 3.0 software was used to quantitatively assess the band intensity (Bio-Rad), the data of target protein has been normalized to the GAPDH level through image lab 3.0 software.
## 2.8 Statistical analysis
All data are described as the mean ± standard deviation (SD). Comparisons among the groups were performed using one-way ANOVA with Tukey’s multiple comparison test. All analyses were performed with GraphPad Prism 9 software (GraphPad Software Inc., United States). In all tests, statistical significance was set at ∗p-value <0.05, ∗∗p-value <0.01 and ∗∗∗p-value <0.001.
## 3.1 Characterization of exosomes and miR-132-exo
The cultured adipose mesenchymal stem cells exhibited typical spindle shaped morphology (Figure 1A). The exosomes extracted through ultracentrifuge were characterized by TEM, NTA and Western blot analysis. TEM results showed that the naturally isolated exosomes exhibited spherical shape in morphology (Figure 1B). NTA data showed that the size of exosomes ranged from 80 to 150 nm (Figure 1C). In addition, Western blot results showed these exosomes exhibited high expression of the exosomal marker proteins, including CD63, CD81, and TSG101 (Figure 1D). These results indicated that exosomes derived from ADSCs were successfully extracted and obtained in this study.
**FIGURE 1:** *Characterization of ADSCs derived exosomes and the transduction efficiency. (A) The typical cobblestone-like morphology of ADSCs (left), and the morphology of ADSCs transfected with lentivirus labled with green fluorescent protein (GFP) (right); (B) The transmission electron microscopy (TEM) image of ADSCs-Exo (Scale bar = 100 nm, 500 nm); (C) Size distribution of exosomes by nanoparticle tracking analysis (NTA); (D) Western blot analysis of exosomes’ specific markers of CD63, CD81, and TSG101; (E) Loading efficiency of the Lentivirus in ADSCs derived exosomes calculated by qRT-PCR; miR-132 were used as a positive control, and expression was normalized to the housekeeping gene U6. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 3.*
The exosomes derived from miR-132 enriched ADSCs were also isolated. The qRt-PCR results showed that exosomes derived from ADSCs transfected with miR-132 expressed almost 5 times higher level of miR-132 than control, indicating that miR-132 has been successfully loaded into exosomes (Figure 1E).
## 3.2 Effect of miR-132-exo on proliferation and migration of HUVECs
The effect of miR-132-exo on proliferation of endothelial cells was evaluated by EDU assay. As shown in Figures 2A,D, the average edu-positive cell rate of the control group was $14.26\%$, while that of Exo group and miR-132-exo group was $26.41\%$ and $43.90\%$, respectively, it can be clear to see that both miR-132-exo and Exo promoted the proliferation of endothelial cells to a certain extent when compared with control, whereas the miR-132-exo exhibited the best effect on promoting proliferation of HUVECs.
**FIGURE 2:** *The effect of miR-132-exo on the proliferation and migration of HUVECs in vitro. (A) Representative fluorescence images of EdU staining of HUVECs treated with PBS, exosomes, and miR-132-exo for 24 h (scale bar = 100 μm); (B) Scratch wound assay of HUVECs following treatment with PBS, exosomes, and miR-132-exo at 0, 24 and 48 h (scale bar = 200 μm); (C) Representative transwell migration assay images of HUVECs treated with PBS, exosomes, and miR-132-exo for 12 h (scale bar = 100 μm); (D) Quantitative analysis of the EdU staining for proliferation rates in each group; (E) Quantitative data of the percentage of scratch wound closure in each group; (F) Quantitative analysis of the migrated cells HUVECs in each group. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 6.*
The in vitro wound healing ability of miR-132-exo on HUVECs was evaluated by in vitro scratch test. All endothelial cells were incubated in a medium containing $1\%$ FBS to eliminate the promoting effect of serum on cell proliferation. As shown in Figures 2B,E, after 24 h of cell scratch, the wound closure rate of control group was $33.45\%$. Compared with control, the Exo and miR-132-exo group showed statistically higher wound closure rates, which were $45.54\%$ and $63.36\%$, respectively. After 48 h, this trend was more prominent. The wound closure rate of the miR-132-exo group raised up to $83.28\%$ with shortest distance of scratch gap, while control group only achieved $46.22\%$, indicating miR-132-exo can enhance the cell migration ability of HUVECs. We used a transwell migration assay with crystal violet staining to evaluate cell movement via a transwell membrane, confirming that miR-132-exo can stimulate cell migration. The results (Figures 2C,F) also showed that the miR-132-exo group had the highest number of stained HUVECs that migrated, indicating that miR-132 can significantly promote cell migration ability of HUVECs. The above results indicated that miR-132-exo promoted the proliferation and migration of HUVECs in vitro, which should be beneficial for wound healing.
## 3.3 Effect of miR-132-exo on the survival of diabetic random skin flaps
STZ was injected intraperitoneally into mice to produce a model of diabetes. And a standard random skin flap model was established by surgery (Figure 3). The morphological changes of flap necrosis after 3 and 7days treatment (miR-132-exo, Exo or saline) were visually recorded by gross observation images. During the modeling process, the vascular pedicle was cut off, so the distal end part of the random flap was susceptible to ischemic necrosis. At day 3, we found small areas of necrosis began to appear in flaps, especially in control group, while the necrosis in miR-132-exo and Exo group was not noticeable. At day 7, different degrees of distal flap necrosis occurred in each group, whilst control group had the largest necrotic area. In contrast, the Exo and miR-132-exo treatment showed a much smaller necrotic area of the flaps, and the treatment of miR-132-exo was better than that of Exo (Figures 4A,E). These results indicated that miR-132-exo treatment can largely enhance the survival of diabetic skin flaps.
**FIGURE 3:** *Schematic framework and animal surgery representative images. (A) The schematic framework of the animal experimental design; (B) The gross image of the construction of the random flap model (left) and the treatment of miR-132-exo on mice diabetic flap model (right); (C) The treatment of miR-132-exo on mice diabetic wound model.* **FIGURE 4:** *The miR-132-exo ameliorated the survival of diabetic random skin flaps in vivo. (A) Gross observation images of diabetic random skin flaps on the third and seventh day after treatment with normal saline, exosomes, and miR-132-exo; (B) Images reflected the tissue edema on postsurgery day 7, the edematous flap area is marked with a black arrow; (C) H&E staining images of the three groups on day 7; (D) Masson staining images of the three groups on day 7, scale bar = 250 μm; (E) Quantitative analysis of the percentage of survival area in three groups; (F) The percentage of tissue water content of the skin flaps in each group; (G) Quantitative analysis of collagen deposition in each group. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n > 3.*
The edema of the flap tissue was measured to evaluate the survival rate of the flaps. Compared with control, the miR-132-exo group showed lower edema and subcutaneous venous congestion (Figure 4B). The Exo group also had a positive effect on decreasing the edema of the tissue compared with Control. The data of tissue water content at day 7 also confirmed that miR-132-exo treatment showed a statistical better effect on decreasing the edema extent and promote the survival of the flaps than Exo treatment (Figure 4F).
Hematoxylin-eosin (H&E) and Masson staining were also performed on the sections at the junction of necrosis and survival tissue at day 7. As shown in Figure 4C, compared with the control group, the Exo and the miR-132-exo group had prominent microvessels with complete skin structure and much fewer inflammatory cells, while the control group had a small number of fragile microvessels appeared at the bottom of the flap with the inferior blood supply and large numbers of inflammatory cells. Masson staining (Figures 4D,G) showed that the collagen fibers in the miR-132-exo group were densely arranged with order, while the collagen fibers in control were irregular and loose. All the above results confirmed that miR-132-exo facilitated the survival of diabetic random skin flaps in vivo.
## 3.4 Effect of miR-132-exo on promoting angiogenesis and vascular network formation in the random skin flaps
Angiogenesis is essential for the survival of skin flaps. Therefore, the vascular status of the skin flaps was then evaluated by the blood flow laser Doppler equipment at day 3 and 7. As shown in Figure 5A, small amount of microvessels can be seen at the edge of wounds in miR-132-exo and Exo group at day 3, while no obvious newly-formed microvessels can been found in control group. With the increase of time, the miR-132-exo treatment significantly promoted the growth of microvessels in the flap pedicle and wound edge and increased the microvascular proliferation of the whole flap when compared with control. The blood flow signal (Figure 5D) data also confirmed that the flow intensity of the miR-132-exo group was significantly the highest among the three groups, followed by Exo and control group. The results indicated that the blood flow of the skin flaps can be restored by the miR-132-exo.
**FIGURE 5:** *The miR-132-exo promoted angiogenesis and vascular network formation in diabetic random skin flaps. (A) Representative laser Doppler images on postsurgery day 3 and day 7; the arrows indicate the new blood vessels; (B) Representative immunofluorescence staining images of α-SMA-positive microvessels in each group on day 7, scale bar = 100 μm; (C) Representative immunofluorescence staining images of ZO-1 (red) and α-SMA (green) co-staining in each group on day 7, scale bar = 50 μm; (D) Quantitative data of signal intensity of blood flow in random skin flaps in each group; (E) Quantification of newly formed vessels per field corresponding to immunostaining of α-SMA; (F) Quantification of mean fluorescence intensity per field corresponding to co-staining of ZO-1 and α-SMA. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 3.*
Meanwhile, α-SMA immunofluorescence staining was also performed to evaluate the angiogenesis of tissue flaps. The results in Figures 5B,E showed that the number of α-SMA labeled microvessels in miR-132-exo group was significantly higher than that in Exo and control groups. The newly formed microvessels helped deliver oxygen and nutrition to the distal end of the flaps and further benefited their survival. Tight junction protein ZO-1 is crucial for maintaining the mechanical barrier and permeability of epithelium. It not only participates in the regulation of cell material transport and maintenance of epithelial polarity but also plays an important part in cell proliferation and differentiation, which is closely related to the formation of new microvessels and the link between blood vessels. The ZO-1 and α-SMA co-staining (Figures 5C,F) showed that both of them were highly expressed in miR-132-exo group, indicating that miR-132-exo could promote the generation of neovascularization in flaps and the reconstruction of microtubules between vessels, which was more conducive to the generation of microvascular network.
Based on the above results, it can be inferred that miR-132-exo can upregulate the expression of ZO-1 and α-SMA and promote the angiogenesis and vascular network formation of the tissue, which restored the blood re-perfusion and further increased the survival rate of the random skin flaps.
## 3.5 Effect of miR-132-exo on stimulating M2 polarization of macrophages in diabetic random skin flaps
The flap regeneration is dependent on proper macrophage infiltration, and M2 macrophages are dominant and responsible for inflammation resolution and flap survival. We selected flap tissue sections of day 7, and co-stained CD86 (M1) and CD206 (M2). Figures 6A,B showed that more M2 macrophages and fewer M1 phenotype macrophages appeared in the miR-132-exo group. The results indicated that miR-132-exo can increase M2 macrophage infiltration during flap regeneration, further reduce inflammation and promoted the survival of the random skin flaps.
**FIGURE 6:** *The miR-132-exo stimulated M2 polarization of macrophages in diabetic random skin flaps. (A) Representative co-staining images of CD86 and CD206 in each group, scale bar = 50 μm; (B) Quantification analysis of positive stained macrophages by CD86 and CD206. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 3.*
## 3.6 Effect of miR-132-exo on accelerating wound healing in diabetic mice
A mouse diabetic model was established by intraperitoneal injection of STZ, and a standard full-thickness skin defect model was created by surgery. Exo, miR-132-exo, or an equal volume of saline were injected subcutaneously around the wound and the morphological changes (Figure 7A) of wound healing at 0, 3, 7, and 14 days after operation were visually recorded. The blue area (Figure 7B) represented the wound size of each group on day 0, 3, 7, and 14. Although wound area decreased over time in all groups (Figure 7C), the difference in unhealed wound area between the three groups was not significant at day 3. However, at day 7 and 14, the area of unhealed wounds in Exo and miR-132-exo groups was significantly smaller than that in control group, which also indicated the strong potential of miR-132-exo in the treatment of diabetic wounds.
**FIGURE 7:** *The miR-132-exo accelerated wound repair and regeneration in diabetic wounds. (A) Representative images of wound closure in a diabetic mouse model at the end of days 0, 3, 7, and 14 days following treatment with normal saline, exosomes, and miR-132-exo; (B) Schematic diagram of the wound area of each group; (C) Wound healing percentage of each groups at different time points; (D) Quantification of wound length assessed by H&E staining images in each group; (E) Representative images of H&E staining in each group on day 14, scale bar = 250 μm *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 6.*
To further evaluate the healing pathology of the wounds, we selected a 14-day biopsy for H&E staining to assess epithelial formation, granulation tissue, and wound length. From Figures 7D,E it can be seen clearly that miR-132-exo group showed the smallest scar area with shortest wound lengths, followed by Exo group. Besides, miR-132-exo group also showed intact new epidermis with a clear and complex structure and more granulation tissue than control group.
## 3.7 Effect of miR-132-exo on promoting collagen deposition and ECM fibronectin hyperplasia in diabetic wounds
The main components of the extracellular matrix are collagen, elastin, fibronectin, and other structural proteins. Hence, we select 14-day tissue sections for Masson trichrome staining to observe the collagen fibers in wound tissue. Figure 8A shows that all three groups had a wide distribution of blue color, but miR-132-exo group showed a darker blue color with more orderly arrangement than other groups, indicating that miR-132-Exo group had a faster collagen maturation with relatively well-arranged thick collagen fiber bundle. The COL I and COL III, which are the major collagen types in skin, are identified by immunostaining. As shown in Figures 8B,D, the positive staining of COL I and COL III in the miR-132-exo group were higher than in control, which are also consistent with the Masson staining results.
**FIGURE 8:** *The miR-132-exo promoted collagen deposition and ECM fibronectin accretion in diabetic wounds. (A) Representative images of Masson staining in each group on day 14, scale bar = 250 μm; (B) Representative images of immunohistochemistry staining of collagen I and collagen III in each group on days 14, scale bar = 100 μm; (C) Representative images of Fibronectin immunostaining in each group on day 14, scale bar = 50 μm; (D) Quantitative data of relative density of collagen I and collagen III on days 14; (E) Quantification of mean fluorescence intensity per field of Fibronectin. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 3.*
Fibronection is a multifunctional protein that is rich in the extracellular matrix (Brem and Tomic-Canic, 2007; Andreeva et al., 2016). In the process of dynamic wound healing and remodeling, Fibronection enhances the surrounding matrix formation with its fibrous structure. It can also be used as a biological glue to mediate the interaction between cells and other ECM components. The immunofluorescence staining of Fibronection in Figure 8C showed that the miR-132-exo group expressed more fibronectin, which could make various extracellular matrices and microvessels grow more closely and then benefit the diabetic wound healing.
## 3.8 Effect of miR-132-exo on promoting angiogenesis in diabetic wounds
The angiogenesis is vital in wound healing due to the function of transporting nutrition and oxygen of blood vessels to the wound site. To further verify the role of miR-132-exo in promoting the process of wound neovascularization, Laser Doppler scanning imaging (Figure 9A) was used to evaluate the recovery of vascular bed in the whole flap area at Day 3, 7 and 14. The results (Figure 9D) showed that Exo group exhibited higher blood flow level than Control at day 7 and 14. However, even at the early time of healing (day 3), miR-132-exo group already presented the highest blood flow level among the three groups and lasted till day 14. Further, the immunofluorescence staining of the marker of vascular endothelial cells (CD31) and the marker of vascular smooth muscle cells α-smooth muscle actin (α-SMA) were performed. We found that CD31 and α-SMA (Figures 9B,E) were widely expressed in wound beds treated by miR-132-exo. Compared with control and Exo group, miR-132-exo group showed more new and mature blood vessels, which indicated that miR-132-exo stimulated the regeneration of blood vessels in diabetic wounds. Tight junction protein ZO-1 is one of the crucial proteins to maintain epithelial mechanical barrier and permeability. ZO-1 co-stained with α-SMA results (Figures 9C,F) indicated that miR-132-exo could promote tight junction between the new blood vessels in wounds, which are helpful for the generation of microvascular networks. These results further proved that miR-132-exo stimulated the angiogenesis process in diabetic wounds, which can contribute to the faster healing observed in the above gross observation results.
**FIGURE 9:** *The miR-132-exo promoted angiogenesis and vascular network formation in diabetic wounds. (A) Representative laser Doppler images on postsurgery day 3, 7, and 14; (B) Representative co-staining images of α-SMA and CD31 positive microvessels in each group on day 14, scale bar = 50 μm; (C) Representative immunofluorescence staining images of ZO-1 (red) and α-SMA (green) co-staining in each group on day 14, scale bar = 100 μm; (D) Quantitative data of signal intensity of blood flow in diabetic wounds in each group; (E) Quantification of newly formed vessels per field corresponding to co-staining of α-SMA and CD31; (F) Quantification of mean fluorescence intensity per field corresponding to co-staining of ZO-1 and α-SMA. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 3.*
## 3.9 Effect of miR-132-exo on relieving inflammation and stimulating M2 polarization of macrophages in diabetic wounds
Macrophages play an essential role in wound healing. M1 phenotype macrophages are usually involved in pro-inflammatory responses, while M2 macrophages are associated with anti-inflammatory responses. In the wound healing stage, the expression of IL-6 and TNF-α is the central link of inflammation and also reflects the severity of inflammation. The tissue immunostaining (Figures 10A,B) showed lower level of TNF-α and IL-6 when compared with Exo and Control group, indicating the inflammation in wound tissue treated by miR-132-exo was gradually reduced. In order to further study the relationship between inflammation and macrophage polarization, we selected wound tissue sections of day 14, and co-stained CD86 (M1) and CD206 (M2) using immunofluorescence staining. It was shown (Figures 10C,D) that more M2 macrophages and fewer M1 phenotype macrophages appeared in miR-132-exo group. The immunostaining of INOS (M1) and ARG (M2) (Figures 10E,F) also showed the similar trend. The results here indicated that miR-132-exo promoted the polarization of macrophages to M2 macrophages and had a good anti-inflammatory ability, which can reduce the inflammation in diabetic wounds and accelerate the healing process.
**FIGURE 10:** *The miR-132-exo relieved inflammation and stimulated M2 polarization of macrophages in diabetic wounds. (A, B) Representative images (A) and positive staining ratio of immuostaining of TNF-α and IL-6 in each group on day 14, scale bar = 50 μm; (C, D) Representative images and quantification analysis of co-staining of CD86 and CD206 in each group on day 14, scale bar = 50 μm; (E, F) Representative images and quantification analysis of co-staining of INOS and ARG-1 in each group on day 14, scale bar = 50 μm *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 3.*
## 3.10 Effect of miR-132-exo on inhibiting the NF-KB pathway in diabetic wounds
The mechanism by which miR-132-exo stimulating M2 macrophage polarization was also investigated. Western blotting results showed that the expression of p-p65 and p-IκB was lower in miR-132-exo group than that in control and Exo groups (Figures 11A–C), suggesting that the miR-132-exo treatment notably inhibited the phosphorylation of NF-κB p65 and IκBα in wound tissue of diabetic mice, thereby reducing the expression of inflammatory cytokines of TNF-α and IL-6. In conclusion, miR-132-exo may promote the polarization of macrophages and reduce the expression of inflammatory factors by inhibiting the NF-κB pathway, thereby reducing inflammation and promoting the transition from inflammation to proliferation in the process of wound healing.
**FIGURE 11:** *The miR-132-exo inhibited the NF-KB pathway in diabetic wounds. (A–C) Western blot bands and quantitative analysis of P65, PP65, lκB, P-lκB in each group. *p < 0.05, **p < 0.01, ***p < 0.001, data are presented as the mean ± SD, n = 3.*
## 4 Discussion
The diabetic wounds have become one of the main types of wounds in clinic. The impaired healing of diabetic wounds is mainly characterized by long-term severe inflammation, insufficient and low-activity growth factors, difficult to vascularization, excessive ROS, and easy infection (Goswami et al., 2022). Minor defects of diabetic skin can often be healed by drugs, growth factors, dressings, and other methods. In contrast, large-scale wounds need to be grafted with flaps, which usually face the risk of necrosis in diabetic wounds (Dolati et al., 2020). Therefore, new therapeutics that help the tissue reconstruction and the survival of skin flaps are highly needed for the treatment of diabetic wounds (Houlind, 2020). Previous studies have shown that exosomes derived from adipose stem cells has a good effect on promoting wound healing, and loading functional miRNA into exosomes can conquer some of the difficulties in diabetic wound healing (Wang et al., 2021). In this study, the engineered exosomes derived from miR-132-overexpressing ADSCs was constructed. Endothelial cells can have their proliferation and migration boosted by miR-132-exo. In addition, both the diabetic wound healing and the survival of random skin flaps were significantly improved by the miR-132-exo through alleviating inflammatory response and promoting angiogenesis, which were closely related to the higher level of M2-macrophages polarization mediated by inhibiting the NF-kB signaling pathway.
Reducing severe inflammation is critical to the healing outcomes of diabetic wounds (Aitcheson et al., 2021). In the process of wound healing, macrophages play an essential part in the inflammatory response (Liu et al., 2020a). According to their different phenotypes and functional plasticity, macrophages can be divided into M1-like phenotype macrophages and M2-like phenotype macrophages, whereas diabetes may cause the difficulty for the transition of M0/M1 macrophages polarizing into M2 macrophages and hinder the healing process (Kuo et al., 2021). In our study, to further determine the type of macrophages in skin flaps/wounds, the molecular marker of M1/M2 macrophages were labelled by CD86 or INOS (M1) and CD206 or Arg-1 (M2). We found that the miR-132-exo group showed more M2 macrophages than other two groups in damaged area, while control group showed much more M1 macrophages with few M2 macrophages, which indicated that miR-132-exo could promote the polarization of macrophages into the M2 phenotype during healing and tissue reconstruction. This may be due to the function of the enriched miR-132 in exosomes, which has been reported that it can induce the polarization of M2 macrophages and regulate the inflammatory response (Liu et al., 2020a). Then, we detected the expression of inflammation-related proteins in tissue, and the results of immunohistochemical staining of TNF-α and IL-6 showed that the miR-132-exo group has lower inflammation compared with control, which could be the result of the induced polarization of M2 macrophages by miR-132-exo (Kotwal and Chien, 2017). The H&E staining results also confirmed the reduced inflammatory response in miR-132-exo treated wounds. Hence, miR-132-exo can effectively reduce the excessive inflammation around the wounds and the transplanted skin flaps under diabetes condition, which presented great potential in promoting the survival of diabetic skin flaps and the regeneration of diabetic wounds.
Another important factor that controls the healing rate of diabetic wounds is the ability of angiogenesis and further vascularization in the regenerated tissue (Okonkwo and DiPietro, 2017). The newly-formed blood vessels can provide oxygen and nutrition for the metabolism of cells around diabetic wounds (Wang et al., 2018). Emerging studies have shown the angiogenic ability of exosomes derived from adipose stem cells (Chen et al., 2022). Besides, the advantage of exosomes’ natural availability and biocompatibility makes them an excellent tool for miR-132 transport for the precision therapy in diabetic wound repair and skin reconstruction (Yong et al., 2019; Joo et al., 2020; Zhou et al., 2020). It has also been found that miR-132 can regulate angiogenesis by inhibiting the NF-κB pathway and activate the VEGF pathway in ICD diseases (Che et al., 2018). In this study, miR-132-exo showed excellent pro-angiogenic ability both in vivo and in vitro. In vitro endothelial cell experiments proved that miR-132-exo could promote endothelial cell proliferation and migration, which is the beginning and fundamental of the angiogenesis process. The expression of CD31 and α-SMA showed higher numbers of newly-formed vessels in flaps and wound tissues treated by miR-132-exo, which was consistent with the laser Doppler results that clearly showed the higher blood flow in miR-132-exo group compared with exo and control during the regeneration of diabetic wounds and the skin flaps. Compared with control, the staining of tight junction protein ZO-1 also showed that miR-132-exo contributed to the interconnection and communication between microvessels and was more conducive to the communication of blood flow and the delivery of nutrients and oxygen (Kuo et al., 2021). This excellent angiogenic effect stimulated by miR-132-exo should be due to the synergetic effect of both miR-132 and the exosomes themselves, which also lays a foundation for the rapid formation of granulation tissue and epithelial re-formation in diabetic wounds (Shaabani et al., 2022). At the same time, this angiogenic ability of miR-132-exo also solved the problem of necrosis in the distal part of skin flaps cause by insufficient vascular circulation after transplantation, thus enhanced the survival rate of the skin flaps.
Due to the excellent performance of miR-132-exo in anti-inflammation and promoting angiogenesis, after the inflammatory stage, granulation tissue began to grow around diabetes wounds with an enormous number of cells, extracellular matrix, and new capillaries (Xu et al., 2015). The miR-132-exo group showed abundant granulation tissue and significantly shortened wound length, while the control group showed relatively less granulation tissue and ECM, resulting in a lack of matrix to facilitate effective communication between endogenous cells (Liu et al., 2022). With the increase of healing time, collagen fibers gradually replaced the granulation tissue. Neatly arranged collagen fibers were deposited in wounds treated with miR-132-exo with abundant type I and type III collagen in diabetic tissues, and this abundant extracellular matrix provides a good growth space for endogenous cells to grow (Liu et al., 2022). In the transplanted skin flaps, a more complete epithelial structure with abundant granulation tissue and arranged collagen fibers can be clearly found in miR-132-exo group, hence resulting in a faster and better healing results of the diabetic wounds/skin flaps.
In LPS-induced inflammation, the NF-κB signaling pathway is the most critical pathway for macrophage activation and polarization (Liu et al., 2020b). In this study, miR-132-exo significantly inhibited the phosphorylation of NF--κB p65 and IκB during diabetic wound healing, which indicated that miR-132-exo inhibited the NF-κB signaling pathway and further stimulated the M2 macrophages polarization to reduce the expression of inflammatory factors TNF-a and IL-6. This could reduce the inflammatory response in diabetic wounds and skin flaps and further accelerated wound repair and skin reconstruction.
In this study, the anti-inflammatory, pro-angiogenic, pro-healing, and the possible underlying mechanisms of miR-132-exo in promoting diabetic wound healing and the survival of random skin flaps have been identified. Due to the synergetic effect of miR-132 and exosomes, the miR-132-exo achieved good anti-inflammatory ability through inducing the M2-macrophages polarization, improved vascularization, and reached faster granulation tissue formation and collagen formation, together accelerated the diabetic wound healling and enhanced the survival of random skin flaps. This study mimicked two circumstances of diabetic wounds, the relatively small one and a large one that needs the skin graft, and miR-132-exo showed potent positive healing effect on both wounds. Taken together, miR-132-exo can be an excellent candidate in promoting the healing of diabetic wounds and skin flaps. In diabetic patients, it is worth noting that the chronic non-healing wounds involve more complex cross-talking among pathogenic agent, immune cells, tissue cells in the disease process compared with experimental models. Prior to preclinical testing, additional research is required to understand the exact mechanisms of miR-132-exo on diabetic wounds.
## 5 Conclusion
In conclusion, we combined exosome therapy with precision delivery of miR-132 into adipose stem cell-derived exosomes for diabetic wound healing. The miR-132-exo can accelerate diabetic wound healing and enhance the survival of random skin flaps by inducing the M2-macrophages polarization, accelerating angiogenesis and vascularization, and increasing collagen remodeling. In addition, the M2 macrophages polarization induced by miR-132-exo may be mediated by inhibiting the NF-κB signaling pathway through suppressing the phosphorylation of p65 and IκB, which further reduced the inflammation and promoted the angiogenesis of random skin flaps and in wound tissue (Figure 12). These results indicated that engineered miR-132-exo can be an excellent candidate for precision treatment of diabetic wounds and other inflammatory-related disease.
**FIGURE 12:** *Schematic diagram of the function of miR-132-exo in wound repair and skin reconstruction and its possible mechanism.*
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by the Animal Care and Use Committee at Wenzhou Medical University.
## Author contributions
LG and KW implemented the experiments and drafted the manuscript. HL, ET, WX, and FW participated in the experiments and accomplished the data processing. YF and CM designed the experiment and revised the manuscript. All authors read and approved the final manuscript. LG, KW, and HL are deemed as co-first authors.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1129538/full#supplementary-material
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|
---
title: 'The influence of family function on online prosocial behaviors of high school
students: A moderated chained mediation model'
authors:
- Lulu Cui
- Zhaoliang Li
journal: Frontiers in Psychology
year: 2023
pmcid: PMC10014605
doi: 10.3389/fpsyg.2023.1103897
license: CC BY 4.0
---
# The influence of family function on online prosocial behaviors of high school students: A moderated chained mediation model
## Abstract
The frequency of cyberbullying incidents is gradually increasing, and the seriousness of the consequences is gradually becoming more prominent. Previous studies have shown that cyberbullying bystander behaviors play an important role in reducing cyberbullying. This study aims to explore the mechanisms that high school students’ family function, empathy, and social support levels how to affect their implementation of online prosocial behaviors when they act as cyberbullying bystanders. The study was conducted in 1961 high school students ($M = 16.84$ years; SD = 1.08) in China. Results found that family function promotes online prosocial behaviors through (a) empathy, (b) social support, and (c) chain mediating path of empathy and social support. There were interactions between gender and family function as well as social support, which played a moderating role in the paths of family function and online prosocial behaviors and social support and online prosocial behaviors, respectively. We investigated how family function affected online prosocial behaviors in high school students and how empathy and social support worked to promote them to carry out online prosocial behaviors.
## Introduction
According to the 50th China Internet Development Statistical Report released by the China Internet Network Information Center in 2022, the number of netizens in China reached 1.051 billion until June 2022, and the Internet penetration rate reached $74.4\%$, of which the scale of netizens aged 10–19 reached 142 million, accounting for $13.5\%$ of the total netizens (China Internet Network Information Center, 2022). It could be concluded from the data of the report that the Internet occupied a high penetration rate in people’s life, and some behavioral tendencies of people could be reflected through the network social platforms (Chen et al., 2022). With the further expansion of the scale of netizens in China and the further increase in the proportion of adolescents and minors, cyberbullying has gradually become the focus of scholars and researchers. Cyberbullying refers to the use of electronic media (such as social networks, email, chat, text, MMS, etc.) in an online environment to do harm to someone who is unable to defend themselves. It is intentional aggressive behavior that demonstrates strength or competence characteristics of imbalance (Palermiti et al., 2017). In a study of bullying behavior among 7,182 middle and high school students in the United States, $8.3\%$ of students reported that they had committed cyberbullying, $9.8\%$ of them reported that they had been subjected to cyberbullying, and $13.6\%$ of students reported had been both the perpetrator and the bullied in cyberbullying in the past 2 months (Wang et al., 2009). Many researchers at home and abroad have shown through empirical researches that cyberbullying had a significant negative impact on the victim’s campus environment, physical and mental health, interpersonal communication, and emotions (González-Cabrera et al., 2018). As a result, both the cyberbullies and the perpetrators showed higher tendencies to depression and loneliness (Varghese and Pistole, 2017), greater insecurity and attachment behaviors, lower levels of self-esteem (Varghese and Pistole, 2017; González-Cabrera et al., 2018), a higher level of social anxiety (Pabian and Vandebosch, 2015), higher suicidal tendencies (Mitchell et al., 2016) and more alcohol abuse behaviors (Selkie et al., 2015).
## Online prosocial behavior
There are three important roles in both traditional bullying and cyberbullying incidents, that is, cyberbullying perpetrators, cyberbullies, and cyberbullying bystanders (Li, 2010). Among them, bystanders refer to individuals who are at present but do not participate in events or situations, and their synonymous expressions are passers-by, witnesses, etc. ( Stueve et al., 2006). In a study of Nichatolas Brody, it was pointed out that among the three roles in cyberbullying, researchers paid more attention to the motivation and implementation of the perpetrator and the bullied, while the research papers from the perspective of bystanders were relatively in a small quantity (Brody and Vangelisti, 2015). Foreign researchers have divided the responses of bystanders to cyberbullying into two modes, namely the positive mode of protecting the bullied and the negative mode of helping the bullying (Olenik-Shemesh et al., 2016). Domestic researchers believed that when individuals faced cyberbullying incidents and acted the role of bystanders, their responses could be roughly divided into three categories, specifically behaviors that promote bullying, behaviors that protect the bullied, and outsider behaviors (Teng, 2015). In cyberbullying incidents, the reactions of protecting the victim were seen as a cyber-prosocial behavior or online prosocial behavior, which played a significant role in the intervention of cyberbullying incidents, and it might affect the frequency of bullying incidents and the developmental outcomes of incidents (Salmivalli et al., 2011). Online prosocial behaviors refer to behaviors that are beneficial to others and the society in online context, and are not based on receiving rewards as the aim of these behaviors (Jiang et al., 2017). In a study of Eisenberg, there was no clear definition and conceptual distinction between altruistic behaviors and prosocial behaviors, so this study did not distinguish between online altruistic behaviors and online prosocial behaviors as well (Eisenberg et al., 2002). Applying naturalistic observations research methods on school playgrounds investigations, researchers studied peer intervention for bullying and found that in two-thirds of cases, peer intervention was effective in stopping bullying within 10 s and interventions in preventing bullying were equally effective both in boys and girls (Hawkins et al., 2001). Some researchers also used the method of constructing a multi-level model for 6,764 participants and came to the conclusion that with the face of bullying incidents, bystanders adopted prosocial behaviors to protect the bullied could effectively reduce the frequency of bullying (Salmivalli et al., 2011). Moreover, protecting the bullied person could also effectively reduce the victims’ feelings of stress, anxiety, and depression, and allowed the victim to receive positive feedback (Salmivalli, 2010).
Guided by the General Learning Model, the researchers conducted a series of studies on the impact of the bystander effect on individual behaviors, and the results showed that exposure to online violent games and violent videos could obviously increase the probability of cyberbullying behaviors as well as decrease the probability of online prosocial behaviors (Bartholow and Anderson, 2002), however, watching prosocial videos or listening to prosocial music could increase individual’s prosocial behaviors (Fireman et al., 2010). Some researchers adopted experimental research methods to study the bystander effect in online prosocial behaviors. By comparing the differences between mass sending helping emails online and sending helping emails with a single name on the Internet, the researchers found that the response rate of sending emails for help individually was much higher than mass sending, and non-bulk helping letters received longer replies, more relevant contents and higher intention for help (Barron and Yechiam, 2002; Blair et al., 2010). This could also indicate that with the face of cyberbullying incidents or online help-seeking incidents, whether there were bystanders or not had a significant impact on individual’s implementation of online prosocial behaviors. Therefore, it was very valuable to study online prosocial behaviors from the perspective of cyberbullying bystanders when people were faced with cyberbullying accidents.
## Family function
Family function refers to the effectiveness of various activities between family members, including family communication, family emotional connection, family internal rules and dealing with external things, and so on (Olson et al., 1979). Olson divided family functions into two dimensions, that is, family cohesion and family adaptability. Family cohesion refers to the emotional bonds between family members and the standards of personal autonomy of a family member in their family system. Family adaptability refers to the ability of the family system to change its power structure, role relationships, and corresponding rules when dealing with external situations and developmental pressures. And according to the Circumplex Model of family system which was developed by Olson, 16 kinds of marital and family systems were divided and identified (Olson et al., 1979). In a later study by Olson, family functions were further divided into three dimensions: family cohesion, family flexibility, and family communication skills (Olson, 2000). Beavers believed that family function included two dimensions, the family’s ability to cope with stress and the style of family interaction (Beavers and Hampson, 2000). Mcmaster’s Family Functional Model Theory claimed that the structure and organization of the family were important factors that strongly influenced and even determined the behaviors of family members (Miller et al., 2000). Studies have shown that family function had a significant impact on depression, suicidal tendencies, and so on, with the result that the better the family function was, the less behavioral and psychological problems individuals had (Keitner and Miller, 1990). Family function was not only closely related to the individual’s emotional response ability and emotional involvement ability, but also significantly related to the control of personal behaviors. Individuals with better family function were more likely to carry out prosocial behaviors (Miller et al., 2000). Some studies have found that the quality of parent–child relationship had a significant impact on the impulsivity of adolescents’ bad behaviors as well as their altruistic behaviors (Lu et al., 1998). Other studies have shown that adolescents’ socialization willingness and socialization results were important reflections of the socialization process of people’s parenting styles and parenting practices, and prosocial behaviors were important parts of adolescent socialization (Darling and Steinberg, 1993), so family functions had a significant impact on online prosocial behaviors. Therefore, the first hypothesis of this study is put forward, H1: family function will positively predict individual’s online prosocial behaviors, and people who have better family function will deliver more online prosocial behaviors.
## Empathy
Empathy, as a critical capacity in our emotional and social lives, is conceptualized as the ability to share the feelings of others (Bernhardt and Singer, 2012). In two other studies by Singer, it was believed that when an individual observed or imagined the emotional state of another person, the observer would develop the state of empathy (Singer and Lamm, 2009; Bernhardt and Singer, 2012). Many researchers held that empathy consists of two components, cognitive empathy and affective empathy (Gladstein, 1983). Davis divided empathy into four dimensions, namely perspective taking (PT), fantasy (FS), empathy concern (EC), and personal distress (PD) (Davis, 1983). In a longitudinal study of 180 children, the results showed that parental emotional warmth and positive expressions could significantly promote children’s empathy-related responses and their social functions (Zhou et al., 2002). In another longitudinal survey of 678 high school students of Belgian nationality, the results demonstrated that to a certain extent, students who grew with better family function were more likely to develop better empathy ability (Miklikowska et al., 2011), which provided evidence for the impact of perceived supportive parenting during adolescence on the development of empathy. In a study of adolescents aged 13 to 18, empathy significantly predicted prosocial behaviors (Silke et al., 2018). And in a study on the role of empathy in improving inter-group relations, the results revealed that empathy could enhance inter-group relations and promote individual’s prosocial behaviors (Stephan and Finlay, 1999). According to previous empirical researches and logical reasoning, the second hypothesis of this study is proposed, H2: Empathy plays a mediating role in the influence of family function on online prosocial behaviors, that is family function can indirectly influence the implementation of online prosocial behaviors through the mediating effect of empathy.
## Social support
Social support refers to information that leads individuals to believe that they are cared for, loved, and respected, additionally, each of them is a part of a team (Cobb, 1976). The concept also aims to draw attention to and focus on resources that may amortize or attenuate the impact of life events and other pressure sources (Coyne and Downey, 1991). In a study of 863 Australian suburban residents by Gavin, social support was divided into three dimensions, that is, support in crisis situations, interaction between neighbors, and community participation (Andrews et al., 1978). The Chinese Scholar Xiao Shuiyuan conducted in-depth and detailed researches on social support and compiled a social support rating scale. He divided social support into three dimensions, namely subjective support, objective support, and utilization of social support (Xiao, 1994). Previous studies have shown that the family environment could affect the acquisition of individual social support, specifically, a family environment with high intimacy and strong organization was significantly able to improve the level of college students’ acquisition of social support (Wu and Lu, 2006). In the relationship between social support and prosocial behaviors, studies have shown that the level of social support that individuals feel from teachers, peer groups, and family could significantly and positively predict their prosocial behaviors (Qiu and An, 2012). On the basis of the relevant research results, the third hypothesis of this study is proposed, H3: Social support plays a mediating role in the influence of family function on online prosocial behaviors, that is family function can indirectly influence the implementation of online prosocial behaviors through the mediating effect of social support.
According to Eisenberg’s Prosocial Behavior Model Theory, prosocial behaviors could be divided into three stages in line with their psychological change process, that is, the stage of paying attention to the needs of others, the stage of determining the intention to help others, and the stage of linking intention and behaviors (Eisenberg, 2014). To begin with, the first stage to pay attention to others’ need was the initial stage of the implementation of individual prosocial behaviors. At this stage, Eisenberg believed that whether an individual could pay attention to others was affected by two factors, one of which was the relevant individual characteristics, and the second was the individual’s interpretation of a particular situation. The individual factors included individual characteristics formed in the acquired social environment, the parenting style, one’s family function, and so on, which were all important components. And then, the second stage to determine prosocial behaviors intention was divided into two situations, which included the determination of helping intention in emergency situations and the determination of helping intention in non-emergency situations. In emergencies, the critical factors in the decision-making process were emotional factors, such as personal pain, empathy, perspective taking, and guilt; while in non-emergency situations, the individual’s personality traits were the determining factors. Despite under which circumstances, individuals with high empathy ability were more likely to put themselves into the perspective of people who were faced with the events, having stronger emotional involvement and deeper psychological experience, having a relatively more positive attribution and risk–benefit assessment, making it easier to determine the intention of prosocial behaviors. At last, the third stage to establish the connection between intention and behaviors was mainly affected by the individual’s ability to help others and the change between person and the situation (Wang and Pang, 1997). On the basis of Social Learning Theory, which was put forward by behaviorist Bandura, the individual’s performance of certain behavior itself was capable of strengthening one’s own behaviors, and it was often referred as the concept of direct reinforcement. Individuals who received more social support at this stage would have a higher level of self-efficacy in their own abilities, and would feel that they were more competent to put prosocial behaviors into practice. Moreover, positive intimation of self-worth and highly-praised evaluation of others after people implemented prosocial behaviors in the past would also further enhance one’s self-efficacy, and it was much easier to associate the intentions and actions of prosocial behaviors with implement of prosocial behaviors (Guo, 2005). As can be seen in these studies that individuals who had better family function, higher empathy ability, and higher level of social support were more likely to perform online prosocial behaviors. Based on previous empirical research papers and theoretical reasoning, the fourth hypothesis in this study is brought forward, H4: Empathy and social support play a chain mediating role in the influence of family function and online prosocial behaviors. To be specific, individuals with better family function, higher empathy, and higher levels of social support are more likely to develop online prosocial behaviors.
## The role of gender
Relevant studies have shown that gender could moderate the relationship between family function, prosocial behaviors, and online prosocial behaviors (Wang et al., 2020, 2022). The family’s socioeconomic status, parents’ expectations for their children, parents’ own educational experience and life background, as well as clan culture, all of these factors had significant impacts on the parenting style, parents’ attention, and educational investment on their children of different genders (Currie and Moretti, 2007; Ding et al., 2018). Campbell Leaper together with other scholars conducted a four-year follow-up study and found that males were more susceptible to interpersonal interactions and environmental influences than females (Leaper et al., 1989). In the light with Human-situation Interaction Theory, an individual and his situation commonly constituted a whole system (Zeng and Sang, 2005), and individual’s family situation would have an obvious affect on people’s behavioral characteristics when the children’s genders were different, as a result that males and females would have different behavioral response tendencies due to the interaction of family function and gender. Rosa Rosnati and his coworkers recruited 276 Italian families with children aged 11 to 17 as a research sample and found that whether in native or adoptive families, parents’ parenting methods for children of different genders in emotional communication and life interaction events were significantly different (Rosnati et al., 2007), therefore the interaction of family factors and gender can affect people’s behavior patterns and behavioral tendencies, and online prosocial behavior is one of them. Studies have indicated that owning to parents had different parenting goals for males and females and society had different gender requirements for individuals, females might deliberately maintain and strengthen their own prosociality in order to gain the approval of their parents and the acceptance of other people (Wang et al., 2022). In addition, because of the influence of gender stereotypes, family and social acceptance was much higher when males show mischievous actions, while females were always taught to be polite and quiet. Families’ character shaping and expectations of children of different genders in terms of personality were also influential on the frequency of individual prosocial behaviors. Therefore, the fifth hypothesis of this study is proposed, H5: Gender plays a moderating role on the direct path of family function and online prosocial behaviors. In previous studies, some scholars have shown that there were gender differences between males and females in social support (Colarossi, 2001). According to the Gender Schema Theory (Hair et al., 2008) and the expectation of males and females’ gender roles in traditional culture, females were often considered weak and need more support, while males were considered strong, brave and independent, so the level of social support would be different in different genders. Based on what we have talked above, this study puts forward the sixth hypothesis, H6: Gender has a moderating effect on the influence of social support on online prosocial behaviors, that is to say, on the intermediary path from social support to online prosocial behaviors, gender plays a moderating role in both the direct path and the second half path.
Most of the high school students are adolescents, and they are at a very pivotal and relatively special stage in their life (Liu et al., 2020). For one thing, in terms of age, they are in a special transition stage when they have just passed adolescence and then immediately enter into adulthood. For another, in terms of psychology, they are in a special stage of rapid development of self-identity and self-awareness. In the external environment, they are under ardent expectations and enormous pressure from both teachers and parents, and they also face vital tests such as the scores of college entrance examination and academic performance. So is there bullying happening around high school students? Especially in the period of online classes, will cyberbullying occur when online exposure increases? Will they give a helping hand as a bystander with the face of cyberbullying? What factors can promote online prosocial behaviors among high school students? These are what this article is going to study.
## Participants
The simple random sampling method was adopted in this study, and questionnaires of the study were gathered in February 2022 in certain normal high schools in Hebei Province and Henan Province in China. The data of this study was distributed and collected online in the form of questionnaires, and the students who were participated in this study used their mobile phones as well as computers to answer the questions independently after class and during vacations. The final size of questionnaires obtained in this study is 1961, and 1861 valid data remained after excluding the invalid ones. The effective ratio of the questionnaire is $94.90\%$. Among them, 884 are boys ($47.5\%$), and 977 are girls ($52.5\%$); 720 are in the first grade ($38.7\%$), and 645 are in the second grade ($34.7\%$), 496 are in the third grade ($26.7\%$) as well; 167 are the only children in their family ($9\%$), and 1,694 are non-only children ($91\%$); 1710 are from rural areas ($91.9\%$) and 151 are from non-rural areas ($8.1\%$). The average age of the subjects is 16.84 ± 1.08 years; the average Internet age of the subjects is 5.42 ± 2.45 years; during the winter and summer vacations, the time used on *Internet is* 3.03 ± 0.92 h every day; during the non-winter and summer vacations, the time spent on the *Internet is* 2.07 ± 1.02 h.
## Family function scale
This study applied the Family Intimacy and Adaptability Scale (FACESII) which was developed by Olson and his colleagues [1982] to measure the subjects’ family function. The scale was modified by Felipeng and his coworkers (Fei et al., 1991) for localization to be suitable for Chinese, which was named Family Intimacy and Adaptability Scale (FACESII-CV). The revised scale has a total number of 30 items, including two dimensions, intimacy and adaptability. The scale uses a five-point Likert scale, ranging from 1 to 5 to indicate “never” to “always.” Participants who gets the higher scores meant that their family has a higher degree of intimacy and adaptability. In this study, the Cronbach’s α = 0.814 and 0.846 respectively, and the Cronbach’s α = 0.908 of the total scale.
## Interpersonal response indicator scale
This study applied the Interpersonal Response Indicator scale (IRI) which was compiled by Davis [1983], and the scale was localized and revised by Zhang Fengfeng and his coworkers (Zhang et al., 2010) into the Chinese version of the Interpersonal Response Indicator Scale (IRI-C) to measure the subjects’ empathy ability (Zhang et al., 2010). The revised scale has a total number of 22 items, including four dimensions, namely Perspective Taking (PT), Fantasy (FS), Empathy Concern (EC), and Personal Distress (PD). The scale uses a five-point Likert scale, ranging from 0 to 4 to indicate “inappropriate” to “very appropriate.” The higher the score is, the higher the empathy level of the subjects have. In this study, Cronbach’s α = 0.783.
## Social support rating scale
In this study, the Social Support Rating Scale (SSRS) developed by Xiao [1994] was used to measure the social support level of the subjects. The scale has a total number of 10 items, including three sub-dimensions, that is, objective support (3 items), subjective support (4 items), and utilization of social support (3 items). The final scores less than 20 represent “low level social support,” scores between 20 and 30 represent “medium level social support,” and scores greater than 30 represent “high level social support” (Liu et al., 2020). In this study, the mean score of the subjects’ social support level was 39.01, which is a high level of social support. In this study, Cronbach’s α = 0.727.
## Questionnaire on bystander behavior in cyberbullying
In this study, the Bystander Behavior Questionnaire in Cyberbullying developed by Teng [2015] was used to measure the subjects’ reactions to cyberbullying. The scale consists of 20 items and includes three sub-dimensions, namely, behaviors that promote bullying (7 items), behaviors that protect the bullied (9 items), and outsider behaviors (4 items). The scale is scored on a seven-point scale, ranging from 1 to 7 to indicate “completely disagree” to “totally agree.” The scale is scored on three sub-dimensions respectively, and the higher the score is, the higher the tendency of the subjects to approach this behavior is. In this study, Cronbach’s α = 0.927, 0.958, and 0.904, respectively.
## Statistical analysis and common method bias test
This study used SPSS 21.0 to perform descriptive statistics, t-test, and correlation analysis on the collected data, and used the PROCESS macro program of Hayes [2013] to test and analyze mediating and moderating effects. Since data were collected in a self-reported manner, results may be subject to common method biases (Zhou and Long, 2004). In this study, in order to control the confusion of the research results caused by the common method bias, in the aspect of program control, it was firstly stated in the instruction setting of the test questionnaire that this questionnaire will be filled in anonymously, and the answers to the questionnaire will be strictly confidential and the answers will only be for academic research (Hu et al., 2019). Secondly, the subjects who participated in the study came from different provinces and cities such as Hebei Province and Henan Province, and were selected from different school levels and school types. As for the term of statistical control, Harman’s Univariate Method was used to test for common method bias in this study. The results showed that the eigenvalues of 11 factors were greater than 1 in total, and the variance explained by the largest factor was $19.94\%$, which was less than $40\%$, indicating that there was no serious common method bias effect in this study (Xiong et al., 2012).
## Gender and grade differences
Gender differences in family function, empathy, social support, and cyberbullying bystander behaviors were investigated by independent-samples t-test statistical methods in this study. The results showed that there are significant differences in the other variables except for the variable of social support, which had no significant gender difference. There was a significant gender difference in family function ($t = 2.76$, $p \leq 0.01$). Compared with females, males had a higher level of family function. There was a significant gender difference in empathy (t = −5.76, $p \leq 0.001$). Compared with males, females had a higher level of empathy. There was a significant gender difference in the behavior of promoting bullying ($t = 2.91$, $p \leq 0.01$), and males had more bullying-promoting behaviors compared with females. There was a significant gender difference in the behavior of protecting the bullied (t = −2.57, $p \leq 0.05$). Specifically, females had more behaviors to protect the bullied compared with males. There was a significant gender difference in bystander behavior ($t = 2.98$, $p \leq 0.01$). Compared with females, males have more bystander behaviors.
The differences caused by grade in family function, empathy, social support, and cyberbullying bystander behaviors were investigated by ANOVA test statistical methods in this study. The results showed that only the variable of empathy ability had a significant difference in grades ($F = 4.648$, $p \leq 0.05$). The LSD post-hoc test showed that the empathy ability of Grade 1 had the highest score, and the empathy ability of Grade 3 was the lowest. And except for empathy, no significant differences in grades were found in other dimensions in this study (See Table 1).
**Table 1**
| Unnamed: 0 | Male (N = 884) | Male (N = 884).1 | Female (N = 997) | Female (N = 997).1 | t | Grade 1 (N = 720) | Grade 1 (N = 720).1 | Grade 2 (N = 645) | Grade 2 (N = 645).1 | Grade 3 (N = 496) | Grade 3 (N = 496).1 | F |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | M | SD | M | SD | t | M | SD | M | SD | M | SD | F |
| FF | 144.43 | 19.38 | 142.04 | 17.87 | 2.76** | 142.51 | 18.35 | 143.08 | 18.41 | 144.27 | 19.31 | 1.318 |
| E | 48.89 | 10.94 | 51.78 | 10.70 | −5.76*** | 51.04 | 11.37 | 50.66 | 10.5 | 49.16 | 10.66 | 4.648* |
| SS | 39.02 | 6.45 | 39.01 | 5.96 | 0.04 | 39.09 | 6.12 | 38.9 | 6.24 | 39.06 | 6.26 | 0.178 |
| BPromoteB | 13.85 | 9.89 | 12.57 | 8.95 | 2.91** | 12.88 | 9.39 | 13.06 | 9.17 | 13.76 | 9.8 | 1.362 |
| BProtectB | 42.73 | 15.83 | 44.47 | 13.05 | −2.57* | 44.43 | 14.60 | 43.41 | 14.39 | 42.79 | 14.32 | 2.021 |
| BStanderB | 12.61 | 6.57 | 11.74 | 5.95 | 2.98** | 11.84 | 6.30 | 12.12 | 6.05 | 12.64 | 6.46 | 2.406 |
## Correlations among all variables
The study applied Pearson Product–moment Correlation to test the correlations among all variables. It was found that except for few dimensions, all dimensions basically showed significant pairwise correlations. Family function was significantly positively correlated with empathy, social support, bullying-promoting behavior, and bullying-protecting behavior, and significantly negatively correlated with bystander behavior. Empathy was significantly positively correlated with social support and bullying-protecting behavior, and negatively correlated with bullying-promoting and bystander behavior. Social support was significantly positively correlated with bullying-protecting behavior, and negatively correlated with bystander behavior. Bullying-promoting behavior was significantly positively correlated with bullying-protecting behavior and bystander behavior. Bullying-protecting behavior was significantly negatively correlated with bystander behavior (See Table 2).
**Table 2**
| Unnamed: 0 | M | SD | 1 | 2 | 3 | 4 | 5 | 6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1. FF | 143.18 | 18.64 | 1 | | | | | |
| 2. E | 50.41 | 10.91 | 0.24**(0.00) | 1 | | | | |
| 3. SS | 39.01 | 6.2 | 0.44**(0.00) | 0.16**(0.00) | 1 | | | |
| 4. BPromoteB | 13.18 | 9.43 | 0.05*(0.02) | −0.07**(0.01) | 0.04 | 1 | | |
| 5. BProtectB | 43.64 | 14.46 | 0.22**(0.00) | 0.26**(0.00) | 0.33**(0.00) | 0.06*(0.02) | 1 | |
| 6. BStanderB | 12.15 | 6.26 | −0.14**(0.00) | −0.21**(0.00) | −0.24**(0.00) | 0.18**(0.00) | −0.32**(0.00) | 1.0 |
## Chain mediating effects of empathy and social support between family function and bullying-protecting behavior
According to the results of the correlation analysis in this study and the statistical preconditions of the mediation effect, further mediation effect analysis of empathy and social support can be carried out (Wen and Ye, 2014). In order to study the role of empathy and social support in family function and bullying-protecting behavior in the face of cyberbullying, the study used the bias-corrected percentile Bootstrap method in the SPSS macro program Process compiled by Hayes [2013] to analyze the mediating effect (Fang et al., 2015), and Model 6, which specialized in analyzing chain mediation effects, was used for testing. This study used a Bootstrap sample size of 5,000 times to test the chain mediation effect of empathy and social support with a $95\%$ confidence interval. Among the demographic variables in this study, the proportion of students’ origin and whether they were only children was quite different, and the grade factor had little effect on the variables to be studied in the previous ANOVA analysis, so these factors were not controlled in the next tests. However, in the independent sample t-test on sex, it was found that most variables in this study had significant differences on it. In order to avoid the error caused by this factor on the research results, gender was used as a covariate to control for the chain mediation effect test.
The results of regression analysis showed that family function significantly and positively predicted the bullying-protecting behaviors (β = 0.172, $p \leq 0.001$) and empathy (β = 0.146, $p \leq 0.001$). After incorporating social support into the regression equation of family function and empathy, the results suggested that family function significantly and positively predicted social support (β = 0.141, $p \leq 0.001$), and empathy also significantly as well as positively predicted social support (β = 0.031, $p \leq 0.05$). And then, after incorporating the bullying-protecting behaviors into the regression equation of family function, empathy, and social support, it was indicated that family function significantly and positively predicted the bullying-protecting behaviors (β = 0.044, $p \leq 0.05$), and empathy also significantly and positively predicted the bullying-protecting behaviors (β = 0.256, $p \leq 0.001$), and social support also significantly and positively predicted the bullying-protecting behaviors (β = 0.647, $p \leq 0.001$) as well (See Table 3).
**Table 3**
| Outcome variable | Predictor variable | R | R2 | F | β | t |
| --- | --- | --- | --- | --- | --- | --- |
| BProtectB | | 0.221 | 0.049 | 96.687*** | | |
| | FF | | | | 0.172 | 9.777*** |
| E | | 0.282 | 0.08 | 80.454*** | | |
| | FF | | | | 0.146 | 11.201*** |
| SS | | 0.44 | 0.193 | 148.444*** | | |
| | FF | | | | 0.141 | 19.646*** |
| | E | | | | 0.031 | 2.517* |
| BProtectB | | 0.396 | 0.157 | 86.073*** | | |
| | FF | | | | 0.044 | 2.320* |
| | E | | | | 0.256 | 8.684*** |
| | SS | | | | 0.647 | 11.684*** |
The results of the mediation effect analysis showed that empathy and social support played a significant mediating role between family function and the bullying-protecting behavior. The total mediation effect value was 0.132 (Boot $95\%$ CI = [0.109, 0.156]), accounting for $75.43\%$ of the total effect of family function on the bullying-protecting behavior. Specifically, the mediating effect of family function and the bullying-protecting behavior is composed of indirect effects generated by three paths, path 1:family function → empathy →the bullying-protecting behavior (effect value 0.038, Boot $95\%$ CI = [0.027, 0.050]); Path 2: family function → empathy → social support → the bullying-protecting behavior (effect size 0.003, Boot $95\%$ CI = [0.001, 0.006]); Path 3: family functioning→social support→the bullying-protecting behavior (effect size 0.091, Boot $95\%$ CI = [0.072, 0.112]). The ratios of the indirect effects of the three pathways to the total effects were $21.71\%$,$1.71\%$, and $52.01\%$, respectively. Moreover, the $95\%$ confidence intervals of the three indirect effects did not contain 0 values, indicating that the three indirect effects reached a significant level. Use the options in Model 6 of Process to compare indirect effects, compare three different indirect path effects in pairs, and explore whether there are significant differences among them. The results showed that, comparison 1 showed that the Bootstrap $95\%$ confidence interval of the difference between indirect effect 1 and indirect effect 2 did not contain 0 value, indicating that there was a significant difference between indirect effect 1 and indirect effect 2; Comparison 2 showed that the Bootstrap $95\%$ confidence interval for the difference between indirect effect 1 and indirect effect 3 did not contain 0 value, indicating that there was a significant difference between indirect effect 1 and indirect effect 3; Comparison 3 showed that the Bootstrap $95\%$ confidence interval for the difference between indirect effect 2 and indirect effect 3 did not contain 0 value, indicating that there was a significant difference between indirect effect 2 and indirect effect 3 (See Table 4 and Figure 1).
## Analysis of the moderating effect of gender among family function, empathy, and the bullying-protecting behavior
Equation 1 According to the above test, it can be shown that empathy and social support have a mediating effect between family function and the bullying-protecting behavior. However, the effect value of indirect effect 2 (family function → empathy → social support → bullying-protecting behavior) is relatively low ($1.71\%$). Therefore, we will only explore the moderating effect of gender on the two mediating effect pathways of indirect pathway 1 (family function → empathy → bullying-protecting behavior) and indirect pathway 3 (family function → social support → bullying-protecting behavior). According to the moderated mediation model test method suggested by Wen and Ye [2014], this study first standardized all variable data, and coded the gender variable as a dummy variable (1 for male and 0 for female). Next, the Process program was used to test the moderated mediation model with family function as the independent variable, empathy as the mediator variable, gender as the moderator variable, and the bullying-protecting behavior as the dependent variable. The results showed that family function had a significant positive predictive effect on empathy (β = 0.250, $t = 11.156$, Boot $95\%$ CI = [0.205, 0.293], $p \leq 0.001$); Gender had a significant negative predictive effect on empathy (β = −0.287, t = −6.420, $95\%$ CI = [−0.374, −0.200], $p \leq 0.001$); The interaction term of family function and gender had no significant effect on the prediction of empathy (β = 0.076, $t = 1.700$, $95\%$ CI = [−0.012, 0.163]) (See Table 5 Equation 1).
**Table 5**
| Predictor variable | Equation 1 E(M) | Equation 1 E(M).1 | Equation 1 E(M).2 | Equation 2 BProtectB(Y) | Equation 2 BProtectB(Y).1 | Equation 2 BProtectB(Y).2 |
| --- | --- | --- | --- | --- | --- | --- |
| Predictor variable | B | SE | 95%CI | B | SE | 95%CI |
| Grade | −0.043 | 0.028 | [−0.097, 0.111] | −0.053 | 0.027 | [−0.107, 0.001] |
| Age | −0.046 | 0.028 | [−0.100, 0.008] | 0.027 | 0.027 | [−0.027, 0.081] |
| FF | 0.250*** | 0.022 | [0.205, 0.293] | 0.177*** | 0.023 | [0.132, 0.222] |
| Gender | −0.287*** | 0.045 | [−0.374, −0.200] | −0.086 | 0.045 | [−0.174, 0.002] |
| FF × Gender | 0.076 | 0.045 | [−0.012, 0.163] | −0.103* | 0.046 | [−0.193, −0.013] |
| E | | | | 0.208*** | 0.023 | [0.163, 0.254] |
| E × Gender | | | | 0.075 | 0.046 | [−0.015, 0.165] |
| R 2 | 0.087 | 0.087 | 0.087 | 0.099 | 0.099 | 0.099 |
| F | 35.488*** | 35.488*** | 35.488*** | 29.171*** | 29.171*** | 29.171*** |
Family function had a significant positive predictive effect on the bullying-protecting behavior (β = 0.177, $t = 7.731$, Boot $95\%$ CI = [0.132, 0.222], $p \leq 0.001$); The interaction item of family function and gender had a significant predictive effect on the bullying-protecting behavior (β = −0.103, t = −2.250, Boot $95\%$ CI = [−0.193, −0.013], $p \leq 0.05$); Empathy had a significant positive predictive effect on the bullying-protecting behavior (β = 0.208, $t = 9.026$, Boot $95\%$ CI = [0.163, 0.254], $p \leq 0.001$); The interaction term of empathy and gender had no significant effect on the bullying-protecting behavior (β = 0.075, t = −1.629, Boot $95\%$ CI = [−0.015, 0.165]). The results of this model verified that empathy mediated between family function and the bullying-protecting behavior, and that the direct pathway of this model is moderated by gender (See Table 5 Equation 2).
The method of simple slope analysis was used to further analyze the moderating effect of gender when empathy was the mediating variable of family function and high school students’ online prosocial behavior. The results showed that in the male group, family function had a significant effect on predicting online prosocial behavior (simple slope = 0.123, $t = 7.000$, $p \leq 0.001$); in the female group, family function also had a significant predictive effect on online prosocial behavior (simple slope = 0.226, $t = 3.820$, $p \leq 0.001$). However, the effect sizes of the two groups were different, indicating that gender can play a moderating role between family function and online prosocial behavior (See Figure 2).
**Figure 2:** *The interaction of family function and gender on online prosocial behaviors (the mediating variable is empathy).*
## Analysis of the moderating effect of gender among family function, social support, and the bullying-protecting behavior
Equation 1 the Process program macro was used to test the moderated mediation model with family function as the independent variable, social support as the mediator variable, gender as the moderator variable, and the bullying-protecting behavior as the dependent variable, as well as grade and age as covariates. The results showed that family function had a significant positive predictive effect on social support (β = 0.436, $t = 20.827$, Boot $95\%$ CI = [0.396, 0.479], $p \leq 0.001$); the interaction term of family function and gender had no significant predictive effect on social support (β = 0.018, $t = 0.434$, Boot $95\%$ CI = [−0.046, 0.100]) (See Table 6 Equation 1).
**Table 6**
| Predictor variable | Equation 1 SS(M) | Equation 1 SS(M).1 | Equation 1 SS(M).2 | Equation 2 BProtectB(Y) | Equation 2 BProtectB(Y).1 | Equation 2 BProtectB(Y).2 |
| --- | --- | --- | --- | --- | --- | --- |
| Predictor variable | B | SE | 95%CI | B | SE | 95%CI |
| Grade | −0.021 | 0.026 | [−0.071, 0.031] | −0.056 | 0.027 | [−0.108, 0.003] |
| Age | 0.003 | 0.026 | [−0.048, 0.054] | 0.016 | 0.027 | [−0.037, 0.070] |
| FF | 0.436*** | 0.021 | [0.396, 0.479] | 0.106*** | 0.024 | [0.059, 0.154] |
| Gender | −0.054 | 0.042 | [−0.136, 0.030] | −0.132** | 0.044 | [−0.217, −0.046] |
| FF × Gender | 0.018 | 0.042 | [−0.064, 0.100] | −0.149** | 0.048 | [−0.243, −0.054] |
| SS | | | | 0.283*** | 0.024 | [0.059, 0.154] |
| SS × Gender | | | | 0.172*** | 0.048 | [0.077, 0.266] |
| R 2 | 0.191 | 0.191 | 0.191 | 0.132 | 0.132 | 0.132 |
| F | 87.654*** | 87.654*** | 87.654*** | 40.181*** | 40.181*** | 40.181*** |
Family function had a significant positive predictive effect on the bullying-protecting behavior (β = 0.106, $t = 4.382$, Boot $95\%$ CI = [0.059, 0.154], $p \leq 0.001$); Gender had a significant negative predictive effect on the bullying-protecting behavior (β = −0.132, t = −0.319, Boot $95\%$ CI = [−0.217, −0.046], $p \leq 0.005$); The interaction term of family function and gender had a significant predictive effect on the bullying-protecting behavior (β = −0.149, $t = 3.081$, Boot $95\%$ CI = [−0.243, −0.054], $p \leq 0.005$); Social support had a significant positive predictive effect on the bullying-protecting behavior (β = 0.283, $t = 4.383$, Boot $95\%$ CI = [0.059, 0.154], $p \leq 0.001$); The interaction term of social support and gender had a significant predictive effect on the bullying-protecting behavior (β = 0.172, $t = 3.566$, Boot $95\%$ CI = [0.077, 0.266], $p \leq 0.005$). The results of this model verified that social support mediated the relationship between family function and the bullying-protecting behavior, and that the direct pathway and the second half pathway of this model are moderated by gender (See Table 6 Equation 2).
The method of simple slope analysis was used to further analyze the moderating effect of gender when social support was the mediating variable of family function and high school students’ online prosocial behavior. A simple slope plot was used to determine the differences in the influence of family function on online prosocial behaviors for different genders. The results showed that in the male group, family function had no significant effect on predicting online prosocial behavior (simple slope = 0.280, $t = 0.829$, $p \leq 0.05$); In the female group, family function had a significant effect on the prediction of online prosocial behavior (simple slope = 0.177, $t = 5.124$, $p \leq 0.001$), indicating that gender can play a moderating role between family function and online prosocial behavior (See Figure 3).
**Figure 3:** *The interaction of family function and gender on online prosocial behaviors (the mediating variable is social support).*
The method of simple slope analysis was used to further analyze the moderating effect of gender between social support and online prosocial behavior of high school students. A simple slope plot was used to determine the differences in the influence of social support on online prosocial behavior for different genders. The results showed that in the male group, social support had a significant effect on predicting online prosocial behavior (simple slope = 0.385, $t = 12.686$, $p \leq 0.001$); In the female group, social support also had a significant predictive effect on online prosocial behavior (simple slope = 0.276, $t = 8.837$, $p \leq 0.001$), indicating that gender can play a moderating role between social support and online prosocial behavior (See Figure 4).
**Figure 4:** *The interaction of social support and gender on online prosocial behaviors (the mediating variable is social support).*
## Discussion
This study mainly discussed the chain mediating pathway of family function and online prosocial behaviors of high school students as bystanders of cyberbullying incidents. The results indicated that family function influenced online prosocial behaviors through the indirect pathways of empathy, social support, and the chain mediating pathway of empathy and social support, as well as that gender moderated the two indirect pathways.
## The predictive effect of family function on online prosocial behavior
This study investigated the relationship between family function and high school students’ online prosocial behaviors with the face of cyberbullying, and found that family function could directly and positively predict high school students’ online prosocial behaviors, which confirmed the first hypothesis of this study. Consistent with previous research findings, positive parenting, high levels of parent–child relationships, and good family function could promote the implementation of individual’s prosocial behaviors (Pastorelli et al., 2016). According to Bronfenbrenner’s Ecological Systems Theory, the innermost micro-system of the environmental level was the direct environment of high school students’ interactions and activities. So the parenting styles in the family environment, parents’ expectations, family intimacy, and adaptability all played essential roles in directly affecting people’s social behaviors (Ungar et al., 2013; Zhou et al., 2020). In light with The Family Circumplex Model proposed by Olson, family function was divided into two indicators, family intimacy, and family adaptability (Beavers and Voeller, 1983). Studies have shown that family intimacy made a difference to promoting people’s prosocial behaviors (Li et al., 2020), and family adaptability also positively predicted adolescents’ school participation, actively making friends and helping conducts (Annunziata et al., 2006). The Theory of Marriage and Family Function which was put forward by Chinese scholar Fei Xiaotong believed that people’s family was the first place for individual’s socialization. The family shaped children’s social roles, taught individual’s social norms, formulated their life goals, and cultivated individual socialization to become a social person and integrate into the society (Pan, 2010). High school students with good family function received more company time (Dou et al., 2022), financial and psychological support, reduced their anxiety, interacted more favorably with others, and were more likely to engage in prosocial behaviors (Chen et al., 2021). Therefore, online prosocial behaviors, which acted as an unmissable component of individual socialization behaviors, the quality of one’s family function had a very significant impact on it.
## The mediating role of empathy in the influence of family function on online prosocial behavior
This study found that empathy played a mediating role between family function and high school students’ online prosocial behaviors. Specifically, family function positively predicted an individual’s empathy ability, thereby further promote online prosocial behaviors, which was consistent with previous research results (Fan et al., 2020), and verified the second hypothesis of this study. This indirect effect accounted for $21.71\%$ of the total effect. According to Davis’s Multi-dimensional Theoretical Construction based on empathy, empathy included two orientations, cognitive empathy and emotional empathy (Davis, 1983). Referring to the Theoretical Model of Empathy’s Life-long Development, emotional empathy was greatly affected by innate factors, and cognitive empathy was significantly affected by the acquired environment (Decety and Svetlova, 2012). Therefore, family environment which acted as an important acquired factor for individuals was of great importance to one’s empathy. In light with the Empathy-altruism Hypothesis put forward by Batson, the greater the emotional intensity of a person’s empathy was, the higher the motivation for altruistic behaviors was, and the easier it was to carry out altruistic behaviors (Batson et al., 1991; Zhang et al., 2020).
## The mediating role of social support in the influence of family function on online prosocial behavior
This study found that empathy and social support played a chain mediating role between family function and high school students’ online prosocial behaviors. Specifically, family function first worked on empathy, then empathy affected social support, and finally social support acted on online prosocial behaviors, forming the path of “family function—empathy—social support—online prosocial behaviors.” Because the mediation path of “family function—empathy—online prosocial behaviors” had been discussed before, the next part will be discussed from the mediation effect chains “family function—social support—online prosocial behaviors” and “family function—empathy—social support—online prosocial behaviors” separately.
This study found that family function would have an indirect effect on high school students’ online prosocial behaviors through social support, which confirmed the third hypothesis of this study. This indirect effect accounted for $52.01\%$ of the total effect. Moreover, a strong connection between family function and social support had been supported in many previous studies (Bokhorst et al., 2010). According to the Reciprocity Theory of Altruistic Behavior proposed by Trivers, the altruistic behaviors between individuals were mutual, and the social support that an individual received would have an important impact on his altruistic behaviors. Generally speaking, the more social support an individual felt, the more altruistic behaviors people would perform (Helsen et al., 2000). Based on Bandura’s Social Learning Theory (Yildirim et al., 2020), an important factor that promoted or inhibited people’s prosocial behaviors was the observation of the practices of people around you, that is, the concept of indirect reinforcement. Overall, individuals with good family functions had a higher degree of harmony in family relationships, and the mutual aid behaviors shown by family members would provide more mental power, psychological safety, and support to each other, including material support, spiritual support, emotional support, and so on. In addition, individuals living in an environment with good family function, family intimacy and adaptability would also perceive more social support from the family, thus showing more prosocial behaviors. Moreover, according to Deutsch and Lamberti, when the helping behaviors implemented by individuals were reinforced by gratitude, praise, and other positive feedback, individuals would be more inclined to show higher frequency of prosocial behaviors (Deutsch and Lamberti, 1986). A well-functioning family environment was an environment full of encouragement, affirmation, recognition, positive atmosphere, and appreciation, which would make individuals feel more social support for their prosocial behaviors, thereby further strengthening their prosocial behavioral motivation and implementation of prosocial behaviors.
## The chain mediating role of empathy and social support in the influence of family function on online prosocial behavior
This study also found that family function could also have an indirect effect on high school students’ online prosocial behaviors through the chain mediating effect of empathy and social support, which verified hypothesis 4 of this study. This indirect effect accounted for $1.71\%$ of the total effect. Chinese scholar Xianliang Zheng, in his book The Theory and empirical Research on Internet Altruistic Behavior, believed that the influencing factors of Internet altruistic behaviors were mainly involved in three aspects, namely factors of helpers, helping seekers, and network environment (Zheng, 2013). In the helper factor, the helper’s family environment and family function had a significant impact on individual’s acquired personality quality, and the individual’s empathy ability was an important part of these qualities. As for the helping seeker factor, the homogeneity of the helping seekers and the potential helpers was closely related to whether to perform the helping behaviors or not. Individuals with strong empathy were capable of perceiving and experiencing the unfavorable situation of the online bullied people, psychologically enhancing the homogeneity of potential helpers and helping seekers. In the network environment, due to the various characteristics of the Internet, it was easier for individuals to carry on network altruistic behaviors (Guo and Wang, 2010). For example, the anonymity of the Internet gave the helping seekers greater courage to self-disclose, and the potential helpers could better understand the situation of the seekers, which increased the possibility of giving social support to them. The timeliness of Internet made the implementation of relational online altruistic behaviors more rapid, and the conducts of online helpers could be reflected in the events of the helper in a timely manner. The interactive nature of the Internet made it more efficient for individuals to implement online prosocial behaviors as bystanders of cyberbullying accidents (Cheng, 2002). According to Erickson’s Theory of Social Personality Development Stages, the main contradiction for high school students was the contradiction between individual role identity and role confusion. The core problem faced by high school students at this stage was the determination of self-awareness and self-role formation. Self-identity could help high school students coordinate the relationship between various people and surrounding matters, and make the transition to adulthood smoothly (Erikson, 1994). High school students with good family functions have developed higher empathy abilities, and their ability to empathize made individuals more friendly to those around them, meanwhile it was easier to obtain higher quality of friendship and social support. In Batson’s view to analyze people’s characteristics and motivation of prosocial behaviors, individuals with high social support actively implemented online prosocial behaviors so as to relieve others’ troubles and help others solve their problems. At the same time, they would also further gain the positive evaluations of themselves and others, even get rewards, social approval, and reduce aversive arousal (Batson et al., 1991). As a result, helpers could obtain higher self-efficacy and a higher sense of self-identity, solve the psychological development contradictions faced by high school students during adolescence, and achieve the purpose of improving the level of individual physical and mental health development.
## The moderating role of gender in family function-empathy-online prosocial behavior
The study found that when the variable of gender was introduced into the study of family function and senior high school students’ online prosocial behaviors, then the results showed that gender played a moderating role in both the pathways of family function-empathy-online prosocial behaviors and family function-social support-online prosocial behaviors, which was consistent with previous research results (Earnshaw et al., 2011; Brown et al., 2014), and verified the fifth hypothesis of this study. Based on the results in pathway of family function-empathy-online prosocial behaviors, it could be found that there were interactions between family function and gender. Compared with males, females with a high family function level were more likely to perform online prosocial behaviors. In conformity with the Gendered Family Process Model, there were different ways for parents to raise males and females, as a result, the differences between education methods and family functions would shape the differences in their characteristics, social communication styles, and behavior tendencies of males and females (Endendijk et al., 2018). Generally speaking, females would be educated by parents to be kind, benevolent, and helpful, so it was easier for them to extend a helping hand in the face of cyberbullying. By contrast, the orientation of males’ family education was supposed to be reserved, stable, restrained, and unassuming. Therefore, when males found that others were in the state of being bullied online, they were more likely to turn a blind eye and remain silent. Therefore, family functions showed interactions on different genders, and gender played a moderating role in the direct pathway from family functions to online prosocial behaviors.
## The moderating role of gender in family function-social support-online prosocial behavior
On the intermediary path of family function-social support-online prosocial behaviors, it could find that gender not only played a moderating role in the direct path, but also played a moderating role in the second half of the path. This result verified the sixth hypothesis of this study. On one hand, in the direct path of mediating family function and online prosocial behavior with social support, females with high family function were more likely to implement online prosocial behavior than males. According to the Relational Theory put forward by Portman, in comparison with adolescent males, adolescent females matured earlier than males in both physical and psychological aspects (Portman et al., 2010), so the surrounding environmental factors and the family influence they have received would also have a greater impact on females. Family function which acted as an important part of environmental factors, would also have a greater impact on females, making family function interact with sex, so that the females who had higher level family function showed a higher level of online prosocial behaviors. On the other hand, compared with females, males who received high-level social support were more likely to show online prosocial behaviors. Compared with females who were introverted and gentle, males were more extroverted and strong. In addition, no matter the knights saved the princess in western culture or the heroes saved the beauty in eastern culture, under the expectation of social roles, males are endowed with higher role expectations in helping others than females. Therefore, when they received a high level of social support, they were more determined to act as protectors and messengers of justice, and were more likely to carry out online prosocial behavior than females.
In summary, all the six research hypotheses in this study have been verified. Family function of high school students had a direct predictive effect on online prosocial behaviors. Empathy and social support not only played a mediating role, respectively, in the influence of family function on online prosocial behaviors, but also played a chain mediating role between them. Therefore, two mediation models and one chain mediation model were obtained. In the mediating model of family function-empathy-online prosocial behaviors, gender played a moderating role in the direct pathway. In the mediating model of family function-social support-online prosocial behaviors, gender played a moderating role in the direct path and the latter half path, respectively.
## Limitation
This study also had certain limitations. To begin with, from the perspective of research methods, this research was based on the previous theoretical constructions and research models, which adopted a cross-sectional research method through questionnaires and scales to conduct the research. Yet this research design failed to explore the causal relationship between independent variable and dependent variable Future research can design experiments or use cross-lagged studies to further explore the causal relationship between them. In addition, the online prosocial behaviors of high school students measured in this study were only one of the responses of high school students when they faced cyberbullying situations, so little attention was paid to whether they would promote cyberbullying or turn a blind eye. Future research can also focus on other varieties of responses of high school students in the face of cyberbullying and its impact mechanism.
## Conclusion
This study found that family function positively predicted high school students’ online prosocial behaviors, in which empathy and social support played a chain mediating role between them, and gender moderated this mediation model. This paper further revealed the mechanism of family function on high school students’ online altruistic behaviors, enriching the research on high school students’ online prosocial behaviors. And it can provide empirical and theoretical basis for cultivating high school students’ online prosocial behaviors in family, school, and society.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of College of Philosophy and Sociology of Jilin University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
LC conceived the study and designed the trial and drafted the manuscript. ZL and LC supervised the conduct of the trial and data collection, provided statistical advice on study design, and analyzed the data. ZL took responsibility for the manuscript as a whole. All authors contributed substantially to its revision.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Effectiveness of a mHealth intervention on hypertension control in a low-resource
rural setting: A randomized clinical trial'
authors:
- Zhang Yuting
- Tan Xiaodong
- Wang Qun
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10014612
doi: 10.3389/fpubh.2023.1049396
license: CC BY 4.0
---
# Effectiveness of a mHealth intervention on hypertension control in a low-resource rural setting: A randomized clinical trial
## Abstract
### Background
Despite the increasing popularity of mHealth, little evidence indicates that they can improve health outcomes. Mobile health interventions (mHealth) have been shown as an attractive approach for health-care systems with limited resources. To determine whether mHealth would reduce blood pressure, promote weight loss, and improve hypertension compliance, self-efficacy and life quality in individuals with hypertension living in low-resource rural settings in Hubei, China.
### Methods
In this parallel-group, randomized controlled trial, we recruited individuals from health-care centers, home visits, and community centers in low-resource rural settings in Hubei, China. Of 200 participants who were screened, 148 completed consent, met inclusion criteria, and were randomly assigned in a ratio of 1:1 to control or intervention. Intervention group participants were instructed to use the Monitoring Wearable Device and download a Smartphone Application, which includes reminder alerts, adherence reports, medical instruction and optional family support. Changes in the index of Cardiovascular health risk factors from baseline to end of follow-up. Secondary outcomes were change in hypertension compliance, self-efficacy and life quality at 12 weeks.
### Results
Participants ($$n = 134$$; 66 in the intervention group and 68 controls) had a mean age of 61.73 years, $61.94\%$ were male. After 12 weeks, the mean (SD) systolic blood pressure decreased by 8.52 (19.73) mm Hg in the intervention group and by 1.25 (12.47) mm Hg in the control group (between-group difference, −7.265 mm Hg; $95\%$ CI, −12.89 to −1.64 mm Hg; $$P \leq 0.012$$), While, there was no difference in the change in diastolic blood pressure between the two groups (between-group difference, −0.41 mm Hg; $95\%$ CI, −3.56 to 2.74 mm Hg; $$P \leq 0.797$$). After 12 weeks of follow-up, the mean (SD) hypertension compliance increased by 7.35 (7.31) in the intervention group and by 3.01 (4.92) in the control group (between-group difference, 4.334; $95\%$ CI, 2.21 to −6.46; $P \leq 0.01$), the mean (SD) hypertension compliance increased by 12.89 (11.95) in the intervention group and by 5.43 (10.54) in the control group (between-group difference, 7.47; $95\%$ CI, 3.62 to 11.31; $P \leq 0.01$), the mean (SD) physical health increased by 12.21 (10.77) in the intervention group and by 1.54 (7.18) in the control group (between-group difference, 10.66; $95\%$ CI, 7.54–13.78; $P \leq 0.01$), the mean (SD) mental health increased by 13.17 (9.25) in the intervention group and by 2.55 (5.99) in the control group (between-group difference, 10.93; $95\%$ CI, 7.74 to 14.12; $P \leq 0.01$).
### Conclusions
Among participants with uncontrolled hypertension, individuals randomized to use a monitoring wearable device with a smartphone application had a significant improvement in self-reported hypertension compliance, self-efficacy, life quality, weight loss and diastolic blood pressure, but no change in systolic blood pressure compared with controls.
## Introduction
Hypertension is the most common chronic condition for cardiovascular and cerebrovascular events worldwide, affecting $32.6\%$ of US adults, and has an estimated annual medical expenses exceeding $50 billion [1, 2]. Worldwide, 422.7 million people diagnosis with cardiovascular disease [3], and causes 16.7 million deaths each year, $80\%$ of which occur in low-income and middle-income countries [4]. According to a recent investigation, in rural China, the control and control under-treatment rate of hypertension were only 8.6 and $19.8\%$, respectively [5]. Decades of research have shown that even the modest reductions in blood pressure (BP) would reduce the premature mortality and the risk of associated morbidity [6]. However, despite the widespread availability of well-tolerated, effective, and inexpensive drugs, approximately half of treated patients do not have well-controlled BP [7]. Lack of patient engagement, poor medication adherence, and therapeutic inertia are major contributors to patients not reaching their recommended BP levels [8].
Many types of intervention methods have been conducted to improve therapeutic targets and BP control. Systematic reviews summarizing more than 3 decades of research advocate for specific lifestyle modifications in populations with high risk of cardiovascular disease [9, 10]. In addition, improvement of patients' self-management, nurses and pharmacists have also been proved to be effective in hypertension control in team-based care [11, 12]. However, in favor of lifestyle modifications for the reduction of cardiovascular disease risk is mostly restricted to trials done in high-income countries [13]. Few trials have been done in low-income and middle-income countries, despite robust evidence supporting their effectiveness [14].
With the rapid rise and popularity in mobile phone use, mobile health (mHealth) could become a potential way to address several health-care system constraints in low and middle income countries, such as limited medical resources, overburdened health-care workforce, and an increasing prevalence of chronic diseases [4]. In view of all these constraints, it is very challenging to extend the health care to difficult-to-reach populations. Strategies that depend on offering education, providing reminders for medication taking and refilling, or facilitating social interactions have been shown to increase physical activity, promote weight loss, encourage behavior change and improve patient-provider communication (15–17). In a systematic review, use of mobile apps and SMS messaging was found to improve physical health and reduce stress, anxiety, and depression, and the review showed using mobile apps and SMS text messaging as promising mHealth interventions [18]. However, a systematic review [19] showed that m-health interventions had a positive effect on chronic diseases and also highlighted the need for more rigorous research in developing countries. Since, only 9 trials from low and middle-income countries were included in the analysis, and only 1 of them conducted in China.
In our research, we aimed to investigate whether mHealth including wearable monitoring device support home-based self-monitoring weekly counseling phone calls and advice for lifestyle modification could reduce BP, promote weight loss, and improve hypertension Compliance, self-efficacy and quality of life in adults with hypertension living in low-resource rural settings in China.
## Study design
The Self-Monitoring Intervention Programme for Hypertension Control was a randomized trial conducted among 6 primary care centers within a remote mountainous districts of Hubei province, China. Details of the Program's study design and organizations have been published elsewhere [20].
All the selected primary care centers were located in a poor rural area and provided free medication and health care to hypertensive patients. Three centers were assigned to the mobile health intervention and the other 3 centers to usual health care. All participants were included consecutively to avoid selection bias. Given the nature of the behavioral intervention, no action was taken to balance the recruitment for individuals that refused consent.
## Conceptual framework
We adopted an integrating of constructs adapted from the following conceptual models: the Task-Technology Fit [21], the Theory of Planned Behavior [22] and the Process Virtualization Theory [23]. A generic schema of various factors are comprised in this conceptual framework. It includes 6 primary constructs (X1-X6): user friendly, high user benefit, remote monitoring, emergency contacts, unique identifiers *And data* security, timey feedback and 3 moderating constructs (Y1-Y3): representation, reach and security and privacy. The primary constructs have negative influence on Fit, while the 3 moderating constructs can moderate the potential negative effect of the 6 primary constructs (Figure 1).
**Figure 1:** *Conceptual framework for designing mHealth solutions.*
## Study population
Eligibility criteria were an age of more than forty, definite diagnosis of hypertension: Systolic blood pressure (SBP) ≥140 mmHg and/or Diastolic blood pressure (DBP) ≥90 mmHg or being treated with antihypertensive medication, no cognitive deficit and able to possess communication proficiency to carry out study tasks. Participants were excluded if they had cognitive dysfunction, developed serious health conditions that led to hospitalization or death, or had no smart phones to perform the mobile healthcare. Moreover, written informed consent was obtained from all participants during screening.
Study data were collected at baseline and at 12 weeks. The mHealth intervention program included education of healthcare providers, adherence to drug treatment, home-based lifestyle modification, and a mobile health intervention.
## Patient recruitment and randomization
Participants were recruited though the cooperation of local Health and Family Planning Committee (HFPC), which composed of a diverse group of community leaders, township health centers personnel. These cooperative relationships were maintained with regular health advocacy meetings, face-to-face contact, and HFPC events, as described elsewhere [24]. Potential participants were directed to local healthcare clinical centers to assess eligibility and to provide informed consent. Eligible participants completed a baseline measurements including BP, waist and hip circumference, height and weight, and a survey consisting of demographics, the Compliance of Hypertensive Patients' Scale (CHPS), self-efficacy, and quality of life. The CHPS is widely used tool for self-reported hypertension compliance scale that was found to be reliable (Cronbach's α = 0.80) [25, 26]. This study used the Hypertension Self-efficacy Scale original designed by Han [27] to evaluate the self-efficacy of patients. The test-retest reliability and content validity of the revised version were 0.87 and 0.92, respectively [28]. Health-related quality of life was assessed using SF-12 which was a short alternative to the SF-36 [29]. The SF-12 has been validated among hypertensive patients and the Cronbach's alpha was 0.801 in our study [30].
Upon receipt of the Bluetooth-enabled BP monitor, potentially eligible individuals were provided with a written instruction manual on how to set up the monitor and properly take a BP measuring. The BP monitor has been approved by BP associations for its accuracy in home use, as described elsewhere [31]. Participants were recruited and randomized in a ratio of 1:1 to the control or intervention or using a random number generator. The study staff interacting with patients were not blinded to group assignment, while all the study investigators and data analysts remained blinded until the primary analytic strategies were finalized and all follow-up data were obtained.
## Intervention
The mobile health intervention was the key element, with a complementary text messaging, BP warning, and home-visited intervention. The research team members, who were part of the staff of the local primary care centers, were trained in interactive intervention techniques, performing wearable device, measuring BP, providing life-style modification skills based on the Change Model Stages [32]. The motivational training was conducted in a 1-day session, followed with onsite field testing. The research team members visited participants weekly in the first month and every other week thereafter. The mobile health system was developed and formulated though a consensus team including electronics technicians, health care physicians, pharmacists, and patient's family. It included monitoring wearable wristband, mHealth app and website.
All participants were given written information about hypertension and health promotion, and continued to receive routine hypertension management from local clinical centers. Each intervention group participants received a home-based BP monitor wearable wristband that stored and uploaded BP data to a secure website via Bluetooth, and then were instructed to transmit at least 1 BP measurements daily. During the first 1 week of the intervention, patients and medical staff of local health centers met everyday via telephone until BP measurements data was uploaded and sustained for the whole week, and then the frequency was reduced to weekly.
## Using the model to develop a mHealth intervention
We conducted the mHealth intervention programmes under the guidance of the conceptual framework. Also, we adopted the same conceptual framework to design the intervention strategies which were similar in the constructs but different in detailed content of care needs for the two groups of patients. Table 1 presents examples how we delivered intervention strategies for hypertensive patients based on the conceptual model.
**Table 1**
| Model element | Strategies included in intervention |
| --- | --- |
| Individual requirements | Individual requirements |
| User friendly (X1) | We kept the user interface as simple as possible with self-explaining navigation icons. Considering the remoteness of some villages where the internet is unavailable or weak, we made the app operate even without the internet. |
| High user benefit (X2) | The app can send reminder alerts for high BP and due medication to patients. The chat platform in the mHealth app can provide timely support or response from medical staff when patients report any alert signs. These interactive app functions can promote the initiative for app use. |
| Process requirements | Process requirements |
| Remote monitoring (X3) | Considering the sensory requirements are costly and difficult to virtualize in remote mountains areas, we developed a separate chat platform where multimedia messaging is available. |
| Emergency contacts (X4) | Interaction between health care providers and patients is crucial for health concerns. Virtualization of face-to-face communication via video over Internet technology was not feasible in app settings due to high data consumption and limited bandwidth. For any situation that requires emergency medical care, we provided the phone numbers of contracted doctor. |
| Unique identifiers and Data security (X5) | Identification of patients, caregivers and medical care providers is crucial. The patients' mobile phones are the unique identifiers. Health care providers who registered in our mHealth app system have access to all the registered patients. All identifiers and patients' health demographics data security were protected through an encrypted mechanism. |
| Timely feedback (X6) | The patients were provided with website to log health information and free BP monitors which give timely feedback and real-time graphical display about blood pressure fluctuation. Subsequently, health care providers worked with patients to identify health goals and help them link to further health readings available on the website (eg, patient forums, diet advice, videos, and exercise advice). |
| Moderating constructs | Moderating constructs |
| Representation (Y1) | Representation refers to the capability of mHealth chat platform to allow communication between patients and medical staff, moderating the potential negative impact of user friendly, remote monitoring and emergency contacts. |
| Reach (Y2) | Reach refers to the capability of mHealth to minimize the medical load and ensure the availability of health care at the fingertips at any time. Consequently, this construct moderate the emergency contacts and timely feedback. |
| Security and Privacy (Y3) | Security and privacy features of mHealth app system can ensure patients' trust in the application. Hence, this construct moderate high user benefit and timely feedback. |
## Follow-up assessment
Follow-up assessments were performed at baseline and 12 weeks after enrollment based on intention-to-treat principles for participants. Each assessment included BP measurement using the provided wearable BP monitor, measurement of waist and hip circumference, height and weight, and questionnaire survey.
## Outcomes
The primary outcomes were change in SBP and DBP, and the co-primary outcomes were change in waist and hip circumference, height and weight. The second outcomes were change in self-reported CHPS, self-efficacy, and quality of life.
## Ethical consideration
The study protocol was approved by the ethics committee of School of Health Sciences, Wuhan University in China (Ethical approval number: 2019YF2054). At the process of recruitment, clear explanations about the study objection were provided to all the participants and written informed consent was obtained.
## Statistical analysis
According to the results of our protocol, after the mobile platform management, the BP compliance rate of patients was more than $70\%$. According to the epidemiological survey conducted by Lin [33] in 125 hospitals in 31 cities in China, the blood pressure compliance rate of outpatients with hypertension was $33.68\%$, with a conservative estimate of $40\%$. The parameters and calculation formula of sample size required for the comparison of two sample rates are as follows: We sought to recruit at least 134 patients to have $90\%$ power to detect a 5-mm Hg difference in SBP between treatment arms, with an α of 0.05.
We conducted our analyses according to intention-to-treat principles. Means and frequencies of baseline characteristics were calculated between two group differences despite randomization. The primary outcomes and the secondary outcomes were analyzed using univariate linear regression models. We defined statistical significance as $P \leq 0.05$ and did not adjust our P-value threshold for our outcomes, which we assumed would be correlated. In sensitivity analyses, we repeated our analyses for whom the whole complete outcome data were available. Also, we evaluated changes in BP measurements at baseline and the subsequent follow-up assessment using generalized estimating equations with autoregressive errors and an identity link function.
In subgroup analyses, we evaluated differential effects of the intervention on the outcomes with respect to gender, age, number of concomitant diseases, years of hypertension, baseline BMI, baseline hypertension compliance, baseline self-efficacy, and baseline SBP based on the statistical significance of the interaction term for the subgroup of interest in the multivariable model.
All data analyses were conducted using SAS software (version 9.4).
## Participants
From Nov 2017 to Jul 2018, we screened 200 participants, of whom 148 met eligibility criteria and randomly divided into two equal groups. Eight participants from the intervention group and 6 participants from the control group were lost to follow-up because they could not attend the scheduled meetings despite being contacted by research personnel. Therefore, 66 patients in the intervention group and 68 in the control group completed the final assessment at 12 weeks and were included in the intention-to-treat analysis.
## Baseline characteristics
Demographic and socioeconomic characteristics, systolic blood pressure (SBP), diastolic blood pressure (DBP), BMI, waist circumference (WC), hip circumference (HC), hypertension compliance, self-efficacy, physical health and mental health in the intervention group were similar to those of the control group participants (Table 2).
**Table 2**
| Characteristics | Intervention | Control | χ2/t | P |
| --- | --- | --- | --- | --- |
| | (n = 66) | (n = 68) | | |
| Gender, no. (%) | | | 2.149 | 0.158 |
| Male | 45 (68.18) | 38 (55.88) | | |
| Female | 21 (31.82) | 30 (44.12) | | |
| Age, mean (SD), y | 61.37 (11.73) | 62.09 (10.66) | 0.136 | 0.713 |
| Ethnic, no. (%) | | | 3.170 | 0.205 |
| Han | 15 (22.73) | 25 (36.76) | | |
| Tujia | 28 (42.42) | 23 (33.83) | | |
| Others | 23 (34.85) | 20 (29.41) | | |
| Marital status, no. (%) | | | 1.337 | 0.366 |
| Married | 62 (93.94) | 60 (88.24) | | |
| Single | 4 (6.06) | 8 (11.76) | | |
| Years of schooling, y | | | 2.080 | 0.556 |
| ≤ 6 | 17 (25.76) | 25 (36.76) | | |
| 7–9 | 10 (15.15) | 8 (11.76) | | |
| 10–12 | 15 (22.73) | 15 (22.06) | | |
| ≥13 | 24 (36.36) | 20 (29.42) | | |
| Years of hypertension, y | | | 2.231 | 0.693 |
| <1 | 8 (12.12) | 11 (16.18) | | |
| 1–3 | 14 (21.21) | 13 (19.12) | | |
| 3–5 | 10 (15.15) | 7 (10.29) | | |
| 5–10 | 18 (27.27) | 15 (22.06) | | |
| >10 | 16 (24.24) | 22 (32.35) | | |
| Number of concomitant diseases, No. (%) | | | 5.294 | 0.151 |
| 0 | 25 (37.88) | 19 (27.94) | | |
| 1 | 20 (30.30) | 33 (48.53) | | |
| 2 | 9 (13.64) | 9 (13.24) | | |
| ≥3 | 12 (18.18) | 7 (10.29) | | |
| SBP, mean (SD), mmHg | 152.59 (23.44) | 148.85 (20.70) | −0.979 | 0.329 |
| DBP, mean (SD), mmHg | 92.85 (14.93) | 91.34 (15.31) | −0.578 | 0.564 |
| BMI, mean (SD), kg/m2 | 25.55 (2.95) | 25.99 (4.20) | 0.701 | 0.485 |
| WC, mean (SD), cm | 91.42 (12.92) | 90.37 (9.45) | −0.541 | 0.589 |
| HC, mean (SD), cm | 96.74 (8.81) | 98.01(6.66) | 0.945 | 0.347 |
| Hypertension compliance, mean (SD) | 46.70 (6.69) | 46.46 (6.89) | −0.205 | 0.838 |
| Self-Efficacy, mean (SD) | 59.21 (10.44) | 57.84 (11.70) | −0.716 | 0.475 |
| Physical health, mean (SD) | 41.68 (9.39) | 40.12 (10.30) | −0.912 | 0.363 |
| Mental health, mean (SD) | 48.62 (11.09) | 48.72 (9.87) | 0.056 | 0.955 |
## Blood pressure
At baseline, the mean (SD) SBP was 152.59 (23.44) mmHg in the intervention group and 148.85 (20.70) mmHg among controls. After 12 weeks of follow-up, the mean (SD) SBP decreased by 8.52 (19.73) mmHg in the intervention group and by 1.25 (12.47) mmHg in the control group (between-group difference, −7.265 mm Hg; $95\%$ CI, −12.89 to −1.64 mm Hg; $$P \leq 0.012$$) (Table 3). While, there was no difference in the change in DBP between the two groups (between-group difference, −0.41 mm Hg; $95\%$ CI, −3.56 to 2.74 mm Hg; $$P \leq 0.797$$).
**Table 3**
| Variable | Intervention group | Intervention group.1 | Intervention group.2 | Control group | Control group.1 | Control group.2 | Unadjusted effect estimate | Unadjusted effect estimate.1 | Adjusted effect estimate | Adjusted effect estimate.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variable | | | | | | | | | | |
| | Wk 0 | Wk 12 | Change | Wk 0 | Wk 12 | Change | Absolute Difference | P-value | Absolute Difference | P-value |
| SBP, mmHg, mean (SD) | 152.59 (23.44) | 144.08 (14.19) | −8.52 (19.73) | 148.85 (20.70) | 147.6 (17.27) | −1.25 (12.47) | −7.265 (-12.89 to −1.64) | 0.012 | −0.82 (−4.19 to 2.55) | 0.631 |
| DBP, mmHg, mean (SD) | 92.85 (14.94) | 92.42 (14.12) | −0.42 (10.91) | 91.34 (15.31) | 91.32 (13.13) | −0.01 (7.19) | −0.41 (-3.56 to 2.74) | 0.797 | −7.20 (-13.12 to −1.27) | 0.018 |
| WC, cm, mean (SD) | 91.42 (12.92) | 89.29 (12.74) | −2.14 (2.61) | 90.37 (9.45) | 90.12 (9.34) | −0.25 (0.61) | −1.89 (−2.53 to −1.25) | < 0.01 | −1.84 (−2.53 to −1.17) | < 0.001 |
| HC, cm, mean (SD) | 96.74 (8.81) | 96.44 (8.87) | −0.30 (1.38) | 98.01 (6.66) | 98.01 (6.65) | −0.01 (0.04) | −0.30 (−0.63 to 0.04) | 0.079 | −0.32 (−0.67 to 0.03) | 0.075 |
| Hypertension compliance, mean (SD) | 46.7 (6.69) | 54.05 (5.17) | 7.35 (7.31) | 46.46 (6.89) | 49.47 (5.62) | 3.01 (4.92) | 4.334 (2.210 to 6.46) | < 0.01 | 3.92 (1.68 to 6.16) | 0.001 |
| Self-Efficacy, mean (SD) | 59.21 (10.44) | 72.11 (4.14) | 12.89 (11.95) | 57.84 (11.71) | 63.26 (9.73) | 5.43 (10.54) | 7.47 (3.62 to 11.31) | < 0.01 | 7.89 (3.81 to 11.98) | < 0.001 |
| Physical health, mean (SD) | 49.52 (10.10) | 61.72 (6.64) | 12.21 (10.77) | 49.59 (9.11) | 51.13 (7.48) | 1.54 (7.18) | 10.66 (7.54 to 13.78) | < 0.01 | 10.47 (7.72 to 13.22) | < 0.001 |
| Mental health, mean (SD) | 41.68 (9.39) | 54.85 (2.04) | 13.17 (9.25) | 40.12 (10.30) | 42.67 (9.19) | 2.55 (5.99) | 10.62 (7.97 to 13.28) | < 0.01 | 10.93 (7.74 to 14.12) | < 0.001 |
Subgroup analyses of the association of the intervention with SBP by gender, age, number of concomitant diseases, years of hypertension, baseline BMI, baseline hypertension compliance and baseline self-efficacy showed no significant between-group differences, while was significant by baseline SBP ($P \leq 0.001$) (Table 3).
## Waist and hip circumference
At baseline, the mean (SD) WC was 91.42 (12.92) cm in the intervention group and 90.37 (9.45) cm in the control group. After 12 weeks of follow-up, the mean (SD) WC decreased by 2.14 (2.61) cm in the intervention group and by 0.25 (0.61) cm in the control group (between-group difference, −1.89 cm; $95\%$ CI, −2.53 to −1.25 cm; $P \leq 0.01$) (Table 3). While, there was no difference in the change in HC between the two groups (between-group difference, −0.30 cm; $95\%$ CI, −0.63 to 0.04 cm; $$P \leq 0.079$$).
## Hypertension compliance
At baseline, the mean (SD) hypertension compliance was 46.70 (6.69) in the intervention group and 46.46 (6.89) among controls. After 12 weeks of follow-up, the mean (SD) hypertension compliance increased by 7.35 (7.31) in the intervention group and by 3.01 (4.92) in the control group (between-group difference, 4.334; $95\%$ CI, 2.21 to −6.46; $P \leq 0.01$) (Table 3).
Subgroup analyses of the association of the intervention with hypertension compliance by gender, age, number of concomitant diseases, years of hypertension, baseline BMI, baseline self-efficacy and baseline SBP showed no significant between-group differences, while was significant by baseline hypertension compliance ($$P \leq 0.003$$) (Table 4).
**Table 4**
| Subgroup | SBP difference between intervention and control groups (95% CI) | Interaction P-value | Hypertension compliance difference between intervention and control groups (95% CI) | Interaction P-value.1 | Self-Efficacy difference between intervention and control groups (95% CI) | Interaction P-value.2 | Physical health difference between intervention and control groups (95% CI) | Interaction P-value.3 | Mental health difference between intervention and control groups (95% CI) | Interaction P-value.4 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Gender | | 0.743 | | 0.645 | | 0.478 | | 0.038 | | 0.248 |
| Male | −7.20 (-12.78 to −1.63) | | 4.43 (2.33 to 6.53) | | 7.61 (3.80 to 11.42) | | 10.20 (7.62 to 12.78) | | 10.46 (7.38 to 13.54) | |
| Female | −7.53 (-13.81 to −1.24) | | 3.94 (1.57 to 6.31) | | 6.89 (2.59 to 11.19) | | 12.35 (9.45 to 15.26) | | 11.50 (8.02 to 14.97) | |
| Age | | 0.420 | | 0.126 | | 0.903 | | 0.788 | | 0.078 |
| At or below median | −5.75 (-11.38 to −0.13) | | 4.99 (2.88 to 7.11) | | 7.84 (3.94 to 11.74) | | 10.26 (7.57 to 12.95) | | 11.13 (7.97 to 14.28) | |
| Above median | -9.44 (-15.25 to -3.63) | | 3.38 (1.20 to 5.57) | | 6.93 (2.90 to 10.97) | | 11.14 (8.36 to 13.92) | | 9.99(6.73 to 13.26) | |
| Number of concomitant diseases | | | | 0.068 | | 0.097 | | 0.353 | | |
| 0 | | 0.092 | | | | | | | | 0.774 |
| 1 | −8.56 (-14.51 to −2.81) | | 3.56 (1.39 to 5.74) | | 8.52 (4.53 to 12.51) | | 10.22 (7.44 to 12.99) | | 10.67 (7.41 to 13.94) | |
| 2 | −7.02 (-13.42 to −0.63) | | 3.64 (1.27 to 6.01) | | 7.71 (3.35 to 12.07) | | 10.65 (7.62 to 13.69) | | 11.15 (7.58 to 14.72) | |
| ≥ 3 | −5.68 (-11.67 to 0.31) | | 5.46 (3.24 to 7.68) | | 6.14 (2.06 to 8.68) | | 11.10 (8.26 to 13.94) | | 10.50 (7.16 to 13.85) | |
| Years of hypertension | | 0.111 | | 0.636 | | 0.921 | | 0.867 | | 0.092 |
| < 1 | −1.06 (−8.22 to 6.10) | | 6.99 (4.30 to 9.67) | | 11.15 (6.23 to 16.06) | | 8.58 (5.16 to 12.01) | | 9.60 (5.55 to 13.66) | |
| 1–3 | −7.80 (-13.57 to −2.20) | | 3.99 (1.83 to 6.16) | | 6.47 (2.51 to 10.43) | | 10.73 (7.97 to 13.49) | | 10.35 (7.08 to 13.62) | |
| 3–5 | −7.82 (-13.50 to −2.10) | | 4.20 (2.07 to 6.33) | | 7.75 (3.84 to 11.65) | | 10.87 (8.15 to 13.59) | | 11.11 (7.89 to 14.33) | |
| 5–10 | | | | | | | | | | |
| >10 | | | | | | | | | | |
| Baseline BMI | | 0.950 | | 0.475 | | 0.317 | | 0.215 | | 0.163 |
| < 18.5 | −15.48 (-47.84 to 16.87) | | 23.01 (10.93 to 35.08) | | 51.53 (30.16 to 72.90) | | 11.79 (-3.79 to 27.31) | | 22.24 (3.91 to 40.57) | |
| 18.5–24.9 | −8.11 (-13.82 to −2.39) | | 4.47 (2.34 to 6.60) | | 8.19 (4.41 to 11.96) | | 11.38 (8.64 to 14.12) | | 11.21 (7.97 to 14.44) | |
| ≥ 25 | −5.58 (-11.28 to 0.11) | | 4.95 (2.83 to 7.08) | | 8.41 (4.65 to 12.17) | | 9.79 (7.06 to 12.52) | | 10.43 (7.21 to 16.36) | |
| Baseline Hypertension compliance | | 0.583 | | 0.003 | | 0.445 | | 0.514 | | 0.905 |
| At or below median | −5.31 (−11.42 to 0.81) | | 7.30 (5.27 to 9.33) | | 12.19 (8.38 to 16.00) | | 9.91 (7.01 to 12.81) | | 10.43 (7.01 to 13.85) | |
| Above median | −7.89 (−13.45 to −2.34) | | 3.38 (1.54 to 5.23) | | 5.95 (2.49 to 9.41) | | 10.85 (8.22 to 13.48) | | 10.74 (7.63 to 13.84) | |
| Baseline Self-Efficacy | | 0.328 | | 0.06 | | < 0.001 | | 0.793 | | 0.926 |
| At or below median | −5.08 (−13.32 to 1.16) | | 6.84 (4.64 to 9.05) | | 14.58 (11.11 to 18.06) | | 10.27 (7.30 to 13.24) | | 10.81 (7.33 to 14.31) | |
| Above median | −7.80 (−13.33 to 7.62) | | 3.73 (1.77 to 5.68) | | 5.74 (2.66 to 8.83) | | 10.71 (8.08 to 13.34) | | 10.62(7.53 to 13.72) | 0.416 |
| Baseline SBP | | < 0.001 | | 0.684 | | 0.842 | | 0.712 | | |
| ≤ 160 mmHg | −2.72 (−7.04 to 1.61) | | 4.54 (2.41 to 6.67) | | 7.77 (3.91 to 11.63) | | 10.55 (7.88 to 13.22) | | 10.80 (7.66 to 13.94) | |
| >160 mmHg | −17.29 (-21.98 to −12.61) | | 3.89 (1.58 to 6.20) | | 6.81 (2.62 to 10.99) | | 10.79 (7.89 to 13.68) | | 10.36 (6.96 to 13.76) | |
## Self-efficacy
At baseline, the mean (SD) self-efficacy was 59.21 (10.44) in the intervention group and 57.84 (11.71) among controls. After 12 weeks of follow-up, the mean (SD) hypertension compliance increased by 12.89 (11.95) in the intervention group and by 5.43 (10.54) in the control group (between-group difference, 7.47; $95\%$ CI, 3.62 to 11.31; $P \leq 0.01$) (Table 3).
Subgroup analyses of the association of the intervention with self-efficacy by gender, age, number of concomitant diseases, years of hypertension, baseline BMI, baseline hypertension compliance and baseline SBP showed no significant between-group differences, while was significant by baseline self-efficacy ($P \leq 0.001$) (Table 4).
## Quality of life
At baseline, the mean (SD) physical health was 49.52 (10.10) in the intervention group and 49.59 (9.11) in the control group, and the mean (SD) mental health was 41.68 (9.39) in the intervention group and 40.12 (10.30) in the control group. After 12 weeks of follow-up, the mean (SD) physical health increased by 12.21 (10.77) in the intervention group and by 1.54 (7.18) in the control group (between-group difference, 10.66; $95\%$ CI, 7.54 to 13.78; $P \leq 0.01$), the mean (SD) mental health increased by 13.17 (9.25) in the intervention group and by 2.55 (5.99) in the control group (between-group difference, 10.93; $95\%$ CI, 7.74–14.12; $P \leq 0.01$) (Table 3).
Subgroup analyses of the association of the intervention with mental health by age, number of concomitant diseases, years of hypertension, baseline BMI, baseline hypertension compliance, baseline self-efficacy, and baseline SBP showed no significant between-group differences, while was significant by gender ($$P \leq 0.038$$) (Table 4). While, Subgroup analyses of the association of the intervention with mental health by all of them showed no significant between-group differences (Table 4).
## Discussion
To our knowledge, this study is the first randomized controlled trial to assess mHealth intervention to improve cardiovascular factors and promote healthier lifestyle behaviors among individuals at high risk of cardiovascular disease in low-resource rural settings in China. This study aimed to evaluate the effects of mobile phone-based intervention on BP control, waist and hip circumference, self-reported hypertension compliance, self-efficacy, and quality of life. Our findings show that compared with local usual primary care, mHealth BP monitoring intervention resulted in significant improvements in SBP and other cardiovascular factors. Compared with usual community-based management of hypertension patients, mHealth intervention patients had greater controlled SBP, waist and hip circumference. Moreover, the intervention also improved some aspects of self-reported hypertension compliance and self-efficacy, and appeared to have an acceptable level of quality of life.
The results of this randomized control trial showed that the wearable BP wristband and app-based management could decreased SBP by 8.52 (19.73) mm Hg ($95\%$ CI, −12.89 to −1.64 mm Hg; $$P \leq 0.012$$), which showed similar treatment effects of medication treatment. A recent meta-analysis [34] that analyzed 14 RCTs showed that intensive BP-lowering medication treatment could decrease SBP by an additional 8.3 mmHg ($95\%$ CI: 2.1–14.1 mmHg), which could resulted in $14\%$ reduction of cardiovascular disease (CVD) risk. In line with our findings, a RCT on 1,372 hypertension patients reported that mobile phone text messages could resulted in a small reduction in SBP compared with usual care after 12 months intervention [6]. Also, it was reported that observations, including in-person visits, telephone support, and text messaging may have important implications when conducting internet-based interventions [35, 36]. So we added mobile devices, including phone calls, short message service, face-to-face communication via video and in-person visits as our intervention methods. However, according to our literature review, there is no clear explanation for the different intervention results of the SBP and DBP.
Unique features of our study were the significant improvement in self-reported hypertension compliance and self-efficacy with corresponding reductions in SBP. In our study, readings from the home-based BP monitoring wearable devices were used to evaluate trial outcomes. A possible explanation might be that the reductions in BP from baseline to the 12 weeks of follow-up that we observed in both the control and intervention group were resulted from fluctuations in these home BP monitoring readings, and that the magnitude of these fluctuations was larger than the hypothesized effect from the smartphone application [37]. Hence, all participants were engaged in some level of self-monitoring. In this respect, the home-based BP monitoring intervention have significant positive effects on BP control [38], hypertension compliance [6] and self-efficacy [39] and may have been particularly motivating for the patients in our trial.
It is interesting to note a net reduction in the waist circumference, while, no changes were seen in levels of hip circumference. It might be related to the amount of exposure to the intervention domains defined by the patients during motivational suggestion, following the autonomy support on the basis of principle. Thus, target behavior including reduction of high-sugar and high-fat foods intake was most commonly chosen during motivational home visit or counseling calls. In line with our findings, Partridge et al. [ 40] conducted a 12-week mHealth prevention program, with weekly goal setting to prevent weight gain and improve lifestyle behaviors among overweight young adults.
How could home-based BP monitoring wearable devices enhance the quality of life for patients with hypertension? While the wearable devices we tested has received high usability scores? It may be the reason that patients with hypertension in low-resource rural settings have needs that differ from those with other conditions [20, 24]. Therefore, smart tools shown greater effects on clinical outcomes when they linked with additional support, especially though connection to health care professionals [41]. Meanwhile, it seems the individuals would be highly adherent to their hypertension compliance to derive clinical benefits [42]. If the highly adherent from the intervention could persist more than 12-week duration of our trial, it may be possible that we could have observe more significant life quality improvements with longer follow-up. Finally, quality of life was measured by self-report. Although, the SF-12 questionnaires has been validated and extensively used, self-reported tools are difficult to avoid social desirability bias and may overestimate true condition [43]. As such, after exposure to a home-based BP monitoring device that very clearly encouraged adherence, intervention group participants may have been more likely to report higher level life quality without actually changing their physical or mental health condition.
Several limitations should be considered of this trial. The sample size was small and included only 6 primary care centers, and excluded those had no smartphones, which may contributed discrepancies in participant baseline characteristics and lack of power to detect differences of the secondary and subgroup analyses outcomes between two groups. Also, the hypertension compliance, self-efficacy and SF-12 questionnaires were all self-reported measurements, therefore we cannot conclude the findings to a broader population. In addition, the trial was not double-blinded, which may lead to an effect on the reporting bias such as recall error, social desirability or other subjective outcomes. However, BP recordings were measured by automated wearable devices with a standard protocol, which was unlikely to have been biased. Lack of information on long-term intervention effects, reimbursement mechanisms, and return on investment have been revealed as barriers to trail implementation [44]. Future studies should be conducted to address these issues when a planned long-term follow-up study.
## Conclusions
Despite the popularity of smartphone health-related apps has increased quickly, there has been a lack of rigorous studies which including a clinically important outcome [45, 46]. Our trial, to our knowledge, is one of the first randomized clinical studies using a conceptual framework [47], reporting the effect of a stand-alone mHealth platform to improve DBP control and increase hypertension compliance, self-efficacy and life quality. We found mHealth platform was safe and effective for promoting hypertension compliance, self-efficacy, life quality and DBP control, but no difference in SBP between the control and intervention groups during 12 weeks. If these finding are found to be stable and cost-effective during an even longer intervention period, it should spur wider testing and dissemination of similar alternative platform to manage hypertension and other chronic conditions.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the School of Public Health, Wuhan University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
ZY conceived the study and completed the original draft preparation. ZY and TX collected data. TX provided the recruitment resources. WQ reviewed, edited the final draft, and received the funding. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Trend in health-related physical fitness for Chinese male first-year college
students: 2013–2019'
authors:
- Xiaoxi Dong
- Fan Huang
- Gerene Starratt
- Zheyi Yang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10014614
doi: 10.3389/fpubh.2023.984511
license: CC BY 4.0
---
# Trend in health-related physical fitness for Chinese male first-year college students: 2013–2019
## Abstract
### Introduction
Physical fitness is a health indicator contributing to the prevention of non-communicable diseases that threaten public health. Studies across a number of global populations indicate that physical fitness is generally declining. This study investigated the trend in physical fitness of Chinese male first-year college students from 2013 to 2019 to offer critical information for fostering individual and public health.
### Methods
This study used archival data and a natural experiment design capturing 4 years of data prior to implementation of the Healthy China 2030 initiative and 3 years following. Physical fitness tests were based on the Chinese national student physical fitness standards for males including body mass index, vital capacity, standing-long-jump, sit-and-reach, pull-ups, 50 m sprint, and 1,000 m run. Because the physical fitness tests set different standards for males and females, female data will be reported separately. Data from a total of 3,185 Chinese male first-year college students from a private university in Hebei Province of China were included in the study. A one-way multivariate analysis of variance was used for analyzing the research data.
### Results
The results indicated an overall significant difference in health-related physical fitness of Chinese male first-year college students, with scores on health indicators generally declining from 2013 to 2019. Despite improvement on some fitness variables in some years, performance on virtually all indicators was diminished compared to baseline years.
### Discussion
These findings can contribute to the existing global literature in the field of public health showing general declines in physical fitness. Chinese universities have the opportunity to support Healthy China 2030 goals and cultivate individuals' physical fitness by offering physical education course that encourage college students to participate in moderate-to-vigorous-intensity physical activities in order to support physical fitness development.
## 1. Introduction
Non-communicable diseases (NCDs) are implicated in approximately 41 million deaths each year worldwide, making them a crucial health issue threatening both public and individual health [1]. Physical inactivity has been identified both as a risk factor [2] and a cause [3] contributing to fatal NCDs around the globe. Unfortunately, a high level of global prevalence of physical inactivity has been reported (4–7). For instance, in 2018, WHO estimated that more than $20\%$ of adults and $80\%$ of adolescents across the world did not engage in the level of physical activities that WHO [2] guidelines recommend for health. Not surprisingly, the global high prevalence of physical inactivity has imposed a substantial cost burden on the public healthcare system [8]. As such, the individual challenge of physical inactivity has also become a public health challenge [9]. It is reasonable to assume that strategies that could contribute to increases in physical activity may have the potential to reduce the prevalence of NCDs.
In contrast, physical fitness is a remarkable healthy indicator for preventing critical NCDs, e.g., cardiovascular mortality [10] and vascular structure [11]. For example, Clausen et al. [ 12], who examined midlife cardiorespiratory fitness and all-cause and cardiovascular mortality, found that the level of midlife cardiorespiratory fitness was positively correlated to an increase in longevity and was a significant indicator of protection from all-cause mortality. Moreover, physical fitness is also positively associated with well-being [13], physical activity [14], and academic performance [15]. Physical fitness can be conceptualized as a series of an individuals' capabilities of performing and/or participating in physical activity [16].
Physical fitness has been theorized as two types of fitness: skill-related physical fitness and health-related physical fitness (HRPF) [17, 18]. Skill-related physical fitness highlights the development of motor skills associated with sports performance, e.g., speed, power, coordination, and agility [17]. Arguably, skill-related physical fitness may not be a reliable and appropriate measure providing meaningful information to foster a healthy living for people in general. On the other hand, HRPF emphasizes the essential development of overall health and disease prevention [19]. HRPF consists of five components, e.g., body composition, cardiovascular endurance, flexibility, muscular strength, and muscular endurance [16, 20]. The effectiveness of HRPF in contributing to overall health and disease prevention for encouraging healthy living has been extensively studied. Findings indicate that the factors of HRPF promote healthy living (10–12). Therefore, HRPF is a critical health factor that can be used to assist with planning for countering the challenge of NCDs while cultivating public health.
College life is a unique period that represents the first major transition from adolescence to early adulthood in life [21], and habitual behavior established in the period would affect people's lifestyles in the future [22]. In addition, a decrease in cardiorespiratory fitness measured by a 6-min run test was found among 11–14 years old Croatian children and adolescents from years 1999 to 2014 [23]. This decreased cardiorespiratory fitness may cause individuals to be at-risk for unhealthy living in the future. For instance, children with a lower level of cardiorespiratory fitness are likely to experience increased health challenges in obesity and insulin resistance in adulthood [24]. One of the possible strategies to overcome the health challenges is physical activity. However, there is a serious concern about engagement in physical activity during college life, due to a lack of participation in physical activity during the transition from adolescence to college life [25]. Approximately, four in ten college students inadequately participated in physical activity for promoting health across 23 countries [26]. Consequently, this high prevalence of physical inactivity might have a negative effect on HRPF among college students, even with different generations. Kaj et al. [ 27], who conducted a study to compare Hungarian college students' fitness between 1997–1998 and 2011–2012, found that fitness indicators, such as sit-and-reach, body fat percentages, and sit-up, performed better in the year of 1997–1998 than 2011–2012. The downward trend in HRPF fitness is also found among Chinese medical students from the years 2014 to 2016 [28]. Ultimately, this downward trend in HRPF would harm individuals and public health.
In order to cultivate both individual and public health, the guidance of Healthy China 2030 was released by the Ministry of Human Resources and Social Security of the People's Republic of China [29]. One of the healthy targets recommended by Healthy China 2030 for improving the health level is to increase the level of HRPF among individuals. However, limited studies have been conducted to examine the differences in HRPF among Chinese first-year college students, especially in relation to the implementation of the Healthy China 2030 guidance designed to improve physical fitness. To address this knowledge gap, the purpose of this study was to investigate the differences in HRPF of Chinese male first-year college students before and after Healthy China 2030 was issued in order to assess the physical fitness indicators as measures of individual and public health. Specifically, the study used a natural experiment design to examine the trends in physical fitness indicators from a baseline average of 4 years before the implementation of Healthy China 2030 (2013–2016) compared to 3 years following (2017–2019). The research question is “Are there differences in HRPF for Chinese male first-year college students between the years 2013 to 2019?” Specifically, it was hypothesized that there would be statistically significant differences in the linear combination of body mass index (BMI), vital capacity, 50 m sprint, standing-long-jump, sit-and-reach, 1,000 m run, and pull-ups for Chinese male first-year college students between the years 2013 to 2019.
## 2.1. Participants
The population for the present study was Chinese male first-year college students enrolled in any year from 2013 to 2019. A convenience sampling strategy was used for recruiting universities in Hebei Province of China to provide archival data for this study. In the end, only one private university agreed to participate. The archival data that were obtained included physical fitness records for both male and female Chinese first-year college students from years 2013 to 2019 who were enrolled in variety of departments (e.g., finance, engineering) and physical education, which is a compulsory class for every student in the university. These years were selected in order to have four baseline years of data before the implementation of Healthy China 2030 and 3 years after. This timeframe was also selected to end prior to the start of the COVID−19 pandemic to preserve the integrity of the comparisons. Because female and male college students took different physical fitness tests (e.g., female sit-and-up vs. male pull-ups) during those years, data for female students will be reported separately. The sample in the present study included 3,185 Chinese male first-year college students (age, $M = 18.00$, SD = 1.21; BMI, $M = 21.06$, SD = 2.86) who were enrolled in the participating university between 2013 and 2019. The number of participants and descriptive statistics for dependent variables (DVs) are shown in Table 1. The unequal sample sizes for each year occurred naturally as a result of the variation in college enrollment for the university every year. According to Tabachnick and Fidell [30], the unequal sample sizes could not pose specific issues with one-way analysis. Hence, no specific concern was raised with the minor unequal sample sizes for each year.
**Table 1**
| Unnamed: 0 | BMI | Vital | 50 m | SLJ | SR | 1,000 m | Pull-ups |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Year 2013 (n = 535) | Year 2013 (n = 535) | Year 2013 (n = 535) | Year 2013 (n = 535) | Year 2013 (n = 535) | Year 2013 (n = 535) | Year 2013 (n = 535) | Year 2013 (n = 535) |
| M | 21.16 | 4331.89 | 7.32 | 232.82 | 13.83 | 231.08 | 15.73 |
| SD | 2.73 | 477.48 | 0.57 | 15.56 | 4.59 | 24.81 | 3.97 |
| skewness | 0.42 | −0.14 | −0.06 | 0.14 | 0.23 | 0.09 | 0.59 |
| kurtosis | −0.41 | 0.36 | −0.69 | −0.02 | −0.18 | −0.98 | −0.21 |
| Year 2014 (n = 284) | Year 2014 (n = 284) | Year 2014 (n = 284) | Year 2014 (n = 284) | Year 2014 (n = 284) | Year 2014 (n = 284) | Year 2014 (n = 284) | Year 2014 (n = 284) |
| M | 20.50 | 4327.96 | 7.46 | 223.93 | 12.80 | 247.26 | 10.96 |
| SD | 2.66 | 583.15 | 0.63 | 17.42 | 5.07 | 25.00 | 4.29 |
| skewness | 0.70 | 0.11 | 0.33 | 0.39 | 0.38 | 0.29 | 0.45 |
| kurtosis | −0.05 | −0.28 | 0.02 | −0.56 | −0.38 | 0.79 | 0.53 |
| Year 2015 (n = 452) | Year 2015 (n = 452) | Year 2015 (n = 452) | Year 2015 (n = 452) | Year 2015 (n = 452) | Year 2015 (n = 452) | Year 2015 (n = 452) | Year 2015 (n = 452) |
| M | 20.93 | 4126.45 | 7.59 | 220.43 | 11.27 | 255.56 | 7.09 |
| SD | 2.80 | 387.91 | 0.65 | 16.76 | 5.27 | 29.28 | 4.70 |
| skewness | 0.51 | −0.05 | 0.22 | 0.46 | 0.53 | 0.07 | 0.59 |
| kurtosis | −0.25 | 0.24 | −0.16 | −0.27 | −0.08 | 0.32 | 0.08 |
| Year 2016 (n = 562) | Year 2016 (n = 562) | Year 2016 (n = 562) | Year 2016 (n = 562) | Year 2016 (n = 562) | Year 2016 (n = 562) | Year 2016 (n = 562) | Year 2016 (n = 562) |
| M | 21.13 | 4214.94 | 7.54 | 222.93 | 11.09 | 252.60 | 9.05 |
| SD | 2.79 | 498.37 | 0.68 | 17.06 | 5.00 | 27.59 | 5.25 |
| skewness | 0.55 | 0.33 | 0.31 | 0.51 | 0.42 | 0.32 | 0.41 |
| kurtosis | −0.15 | 0.33 | 0.01 | −0.19 | −0.32 | 0.58 | −0.29 |
| Year 2017 (n = 533) | Year 2017 (n = 533) | Year 2017 (n = 533) | Year 2017 (n = 533) | Year 2017 (n = 533) | Year 2017 (n = 533) | Year 2017 (n = 533) | Year 2017 (n = 533) |
| M | 20.94 | 3729.61 | 7.53 | 222.02 | 11.02 | 257.12 | 8.46 |
| SD | 2.89 | 528.69 | 0.59 | 16.07 | 5.80 | 31.34 | 4.83 |
| skewness | 0.51 | 0.42 | 0.23 | 0.63 | 0.55 | 0.26 | 0.50 |
| kurtosis | −0.36 | 0.13 | −0.15 | 0.04 | −0.50 | −0.19 | 0.19 |
| Year 2018 (n = 394) | Year 2018 (n = 394) | Year 2018 (n = 394) | Year 2018 (n = 394) | Year 2018 (n = 394) | Year 2018 (n = 394) | Year 2018 (n = 394) | Year 2018 (n = 394) |
| M | 21.20 | 3862.07 | 7.62 | 225.81 | 11.16 | 252.03 | 9.15 |
| SD | 3.06 | 636.90 | 0.68 | 17.03 | 6.17 | 32.55 | 4.97 |
| skewness | 0.38 | −0.02 | 0.38 | 0.62 | 0.43 | 0.27 | 0.28 |
| kurtosis | −0.60 | −0.60 | −0.30 | −0.43 | −0.68 | −0.11 | −0.26 |
| Year 2019 (n = 425) | Year 2019 (n = 425) | Year 2019 (n = 425) | Year 2019 (n = 425) | Year 2019 (n = 425) | Year 2019 (n = 425) | Year 2019 (n = 425) | Year 2019 (n = 425) |
| M | 21.36 | 4007.85 | 7.87 | 225.65 | 12.03 | 270.48 | 8.40 |
| SD | 3.04 | 572.38 | 0.72 | 20.15 | 5.38 | 28.41 | 4.74 |
| skewness | 0.30 | 0.43 | 0.22 | 0.29 | 0.38 | 0.13 | 0.50 |
| kurtosis | −0.66 | 0.18 | −0.91 | −0.96 | −0.79 | −0.20 | 0.03 |
| All (N = 3,185) | All (N = 3,185) | All (N = 3,185) | All (N = 3,185) | All (N = 3,185) | All (N = 3,185) | All (N = 3,185) | All (N = 3,185) |
| M | 21.06 | 4079.60 | 7.56 | 224.89 | 11.85 | 252.00 | 9.89 |
| SD | 2.86 | 566.33 | 0.67 | 17.53 | 5.42 | 30.73 | 5.48 |
| skewness | 0.48 | 0.02 | 0.31 | 0.40 | 0.36 | 0.24 | 0.33 |
| kurtosis | −0.37 | 0.01 | −0.14 | −0.43 | −0.48 | 0.02 | −0.28 |
## 2.2. Physical fitness measures
The physical fitness tests utilized in the study were based on the Chinese national student physical fitness standards, which is a reliable and valid physical fitness test used by different researchers [28, 31]. In addition, the physical fitness test battery included height and weight, vital capacity, standing-long-jump, sit-and-reach, pull-ups, 50 m sprint, and 1,000 m run. The participants were required to warm up before taking the physical fitness tests. The physical fitness tests were implemented by the physical education professors at their university.
## 2.2.1. Test of vital capacity
The test of vital capacity was measured with an air spirometer with a dry and sterilized plastic mouthpiece. The participants took a deep breath and exhaled slowly into the mouthpiece until they could no longer exhale. The air spirometer automatically calculated the maximum volume of air (lung capacity per milliliter) and displayed the results after the blowing was completed. The participants took the test twice with an interval of 15 s, and the better of the two scores was recorded.
## 2.2.2. Test of standing-long-jump
The participants were asked to stand behind a starting line with their feet apart in a natural stance and to jump forward. In addition, the participants were instructed that both feet should jump simultaneously from the standing position, and no additional movements should be made. The horizontal distance was measured from the trailing edge of the starting point to the trailing edge of the nearest landing point. Each student was permitted three jump attempts. The longest jump was recorded in centimeters.
## 2.2.3. Test of sit-and-reach
In order to examine the flexibility of the participants, the participants were required to take a sit-and-reach test with an equipment of sit-and-reach box and a sitting position and keeping legs straight. The upper body leaned forward with the arms stretching out straight forward simultaneously and the participants' feet were separated between 10 and 15 cm while pushing forward horizontally against the test board. The fingertips of the participants reached out and gradually pushed the test bar forward until the participants could not push further. Each student was permitted two attempts. The furthest distance was recorded in centimeters.
## 2.2.4. Test of pull-ups
In order to examine the participants' upper body strength, the test of pull-ups was used. The participants were instructed to hold a bar with their hands the same width as their shoulders to form a straight arm suspension. Subsequently, the participants pulled their entire bodyweight upward only with both arms simultaneously while body and arms stayed straight. Additionally, the participants' bodies could not have additional actions, e.g., swing or wiggle. To complete one pull-up, participants were required to pull themselves up until their lower jaws were beyond the upper edge of the bar. The number of pull-ups was recorded.
## 2.2.5. Test of 50 m sprint
The participants were directed to the starting line of the 50 m straight track and took the standing position. Moreover, the participant was directed to begin in response to the words “ready-and-go” from the instructor. The timekeeper began recording the time upon the instructor's direction to go. The time was recorded as the moment that the participants passed the finish line with their chests. The time recoding was measured in seconds, accurate to one decimal place. The second digit after the decimal point was rounded in terms of the principle of non-zero into 1. Each participant was permitted two attempts and the better performance was recorded.
## 2.2.6. Test of 1,000 m run
The participants were asked to take a standing position behind the starting line of the 1,000 m run. The test of the 1,000 m run began with the instruction of ready-and-go given by the instructor. Simultaneously, the timekeepers started recording when the instructor directed the participant to begin. Then, the timekeeper finished the recording when the participants passed the finish line. The time recoding was measured in minutes and seconds, accurate to one decimal place. The second digit after the decimal point was rounded in terms of the principle of non-zero into 1. Each participant only had one attempt to take the test.
## 2.2.7. Test of body mass index (BMI)
BMI was used to measure a degree of overweight and obesity among participants and the formula used for calculating BMI is weight (kg) / height (m)2. The criteria for BMI defined by WHO was used, which is scored as equal and higher than 28 (kg/m2) defined as obesity, between 24 and 27.9 defined as overweight, between 18.5 and 23.9 defined as normal weight, and smaller than 18.5 defined as low weight [28]. Participants' heights and weights were measured by using an electronic weight and height scale. Participants were instructed to take off their shoes and step on the electronic weight and height scale with standing straight. Then, the electronic measure automatically measured individuals' weights and heights with accurate to 0.1 cm.
## 2.3. Procedures
The research setting was a private university in Hebei province of northern China. Researchers contacted the physical education professors at the department of physical education to obtain consent to share their existing physical fitness tests data from 2013 to 2019. The research data were sent to the researchers after the physical education professors agreed to share their research data and cooperate with the researchers in conducting the study.
During each year for which the study data were supplied, the physical education professors scheduled a time for physical fitness tests with their students based on enrollment in their classes. The physical fitness tests were started after an agreement on the time of physical fitness tests from the students was obtained. In addition, every physical fitness test class varied from 20 to 30 students, and each class needed a maximum of 50 min to complete the physical fitness tests. The physical fitness tests were conducted every October from 2013 to 2019. The study was approved by the Institutional Review Board.
## 2.4. Data analysis
The seven DVs for the study were BMI, vital capacity, 50 m sprint, standing-long-jump, sit-and-reach, 1,000 m run, and pull-ups, while the independent variable (IV) was the different years from 2013 to 2019. To examine the differences in HRPF among Chinese male first-year college students, 4 years of data were averaged (20013–2016) for each variable to serve as baseline prior to the issue of Healthy China 2030 in 2016. The baseline for each variable was then compared to the average scores for each variable for the years 2017, 2018, and 2019. First, the R package psych [32] was used to produce descriptive statistics and Pearson correlation coefficient for the study variables. Second, the histogram plot, boxplot, Q-Q plot, and scatter plot were conducted to assess the degree to which the assumptions of multivariate normality, multicollinearity, and multivariate outliers, as this study included a large sample size, recommended by Field et al. [ 33]. The visual results indicated that the assumptions were met. Third, the R package MASS [34] was used to conduct a one-way multivariate analysis of variance (MANOVA). According to Field et al. [ 33], one-way MANOVA can be used to examine whether there is a group discrepancy with a combination of multiple dimensions. Hence, a one-way MANOVA was used to simultaneously examine the differences in the linear combination of the seven DVs between the years 2013 to 2019. Fourth, one-way univariate analysis of variance (ANOVA), as follow-up tests to the significant one-way MANOVA, was used to examine the significance of the multivariate effect on each DV separately. Moreover, an alpha of 0.05 was used to determine statistical significance.
## 3.1. Descriptive statistics
The descriptive statistics to summarize the study variables by year are displayed in Table 1. Moreover, Pearson's correlation analysis showed that virtually all of the correlations among the seven DVs were statistically significant with the exception of the correlation between BMI and standing-long-jump, as displayed in Table 2. The negative correlations between the DVs of 50 m sprint and 1,000 m run and other four DVs (i.e., vital capacity, standing-long-jump, sit-and-reach, and pullups) simply reflect the fact that shorter running times (better performance) were associated with higher scores (better performance) on the other variables. Ultimately, the participants who tended to do well on any test tended to also do well on the other tests. Furthermore, the ranges of associations between the DVs were below an absolute value of 0.70, which satisfied the assumption related to multicollinearity.
**Table 2**
| Unnamed: 0 | BMI | Vital | X50 m | SLJ | SFB | X1,000 mm | Pullups |
| --- | --- | --- | --- | --- | --- | --- | --- |
| BMI | 1 | | | | | | |
| Vital | 0.06*** | 1 | | | | | |
| X50 m | 0.05** | −0.08*** | 1 | | | | |
| SLJ | −0.11*** | 0.12*** | −0.37*** | 1 | | | |
| SFB | −0.01 | 0.05** | −0.1*** | 0.17*** | 1 | | |
| X1,000 mm | 0.1*** | −0.13*** | 0.3*** | −0.29*** | −0.12*** | 1 | |
| Pullups | −0.18*** | 0.11*** | −0.25*** | 0.34*** | 0.19*** | −0.36*** | 1.0 |
## 3.2. MANOVA analysis
A one-way four-level MANOVA was used to examine the differences in a combination of seven DVs across baseline and the years 2017, 2018, and 2019. Specifically, the average baseline was calculated from four years of data prior to and including 2016 when the guidance of Healthy China 2030 was released. The other three levels represented years 2017, 2018, and 2019. Box's M test was used to examine the equality of variance-covariance matrices, and the Box's M test yielded a significant result (χ[84]2 = 473.95, $p \leq 0.01$), which revealed that the dependent covariance matrices were not equal across the four levels from the baseline level to 2019. However, the significant result of the Box's M might be due to the large sample size. According to Field et al. [ 33], the Box's M test could have significant results with a large sample size even if the dependent covariance matrices were equal across the different levels of the IV. Further, Pillai's Trace test was used to examine the significance of the multivariate effects. The results of Pillai's Trace test indicated a statistically significant difference in mean values for the linear combination of the seven DVs with respect to the HRPF among four levels, years from before 2017 to 2019 (Pillai'$s = 0.25$, F[3,3181] = 42.09, $p \leq 0.0001$, partial η2 = 0.25). In addition, the results illustrated that $25\%$ of the total variance of the composite seven DVs could be explained by the four timeframes, from the baseline through to 2019, indicating a large effect size [35].
## 3.2.1. One-way ANOVA for vital capacity
Subsequently, a series of univariate ANOVAs were conducted to assess the significance of the multivariate effect on each DV among the four timeframes representing the years before 2017 to 2019 separately as follow-up tests to the significant one-way MANOVA. The result indicated a significant univariate effect on vital capacity across the four levels, (F[3,3181] = 163.08, $p \leq 0.0001$, partial η2 = 0.13), with a moderate to large effect size, suggested by Cohen [35]. Further, Tukey's HSD was conducted to assess the nature of the mean differences in vital capacity between the four levels. The post-hoc analysis revealed that the baseline level had significantly higher mean scores in vital capacity than the years 2017 to 2019, as displayed in Table 3. Moreover, the year of 2019 had significantly higher mean scores in vital capacity than the years 2017 and 2018. After a marked drop in the means of the vital capacity from baseline to 2017 was found, an increasing trend from 2017 to 2019 was found, as displayed in Figure 1. For this variable, higher scores indicate better performance.
## 3.2.2. One-way ANOVA for 50 m sprint
Similarly, there were statistically significant differences in the mean scores of the 50 m sprint across the four timeframes, (F[3,3181] = 44.01, $p \leq 0.0001$, partial η2 = 0.04), with a small effect size, according to Cohen [35]. The results of the Tukey's HSD revealed that during the baseline years participants took significantly less time on the test of the 50 m sprint than the years 2018 and 2019, as displayed in Table 4. Moreover, participants in the year 2017 took significantly less time on the test of the 50 m sprint than the years 2018 and 2019. There were no significant differences in the mean score on the test of the 50 m sprint between the baseline and 2017. Figure 2 for the 50 m sprint indicated an increasing trend in time on the 50 m sprint test from baseline to 2019. For this variable lower scores indicate better performance.
## 3.2.3. One-way ANOVA for standing-long-jump
Furthermore, a significant univariate effect was found on standing-long-jump among the four timeframess, (F[3,3181] = 5.85, $p \leq 0.001$, partial η2 = 0.005), with less than a small effect size [35]. The results of the Tukey's HSD indicated a significantly higher mean score on standing-long-jump at baseline, compare to 2017, as displayed in Table 5. Moreover, the years 2019 and 2018 had significantly higher mean scores in standing-long-jump than the year 2017. There was no significant difference in the mean score on the test of the standing-long-jump across the other years. However, there was an upward-and-downward trend from baseline to 2019, as displayed in Figure 3. For this variable, higher scores indicate better performance.
## 3.2.4. One-way ANOVA for sit-and-reach
Similarly, a significant univariate effect on sit-and-reach was found across the four timeframes, (F[3,3181] = 8.99, $p \leq 0.0001$, partial η2 = 0.008), indicating a less than a small effect size, according to Cohen [35]. The results of the Tukey's HSD illustrated that the baseline years had significantly higher mean scores on sit-and-reach than the years from 2017 to 2018, as displayed in Table 6. In addition, the year 2019 had a significantly higher mean score on sit-and-reach compared to 2017. No significant differences were found among other years. Figure 4 shows that sit-and-reach indicated a decreasing curve from baseline to 2017. However, there was a progressing growth from 2018 to 2019. For this variable, higher scores indicate better performance.
## 3.2.5. One-way ANOVA for 1,000 m run
Likewise, the result for the 1,000 m run indicated a significant univariate effect on 1,000 m run among the four timeframes, (F[3,3181] = 83.86, $p \leq 0.0001$, partial η2 = 0.073), indicating a moderate to large effect size [35]. Further, the results of Tukey's HSD revealed that the baseline had significantly lower mean scores on the test of the 1,000 m run than the years 2017 to 2019, as displayed in Table 7. Additionally, there were significant differences in the mean score of the 1,000 m run between 2017 and 2018, 2017and 2019, and 2018 and 2019. The trend shows increasing time completing the 1,000 m run from before 2017 to 2019, as displayed in Figure 5. For this variable, lower scores indicate better performance.
## 3.2.6. One-way ANOVA for pull-ups
Moreover, a significant univariate effect on pull-ups was found across the four timeframes, (F[3,3181] = 44.08, $p \leq 0.0001$, partial η2 = 0.04), indicating a small effect size, suggested by Cohen [35]. The results of Tukey's HSD indicated that the baseline had a significantly higher mean score on pull-ups compared to the years 2017 to 2019, as displayed in Table 8. In addition, there were also significant differences in the mean score of the pull-ups among other years. Further, there was a noticeable declining curve on the mean value of the pull-ups from the year before 2017 to 2019, as displayed in Figure 6. For this variable, higher scores indicate better performance.
## 3.2.7. One-way ANOVA for BMI
Finally, no significant univariate effect on BMI was found (F[3,3181] = 2.57, $p \leq 0.053$,) among the four levels representing baseline to 2019.
## 4. Discussion
The present study examined the difference in HRPF of Chinese male first-year college students from before and after Healthy China 2030 was issued in 2016 in order to contribute to the conversation about individual and public health. Findings of the present study supported the hypothesis, indicating an overall significant difference in HRPF of the Chinese male first-year college students from baseline to 2019. Despite a fluctuating upward-and-downward trend in HRPF of the study population across some years, overall the findings indicated a general decline in physical fitness The main findings of the present study were consistent with the reports of previous studies conducted by Kaj et al. [ 27], Chen et al. [ 28], Pribis et al. [ 36], and Wetter et al. [ 37], who found declines in physical fitness among college students. For instance, Pribis et al. [ 36], reported a decline in physical fitness among college students between 1996 and 2008 in the United States. The factors reported to contribute to the significant decline in HRPF among the student population are varied, e.g., socioeconomic status [38] and eating behavior and patterns [39]. According to Wang [40], Chinese college students' lifestyles of leisure-time entertainment have been altered to show an increase in online consumption, e.g., electronic games. Lepp et al. [ 41] reported that cell phone use was negatively associated with cardiorespiratory fitness among college students. It is reasonable to assume that increased engagement with electronic devices in sedentary activities could be associated with a decline in engagement in physical activities, thus contributing to noted decline in HRPF. Furthermore, Murphy et al. [ 42] found that Irish university students living away from home were more likely to be categorized in a cluster identified with risky health-related behavior. For the population of Chinese college students, most students have left home to live on campus independently, and one impact of the life-environment transition might be for these students to neglect to maintain and/or cultivate healthy behaviors. Arguably, the living environment of college students would be expected to play a role in fostering HRPF. Hence, special attention to cultivating college students' health-related behaviors during the period of campus living needs to be addressed.
For the purpose of the present study, scores on HRPF indictors were obtained for Chinese male first-year college students for 2013 through 2019. The finding did not reveal a significant difference in body composition as measured by BMI among these years. On the other hand, the findings indicated a decline in lower back flexibility, measured by sit-and-reach, from the years before 2017 to 2019. The present findings are consistent with those studies conducted by Kaj et al. [ 27], who found a decreasing trend in flexibility among Hungarian college students between 1997–1998 and 2011–2012, and Wetter et al. [ 37], who reported a decline in flexibility among college students between 2005–2006 and 2010–2011 in the United States. The current findings are consistent with existing research which suggests that these findings may be generalizable beyond this specific population.
It is, however, relevant to note that the present study did show some improvement in some indicators during some timeframes. For example, a gradual increasing trend in lower back flexibility was noted between 2017 and 2019, after a slight decrease trend from years before 2017 to 2017. College students have to take physical education courses as compulsory course, which would have positive influence in college students' lower back flexibility due to first-year students need to do exercise during the college life. Similarly, the present findings showed an increase in vital capacity among Chinese male first-year college students from 2017 to 2019. Moreover, we also revealed improvement in the study population's lower-limb explosive strength, measured by standing-long-jump, among Chinese male first-year college students from 2017 to 2019. While the guidance of Healthy China 2030 was issued in 2016 to encourage educational institutions and public health services to support the development of individuals' physical fitness, mastery of individuals sports skills and engagement in physical activity, the design of this study does not support a causal link between the guidance and these improvements. In fact, despite the improvements in some skills during some timeframes, the overall findings indicate a decline in fitness over the years of the study.
The guidance of Healthy China 2030 consistently highlights the promotion of an individual's physical fitness with various and specific strategies [29]. For instance, the guidance encourages cultivating adolescents' physical exercise hobbies, focuses on the mastery of adolescents' in at least one sports skill, and recommends that every student participates in physical activities for at least one hour during school day [29]. It would be appropriate for educational institutions and public health services to promote cardiovascular fitness for preventing fatal NCDs, as cardiovascular fitness is associated with a reduction in risk of fatal mortality [43].
The present study did not observe improvement in HRPF, as measured by 50 m sprint, pull-ups and 1,000 m run in current years among the Chinese male first-year college students, despite the growth of vital capacity, flexibility, and standing-long-jump from 2017 to 2019. Specifically, the study population's upper body muscular strength, measured by pull-ups, showed a steep descent from baseline to 2017. Subsequently, the data showed a fluctuating upward-and-downward trend in the compositions of HRPF. Moreover, gradual declines in performance on the 50 m sprint and the 1,000 m run from before 2017 to 2019, as reflected in more time to complete the tests, are consistent with the findings of Chen et al. [ 28], who found a decreasing trend in physical fitness with a study population of Chinese medical college students from years 2014 to 2016. One possible explanation for the decrease might be related to a high level of prevalence of physical inactivity and sedentary behavior. For example, < $10\%$ of college students engaged in moderate-to-vigorous-intensity physical activity among a population of 4,747 Chinese medical college students [44]. Hence, the lack of engagement in moderate-and-vigorous-intensity physical activity might impact the decline in muscular strength and endurance, as moderate-and-vigorous-intensity physical activity is positively associated with physical fitness [45]. Although the present study was not able to confirm the association and establish causality, these findings of declines in fitness nonetheless may support a recommendation that educational institutions concentrate on the development of students' muscular strength and endurance in order to sufficiently implement the guidance of Healthy China 2030 as a strategy for coping with the decline in HRPF. The present findings contribute to understanding the trend in HRPF among Chinese male first-year college students.
Although the sample of the present study was from one private college in Hebei Province of China, the study population of students who enrolled at this university had come from different cities in China. Given the alignment of the present finding with previous studies, [e.g., [27, 36, 37]], it is reasonable to assume that these findings would be generalizable at least to similar students enrolled in institutions across China and around the world. To further explore the generalizability of these findings, future research might continue to examine the trend in HRPF with different populations to extend knowledge and inform the development of useful strategies to promote college students' HRPF. To further inform public health policy, future studies might be conducted using an experimental design to examine the cause and effect related to college students' behaviors and HRPF.
The present study reports a trend of a significant decline in HRPF of the Chinese first-year male college students who enrolled in a private university in Hebei Province of China from 2013 to 2019. However, notable findings of the present study reported a gradual increase in the levels of vital capacity, flexibility, and standing-long-jump from 2017 to 2019. The findings describe a manifest downward trend in first-year college male students' muscular strength, muscular endurance, and cardiorespiratory endurance, which might be expected to represent a risk factor that can ultimately contribute to NCDs. Additionally, the declining trend in HRPF among college male students may ultimately represent a negative public health impact as these students age. The theorized causes of the decline in HRPF among college male students are varied, and the possible explanations of the phenomenon may be due to changes in lifestyle, e.g., online consumption (40–42) and a lack of engagement in physical activity [44, 45]. In any event, the present findings suggest that individual and public health may benefit from university efforts to continually cultivate individuals' HRPF, especially muscular strength, muscular endurance, and cardiorespiratory endurance. To overcome the declining trend in HRPF, university physical education courses can provide opportunities for college students to engage in moderate-to-vigorous-intensity physical activities in order to fulfill the needs of promoting college students' HRPF. Chinese universities and other educational institutions, with the benefit of guidance provided by Healthy China 2030 might benefit from collaborating with public health services implement programs to cultivate individual engagement in physical activity through exercise hobbies or the mastery of individual sports skills.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Barry University. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.
## Author contributions
XD, FH, GS, and ZY contributed to conception and design of the study. ZY organized the database. XD and ZY ran the statistics and wrote the first draft of the manuscript. FH and GS revised the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Enhancing endogenous levels of GLP1 dampens acute olanzapine induced perturbations
in lipid and glucose metabolism
authors:
- Kyle D. Medak
- Alyssa J. Weber
- Hesham Shamshoum
- Greg L. McKie
- Margaret K. Hahn
- David C. Wright
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10014622
doi: 10.3389/fphar.2023.1127634
license: CC BY 4.0
---
# Enhancing endogenous levels of GLP1 dampens acute olanzapine induced perturbations in lipid and glucose metabolism
## Abstract
Olanzapine is a second-generation antipsychotic (SGA) used in the treatment of schizophrenia and several on- and off-label conditions. While effective in reducing psychoses, acute olanzapine treatment causes rapid hyperglycemia, insulin resistance, and dyslipidemia and these perturbations are linked to an increased risk of developing cardiometabolic disease. Pharmacological agonists of the glucagon-like peptide-1 (GLP1) receptor have been shown to offset weight-gain associated with chronic SGA administration and mitigate the acute metabolic side effects of SGAs. The purpose of this study was to determine if increasing endogenous GLP1 is sufficient to protect against acute olanzapine-induced impairments in glucose and lipid homeostasis. Male C57BL/6J mice were treated with olanzapine, in the absence or presence of an oral glucose tolerance test (OGTT), and a combination of compounds to increase endogenous GLP1. These include the non-nutritive sweetener allulose which acts to induce GLP1 secretion but not other incretins, the DPPiv inhibitor sitagliptin which prevents degradation of active GLP1, and an SSTR5 antagonist which relieves inhibition on GLP1 secretion. We hypothesized that this cocktail of agents would increase circulating GLP1 to supraphysiological concentrations and would protect against olanzapine-induced perturbations in glucose and lipid homeostasis. We found that ‘triple treatment’ increased both active and total GLP1 and protected against olanzapine-induced perturbations in lipid and glucose metabolism under glucose stimulated conditions and this was paralleled by an attenuation in the olanzapine induced increase in the glucagon:insulin ratio. Our findings provide evidence that pharmacological approaches to increase endogenous GLP1 could be a useful adjunct approach to reduce acute olanzapine-induced perturbations in lipid and glucose metabolism.
## Graphical Abstract
## Introduction
Schizophrenia imposes a disproportionately large economic burden relative to other mental illnesses and non-psychiatric medical disorders (Rupp and Keith, 1993). Antipsychotic drugs (APs) such as olanzapine are commonly used in the treatment of schizophrenia and in a wide list of on- and off-label conditions such as anxiety, sleep disorders, dementia, bipolar, and attention deficit disorder, to name a few (Maher et al., 2011). Olanzapine lessens symptoms of psychosis through action on the dopamine (D2), serotonin (5-HT2A) and muscarinic (M3) receptors (Divac et al., 2014) but is also associated with severe peripheral metabolic consequences including hyperglycemia, insulin resistance, hyperlipidemia, weight gain and the development of type 2 diabetes all of which lead to increases in mortality [(De Hert et al., 2009; Rojo et al., 2015; Castellani et al., 2019; Huhn et al., 2019; Kowalchuk et al., 2019; Barton et al., 2020; Pillinger et al., 2020)].
The metabolic complications induced by APs had initially been attributed to weight gain, which is a common side effect and key co-determinant in AP-induced metabolic dysfunction (Townsend et al., 2018). Contrary to this assertation, acute treatments in preclinical models [(Bush et al., 2018; Castellani et al., 2018; Medak et al., 2019; Shamshoum et al., 2019; Medak et al., 2020; Shamshoum et al., 2021a; Shamshoum et al., 2022)] and in human participants (Albaugh et al., 2011a), (Hahn et al., 2013), have demonstrated profound impairments in glucose metabolism within minutes to hours, highlighting weight gain independent effects of APs. Acute excursions in blood glucose, as would be observed with each treatment of olanzapine, can be harmful as they increase the risk for the development of oxidative stress, inflammation, and cardio-metabolic disease (Chiasson et al., 2002), (Chiasson et al., 2003). Co-treatment strategies which lessen the acute metabolic impairment induced by APs are thus warranted.
Our group has provided evidence that increases in glucagon mediate acute olanzapine-induced hyperglycemia. In support of this, our group and others have shown that 1) acute treatment with olanzapine increases circulating glucagon (Townsend et al., 2018), (Shamshoum et al., 2019), (Shamshoum et al., 2022), (Shamshoum et al., 2021a), 2) the hyperglycemic effects of olanzapine are absent in glucagon receptor knockout mice (Castellani et al., 2017), 3) protection against olanzapine-induced hyperglycemia is often paralleled by reductions in glucagon and/or the glucagon:insulin ratio (Medak et al., 2019), (Shamshoum et al., 2019), and 4) suppressing pancreatic secretions with somatostatin blocked olanzapine-induced hyperglycemia (Castellani et al., 2022).
GLP1 is a hormone which can reduce the glucagon:insulin ratio and is higher in female mice, a model of protection from olanzapine-induced hyperglycemia, compared to male or ovariectomized mice (Medak et al., 2019), (Handgraaf et al., 2018). In recent work we have shown that the pharmacological activation of the glucagon-like peptide-1 (GLP1) receptor with agonists such as liraglutide and exendin 4 protect against olanzapine induced increases in glucagon and hyperglycemia, while antagonizing the GLP1 receptor potentiates the blood glucose response to olanzapine (Medak et al., 2020). Collectively, these data provide evidence demonstrating that targeting the GLP1 receptor is an efficacious approach to limit the acute metabolic perturbations of olanzapine. Unfortunately, injectable agents are associated with adverse effects in patients (Harris and McCarty, 2015). Currently, it is not known if increasing circulating GLP1 using oral compounds would confer the same protective effects against olanzapine-induced hyperglycemia as GLP1 receptor agonism. In the current study we sought to test this premise by cotreating mice with olanzapine and a variety of compounds either alone or in combination which increase endogenous GLP1 concentrations including: the non-nutritive sweetener Allulose (or D-psicose) which acts to induce GLP1 secretion but not other incretins (Hayakawa et al., 2018), (Iwasaki et al., 2018), the dipeptidyl peptidase-4 (DPPiv) inhibitor Sitagliptin which prevents degradation of the active form of GLP1 (Doustmohammadian et al., 2022), and a somatostatin receptor 5 (SSTR5) antagonist to relieve inhibition on GLP1 secretion (Liu et al., 2018a). A similar cocktail of agents has been shown to markedly increase circulating GLP1 and to improve glucose tolerance in mice (Briere et al., 2018). We hypothesized that ‘triple treatment’ with agents which stimulated GLP1 secretion and protected against GLP1 degradation would increase endogenous GLP1 to supraphysiological concentrations (Briere et al., 2018) and protect against acute olanzapine-induced perturbations in glucose and lipid metabolism.
## Animals
All experimental procedures were approved by the University of Guelph Animal Care Committee and followed Canadian Council on Animal Care guidelines. Approximately 10-week-old male C57BL/6J mice were purchased from Jackson Laboratories (Bar Harbor, ME) and individually housed in clear polycarbonate shoebox-style cages (dimensions: 7 1⁄2″ x 11 1⁄2″ x 5”) with wire lids. We used only male mice in these experiments as female mice are already protected against olanzapine-induced hyperglycemia (Medak et al., 2019). Rooms were kept at an ambient temperature of 22 °C with $45\%$ humidity and a 12:12 h light dark cycle. Animals were given free access to water and standard rodent chow (7004-Teklad S-2335 Mouse Breeder Sterilizable Diet; Teklad Diets Harlan Laboratories, Madison WI). Mice were acclimated to our facilities for ∼10 days before experimentation. All olanzapine experiments occurred at the beginning of the animals’ light cycle which coincides with the clinical recommendation for drug administration prior to bedtime (Miller, 2004).
## Materials
Olanzapine (cat. 11937) was purchased from Cayman Chemicals (Ann Arbor, MI, United States). Dimethyl sulfoxide (DMSO) was from Wako Pure Chemical Industries (cat. 67-68-5; Richmond, VA, United States). Kolliphor EL was from Millipore Sigma (Etobicoke, ON, CA; cat. C5135). Allulose (cat. P839620-1), Sitagliptin (cat. 13252), Captisol (cat. S4592), and SSTR5 antagonist (cat. HY-1021037) were purchased from Cedarlane (Burlington, ON, CA). Blood glucose test strips and a Freestyle Lite handheld glucometer were acquired from Abbott Diabetes Care Inc. (Alameda, CA, United States). Injections were carried out using 25-gauge needles purchased from ThermoFisher Scientific (Mississauga, ON, CAN; cat. BD B305122) and 29-gauge insulin needles purchased from VWR (Radnor, PA, United States; cat. 10799-004). ELISAs obtained from Mercodia Inc. (Winston-Salem, NC 27103, United States) were used to measure serum glucagon (cat. 10-1281-01) and insulin (cat. 10-1247-01). Colorimetric assays used to measure Beta-Hydroxybutyrate (cat. 700190) and serum Triglyceride (cat. 10010303) were obtained from Cayman Chemicals (Ann Arbor, MI, United States). ELISAs used to measure total GLP1 (cat. EZGLP1T-36K) and active GLP1 (cat. EGLP-35K) were obtained from Sigma Aldrich (St. Louis, MO, United States). Serum non-esterified fatty acid (NEFA) (Wako Bioproducts, Richmond, VA, United States) and glycerol (F6428; Millipore Sigma, St. Louis, MO, United States) were measured on 96-well plates as our group has previously described (Snook et al., 2016) and as suggested by manufacturer instruction.
## Terminal olanzapine tolerance test
Olanzapine was dissolved in DMSO (1 mg/100 μL) to create a stock solution. Kolliphor EL solution and saline (500 μL/900 mL) were used to dilute 500 μL of the stock olanzapine solution and mice were injected intraperitoneally (IP) with olanzapine (5 mg/kg) or vehicle (DMSO, Kolliphor EL, saline) at the beginning of the light cycle (∼0900). Drug and vehicle were prepared from powdered drug and stored stock solutions (DMSO, Kolliphor EL, and saline) each experimental day. We (Townsend et al., 2018), (Castellani et al., 2017) and others (Ikegami et al., 2013a) have previously used this dose of olanzapine as it mimics human dosing requirements based on dopamine-binding occupancy in rats given olanzapine by subcutaneous injections (Kapur et al., 2003). Allulose was dissolved in water and orally gavaged (1 g/kg). Sitagliptin was dissolved in phosphate-buffered saline (10 mg/kg) and gavaged as a pre-treatment, 30 min before olanzapine treatment. SSTR5 antagonist was dissolved with $30\%$ captisol and saline (15 mg/kg). Blood glucose was measured in mice prior to, 15-, 30-, 60-, 90-, and 120-min post-drug or control administration using a handheld glucometer sampled from a drop of blood taken from the tail vein using a distal tail snip. In a separate cohort of animals, the combination treatment (“triple treatment”) experiment was repeated under glucose stimulated conditions (0.5 g/kg) in which glucose was added to the gavage containing allulose or water and gavaged at the same time as olanzapine treatment. Mice were not fasted prior to the oral glucose tolerance test as this intervention itself ablates the hyperglycemic effect of olanzapine (Shamshoum et al., 2022). At 120 min post treatment mice were anesthetized with sodium pentobarbital (5 mg/100 g body weight) then cardiac blood was collected with 25-gauge needles, allowed to clot for ∼20 min at room temperature, and centrifuged at 5000 g for 10 min at 4°C. Tissues were stored at −80°C until further analysis.
## Serum olanzapine and N-desmethyl-olanzapine measurements
Serum samples (∼500 μL) were collected, and concentrations of olanzapine and the metabolite N-desmethyl-olanzapine (DMO), were assayed using liquid chromatography with tandem mass spectrometry detection (Graff-Guerrero et al., 2015).
## Statistical analyses
Statistical tests were completed using GraphPad Prism v.9.0 (GraphPad Software, La Jolla, CA, USA). Comparison of two groups was done by an unpaired, 2-tailed t-test while the effects of drug co-treatment on glucose AUC and serum measures were analyzed by two-way ANOVA. If a significant interaction was detected a Tukey’s post-hoc analysis was completed in which case the p-value of the discussed group comparison is represented (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001$ between indicated groups). Data sets were analyzed for outliers with Grubbs’ test using Graphpad Outlier Calculator and values were excluded if identified as outliers. Significant main effects are indicated by a line above the graph while bars connected by lines indicate a significant difference between groups. Glucose curves were displayed but not statistically analyzed as this information was represented in AUC values. Normality was assessed using the Shapiro-Wilk test unless a sample size was large enough to use the D’Agostino & Pearson test, as per the recommendation of Graphpad Statistics Guide. A relationship was considered significant when $p \leq 0.05.$
## Allulose increases total serum GLP1 but is not sufficient to protect against acute olanzapine-induced metabolic disturbances
As a follow-up to our previous study in which GLP1 receptor agonists fully protected against olanzapine-induced hyperglycemia and markers of lipid dysregulation (Medak et al., 2020) we aimed to determine if increasing endogenous GLP1 could be protective in the same way. As a first approach we utilized allulose (1 g/kg, gavage) as a GLP1 secretagogue (Hayakawa et al., 2018) to co-treat with olanzapine (5 mg/kg, IP) (Figure 1A). In serum, active GLP1 measured 120-min post-treatment was increased by olanzapine (Mean ± SD: Vehicle-Control = 7.86 ± 0.77, Olanzapine-Control = 10.16 ± 1.41, Vehicle-Allulose = 8.48 ± 1.19, Olanzapine-Allulose = 11.61 ± 3.65; Figure 1B) while there were main effects of both allulose and olanzapine to increase total GLP1 (Mean ± SD: Vehicle-Control = 13.72 ± 5.77, Olanzapine-Control = 30.26 ± 10.86, Vehicle-Allulose = 22.63 ± 5.89, Olanzapine-Allulose = 40.43 ± 17.09; Figure 1C). The minor increase in serum GLP1 did not impact olanzapine induced increases in blood glucose where there was a main effect of olanzapine to increase the glucose AUC (Figure 1D). In line with this finding, there were main effects of olanzapine to reduce insulin (Figure 1E) and increase glucagon (Figure 1F) and the ratio of glucagon:insulin (Figure 1G).
**FIGURE 1:** *Allulose cotreatment does not protect against olanzapine-induced changes in glucose homeostasis. Mice were co-treated with an IP injection of olanzapine (5 mg/kg) and allulose gavage (1 g/kg) (A) for 120 min (n = 6–11 mice/group). (B, C) Active and total GLP1 were measured from serum following 120 min from injection. (D) Blood glucose was measured from the distal tail blood and area under the curve (AUC) calculated. In serum collected from cardiac blood, insulin (E), glucagon (F), the ratio of glucagon:insulin (G), Non-Esterified Fatty Acids (H), and glycerol (I) were measured. Blood glucose AUC and serum hormones/metabolites were analyzed by two-way ANOVA. Lines over graphs indicate a main effect of the described parameter. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 between indicated groups. All data are presented as mean ± SEM.*
Consistent with prior work from our group (Medak et al., 2019)– (Medak et al., 2020), (Shamshoum et al., 2021a), (Shamshoum et al., 2022), there was a main effect of olanzapine to increase serum non-esterified fatty acids (NEFA) (Figure 1H), while circulating glycerol concentrations were unchanged with treatment (Figure 1I). Taken together, the data from this experiment demonstrates that marginally increasing total GLP1 is not sufficient to protect against olanzapine-induced metabolic disturbances and that more robust increases in total and/or active GLP1 may be required to blunt the metabolic consequences of acute olanzapine treatment.
## Sitagliptin is not sufficient to protect against acute olanzapine-induced metabolic disturbances
As slight increases in total GLP1 were not sufficient to protect against olanzapine-induced excursions in blood glucose we reasoned that pharmacologically increasing the active form of this incretin peptide might confer protection against olanzapine. To do this we pretreated mice with sitagliptin (10 mg/kg), 30 min prior to olanzapine treatment (Figure 2A) and tracked changes in blood glucose. Sitagliptin is an inhibitor of DPPiv which degrades the active form of GLP1 (Drucker, 2021). Pretreatment with sitagliptin increased active (Mean ± SD: Vehicle-Control = 7.50 ± 0.62, Olanzapine-Control = 9.01 ± 1.15, Vehicle-Sitagliptin = 9.31 ± 1.38, Olanzapine-Sitagliptin = 20.53 ± 8.64; Figure 2B), but not total (Mean ± SD: Vehicle-Control = 17.96 ± 8.81, Olanzapine-Control = 34.90 ± 10.37, Vehicle-Sitagliptin = 21.51 ± 16.27, Olanzapine-Sitagliptin = 24.04 ± 10.72; Figure 2C) GLP1 under olanzapine stimulated conditions. Despite an ∼ 2-fold increase in active GLP1, olanzapine-induced hyperglycemia (Figure 2D), and reductions in serum insulin (Figure 2E) were unchanged by sitagliptin, while glucagon was elevated in animals that received both drugs (Figure 2F). The ratio of glucagon:insulin (Figure 2G) and serum NEFA (Figure 2H) were increased by olanzapine and unchanged by sitagliptin. Olanzapine increased serum glycerol in vehicle treated mice and this was attenuated in mice pre-treated with sitagliptin (Figure 2I). Collectively, this experiment demonstrates that increasing active GLP1 through the pharmacological inhibition of DPPiv is not sufficient to mute the effects of olanzapine on the development of hyperglycemia.
**FIGURE 2:** *Sitagliptin pretreatment does not protect against olanzapine-induced changes in glucose homeostasis. Mice were treated with oral gavage of sitagliptin (10 mg/kg) 30 min prior to an IP injection of olanzapine (5 mg/kg) (A) (n = 4-6 mice/group). (B, C) Active and total GLP1 were measured from serum following 120 min from injection. (D) Blood glucose was measured from the distal tail blood and area under the curve (AUC) calculated. In serum collected from cardiac blood, insulin (E), glucagon (F), the ratio of glucagon:insulin (G), Non-Esterified Fatty Acids (H), and glycerol (I) were measured. Blood glucose AUC and serum hormones/metabolites were analyzed by two-way ANOVA. Lines over graphs indicate a main effect of the described parameter while bars connected by lines indicate a significant difference between indicated groups. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All data are presented as mean ± SEM.*
## Triple treatment with allulose, sitagliptin, and sstr5 antagonist did not protect against acute olanzapine-induced changes in glucose and lipid metabolism
As either increasing total GLP1 or active GLP1 were not sufficient to protect against olanzapine induced hyperglycemia we reasoned an approach to increase endogenous GLP1 higher might confer protection against olanzapine induced increases in blood glucose. In this light we used a triple treatment of compounds to elevate GLP1. This included allulose (1 g/kg), sitagliptin (10 mg/kg), and SSTR5 antagonist (15 mg/kg) to induce secretion of endogenous GLP1 (Hayakawa et al., 2018), (Iwasaki et al., 2018), prevent the degradation of active GLP1 (Doustmohammadian et al., 2022), and relieve inhibition on GLP1 secretion (Liu et al., 2018a), (Jepsen et al., 2021), (Liu et al., 2018b), respectively. A solution of sitagliptin and SSTR5 antagonist was gavaged 30 min prior to olanzapine treatment while allulose treatment occurred at the same time as olanzapine (Figure 3A). When measured 120 min after olanzapine, our triple treatment significantly increased active GLP1 in serum (Mean ± SD: Vehicle-Control = 5.36 ± 0.99, Olanzapine-Control = 6.33 ± 1.53, Vehicle-TriTreatment = 36.81 ± 7.93, Olanzapine-TriTreatment = 54.29 ± 17.67; Figure 3B) with the largest increase in animals that received triple treatment and olanzapine. Total GLP1 was higher as shown by a significant main effect of both olanzapine and triple treatment (Mean ± SD: Vehicle-Control = 17.46 ± 5.33, Olanzapine-Control = 40.37 ± 10.76, Vehicle-TriTreatment = 47.67 ± 5.63, Olanzapine-TriTreatment = 68.52 ± 14.44; Figure 3C). Surprisingly, despite these large increases in GLP1, triple treatment did not protect against olanzapine-induced increases in blood glucose (Figure 3D), though there was a significant main effect of treatment to increase insulin (Figure 3E). Triple treatment did not blunt olanzapine induced increases in glucagon (Figure 3F), glucagon:insulin (Figure 3G), NEFA (Figure 3H), or glycerol (Figure 3I). In this experiment we demonstrate that increasing serum GLP1 concentrations through a combination of stimulating secretion and preventing degradation does not confer protection against acute olanzapine-induced perturbations in glucose and lipid metabolism.
**FIGURE 3:** *Triple treatment with allulose, sitagliptin, and sstr5 antagonist does not protect against olanzapine-induced changes in glucose or lipid homeostasis. Mice were treated with a mixed oral gavage of sitagliptin (10 mg/kg) and SSTR5 antagonist (15 mg/kg) 30 min prior to an oral gavage of allulose (1 g/kg) and an IP injection of olanzapine (5 mg/kg) (A) (n = 4–6 mice/group). (B, C) Active and total GLP1 were measured from serum following 120 min from injection. (D) Blood glucose was measured from the distal tail blood and area under the curve (AUC) calculated. In serum collected from cardiac blood, insulin (E), glucagon (F), the ratio of glucagon:insulin (G), Non-Esterified Fatty Acids (H), and glycerol (I) were measured. Blood glucose AUC and serum hormones/metabolites were analyzed by two-way ANOVA. Lines over graphs indicate a main effect of the described parameter while bars connected by lines indicate a significant difference between indicated groups. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All data are presented as mean ± SEM.*
## Triple treatment during OGTT improved acute olanzapine-induced changes in glucose and lipid homeostasis
Although triple treatment did not protect against olanzapine induced increases in blood glucose a caveat to this initial experiment was that it was completed in the absence of an additional homeostatic challenge. There is evidence that a nutrient stimulus may be necessary to observe acute AP-induced glucose dysregulation (Castellani et al., 2022), (Albaugh et al., 2011b). This is likely an important consideration given the impact of carbohydrate consumption on GLP1 secretion and the fact that individuals prescribed olanzapine could be consuming carbohydrate after taking their medication. In an effort to test the effects of triple treatment under perhaps more clinically relevant conditions we assessed the blood glucose response to olanzapine and/or triple treatment in mice following an oral gavage of glucose (0.5 g/kg) (Figure 4A). This glucose dose was chosen based on pilot experiments which showed that average glucose values of olanzapine treated animals fell within the detection range of the glucometer.
**FIGURE 4:** *Triple treatment during OGTT improves olanzapine-induced changes in glucose and lipid homeostasis. Mice were treated with a mixed oral gavage of sitagliptin (10 mg/kg) and SSTR5 antagonist (15 mg/kg) 30 min prior to a mixed oral gavage of allulose (1 g/kg) and glucose (0.5 g/kg) and an IP injection of olanzapine (5 mg/kg) (A) (n = 4–6 mice/group). (B, C) Active and total GLP1 were measured from serum following 120 min from injection. (D) Blood glucose was measured from the distal tail blood and area under the curve (AUC) calculated. (E) A correlational analysis was performed between active GLP1 and blood glucose AUC in olanzapine-treated animals. In serum collected from cardiac blood insulin (F), glucagon (G), the ratio of glucagon:insulin (H), Non-Esterified Fatty Acids (I), glycerol (J), Beta-Hydroxybutyrate (K), and Triacylglycerol (L) were measured. Blood glucose AUC and serum hormones/metabolites were analyzed by two-way ANOVA. Lines over graphs indicate a main effect of the described parameter while bars connected by lines indicate a significant difference between indicated groups. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All data are presented as mean ± SEM.*
Triple treatment increased active GLP1 (Mean ± SD: Vehicle-Control = 3.64 ± 0.35, Olanzapine-Control = 5.59 ± 1.72, Vehicle-TriTreatment = 23.90 ± 4.67, Olanzapine-TriTreatment = 36.79 ± 12.70; Figure 4B) with the largest increase in animals that received triple treatment and olanzapine. There was a significant main effect of olanzapine and triple treatment to increase total GLP1 (Mean ± SD: Vehicle-Control = 111.25 ± 36.24, Olanzapine-Control = 272.84 ± 87.42, Vehicle-TriTreatment = 241.19 ± 57.95, Olanzapine-TriTreatment = 371.16 ± 82.25; Figure 4C). Blood glucose AUC was significantly increased by olanzapine and lower in animals given triple treatment (Figure 4D) and there was a trend ($$p \leq 0.0879$$) for the glucose AUC being negatively correlated with active GLP1 in olanzapine-treated animals (Figure 4E). Insulin was increased by triple treatment (Figure 4F) while glucagon was increased by olanzapine (Figure 4G) resulting in the increase in glucagon:insulin with olanzapine being absent in mice receiving triple treatment (Figure 4H).
Markers of perturbed lipid metabolism following olanzapine treatment such as increases in serum NEFA (Figure 4I) and BHB (Figure 4K), an index of whole-body fatty acid oxidation, were reduced with triple treatment. Conversely, serum TAG was elevated by olanzapine and unaffected by triple treatment (Figure 4L). In this experiment we provide evidence that increasing GLP1 to supraphysiological levels during a glucose challenge confers a degree of protection against acute olanzapine induced perturbations in glucose and lipid metabolism. There were no differences in serum levels of olanzapine and N-desmethyl-olanzapine (DMO) between control and triple treatment animals, suggesting that these protective effects are not secondary to differences in circulating drug concentrations (Table 1).
**TABLE 1**
| Unnamed: 0 | Control | Triple | p-value |
| --- | --- | --- | --- |
| | Control | Treatment | p-value |
| Olanzapine (ng/mL) | 124.81 ± 5.14 | 138.12 ± 5.69 | p = 0.11 |
| DMO (ng/mL) | 31.26 ± 1.03 | 30.94 ± 2.95 | p = 0.92 |
## Discussion
Treatment with antipsychotic drugs such as olanzapine can result in severe metabolic consequences such as hyperglycemia, dysregulated lipid metabolism, weight gain and the development of type 2 diabetes, in a population that suffers from premature cardiovascular mortality (Barton et al., 2020), (De Hert et al., 2009). An unappreciated aspect of antipsychotic drugs is the acute perturbations in glucose and lipid metabolism that occur independent of changes in body weight and adiposity (Shamshoum et al., 2019), [(Bush et al., 2018; Castellani et al., 2018; Shamshoum et al., 2021a)], (Albaugh et al., 2012), (Shamshoum et al., 2021b). In the current investigation we use agents which increase and sustain endogenous GLP1, these include allulose, sitagliptin, and an SSTR5 antagonist and demonstrate that, in combination, this cocktail offers a degree of protection against olanzapine-induced perturbations in glucose and lipid homeostasis which occurred independent of any changes in serum olanzapine concentrations.
GLP1 receptor agonists such as liraglutide have proven to be effective pharmacological tools to reduce weight gain in individuals treated with antipsychotics [(Sacher et al., 2008; Ikegami et al., 2013b; Larsen et al., 2017; Lee et al., 2021)]. In addition GLP1 receptor agonism also potently protects against acute olanzapine induced perturbations in carbohydrate and fat metabolism which correspond with reductions in the glucagon:insulin ratio (Medak et al., 2020). In the current study supraphysiological increases in circulating GLP1 offer a degree of protection against olanzapine-induced glucose tolerance and lipidemia, in parallel with a reduction in glucagon:insulin. Interestingly, this triple treatment did not have a protective effect without the metabolic challenge of oral glucose, as opposed to liraglutide, which did (Medak et al., 2020). The distinct response of our treatment with or without glucose could be related to the larger increase in total GLP1 with glucose but this is somewhat confounded by the fact that the active form of GLP1 was the same, at least when measured after 2 h. It could be that peak increases in circulating active GLP1 were missed in the current study, however this seems somewhat unlikely as a similar triple treatment approach in mice has been reported to lead to consistent and sustained increases in active GLP1 for upwards of 2 hours (Briere et al., 2018). SSTR5 antagonist alone is effective at lowering blood glucose when glucose is delivered orally but not intraperitoneally (Jepsen et al., 2021), which is in line with our findings that triple treatment protects against olanzapine-induced glucose intolerance but not olanzapine-induced hyperglycemia. We did not evaluate SSTR5 antagonist as a co-treatment with olanzapine as this intervention is most effective in synergy with other interventions such as DPPiv inhibition (Liu et al., 2018b). Moderate increases in total and/or active GLP1 did not confer protection against olanzapine-induced hyperglycemia. We reasoned that combining triple treatment, the approach which caused the largest increase in GLP1, with an oral glucose challenge, which should further increase GLP1, could potentially uncover a protective effect of increased endogenous GLP1 against olanzapine-induced perturbations in glucose homeostasis. While this proved to be true it should be noted that the relative increase in blood glucose between control and “triple treated” mice appeared to be similar. Moving forward, it will be important to determine if similar protective effects are noted with mono or dual treatment approaches under glucose stimulated conditions.
A potential confounder to the current experiments is that we cannot account for other hormones that might be stimulated by oral glucose and preserved by inhibition of DPPiv such as gastric inhibitory peptide (GIP), an incretin hormone and substrate of DPPiv (Deacon et al., 2001). We surmise that action by GIP is not the main mediator of our protective effect on olanzapine-induced metabolic dysfunction because metabolically compromised individuals have a reduced insulinotropic response to GIP but not GLP1 (Nauck et al., 1993), (Mentis et al., 2011) and GIP is a stimulator of glucagon secretion even during hyperglycemia in individuals with type 2 diabetes (Mentis et al., 2011). To further support this, GIP receptor null mice treated with a similar GLP1 enhancing cocktail displayed marked improvements in oral glucose tolerance (Briere et al., 2018) providing good evidence that the protective effects of the triple treatment were not mediated by GIP.
Treatment with GLP1 receptor agonists are effective and generally well-tolerated but they can be limited by the requirement of repeated injections, dose escalation, nausea, and injection-site reactions (Harris and McCarty, 2015). By treating orally with a cocktail of agents that includes a highly palatable sugar we circumvent these possible complications. From a clinical perspective, treatment with agents which increase endogenous GLP1 could be effective against olanzapine-induced metabolic disturbances in patients when exogenous glucose is ingested, and this is important as AP-treated patients display increased consumption of refined sugar products (Blouin et al., 2008). Future work would be required to determine how often triple treatment should be administered as sitagliptin has a much longer half-life in human (∼12 h) than in rodents (1–2 h) (Kim et al., 2005), (Zerilli and Pyon, 2007). It is not clear whether these supraphysiological increases in GLP1 secretion can be sustained over time as our studies are limited to acute responses, though evidence from metabolic surgery suggests the durability of GLP1 output that is sustained for years in human patients (Briere et al., 2018), (Hutch and Sandoval, 2017).
Findings from the current investigation provide proof of principle that increasing endogenous levels of GLP1 can provide therapeutic benefit in alleviating the metabolic side effects of olanzapine. This ‘triple treatment’ targets complementary mechanisms to increase both active and total GLP1 and protect against olanzapine-induced perturbations in lipid and glucose metabolism, under glucose stimulated conditions. Besides the triple treatment in the current investigation there are other small molecules GPR40, GPR119, and TGR5, for example, that are not yet approved for human use which might also be useful in similar combination therapies to increase endogenous GLP1 to therapeutic levels (Briere et al., 2018), (Gimeno et al., 2020). Future translational research should consider manipulating circulating GLP1 as an adjunct treatment approach to lessen the acute metabolic consequences of APs in those with schizophrenia or other forms of severe mental illness.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by the University of Guelph Animal Care and Welfare Committee.
## Author contributions
KM and DW planned the experiments. KM, AW, HS, and GM performed the experiments. MH aided in analysis. KM and DW drafted the manuscript. All authors edited and approved the final draft of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Cartilage lesion size and number of stromal vascular fraction (SVF) cells strongly
influenced the SVF implantation outcomes in patients with knee osteoarthritis
authors:
- Yong Sang Kim
- Sun Mi Oh
- Dong Suk Suh
- Dae Hyun Tak
- Yoo Beom Kwon
- Yong Gon Koh
journal: Journal of Experimental Orthopaedics
year: 2023
pmcid: PMC10014644
doi: 10.1186/s40634-023-00592-1
license: CC BY 4.0
---
# Cartilage lesion size and number of stromal vascular fraction (SVF) cells strongly influenced the SVF implantation outcomes in patients with knee osteoarthritis
## Abstract
### Purpose
This study evaluated outcomes in patients with knee osteoarthritis following stromal vascular fraction implantation and assessed the associated prognostic factors.
### Methods
We retrospectively evaluated 43 patients who underwent follow-up magnetic resonance imaging 12 months after stromal vascular fraction implantation for knee osteoarthritis. Pain was assessed using the visual analogue scale and measured at baseline and 1-, 3-, 6-, and 12-month follow-up appointments. In addition, cartilage repair was evaluated based on the Magnetic Resonance Observation of Cartilage Repair Tissue scoring system using the magnetic resonance imaging from the 12-month follow-up. Finally, we evaluated the effects of various factors on outcomes following stromal vascular fraction implantation.
### Results
Compared to the baseline value, the mean visual analogue scale score significantly and progressively decreased until 12 months post-treatment ($P \leq 0.05$ for all, except n.s. between the 1 and 3-month follow-ups). The mean Magnetic Resonance Observation of Cartilage Repair Tissue score was 70.5 ± 11.1. Furthermore, the mean visual analogue scale and Magnetic Resonance Observation of Cartilage Repair Tissue scores significantly correlated 12 months postoperatively ($$P \leq 0.002$$). Additionally, the cartilage lesion size and the number of stromal vascular fraction cells significantly correlated with the 12-month visual analogue scale scores and the Magnetic Resonance Observation of Cartilage Repair Tissue score. Multivariate analyses determined that the cartilage lesion size and the number of stromal vascular fraction cells had a high prognostic significance for unsatisfactory outcomes.
### Conclusion
Stromal vascular fraction implantation improved pain and cartilage regeneration for patients with knee osteoarthritis. The cartilage lesion size and the number of stromal vascular fraction cells significantly influenced the postoperative outcomes. Thus, these findings may serve as a basis for preoperative surgical decisions.
### Level of evidence
IV.
## Introduction
Osteoarthritis (OA) is an increasingly prevalent, progressive, and painful chronic joint disorder accompanied by deteriorating joint function [16]. The knee is the principally affected peripheral joint, resulting in pain, stiffness, and progressive loss of function [10]. Knee OA is a painful and debilitating process that significantly affects the patient’s quality of life [3]. The poor intrinsic healing potential of damaged cartilage, which results in progressive degradation of articular cartilage and subsequent widespread degeneration of the joint, is a major clinical problem in knee OA treatment [13]. Hence, restoring the diseased articular cartilage in patients with knee OA is a challenging but important problem for researchers and clinicians [30].
Recently, cell-based therapies have emerged as potential treatment options for managing knee OA [26]. Mesenchymal stem cells (MSCs) from various sources have been extensively evaluated for their ability to restore compromised articular cartilage and slow knee OA progression [44]. The pathogenesis of OA is based on degeneration and inflammation. Thus, the therapeutic properties of MSCs, including paracrine [6, 20], anti-inflammatory [39], and immunomodulatory effects [40], could help restore the intra-articular environment [31]. However, MSCs require culturing, including a few weeks between cell isolation and application, and is also expensive.
Alternatively, adipose-derived stromal vascular fraction (SVF) has received more attention as a stem cell source for managing knee OA at any stage, as lipoaspirates are easy to obtain using a minimally invasive procedure with a low complication rate and minimal donor-site morbidity [17, 41]. Adipose-derived SVF cells are a heterogeneous cell population containing regenerative cells (such as adipose-derived MSCs), macrophages, pericytes, fibroblasts, blood cells, vessel-forming cells (including endothelial and smooth muscle cells), and their progenitors [19]. This heterogeneous cell population includes cells with stem cell elements and is thought to have a synergistic effect with adipose-derived MSCs [37]. Furthermore, adipose-derived SVF and MSCs both result in comparable clinical improvement in patients with knee OA [41].
Several studies have used adipose-derived SVF for knee OA treatment [5, 11, 41, 42]. However, to date, none have assessed factors that influence the outcomes of SVF-based treatment for knee OA. Identifying factors associated with favourable and unfavourable outcomes would provide patients with realistic expectations of outcomes after SVF-based treatment [34]. Accordingly, this study investigated the pain relief and cartilage repair status after arthroscopic SVF implantation in patients with knee OA to identify prognostic factors associated with outcomes. We hypothesised that some factors increase the risk of an unsatisfactory outcome.
## Patient enrolment
We retrospectively reviewed the medical records of 62 consecutive patients with a 12-month follow-up period who underwent arthroscopic SVF implantation for knee OA between September 2019 and April 2021. Our institutional review board reviewed and approved this study. Furthermore, the study was supported by the ‘Conditional Approval System of Health Technology’ grant, funded by the Ministry of Health and Welfare. The study is the result of analysing the parts of participants among the all subjects who were participated in ‘Conditional Approval System of Health Technology’ grant. All participants provided informed consent prior to enrolment.
Medical records and plain radiographs were assessed, and patients with symptomatic knee pain unresponsive to nonoperative treatment were included. The exclusion criteria were previous surgical treatment, knee instability, knee varus or valgus malalignment, and other pathological diseases, including rheumatoid arthritis, haemophilia, and active knee infections. We suggested that all patients undergo follow-up magnetic resonance imaging (MRI), explaining its purpose (to evaluate the cartilage lesion and other pathologic conditions) before surgery. Of the 62 qualified patients, 14 dropped out and 5 were lost during the follow-up. Therefore, 43 patients were enrolled, including 14 men and 29 women with a mean age of 63.4 (range, 53–74) years. The average preoperative body mass index (BMI) was 26.0 (range, 19.5–32.5) kg/m2, and the mean cartilage lesion size was 5.6 (range, 3.2–7.9) cm2 (Table 1).Table 1Baseline characteristicsAge, y63.4 ± 4.1 (53–74)Sex, male/female, n$\frac{14}{29}$Side of involvement, right/left, n$\frac{21}{22}$Body mass index, kg/m226.0 ± 2.8 (19.5–32.5)Lesion size, cm25.6 ± 1.3 (3.2–7.9)Data are presented as means ± standard deviation (range) unless otherwise indicated
## SVF preparation and surgical procedures
One day prior to SVF implantation, samples of adipose tissue were collected from the gluteal regions of the study participants. The collected adipose tissue was suspended in phosphate-buffered saline solution and transported to the laboratory in a sterile box. Prior to implantation, mature adipocytes and connective tissues were separated from the SVF by centrifugation [46], and bacteriologic tests were performed to ensure that the samples were not contaminated; cell viability was assessed using the methylene blue dye exclusion test. A certain amount of adipose tissue was used for cell analyses. After isolating and characterising the adipose-derived cells as described previously [22, 23, 27], we confirmed that they contained MSCs. The isolation and characterisation procedures determined that adipose-derived stem cells made up $9.5\%$ (range, 8.6–$11.2\%$) of the SVF. Consequently, an average of 7.4 × 107 cells (range, 6.7 × 107–8.5 × 107 cells) in the SVF, which contained an average of 7.0 × 106 stem cells (range, 6.4 × 106–8.1 × 106 cells), were used for SVF implantation.
Before SVF implantation, arthroscopic debridement of the damaged or undermined cartilage was performed to smooth the cartilage lesion surface and firm up the edges facing the surrounding cartilage. Before SVF implantation, subchondral drilling was performed to increase the adhesion rate of the applied SVF mixed with fibrin glue. The prepared SVF was loaded into the fibrin glue product from the commercially available Greenplast kit (Greencross, Seoul, Korea), which was used as a scaffold for SVF implantation. After the arthroscopic fluid was extracted, the prepared SVF loaded into the fibrin glue was implanted into the cartilage lesion site under arthroscopic guidance. Then, the applied SVF mixed with fibrin glue was manipulated using the probe to cover the surface of the cartilage lesion evenly. After performing the arthroscopic procedure, the knee was immobilised for two weeks with a knee brace. After the sutures were removed, the patients began range of motion exercises, including active and passive knee joint exercises. Partial weight-bearing activities were initiated two weeks after arthroscopy, and full weight-bearing activities were permitted four weeks postoperatively. Sports and high-impact activities were allowed after three months, and the full return to regular sports or recreational activities was permitted based on the patients’ individual recovery.
## Outcome assessment
All patients were clinically evaluated preoperatively and 1, 3, 6, and 12 months postoperatively at follow-up visits. The visual analogue scale (VAS; range, 0–100) was used for pain assessment and was measured over the follow-up period. Adverse events were recorded for safety evaluation. A follow-up MRI was performed 12 months postoperatively using a 3.0 T MRI scanner. To avoid potential bias, an independent observer, who was a radiologist not involved in patient care and blinded to the study’s purpose, evaluated the MRI scans. Repair tissue evaluations were performed using the follow-up MRI and the Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) scoring system, according to Marlovits et al. [ 28] (Table 2).Table 2The MOCART scores based on the 12-month follow-up MRI examinationVariablesScorenMean ± SD$95\%$ CI1. Degree of defect repair and filling of the defect17.9 ± 3.116.94 – 18.87 Complete2027 Hypertrophy1515 Incomplete > $50\%$ of the adjacent cartilage100 < $50\%$ of the adjacent cartilage51 Subchondral bone exposed002. Integration to border zone7.7 ± 2.76.83 – 8.52 Complete151 Incomplete Demarcating border visible1021 Defect visible < $50\%$ of the length of the repair tissue521 > $50\%$ of the length of the repair tissue003. Surface of the repair tissue2.6 ± 3.21.59 – 3.53 Surface intact103 Surface damaged < $50\%$ of repair tissue depth or total degeneration526 > $50\%$ of repair tissue depth or total degeneration0244. Structure of the repair tissue4.9 ± 0.74.65 – 5.12 Homogenous542 Inhomogenous or cleft formation015. Signal intensity of the repair tissue21.6 ± 7.519.31 – 23.95 Normal (identical to adjacent cartilage)3019 Nearly normal (slight areas of signal alteration)1524 Abnormal (large areas of signal alteration)006. Subchondral lamina4.9 ± 0.84.65 – 5.12 Intact542 Not intact017. Subchondral bone4.8 ± 1.14.44 – 5.10 Intact541 Not intact028. Adhesions3.5 ± 2.32.77 – 4.20 No530 Yes0139. Effusion1.9 ± 2.41.11 – 2.61 No526 Yes027Total10070.5 ± 11.167.06 – 73.87Data are presented as means ± standard deviation (SD) unless otherwise indicatedMOCART Magnetic Resonance Observation of Cartilage Repair Tissue, MRI Magnetic resonance imaging, SD Standard deviation, CI Confidence interval
## Statistical analyses
The principal dependent variables were the VAS scores during the follow-up visits and the MOCART score. Descriptive statistics were calculated as means ± standard deviations unless otherwise indicated. The Wilcoxon signed-rank test was used to evaluate differences between the preoperative and final follow-up values. We divided the patients into subgroups to assess various factors that may influence the outcomes: age (< 60, 60–65, 65–70, and ≥ 70 years), sex (male and female), involved side (right and left), BMI (< 20, 20.0–24.9, 25–29.9, and ≥ 30.0 kg/m2), lesion location (medial femoral condyle, lateral femoral condyle, and trochlea), lesion size (< 3.5, 3.5–5.5, 5.5–7.5, and ≥ 7.5 cm2), and the number of SVF cells (< 7.0 × 107, 7.0 × 107–8.0 × 107, and ≥ 8.0 × 107). Differences between the groups were analysed using the Mann–Whitney U test or the Kruskal–Wallis test for multiple comparisons. The Spearman’s rank-order correlation test was used to evaluate potential bivariate associations between different factors to identify significant correlations. Multivariate logistic regression analyses were used to determine factors independently associated with unsatisfactory outcomes. We defined an unsatisfactory outcome as a VAS score of < 35.9 based on the mean VAS score at the 12-month follow-up (i.e., 35.9) and a MOCART score of < 70.5 based on the mean MOCART score (i.e., 70.5). We calculated odds ratios and $95\%$ confidence intervals (CIs) relative to a chosen reference group for the logistic regression models. Statistical analyses were performed using SPSS, Version 13.0 (IBM Corp., Armonk, NY, USA), and a P-value of < 0.05 was considered statistically significant.
## Pain scores and MRI outcomes
The mean VAS scores at baseline and 1, 3, 6, and 12 months postoperatively were 79.1 ± 6.9, 43.5 ± 8.6, 43.3 ± 9.3, 40.2 ± 8.8, and 35.9 ± 7.1, respectively. The mean VAS score after 1 month was significantly lower than the mean baseline score ($P \leq 0.001$). The mean VAS score did not differ between months 1 and 3 (n.s.), but otherwise, they significantly and progressively decreased during the follow-up period until 12 months post-treatment (all $P \leq 0.001$). The mean MOCART score after 12 months was 70.5 ± 11.1 (Table 2) (Fig. 1). The mean VAS and MOCART scores did not correlate until 6 months after surgery, but they significantly correlated 12 months after surgery ($$P \leq 0.002$$; Table 3).Fig. 1Preoperative (A and B) and follow-up (C and D) coronal and sagittal proton density fat-saturated images of the right knee of a 64-year-old female patient. A and B Cartilage loss is visible in the medial femoral condyle (arrows). C and D Complete filling of the defect along with complete integration with the adjacent native cartilage (arrows; MOCART score, 75 points)Table 3Pain score and MRI outcome correlationsMOCARTS rhoP valueVAS Baseline − 0.148n.s 1 month − 0.203n.s 3 months − 0.228n.s 6 months − 0.201n.s 12 months − 0.4630.002Data are calculated using the Spearman’s rank-order testMRI Magnetic resonance imaging, MOCART Magnetic resonance observation of cartilage repair tissue, VAS Visual analogue scale
## Outcome associations
Tables 4, 5, 6 and 7 present the mean VAS and MOCART scores based on various factors, including age, sex, the involved side, BMI, and lesion location. The mean VAS and MOCART scores did not differ among the age, sex, involved side, BMI, lesion location groups (all n.s.).Table 4The pain and MOCART scores stratified by ageAge, y < 60($$n = 4$$)60–65($$n = 24$$)65–70($$n = 12$$) ≥ 70($$n = 3$$)P value*VAS Baseline74.2 ± 5.180.1 ± 7.178.8 ± 6.478.8 ± 10.2n.s 1 month37.8 ± 11.045.2 ± 8.843.1 ± 7.839.3 ± 2.5n.s 3 months37.5 ± 13.045.8 ± 9.542.0 ± 7.137.0 ± 5.3n.s 6 months34.0 ± 11.141.8 ± 8.6–39.0 ± 7.2n.s 12 months34.4 ± 11.035.7 ± 4.836.1 ± 7.938.7 ± 15.5n.sMOCART73.8 ± 10.373.3 ± 10.565.0 ± 9.565.0 ± 17.3n.sData are presented as means ± standard deviationMOCART Magnetic resonance observation of cartilage repair tissue, VAS Visual analogue scale*Kruskal–Wallis testTable 5Pain and MOCART scores stratified by sex and the involved sideSexInvolved sideMale($$n = 14$$)Female($$n = 29$$)P value*Right($$n = 21$$)Left($$n = 22$$)P value*VAS Baseline77.5 ± 6.679.8 ± 7.1n.s79.5 ± 6.378.7 ± 7.6n.s 1 month42.9 ± 9.943.8 ± 8.0n.s42.5 ± 8.244.5 ± 9.0n.s 3 months43.4 ± 10.043.3 ± 9.1n.s41.1 ± 8.745.5 ± 9.5n.s 6 months40.2 ± 11.740.2 ± 7.2n.s38.2 ± 7.642.2 ± 9.5n.s 12 months34.8 ± 7.736.4 ± 6.8n.s34.7 ± 6.537.0 ± 7.5n.sMOCART72.5 ± 10.169.5 ± 11.5n.s69.8 ± 12.071.1 ± 10.3n.sData are presented as means ± standard deviationMOCART Magnetic resonance observation of cartilage repair tissue, VAS Visual analogue scale*Mann–Whitney U testTable 6Pain and MOCART scores stratified by body mass indexBody mass index, kg/m2 < 20.0($$n = 2$$)20.0–24.9($$n = 15$$)25.0–29.9($$n = 24$$) ≥ 30.0($$n = 2$$)P value*VAS Baseline77.5 ± 0.779.9 ± 7.478.9 ± 7.077.0 ± 8.5n.s 1 month40.0 ± 5.744.7 ± 5.444.0 ± 10.132.5 ± 4.9n.s 3 months37.5 ± 7.844.0 ± 6.543.3 ± 10.945.0 ± 11.3n.s 6 months37.0 ± 8.539.9 ± 5.740.4 ± 10.443.5 ± 12.0n.s 12 months25.8 ± 13.036.4 ± 5.236.1 ± 7.540.0 ± 1.4n.sMOCART67.5 ± 3.570.3 ± 12.971.0 ± 10.967.5 ± 3.5n.sData are presented as means ± standard deviationMOCART Magnetic resonance observation of cartilage repair tissue, VAS Visual analogue scale*Kruskal–Wallis testTable 7Pain and MOCART scores stratified by lesion locationLesion locationMedial femoral condyle ($$n = 41$$)Lateral femoral condyle ($$n = 16$$)Trochlea ($$n = 6$$)P value*VAS Baseline78.5 ± 7.177.1 ± 6.081.0 ± 6.7n.s 1 month46.7 ± 7.245.5 ± 7.750.4 ± 6.8n.s 3 months46.2 ± 8.144.7 ± 10.048.3 ± 8.5n.s 6 months41.5 ± 7.638.0 ± 10.944.8 ± 11.4n.s 12 months37.1 ± 7.233.5 ± 6.934.7 ± 8.0n.sMOCART70.6 ± 10.775.6 ± 10.575.8 ± 12.4n.sData are presented as means ± standard deviationMOCART Magnetic resonance observation of cartilage repair tissue, VAS Visual analogue scale*Kruskal–Wallis test The mean cartilage lesion size was 5.6 ± 1.3 (range, 3.2–7.9) cm2, and Table 8 presents the mean VAS and MOCART scores based on the lesion size. The mean VAS scores at 12 months significantly differed among the lesion size groups ($$P \leq 0.008$$), as did the mean MOCART scores ($$P \leq 0.007$$). Furthermore, the 12-month VAS score and the lesion size significantly correlated (Fig. 2A), as did the MOCART score and the lesion size (Fig. 2C).Table 8Pain and MOCART scores stratified by lesion sizeLesion size, cm2 < 3.5($$n = 4$$)3.5–5.5($$n = 18$$)5.5–7.5($$n = 17$$) ≥ 7.5($$n = 4$$)P value*VAS Baseline81.0 ± 4.878.1 ± 6.380.5 ± 7.375.6 ± 10.2n.s 1 month42.0 ± 4.743.3 ± 10.043.9 ± 8.444.3 ± 7.9n.s 3 months40.8 ± 5.142.1 ± 10.844.2 ± 9.247.8 ± 4.9n.s 6 months40.5 ± 6.438.2 ± 10.641.1 ± 8.040.8 ± 6.6n.s 12 months26.7 ± 6.834.5 ± 5.938.5 ± 6.840.3 ± 4.30.008MOCART80.0 ± 14.773.9 ± 9.267.9 ± 9.756.3 ± 4.80.007Data are presented as means ± standard deviationMOCART Magnetic resonance observation of cartilage repair tissue, VAS Visual analogue scale*Kruskal–Wallis testFig. 2Correlations between the A, B 12-month visual analogue scale (VAS) and C, D magnetic resonance observation of cartilage repair tissue (MOCART) scores and the A, C lesion size and B, D the number of stromal vascular fraction (SVF) cells The mean number of SVF cells was 7.4 × 107 ± 4.8 × 106 (range, 6.7 × 107–8.5 × 107). Table 9 details the association between the number of SVF cells and patient characteristics, none of which correlated. Table 10 reports the mean VAS scores based on the number of SVF cells, and the 12-month mean VAS score significantly differed among the SVF groups ($$P \leq 0.022$$). We also identified a significant correlation between the 12-month VAS score and the number of SVF cells (Fig. 2B).Table 9Patient characteristics and SVF correlationsPatient CharacteristicsNumber of SVF cellsS rhoP valueAge − 0.243n.sSex0.158n.sSide of involvement − 0.008n.sBody mass index − 0.012n.sData are calculated using the Spearman’s rank-order testSVF Stromal vascular fractionTable 10Pain scores stratified by the number of SVF cellsNumber of SVF cells < 7.0 × 107($$n = 12$$)7.0 × 107–8.0 × 107 ($$n = 25$$) ≥ 8.0 × 107($$n = 6$$)P value*VAS Baseline79.1 ± 9.279.5 ± 6.077.4 ± 6.4n.s 1 month44.8 ± 8.142.8 ± 7.744.2 ± 13.3n.s 3 months45.8 ± 8.842.2 ± 8.143.4 ± 14.8n.s 6 months40.0 ± 7.640.5 ± 7.039.4 ± 16.8n.s 12 months39.3 ± 6.935.9 ± 6.328.9 ± 6.10.022Data are presented as means ± standard deviationSVF Stromal vascular fraction, VAS Visual analogue scale*Kruskal–Wallis test Table 11 presents the mean MOCART score based on the number of SVF cells. The mean MOCART score significantly differed among the SVF groups ($$P \leq 0.001$$). Furthermore, some variables comprising the MOCART score significantly differed among the SVF groups, such as the degree of defect repair and defect filling ($$P \leq 0.016$$), border zone integration ($$P \leq 0.018$$), the repair tissue surface ($$P \leq 0.043$$), and repair tissue signal intensity ($$P \leq 0.025$$). We also identified a significant correlation between the MOCART score and the number of SVF cells (Fig. 2D).Table 11The MOCART score stratified by the number of SVF cellsVariablesNumber of SVF cells < 7.0 × 107 ($$n = 12$$)7.0 × 107–8.0 × 107($$n = 25$$) ≥ 8.0 × 107($$n = 6$$)P value*Degree of defect repair and filling of the defect15.8 ± 4.219.0 ± 2.017.5 ± 2.70.016Integration to border zone7.1 ± 2.67.2 ± 2.510.8 ± 2.00.018Surface of the repair tissue1.7 ± 2.52.2 ± 2.95.8 ± 3.80.043Structure of the repair tissue5.0 ± 0.04.8 ± 1.05.0 ± 0.0n.sSignal intensity of the repair tissue17.5 ± 5.822.2 ± 7.627.5 ± 6.10.025Subchondral lamina4.6 ± 1.45.0 ± 0.05.0 ± 0.0n.sSubchondral bone5.0 ± 0.04.6 ± 1.45.0 ± 0.0n.sAdhesions2.9 ± 2.63.6 ± 2.34.2 ± 2.0n.sEffusion1.7 ± 2.51.6 ± 2.43.3 ± 2.6n.sTotal62.1 ± 9.471.2 ± 7.784.2 ± 12.40.001Data are presented as means ± standard deviationMOCART Magnetic resonance observation of cartilage repair tissue, SVF Stromal vascular fraction*Kruskal–Wallis test
Multivariate logistic regression analyses were used to identify factors independently associated with unsatisfactory outcomes. Table 12 presents the final model, which controlled for age, sex, the involved side, BMI, lesion size, and the number of SVF cells. The lesion size and the number of SVF cells were independent predictors of an unsatisfactory outcome after SVF implantation ($$P \leq 0.038$$ and 0.021, respectively). Compared to patients with a lesion < 3.5 cm2, those with a 3.5 to 5.5 cm2 lesion were 2.67 times more likely to have an unsatisfactory outcome ($95\%$ CI, 0.23–31.07). Meanwhile, patients with a 5.5 to 7.5 cm2 lesion were 7.80 times more likely to have an unsatisfactory outcome ($95\%$ CI, 0.65–93.81), and those with a lesion ≥ 7.5 cm2 were 13.4 times more likely to have an unsatisfactory outcome ($95\%$ CI, 2.49–215.36).Table 12Associations between patient factors and an unsatisfactory outcome after SVF implantationFactorsn (%)Unsatisfactory outcome,odds ratio ($95\%$ CI)P valueAge, yn.s < 604 (9.3)1.50 (0.06–40.63) 60–6524 (55.8)1.21 (0.09–145.66) 65–7012 (27.9)0.25 (0.02–3.67) ≥ 703 (7.0)1.00Sexn.s Male14 (32.6)1.00 Female29 (67.4)1.23 (0.34–4.49)Involved siden.s Right21 (48.8)1.67 (0.48–5.74) Left22 (51.2)1.00Body mass index, kg/m2n.s < 20.02 (4.7)1.00 20.0–24.915 (34.8)1.34 (0.08–12.83) 25.0–29.924 (55.8)2.29 (0.17–30.96) ≥ 30.02 (4.7)3.75 (0.29–47.99)Lesion locationn.s Medial femoral condyle41 (65.1)1.00 Lateral femoral condyle16 (25.4)1.41 (0.25–7.86) Trochlea6 (9.5)2.20 (0.32–14.98)Lesion size, cm20.038 < 3.54 (9.3)1.00 3.5–5.518 (41.9)2.67 (0.23–31.07) 5.5–7.517 (39.5)7.80 (0.65–93.81) ≥ 7.54 (9.3)13.4 (2.49–215.36)No. of SVF cells0.021 < 7.0 × 10712 (27.9)7.20 (0.64–81.54) 7.0 × 107–8.0 × 10725 (58.1)1.80 (0.43–7.53) ≥ 8.0 × 1076 (14.0)1.00SVF Stromal vascular fraction, CI Confidence interval Compared to patients with ≥ 8.0 × 107 SVF cells, those with 7.0 × 107 to 8.0 × 107 SVF cells were 1.80 times more likely to have an unsatisfactory outcome ($95\%$ CI, 0.43–7.53). Meanwhile, patients with < 7.0 × 107 SVF cells were 7.20 times more likely to have an unsatisfactory outcome ($95\%$ CI, 0.64–81.54). Age, sex, involved side, and BMI did not independently predict unsatisfactory outcomes after SVF implantation.
## Discussion
Although SVF-based treatment has demonstrated encouraging clinical efficacy for repairing articular cartilage in knee OA [5, 41, 42], we understand little about the preoperative factors that influence the treatment outcomes. This is the first study to assess the effects of various factors, including patient characteristics (age, sex, the involved side, and BMI), cartilage lesion size, and the number of SVF cells, on outcomes after SVF implantation. Understanding the factors associated with clinical outcomes will allow patients with OA to have more realistic expectations after undergoing SVF implantation for their knees.
Patient characteristics may serve as important selection criteria for cell-based repair strategies. For example, older age might significantly affect the SVF quality. Several studies have investigated this, with differing conclusions [2, 8, 12, 14, 43]. Yu et al. [ 43] found a positive correlation between the SVF yield and donor age (linear correlation coefficient $r = 0.30$). Furthermore, Buschmann et al. [ 8] evaluated the SVF yield from 30 donors, reporting that older patients (45–74 years) had a significantly lower SVF yield than middle-aged patients (38–44 years). In contrast, de Girolamo et al. [ 12] identified a significant positive correlation between age and cell yield, indicating that older donors had a larger cell harvest than younger donors. Conversely, Faustini et al. [ 14] performed a linear multiple regression analysis among 125 patients (mean age, 51.31 years; range, 15–87 years) to evaluate how donor age affects the SVF yield, reporting no influence. Finally, Alaaeddine et al. [ 2] compared the number of SVF cells among 58 adults (mean age, 39.4 years; range, 20–71 years) divided into four age groups (< 30, 30–39, 40–49, and ≥ 50) but found no differences among the groups (n.s.). They also found that the number of SVF cells did not differ by sex (n.s.). Similarly, our study found no correlation between the number of SVF cells and patient age or sex (Table 8), nor did we find differences in the mean VAS and MOCART scores among the age and sex subgroups (all n.s.; Tables 4 and 5). Although it remains unclear whether patient age or sex influences the number of SVF cells, we conclude that these variables do not influence the SVF implantation outcomes.
Obesity is a well-established risk factor for OA development and progression, especially in weight-bearing joints [13]. Furthermore, adipose-derived MSCs from overweight patients have a reduced proliferation rate, greater cell senescence, and reduced differentiation to multiple lineages, including chondrogenesis [32]. Some authors have reported a positive correlation between BMI and the SVF yield [2, 43], yet others have reported no correlation [4, 8, 14, 29]. We also found no correlation between the number of SVF cells and BMI (Table 9), nor did we find differences in the SVF implantation outcomes among the BMI groups (Table 6). Obesity [1] is defined as a BMI of ≥ 30.0 kg/m2; in this study, only three patients were classified into the obesity group, meaning that the number of SVF cells from these patients would not have influenced the outcomes. Therefore, further SVF implantation studies that compare outcomes among different BMI groups and include more patients with a BMI of ≥ 30.0 kg/m2 are needed to adequately evaluate the independent effect of BMI.
Strong correlations between the lesion size and outcomes of regenerative procedures for cartilage have been documented. For instance, Salzmann et al. [ 33] reported that microfracture surgeries are usually performed to treat lesions < 3 cm2 in size, and Knutsen et al. [ 24] indicated that full-thickness chondral defects < 4 cm2 respond better to microfracture surgery than lesions > 4 cm2. Furthermore, Koh et al. [ 25] evaluated 37 patients treated with MSC implantation, reporting that cartilage lesions > 5.4 cm2 had significantly worse clinical outcomes and less cartilage regeneration than those < 5.4 cm2. Kim et al. [ 21] also performed MSC implantation in 49 patients (55 knees) with knee OA and compared the outcomes based on the lesion size (< 3.0, 3.0–5.9, 6.0–8.9, and ≥ 9.0 cm2). They found significant differences in clinical outcomes among the groups and suggested that a 6.0 cm2 lesion was the upper size limit for obtaining encouraging outcomes after MSC implantation. Similar results were observed in the present study, where we assessed the patients based on the lesion size (< 3.5, 3.5–5.5, 5.5–7.5, and ≥ 7.5 cm2), finding a significant difference in mean MOCART scores among the groups ($$P \leq 0.007$$; Table 8) and a significant correlation between the MOCART score and the lesion size (Fig. 2C). Our study findings indicated that cartilage regeneration was less favourable after SVF implantation for larger cartilage lesions.
In addition, we found significant correlations between the mean VAS and MOCART scores 12 months after surgery ($$P \leq 0.002$$; Table 3), implying that until the cartilage regenerates, the pain levels are similar, but as regeneration occurs, the pain gradually improves. These results also suggest that at least 12 months is necessary for enough cartilage to regenerate to improve pain levels after SVF implantation. We also found a significant correlation between the 12-month VAS score and the lesion size (Fig. 2A). Together, these results suggest that postoperative pain decreases as the cartilage regenerates, and since the lesion size affects cartilage regeneration, the pain level is related to the lesion size. In addition, we found that the lesion size was an independent predictor of an unsatisfactory outcome after SVF implantation ($$P \leq 0.038$$; Table 12). Therefore, we conclude that the lesion size is a prognostic factor influencing SVF implantation outcomes.
One of the most important questions regarding regenerative treatment using SVF is the optimal number of SVF cells for favourable cartilage regeneration with satisfactory clinical outcomes. Several studies have reported promising results regarding intra-articular injections of SVF cells for knee OA treatment, and the average SVF doses in those studies varied from 1.4 × 107 to 5.0 × 107 cells [35]. Currently, whether the SVF amount affects the knee OA treatment outcome is debatable. For example, Fodor and Paulseth [15] stated that they did not observe a dose-dependent response to the SVF amount in their pilot study in eight patients with knee OA, where they performed an intra-articular injection of SVF (mean, 14.1 × 106; range, 7.0 × 106–14 × 106). However, Simunec et al. [ 36] performed an intra-articular injection of SVF cells in 12 patients with knee OA, reporting a negative correlation between the number of administered cells and an improvement in the Knee injury and Osteoarthritis Outcome Score (KOOS) score (Pearson correlation coefficient: r = − 0.27 at the 3-month follow-up and r = − 0.35 at the 12-month follow-up), indicating that the lower the number of administered cells, the more the KOOS score improved. Meanwhile, other authors reported positive correlations between SVF-based treatment outcomes and the number of SVF cells. Tsubosaka et al. [ 38] compared the 12-month outcomes of 60 patients; 30 received an intra-articular injection with 2.5 × 107 SVF cells (low-dose group), and 30 received an intra-articular injection of 5.0 × 107 SVF cells (high-dose group). They reported that the 12-month postoperative pain and symptom subscale KOOS scores were significantly better in the high-dose group than in the low-dose group. However, they found no differences in the follow-up MRI evaluations between the two groups. Furthermore, Garza et al. [ 17] used freshly isolated SVF cells to treat knee OA, and the patients were allocated to a high-dose (3 × 107 cells), low-dose (1.5 × 107 cells), or placebo group. They found dose-dependent effects, with the higher dose producing more pronounced effects. Similar results were observed in our study. We used a mean of 7.4 × 107 ± 4.8 × 106 (range, 6.7 × 107–8.5 × 107) SVFs and identified a significant correlation between the MOCART score and the number of SVF cells (Fig. 2D). In addition, the mean MOCART scores differed among the four groups with differing SVF amounts ($$P \leq 0.001$$; Table 11). Notably, some variables (e.g., the degree of defect repair and defect filling, border zone integration, repair tissue surface, and repair tissue signal intensity) significantly differed among the four different SVF amount groups ($$P \leq 0.016$$, $$P \leq 0.018$$, $$P \leq 0.043$$, and $$P \leq 0.025$$, respectively; Table 11).
We attribute these results to the SVF characteristics. Unlike the cultured adipose-derived MSCs, which constitute a fairly homogenous cell population, adipose-derived SVF is a heterogeneous cell population containing regenerative cells, such as adipose-derived MSCs, macrophages, pericytes, fibroblasts, blood cells, vessel-forming cells (including endothelial and smooth muscle cells), and their progenitors [19]. Adipose-derived stem and stromal cells contribute to cartilage regeneration by tissue-specific differentiation, extracellular matrix secretion, and various immune-modulating factor secretions [7, 9, 45]. Fibroblasts secrete extracellular matrix components that positively influence cell adhesion, migration, and cell–matrix interactions [18]. Therefore, we speculated that we would identify significant differences between variables related to cartilage regeneration and the number of SVF cells, which we did (Table 11). We also speculated that macrophages in SVF, which secrete immunomodulatory factors and cytokines to induce anti-inflammatory effects, contribute to the significant difference between effusion and the number of SVF cells. This study found that the number of SVF cells was an independent predictor of unsatisfactory outcomes after SVF implantation ($$P \leq 0.021$$; Table 12). Therefore, we conclude that the number of SVF cells is a prognostic factor influencing the outcomes of SVF implantation.
This study has some limitations. First, the number of patients was relatively small, and the 12-month follow-up period was short. Thus, a larger series of cases with longer follow-up periods are required for a more accurate evaluation of the long-term outcomes and the prognostic factors associated with SVF implantation. However, given that no similar studies have been published, we believe these data are important. Second, although a follow-up MRI was performed to evaluate cartilage regeneration after SVF implantation, we did not conduct a histological evaluation to assess the quality of the regenerated cartilage. Second-look arthroscopy with a histological evaluation would help evaluate the quality of the repaired cartilage. Because SVF is a heterogeneous population of cells with variable growth potentials and distinct morphological and functional characteristics, the SVF quality required to achieve adequate cartilage regeneration should be identified to better predict SVF implantation outcomes. In this study, we found that the number of SVF cells was a prognostic factor influencing outcomes following SVF implantation. However, a future study should estimate other SVF characteristics that influence outcomes for a more accurate assessment of the influential prognostic factors. In addition, the optimal number of SVF cells should be determined by evaluating the effects of SVF cells on improved cartilage regeneration to achieve better clinical outcomes. Finally, a follow-up MRI was performed approximately 12 months postoperatively. However, the potential behaviour of regenerated cartilage over time remains unknown, and changes in the influential factors after 12 months cannot be predicted.
## Conclusion
The present study showed encouraging improvement in pain levels and cartilage regeneration after SVF implantation in patients with knee OA throughout the 12-month follow-up period; furthermore, the size of the cartilage lesion and the number of SVF cells significantly influenced patient outcomes following SVF implantation. These factors may serve as a more accurate screening tool, allowing surgeons to better assess which patients with knee OA are good candidates for SVF implantation.
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|
---
title: 'Concomitant fractures in patients with proximal femoral fractures lead to
a prolonged hospital stay but not to increased complication rates or in-house mortality
if treated surgically: a matched pair analysis'
authors:
- Annabel Fenwick
- Michael Pfann
- Jakob Mayr
- Iana Antonovska
- Franziska Von der Helm
- Stefan Nuber
- Stefan Förch
- Edgar Mayr
journal: Aging Clinical and Experimental Research
year: 2023
pmcid: PMC10014667
doi: 10.1007/s40520-023-02348-4
license: CC BY 4.0
---
# Concomitant fractures in patients with proximal femoral fractures lead to a prolonged hospital stay but not to increased complication rates or in-house mortality if treated surgically: a matched pair analysis
## Abstract
### Background
Impact of concomitant fractures on patients sustaining a proximal femur fracture remains unclear. Rising numbers and patient need for rehab is an important issue. The objective of our study was to investigate the impact of concomitant fractures, including all types of fractures, when treated operatively, for proximal femur fractures on the length of hospital stay, in-house mortality and complication rate.
### Methods
Observational retrospective cohort single-center study including 85 of 1933 patients ($4.4\%$) with a mean age of 80.5 years, who were operatively treated for a proximal femoral and a concomitant fracture between January 2016 and June 2020. A matched pair analysis based on age, sex, fracture type and anticoagulants was performed. Patient data, length of hospital stay, complications and mortality were evaluated.
### Results
The most common fractures were osteoporosis-associated fractures of the distal forearm ($$n = 34$$) and the proximal humerus ($$n = 36$$). The group of concomitant fractures showed a higher CCI than the control group (5.87 vs. 5.7 points; $p \leq 0.67$). Patients with a concurrent fracture had a longer hospital stay than patients with an isolated hip fracture (15.68 vs. 13.72 days; $p \leq 0.056$). Complications occurred more often in the group treated only for the hip fracture ($11.8\%$, $$n = 20$$), whilst only $7.1\%$ of complications were recorded for concomitant fractures ($p \leq 0.084$). The in-house mortality rate was $2.4\%$ and there was no difference between patients with or without a concomitant fracture.
### Conclusions
A concomitant fracture to a hip fracture increases the length of hospital stay significantly but does not increase the complication rate or the in-house mortality. This might be due to the early mobilization, which is possible after early operative treatment of both fractures.
## Background
The number of proximal femur fractures is estimated to rise to more than 4.5 million by 2050 [1–4]. Not only are they linked to an exceedingly high mortality rate within the first postoperative year, they also reduce patient mobility and self-sustainability and lead to an impairment of most daily life activities [5–7]. A high proportion of patients need caretaking facilities postoperatively [8]. The economic burden is already by far exceeding health care resources [3, 9]. Treatment consists of arthroplasty or osteosynthesis but depending on fracture morphology especially for femoral neck fractures arthroplasty should be considered for geriatric patients as osteosynthesis failure rate has to be taken into account [10].
Underlying diseases such as diabetes and cardiovascular disorders as well as cognitive impairment have been linked by studies to a higher mortality rate for patients suffering from hip fractures [11, 12]. Women and patients with osteoporosis are at even higher risk sustaining a hip fracture [13]. This leads to a significant number of patients presenting to A&E with concomitant, often osteoporosis-associated fractures. The prognostic value of these concomitant fractures remains unclear, and data are limited [14, 15]. Literature has discussed concomitant fractures as disadvantageous as they lead to lower functionality and higher mortality [8, 16–19]. But the data are mainly limited to concomitant fractures of upper limbs and leaves the specific treatment of the additional fractures open. If there is a disadvantage, the importance of identifying these patients lies in the opportunity to enable more intensive rehabilitation and increase early mobility for a possible return to daily activities and independency.
The objective of our study was to investigate the impact of concomitant fractures, including all types of fractures, when treated operatively, for patients with proximal femur fractures with regards to the length of hospital stay, in-house mortality and complication rate.
## Data acquisition
For our retrospective cohort single centre study (Level III) all patients treated operatively for a proximal femoral fracture (femoral neck, pertrochanteric and subtrochanteric fractures) at our level I trauma centre between January 2016 and June 2020 were evaluated. Exclusion criteria were: primary conservative treatment, greater trochanteric fractures, periprosthetic fractures as well as referrals for revision surgery and polytraumatised patients.
The study conducted was approved by the local Ethics Committee and fulfils the standards of the declaration of Helsinki [20-2155-101].
1933 patients were treated for proximal femur fractures in the mentioned period. Of these patients 95 ($4.91\%$) presented with a concomitant facture at the time of admission. 85 patients were enrolled in our study group as the second fracture was treated surgically during the same hospital admission.
## Matched pair analysis
Patients with concomitant fractures at the time of admission which were subsequently treated operatively during the same hospital stay were extracted to form our study group. A matched pair analysis was carried out. We formed a control group without concomitant fractures which was matched on 4 criteria: age, gender, fracture morphology and anticoagulant medication.
The charts were reviewed for demographic data such as age, gender, BMI, comorbidities including the Charlson Comorbidity Index CCI [19] and ASA American Society of Anaesthesiologists classification [20], fracture morphology, co-geriatric management, medication. Type of surgery and time to surgery from admission were evaluated.
Outcome measures were the length of stay in the intensive care unit as well as the overall length of hospital stay (LHS) and In-house mortality. Complications were analysed and divided into urinary infections, pneumonia, embolism or thrombosis, haematoma, wound infections, mechanical complications, i.e. postoperative fracture or dislocation or cutting out.
## Therapy
One consistent therapy protocol was applied throughout the total period reviewed. Target time to surgery was within 24 h of admission for all patients without anticoagulation or only anti-platelet therapy (AP), including dual AP therapy. Patients with DOACs (Edoxaban, Rivaroxaban, Apixaban) were divided into two groups according to their kidney function (Gr 1: GFR > 50, Gr 2: GFR < 50). If renal clearance was good, surgery was performed within 24 h. If renal function was impaired, surgery was postponed to 24–48 h after admission to reduce risk of bleeding. Depending on pre-operative mobility, comorbidities and fracture morphology total or hemi arthroplasty (cemented or uncemented, Fa. Zimmer Biomet Indiana, US) was performed for femoral neck fractures, intramedullary nailing PFNa, Fa. Synthes Oberdorf, Switzerland, (± cerclage) for pertrochanteric fractures and plate/screw osteosyntheses (DHS, dynamic hip screw, Fa. Synthes) for undisplaced pertrochanteric or lateral femoral neck fractures. The subtrochanteric fractures were addressed by open reduction, cerclage and cephalomedullary nailing in side-positioning. 30 min prior to surgery all patients received an i.v. single shot of 2 g Cefazolin.
Postoperatively, venous thromboembolism prophylaxis was given from day one with Enoxaparin 40 mg subcutaneously. Anticoagulants were substituted with Tinzaparin-Sodium according to patient weight postoperatively. All patients were allowed full weight bearing immediately after surgery and received physiotherapy from day one. In case of a hindfoot fracture full weight bearing was allowed with a VACOped boot.
## Statistical analysis
Statistical analysis was carried out with IBM SPSS Statistics (version 27; IBM Deutschland Ltd., Ehningen, Germany). Normal distribution of all data was verified. The student’s t-test and chi-square were used to determine influencing factors regarding complications and mortality; $95\%$ confidence intervals and standard deviations were calculated. For data without normal distribution the Wilcoxon Rank Test was used. We used Fisher’s exact test for the description of significant differences in mortality between the groups. The significance level was set at $5\%$ (α = 0.05).
## Results
The average age was 80.5 years (range: 34–99; SD 10.8). $74.1\%$ were female and $25.9\%$ male. The mean BMI was 24.35 kg/m2 (range: 14.8–38.1 kg/m2). Each group of 85 patients comprised 37 femoral neck fractures, 41 pertrochanteric and 7 subtrochanteric fractures. In 36 cases total hip endoprothesis was implanted and 33 patients received a hemiarthroplasty. Cephalomedullary nailing was done in 97 cases and osteosynthesis with dynamic hip screw in four cases. Anticoagulant therapy was recorded for 82 patients ($48.2\%$). 56 patients had antiplatelet therapy and 26 were on either Warfarin or DOACs.
## Concomitant fractures
The 85 identified patients with concomitant fractures had 92 fractures. The most common fractures were osteoporosis-associated fractures of the distal forearm ($$n = 34$$) and the proximal humerus ($$n = 36$$) followed by fractures of the distal humerus ($$n = 4$$) and the olecranon ($$n = 4$$). The distal forearm fractures were all treated with locking plate osteosynthesis and the olecranon fractures by tension band wiring. Patients with distal humerus fractures were treated with elbow arthroplasty. Of all the patients with proximal humerus fractures 6 obtained reverse shoulder arthroplasty whilst the other patients were treated with plate- or nail osteosynthesis. Two patients each were also surgically treated for spine fractures, patella fractures, clavicle—and tibial shaft fractures. Furthermore, there was one patient with a talus fracture, one with a calcaneus fracture and one with a metatarsus V fracture. One patient was treated for a metacarpal fracture and one for a radial head fracture. In one case there was a periprosthetic proximal tibia fracture, which received revision arthroplasty.
## Preoperative status
The average CCI for the total cohort was 5.79 points (range: 0–14, SD 2.5). The group of concomitant fractures showed a slightly higher but not significant CCI in comparison to the control group (5.87 vs. 5.7points; $p \leq 0.67$). The ASA classification was also distributed equally amongst both groups with most patients classified ASA II and III ($90.2\%$) (Fig. 1).Fig. 1The average comorbidities a Charlson Comorbidity Index and b ASA classification between patients with concomitant fractures and isolated hip fractures. a CCI, b ASA classification 110 patients ($64.7\%$) had been self-sustaining without caretaking prior to hospital admission. The distribution of amount of caretaking was similarly distributed in both groups (level 1: 12, level 2: 20, level 3: 196; level 4: 18, level 5: 10). Preoperative mobility was assessed and already reduced in $47.6\%$ of the cohort i.e., need of at least a cane or a walker whilst $52.4\%$ had no restrictions in walking or distance of walking.
## Time to surgery
The time from admission to surgery for all patients was on average 24.95 h (range: 2.16–107.18; SD: 17.7). Both groups showed no significant difference in the waiting time to surgery (concomitant: 25.94 h vs. 23.95 h; $p \leq 0.466$).
## Length of hospital stay
The average LHS for the entire cohort was 14.7 days (range: 3–44; SD 6.6). Patients who presented with a further treated fracture had a mean longer hospital stay than patients with an isolated hip fracture (15.68 vs. 13.72 days; $p \leq 0.056$) (Fig. 2). In addition, the length of stay postoperatively in the intensive care unit ICU was significantly longer for patients treated for more than one fracture (1.01 vs. 0.45 days; $p \leq 0.024$).Fig. 2Comparison of length of hospital stay for hip fractures only and concomitant fractures
## Complications and mortality
The entire cohort showed a complication rate of $18.8\%$. In total complications occurred more often in the group treated for only the proximal femur fracture ($11.8\%$, $$n = 20$$) whilst only $7.1\%$ of complications were recorded for the group with concomitant fractures ($p \leq 0.084$). Pneumonia occurred 5 times in both groups, whilst urinary tract infections were more common in the group with isolated hip fracture (5.9 vs. $12.9\%$; $p \leq 0.08$). In each group, there was one case ($1.2\%$) of deep wound infection with the need for surgical revision surgery. Blood loss was significantly higher in the group treated for more than one fracture (1711.09 vs. 1326.2; $p \leq 0.007$).
The overall in-house mortality rate was $2.4\%$ ($$n = 4$$). There were 2 patients with concomitant fractures and 2 patients with a single proximal femur fracture who died postoperatively (Fig. 3). Death causes were pulmonary embolism, cardiac arrest and pneumonia. Fig. 3a Comparison of mortality rate between the two matched groups. b Comparison of complication rate between the two matched groups
## Discussion
There is no question that patients suffering from proximal femur fractures have a high impact on their daily life and a high mortality risk [21]. Furthermore, geriatric patients are particularly vulnerable and often show a worse outcome. Therefore, Di Monaco [22] raised the question of whether a further coinciding fracture could actually lead to an even worse outcome or a higher mortality rate. The percentage of patients affected in our study is similar to previously published studies (3.7–$6.5\%$) making up about $5\%$ of the patients presenting with proximal femur fractures [17, 23, 24]. This may seem a rare condition, but the relevance of the topic is marked by a continuously rising aging population of which $5\%$ makes a substantial number of patients in need of special treatment and rehabilitation.
Uzoigwe [23] demonstrated women are more likely to suffer from a hip fracture and a further fracture whilst Mulhall [25] concluded that higher age was associated with the occurrence of a concurrent fracture. A study conducted by Di Monaco et al. was able to prove that Geriatric Nutritional Risk Index GNRI scores were significantly lower in the subgroup of women with hip fracture and concurrent upper-extremity fracture than in the control group and they concluded that a low GNRI score may have an influence on the genesis of the concurrent fractures [26].
As most studies are restricted to upper limb fractures as the concomitant injury, we enrolled all types of fractures. In agreement with Robinson et al. [ 16] we also found the most common fractures to be associated with proximal femur fracture in our cohort to be fractures of the distal forearm and proximal humerus making up more than $75\%$ of all the fractures included.
A large metanalysis on the topic by Kim et al. involving 217.233 patients with hip fractures and concurrent upper limb fractures found a higher 30-day mortality rate but no difference in the long-term mortality rate [8]. Higher mortality rates for concomitant fractures were also seen by Mulhall et al. [ 25] ($10.3\%$ vs. $5.6\%$) and Buecking et al. [ 17] (increase of $1.8\%$). Furthermore, a study conducted by Thayer et al. [ 18] concluded that this patient group was at a higher risk for in-house mortality than patients with an isolated proximal femur fracture. Our results agree with Ng et al. in not finding any difference in mortality rates amongst the groups compared [27]. Ng et al. found increase of age was linked to 30-day mortality. Our mortality rate for concomitant fractures at $2.4\%$ ($4\%$ ($$n = 78$$) for the overall cohort of 1933 [28] seems relatively low in this cohort but may be linked to our early mobilization program with full weight bearing after surgery for both fractures to diminish complications linked to prolonged immobilization. Furthermore, our geriatric patients are treated on an orthogeriatric ward and studies have been able to show that an interdisciplinary orthogeriatric approach reduces in-house mortality and improve the functional outcome postoperatively, for example, the capability of living at home post-surgery as well as mobility.
[29]. Combined orthogeriatric treatment seems to become even more relevant if more than one injury is preexistent.
All studies agree on the fact that concomitant fractures lead to longer hospitalization [18, 23, 30, 31]. Di Monaco [22] further evaluated the types of fractures and found a prolonged length of stay for patients with a proximal humerus fracture but not for fractures concerning the distal forearm whilst Kim et al. [ 8] did not differentiate between the types of upper limb fractures and saw an increase in length of hospital stay for mean 1.67 days which is very similar to the results of our cohort showing an increase of the average 1.96 days. The largest single study conducted by Ong et al. concluded even when comparing demographic data and outcome compared to the mono-injury cohort the only significant difference was the average inpatient stay [24].
Whilst Robinson et al. [ 16] did not find a significant difference in mobility scoring most studies agree on a worse ambulatory status [30] for patients with concomitant fractures also spending a longer time at rehabilitation and are less likely to be discharged home [8, 18, 27].
The difficulty is that most published studies do specify the treatment patients received for the concomitant fracture. As our treatment aim especially for our geriatric patients within our multimodular approach and geronto- trauma co-management is to enable full weight bearing and extensive range of motion we treated a high percentage of our concomitant fractures surgically and included only these in our study. To minimize anesthetics, we try to perform the second surgery within the surgery needed in any event for the hip fracture. Our mortality and complications rates seem to be comparably low. Buecking et al. [ 17] support our observations that functional recovery could be improved by surgical treatment of the concomitant fracture. They found the postoperative function to be restricted by the hip fracture and preexisting conditions.
Even though our sample size consisting of 85 patients with concomitant fractures is a sizable cohort there are limitations that have to be taken into account. Due to the study design as a matched pair analysis no relevant conclusions on demographic data can be drawn between the groups but the literature agrees mostly women with osteoporosis are prone to these combined injuries. As we included all concomitant fractures the cohort becomes a heterogenous group, and no valuable conclusions can be drawn to single injury patterns. Furthermore, we only evaluated the in-house mortality, and our study is lacking in long-term data.
## Conclusion
Patients with concomitant fractures must be taken into special consideration. Early surgical treatment of concomitant fractures is beneficial for patients with proximal femur fractures as it enables early mobilization and functional recovery and therefore leads to comparable mortality and complication rates even though the overall length of hospital stay is significantly increased.
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|
---
title: 'Functional and Metabolic Imaging in Heart Failure with Preserved Ejection
Fraction: Promises, Challenges, and Clinical Utility'
authors:
- Matthew K Burrage
- Andrew J Lewis
- Jack J J. Miller
journal: Cardiovascular Drugs and Therapy
year: 2022
pmcid: PMC10014679
doi: 10.1007/s10557-022-07355-7
license: CC BY 4.0
---
# Functional and Metabolic Imaging in Heart Failure with Preserved Ejection Fraction: Promises, Challenges, and Clinical Utility
## Abstract
Heart failure with preserved ejection fraction (HFpEF) is recognised as an increasingly prevalent, morbid and burdensome condition with a poor outlook. Recent advances in both the understanding of HFpEF and the technological ability to image cardiac function and metabolism in humans have simultaneously shone a light on the molecular basis of this complex condition of diastolic dysfunction, and the inflammatory and metabolic changes that are associated with it, typically in the context of a complex patient. This review both makes the case for an integrated assessment of the condition, and highlights that metabolic alteration may be a measurable outcome for novel targeted forms of medical therapy. It furthermore highlights how recent technological advancements and advanced medical imaging techniques have enabled the characterisation of the metabolism and function of HFpEF within patients, at rest and during exercise.
## The Demographics of Heart Failure
Half of patients clinically presenting with heart failure (HF) have a preserved left ventricular ejection fraction (HFpEF). Despite this, the symptoms of HFpEF are typically sufficient to cause a substantial reduction in their quality of life. Their disease follows a substantially similar course to patients suffering heart failure with a reduced ejection fraction (HFrEF), with a similar morbidity and mortality burden. HFpEF presents a large and currently unmet need for effective medical therapy and is increasingly recognised as a major and growing pathology with a complex aetiology, increasing at a rate of approximately $1\%$ per year [1]. As its major risk factors, including hypertension, obesity, and type 2 diabetes mellitus (T2DM), are increasing in most populations, HFpEF is expected to emerge as the most prevalent HF phenotype worldwide [2]. It is worth highlighting that patients with HFpEF are as functionally limited as their counterparts with HFrEF: requiring frequent hospitalisation and living with a poor quality of life [3–5], and their overall mortality is similar to that of most HFrEF populations, with observational studies reporting a dismal 5-year survival of only 35–$40\%$ post-hospitalisation for HF—comparable to many cancers.
The demographic of the HFpEF patient is varied and typically comorbid. Population-based studies (e.g. the US Veterans Affairs cohort of 1.8 million individuals [6]) have reported that HFpEF patients are predominantly older women [1], with established cardiovascular risk factors such as obesity, hypertension, chronic kidney disease, coronary artery disease (CAD), anaemia, hyperlipidaemia, T2DM, and atrial fibrillation [7–9] shown via multivariable logistic regression to be associated with increased odds ratios (OR) of developing HFpEF. Whilst the existence of these risk factors is arguably unsurprising, their relative magnitudes differ at times substantially between the conditions: for example, the comparative risk for developing HFpEF following previous MI is approximately half that compared with HFrEF (OR / $95\%$ confidence interval: 2.85 (2.78–2.93) for HFpEF compared to 4.18 (4.10–4.26) for HFrEF); with BMI and diabetes further independently hinting at a distinct mechanism of pathology (c.f. Fig. 1) on a population level alone [10].Fig. 1Multivariable logistic regression derived odds ratios (OR) for risk factors in developing HFpEF and HFrEF in the 1.8 million US Veterans Affairs population (comprising \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 66$$\,831$$\end{document}$$n = 66831$$ HFpEF and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 92$$\,233$$\end{document}$$n = 92233$$ HFrEF patients). Data are reproduced (with permission) from Gaziano et al. [ 6], and have been corrected for age, sex, race, and ethnicity. A thin blue line highlights the differences between HFpEF and HFrEF. Abbreviations: ACE, angiotensin converting enzyme; ARB, angiotensin receptor blocker; BMI, body mass index (coefficient per \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${5}\,{\text{kg}/\text{m}^2}$$\end{document}5kg/m2); CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DBP, diastolic blood pressure (per 10 mmHg); eGFR, estimated glomerular filtration rate (per \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${15}\,{\text{ml}/\text{min}/1.73 \text{m}^2}$$\end{document}15ml/min/1.73m2; HDL-c, high density lipoprotein cholesterol (per 15 mg/dl); HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; LDL-c, low density lipoprotein cholesterol (per 35 mg/dl); MI, myocardial infarction; SBP, systolic blood pressure (per 20 mmHg)
## The Link Between Pressure, Function, and Metabolism
This information, replicated through many different patient studies, provides a strong but subtle cue to examine the molecular pathogenesis of HFpEF: its risk factors such as type 2 diabetes, obesity, and hypertension, are all indepedently associated with impaired cardiac energy metabolism [11–17].
From a fundamental physical point of view, this is self-consistent: the heart as a pump does hydraulic work whilst iterating through a pressure-volume (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P-V$$\end{document}P-V) loop and, from basic thermodynamics, the integral area of that loop, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\oint P\cdot \text {d}V$$\end{document}∮P·dV, necessarily has to be equal to the stroke work undertaken in a cardiac cycle. Broadly speaking, HFpEF is a disease characterised by high filling pressures and mechanically stiff ventricles, with patients falling on a spectrum of diastolic dysfunction [18]. Approximately a third of the patients presenting with HFpEF have a normal collagen volume fraction as ascertained through endomyocardial left-ventricular biopsies [19] (the remainder raised), but all possess LVEDP, LV end-systolic wall stress, and LV stiffness modulus consistent with patients presenting with a raised collagen volume fraction. This suggests that in addition to collagen deposition, intrinsic cardiomyocyte stiffness also contributes directly to diastolic LV dysfunction in HFpEF, a finding experimentally borne out in patient groups where further biopsies are available [19, 20]. The biomechanical effect of these alterations in myocardial stiffness is a marked increase in pulmonary arterial pressure: an increase in myocardial stiffness results in both impaired LV relaxation and filling. This increases the left atrial pressure, exposing the whole of the pulmonary vascular system and ultimately the right heart to an increased pressure workload accordingly, independent of other (comorbid) associated changes in the peripheral vascular resistance that may be present in the patient. This leads to increased pulmonary transcapillary hydrostatic pressure, which drives fluid transudation, potentially increasing capillary diameter from the Young-Laplace relation, and hence leading to pulmonary congestion and breathlessness [21]. What is perhaps often under-appreciated is the role of the right heart in this process. Reduced right ventricular contractile reserve and reduced coupling of the right ventricle to the pulmonary circulation or RV-PA coupling results in increased right-sided pressures, with right atrial dilatation and increased right atrial and systemic venous pressures. This exacerbates pulmonary congestion by reducing clearance of lung water via pulmonary lymphatics [22–25].
Together with an increase in myocyte and ventricular stiffness (i.e. mechanically increasing stroke work), the metabolome in HFpEF patients is significantly altered, and again differentially altered in comparison to HFrEF patients. Plasma metabolomics reveals that, accounting for differences in blood biochemistry known to be due to comorbidities such as T2DM, HFpEF is associated with indices of increased inflammation and oxidative stress (such as higher levels of hydroxyproline and symmetric dimethyl arginine, alanine, cystine), impaired lipid metabolism (lower lysophophatidylcholine, potentially altered coupling between glucose oxidation and glycolysis [26]), increased collagen synthesis, and downregulated nitric oxide signalling. Together, these findings suggest a more predominant systemic microvascular endothelial dysfunction and inflammation linked to increased fibrosis in HFpEF compared with HFrEF [27].Fig. 2A Taken together, these factors are sufficient to delineate HFpEF as a very distinct disease from HFrEF. As detailed by Guazzi et al. [ 28], in HFpEF, concentric myocyte remodelling with increased left ventricular (LV) diastolic stiffness, atrial functional MR, and a stiff left atrium (LA) are the major driving forces to alveolar-capillary stress failure and vascular remodelling, with corresponding differential changes in the end-systolic/end-diastolic pressure-volume relation (ESPVR/EDPVR) compared to healthy controls (shown in blue). B *It is* worth further stressing that, although not typically caused by an ischaemic origin, HFpEF has a similar mortality to HFrEF following MI, as illustrated here in an “all-comers” Ethiopian cohort HF study reporting outcomes on all HF patients treated at the University of Gondar Referral Hospital between 2010 and 2015 [29] As summarised in Fig. 2A, HFpEF therefore remains a disease that is (a) prevalent and expected to become increasingly so; (b) complex and necessarily associated with numerous comorbid pathologies that may help define its molecular milieu; (c) deeply connected with the biophysical mechanics of the heart; and (d) a disease with a high mortality and morbidity (Fig. 2B).
## The Heart as a Metabolic Omnivore
The healthy heart has an enormous energy requirement and consumes more energy and oxygen than any other organ [30]. It continuously produces large amounts of adenosine triphosphate (ATP), which is necessary to sustain both active myocardial contraction and active diastolic relaxation. This is achieved by metabolising a variety of fuels (primarily fatty acids and glucose with additional contributions from lactate, ketones, and amino acids) and oxidative phosphorylation within the cardiac mitochondrial respiratory electron transport chain [31, 32]. In health, the bulk of mitochondrial ATP production, which contributes approximately \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 95\%$$\end{document}∼$95\%$ of myocardial ATP requirements, is derived from fatty acid oxidation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 40$$\end{document}∼40–\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$70\%$$\end{document}$70\%$), with the remainder originating from glucose and lactate via the oxidation of pyruvate [32, 33]. Only about $5\%$ of myocardial ATP demand is met by glycolysis, which does not require oxygen [32, 34, 35]. The heart demonstrates a degree of metabolic flexibility and is able to utilise different energy substrates under different conditions to maintain function, although this may become lost in the failing heart [36]. Given the reliance of actin-myosin interactions on ATP and oxygen availability, it is clear that disruption in cardiac energy metabolism pathways and altered myocardial energetics have significant implications for cardiac function and the pathogenesis of heart failure [30, 32].
## The Failing Heart and Its Fuels
As outlined above, heart failure presents a major societal impact, is increasingly prevalent, and is associated with substantial morbidity and mortality [37]. However, the role of cardiac energy metabolism is perhaps more intuitive in the pathophysiology of HFrEF, in that it is easier to conceptualise that the failing heart with contractile dysfunction is starved of fuel [30]. Impaired cardiac energy metabolism as demonstrated with non-invasive phosphorus magnetic resonance spectroscopy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{31}$$\end{document}31P-MRS) has been shown to correlate with the degree of heart failure and predict mortality in patients with HFrEF due to ischaemic and dilated cardiomyopathy [38–41]. In contrast, HFpEF remains challenging to diagnose, vastly heterogeneous in terms of pathophysiology and clinical risk factors and, until recently [42], had few therapeutic options [43]. Because of this, there is increasing interest to more carefully phenotype individual patients with HFpEF [44]. Recent basic scientific and translational studies have provided further evidence that impaired cardiac energy metabolism also plays a central role in the pathogenesis of HFpEF, with alterations in lipid metabolism, and an increase in mitochondrial stress and ROS together with other inflammatory-related changes in mitochondrial function [45–49]. This is plausible, given that active diastolic relaxation has the greatest energy demand of any phase of the cardiac cycle [50]: rather than a metabolic disorder of contraction, HFpEF is, if anything, a disease of relaxation (but one that is distinct from diastolic dysfunction alone [21]) and moreover one with few universally recognised animal models of the condition, in contrast to HFrEF in which ischaemia-reperfusion models of MI progress routinely to HFrEF. In both conditions, cardiac dysfunction further manifests as a series of profound shifts in the utilisation of fuels through specific pathways, as summarised in Table 1, which potentially appear to be distinct between HFpEF and HFrEF.Table 1A brief summary of some of the main metabolic changes associated with HFpEF (in comparison with HFrEF). The detailed metabolic changes that occur in heart failure are complex, and are dependent not only on the severity and type of heart failure present, but also on the coexistence of common comorbidities such as obesity and type 2 diabetes, and reviewed in detail elsewhere [70]Metabolic pathwayHFrEFHFpEFFatty acid oxidationIncreased in humans, measured in vivo [51, 52]Reduced in humans in vivo [51, 53, 54] and in rodents [55] measured via ex vivo Langendorff perfusionGlucose oxidationReduced in mice, measured via ex vivo perfusion [56, 57]Reduced in LV biopsy [58] from left-ventricular assist device (LVAD) patients with advanced HF and in rodents [59–61] measured via Langendorff perfusionGlycolysisUnchanged [62] or increased [63] in Langendorff-perfused rat heartIncreased in LVAD patients after biopsy [64]; also increased in rats [59].Coupling between glucose oxidation and glycolysisUnchanged in the perfused hearts of mice [56] or reduced in rats [63]Reduced in human LVAD patients [64] and in mice [65].Ketone body oxidationUnknown, insufficient dataIncreased in human advanced HF patients [66, 67] as determined either via biopsy at the time of LVAD implantation or transplantationBranched chain amino acid metabolismUnknown, insufficient dataImpaired in humans [68] and mice [68, 69] as measured via biopsy Multiple cardiometabolic and other risk factors for HFpEF, such as T2DM, obesity, and hypertension, are all independently associated with impaired cardiac energy metabolism, and can be shown through magnetic resonance spectroscopy studies in human patients and volunteers under a variety of cardiometabolic states to quantitatively affect cardiac energetics [11–17]. Diastolic dysfunction is also associated with impaired myocardial energetics in translational and clinical models, but represents a more widely spread disease in patients [71, 72].
It is therefore clear that metabolic modulation may thus be a promising therapeutic strategy for the treatment of heart failure in appropriately phenotyped patients [32, 73]. As detailed subsequently, novel metabolic and functional imaging techniques may help identify those patients who could derive most benefit from such strategies, as well as act as appropriate tools to use in future clinical trials.
## Inflammation and HFpEF
Alongside impaired myocardial energetics, there is a strong body of pre-clinical and clinical evidence that HFpEF is a multi-system disease characterised by systemic inflammation that affects the myocardium, skeletal muscles, vasculature, and kidneys [74], ultimately resulting in increased left ventricular myocardial stiffness and fibrosis. This is one of the hallmarks of HFpEF physiology, where increased myocardial stiffness and impaired diastolic relaxation results in high LV filling pressures and exertional breathlessness. It has been proposed that systemic inflammation is linked to comorbidities associated with HFpEF, particularly diabetes mellitus and obesity, which are associated with a systemic proinflammatory state, haematopoietic activation and systemic leukocytosis [75], and the subsequent development of myocardial stiffness and fibrosis [76].
## Titin as a Link Between Inflammation and Stiffness
Paulus et al. have presented a clear series of pathophysiological mechanisms that link systemic inflammation and myocardial stiffness [74]. Metabolic load associated with comorbidities, such as obesity, diabetes, and chronic kidney disease, induces proinflammatory signalling and systemic inflammation as evidenced by raised plasma levels of inflammatory cytokines [77–80]. Haemodynamic load, as seen in arterial hypertension, also activates proinflammatory and profibrotic signalling pathways [74]. Increased haemodynamic load is sensed by cardiomyocytes, fibroblasts, and resident macrophages resulting in a cascade of structural remodelling and leukocyte recruitment that promotes fibroblast activation and collagen deposition, thereby increasing myocardial stiffness [74]. Modifications in the titin protein are a major contributor to cardiomyocyte stiffness. Titin is the largest protein in the body, spans multiple regions within the human sarcomere, and is responsible for diastolic distensibility. It consists of two main isoforms, the short stiff N2B isoform and the longer, more compliant N2BA isoform. Post-translational titin oxidation or phosphorylation modulates distensibility and contribute to the high myocardial stiffness seen in HFpEF. The N2BA/N2B titin isoform ratio is lower and the N2B isoform is hypophosphorylated in HFpEF [81, 82].
Additionally, reactive oxygen species (ROS) cause formation of disulfide bonds within titin, also contributing to increased cardiomyocyte stiffness [83, 84]. Changes in myocardial collagen homeostasis also lead to abnormalities of the extracellular matrix, resulting in myocardial fibrosis and stiffness [76]. This has been evidenced by an increase in collagen volume fraction on myocardial biopsy in patients with HFpEF [84, 85]. Finally, the myocardial accumulation of degraded proteins, which occurs due to inflammation-triggered expression of inducible nitric oxide synthase in cardiomyocytes, is also thought to drive myocardial stiffness [45, 76]. This increase in stiffness directly causes haemodynamic effects that lead ultimately to the development of the symptoms of the disease, as summarised in Fig. 3.Table 2Human biopsy studies in HFpEF reveal a complex, multifacted disease with increased collagen deposition, alterations in the ECM, and a significant inflammatory component. Reported n is for HFpEF patients in each study; studies listed have substantially different designs and control arms. Abbreviations: ECM, extracellular matrix; EM, electron microscopy; FA, force analysis; HM, histomorphometry; IF, immunofluorescence; IHC, immunohistochemistry; RT-PCR, real-time reverse transcription-polymerase chain reactionPatient populationnAssaysFindingsRef. “All comers” advanced HFpEF108HistologyFibrosis in $93\%$ of patients; hypertrophy in $88\%$; inflammation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.9\times$$\end{document}1.9× higher compared to controls (as measured by CD68 expression); amyloidosis in $14\%$[86]CABG16Transcriptomics743 differentially expressed genes; alterations in ox phos, extracellular matrix proteins, titin, potassium voltage-gated channels[87]Suspected cardiomyopathy, HFrEF, and HFpEF36Western blotting, IHC, IFIncreased E-selectin and intercellular adhesion molecule-1 expression (HFpEF compared to HFrEF); increased NADPH oxidase 2 expression in macrophages and endothelial cells[88]Suspected cardiomyopathy27FA, HM, isoform and phosphorylation assaysStiffer myocardium, N2BA / N2B titin isoform ratio reduced, and N2B isoform hypophosphorylated in HFpEF[81]Worsening HF, suspected cardiomyopathy22FA, HM and EMIncreased collagen content, higher cardiomyocyte diameter, higher passive force, stiffer[82]Restrictive cardiomyopathy12FA, collagen volume fractionStiffer myocytes, often higher collagen volume fraction[19]NYAHA class II+20RT-PCR, collagen assay, histology, TGF-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β ELISAIncreased collagen content, decreased matrix metalloproteinase-1 (which is the primary collagenase of the heart and removes ECM), TGF-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β positive inflammatory cells present[89]Hypertensive CABG patients22FA, collagen assay, isoform and phosphorylation assaysIncreased passive myocardial stiffness; collagen-dependent and titin-dependent stiffness[84] *It is* worth reflecting that many studies which have provided mechanistic and molecular detail on the mechanobiology of HFpEF have done so through the acquisition of myocardial biopsy samples taken from patients listed for operations related either to their condition or their comorbidities, typically at the more severe end of the phenotype. These samples provide histological evidence supporting the twin hypotheses of both differential extracellular matrix content (with HFpEF patients having higher titin expression in their myocytes and a higher collagen content in the surrounding extracellular matrix), leading to mechanically stiffer myocytes with a stronger passive force per unit area (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_\text {passive}$$\end{document}Fpassive); and inflammation associated with HFpEF. In addition, endomyocardial biopsy provides a gold-standard method to diagnose or rule out cardiac amyloidosis, a prevalent aetiology of “HFpEF” that is mechanistically distinct, caused by the extracellular deposition of autologous protein as amyloid fibrils from a variety of more than 30 molecular sources [90] that initially presents as a restrictive cardiomyopathy characterized by progressive diastolic dysfunction that subsequently becomes systolic biventricular dysfunction with oft-fatal arrhythmia. A summary of histological findings in HFpEF is shown in Table 2, which are consistent with the pattern described above. Whilst this tissue is highly valuable, and can be subject to a large number of related differential analyses, it is prudent to be mindful of the fact that human biopsy samples are typically only obtained from patients at the more developed end of the disease spectrum and for whom the surgical intervention is otherwise justified. This selection bias combined with the risks and cost of biopsy motivates the investigation of both non-invasive, or less-invasive diagnostic techniques such as imaging methods, and furthermore the scientific study of less severe disease.
## Imaging Inflammation
The ability to non-invasively characterise myocardial inflammation and fibrosis has important implications for management. Emerging imaging tracers using hyperpolarized magnetic resonance or positron emission tomography enable non-invasive assessment of major cardiac leukocyte populations, including macrophages [91, 92]. Novel PET tracers targeting the cardiac stroma including collagen could be combined with cardiovascular MR for a complete assessment of the extracellular volume fraction. A recent study which identified patients with HFpEF and myocardial fibrosis on cardiovascular magnetic resonance imaging showed that the oral antifibrotic agent pirfenidone may reduce myocardial fibrosis [93]. The effects of this on clinical outcomes and patient symptoms still needs to be confirmed. Other immune-mediated conditions may more directly cause cardiac inflammation and heart failure symptoms. Multiple systemic autoimmune or immune-mediated disorders may cause myocarditis, where inflammation is the primary driver of cardiac dysfunction, while this is most commonly attributable to viral infection [94]. Identification of active primary cardiac inflammation is important as patients are far more likely to respond to immunosuppressive therapies. Advanced myocardial tissue characterisation techniques have shown considerable promise in the detection of myocardial inflammation [95], having identified cardiac involvement in a wide variety of systemic conditions [96–104].Fig. 3A very brief schematic overview of the currently understood pathogenesis of HFpEF. It is important to remember that the HFpEF syndrome is a biventricular process and that the contributions of the right heart are equally important. [ 1] Increased left ventricular stiffness, and a decreased functional reserve, results in [2] increased left-sided filling pressures with left atrial dilatation and increased left atrial pressure. This leads to increased pulmonary transcapillary hydrostatic pressure, which drives fluid transudation, potentially increasing capillary diameter from the Young-Laplace relation, and resulting in pulmonary congestion [3]. Similar maladaptive right heart processes may also occur in parallel. [ 4] Reduced right ventricular contractile reserve, and reduced coupling of the right ventricle to the pulmonary circulation or RV-PA coupling, results in increased right-sided pressures, with right atrial dilatation and increased right atrial and systemic venous pressures [5]. This exacerbates pulmonary congestion by reducing clearance of lung water via pulmonary lymphatics. The underlying pathogenesis of HFpEF is still the subject of ongoing research, but pro-inflammatory changes result in a mechanical increase in the stiffness of the myocardium mediated through alterations in collagen deposition and the biophysics of titin, which occurs concomitantly with metabolic changes leading to an increased oxidative stress on the myocyte
## Functional Imaging and HFpEF
It is therefore highly desirable to attempt to simultaneously ascertain both the function of the heart and measures of its metabolome and microstructure in the context of patients who may well be suffering from relevant comorbid diseases (e.g. T2DM). It is therefore recognised that, in a clinical context, the diagnosis of HFpEF remains challenging, with several diagnostic criteria proposed by learned bodies and in clinical trials [18].
Imaging approaches span a spectrum of complexity, invasiveness, availability, and cost, and these competing concerns have to be balanced against either the scientific rationale for their engagement, or the clarity which they could add to the diagnosis of a condition that exhibits a high misdiagnosis rate [105, 106]. Furthermore, HFpEF is a condition where comparatively subtle physiological changes can be manifest even at the early stages of the disease [18]. The haemodynamic response of the heart to increased myocardial stiffness includes left atrial dilatation (with a volume index \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>{32}\,\text {ml/m}^2$$\end{document}>32ml/m2 shown to increase the risk of cardiovascular mortality), and a reduction of diastolic function as typically quantified by the mitral E velocity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>{90}\,\text {cm/s}$$\end{document}>90cm/s) and septal e’ velocity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<{9}\,\text {cm/s}$$\end{document}<9cm/s) on echocardiography, or equivalently an increase in their ratio (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>9$$\end{document}>9). Whilst some degree of left ventricular hypertrophy is supportive of HFpEF, its absence does not exclude its diagnosis [18].
What is perhaps under-appreciated is the variability present within the condition and the degree to which it can be masked by existing co-morbidities or be confused with other forms of heart failure; one study by Kanagala et al. [ 107] on 154 patients with a provisional HFpEF diagnosis underwent a comprehensive CMR examination (stress/rest perfusion, cine function, and late gadolinium enhancement). Forty-two patients ($27\%$) were found to have an alternative diagnosis (coronary artery disease, microvascular dysfunction, hypertrophic cardiomyopathy, or constrictive pericarditis), with worse outcomes.
## Cardiovascular MR in HFpEF
Cardiovascular magnetic resonance (CMR) is the gold-standard imaging modality for assessing atrial and ventricular volumes and accurately quantifying ejection fraction (in contrast to echocardiography, which suffers both from an increased measurement variability and systematic bias in underestimating cardiac volumes, owing both to increased inter- and intra-observer variability and the conventional assumption that the LV forms a prolate ellipsoid when its mass is estimated through linear measurements made of the wall on the parasternal long-axis view [108–110]). As a consequence, although the full role of CMR for diastolic function assessment is evolving, it currently allows accurate assessment of the structural changes associated with HFpEF, such as left atrial (LA) enlargement, and pathological LV hypertrophy, along with mitral inflow and pulmonary venous flow velocity. These MR flow measurements are obtained via phase-contrast (PC) methods, wherein a bipolar gradient of magnetic field is applied along a given axis, and a change in the phase of the MR signal produced in proportion to the velocity of blood flowing along it. For the most part, the interpretation of these measures largely follows that of Doppler echocardiography, with a comparatively more coarse temporal resolution and a requirement to acquire data over several heartbeats as opposed to in real-time, meaning that CMR measurements of flow are hence more susceptible to arrhythmias. However, in addition to the measurement of mitral and pulmonary flow in one direction, it is possible to quantify the three-dimensional velocity vector field of blood through with CMR, using “4D-flow” sequences in which the direction of the spatial magnetic field gradient that encodes flow information in the phase of the MR signal is varied in 3D space. Over the course of a longer, more complex acquisition, whole-heart flow patterns can be obtained throughout the cardiac cycle which can both reveal complex valvular insufficiency, retained residual blood volume, and the degree of vorticity and turbulent flow in diastole [111, 112]. HFpEF patients are understood to have increased diastolic inflow vortex strength and greater turbulent flow, again consistent with a biomechanical dysfunction of pumping function [113], leading to complex and subtly different responses to fluid-dynamical alterations brought about by, for example, the effect of a presence of an interventricular septal shunt or vasodilation, compared to either healthy individuals or those with HFrEF [114, 115].
Additionally, diastolic dysfunction can also be inferred through alterations of the filling dynamics of each chamber of the heart. The increased availability of dedicated semi-automatic cardiac image analysis and segmentation software (oft using “AI” algorithms in the process) has recently enabled these to be computed relatively more rapidly by those reading the images, removing the requirement to manually segment, e.g. endo- and epicardial contours for the quantification of the LV time-volume filling curve. The resulting peak filling rates and time to peak filling can therefore easily be obtained. Both time to peak filling, peak filling rate, and other novel indices aimed at quantifying these curves have been demonstrated to be of utility in quantifying left ventricular diastolic dysfunction [116].
Furthermore, myocardial strain (and inferred stiffness) can be directly be determined through feature tracking or tagging techniques—which can either be determined post hoc after the acquisition of a conventional cine image or by the use of the spin-physics behind MR to impose structured tags within the myocardial tissue that then deform over time [117]. These thus therefore infer the motion of individual voxels of myocardial tissue, either by imprinting upon the physical myocardium or by estimating its motion in the image domain. As strain is effectively defined as extension over original length, longitudinal, radial, and circumferential strain can be estimated for the heart by the use of either a 2D or 3D mathematical model of deformation [118], once a computational model of the deformation over time of each individual voxel has been determined. This process remains an active research area for both techniques, owing to the fact that multiple complex cardiac motions may result in the same observed deformation [119, 120]. The utility of the technique in HFpEF comes from that fact that, if considered as a rigid body, a stiffer myocardium will be unable to deform as much under the same force, and increased myocardial stiffness can be quantified directly (c.f. Table 3). Perhaps reflecting this, multi-layer feature-tracking derived strain metrics have thus been shown to correlate with NT-proBNP and effectively be able to diagnose HFpEF in a signal scan with an $89\%$ sensitivity and a specificity of $100\%$ [121], and global longitudinal strain has itself been associated with mortality and other hard cardiovascular outcomes in HFpEF [122].Table 3Widely available cardiovascular magnetic resonance techniques permit the detailed assessment of myocardial function and some biophysical properties of the myocardiumNameExample imagePhysical basisIndicationCINE MR Conventional CINE imaging acquires multiple frames throughout the cardiac cycle by the use of either a gradient-echo based or SSFP-based readout. Bright blood contrast is provided predominantly by the fresh magnetisation in blood inflowing into the slice. A wide variety of standard cardiac anatomical parameters are readily determined by CINE MR, such as whole-heart mass and morphology. Dynamic tracing of the volume-time curve permits the determination of measures of chamber filling and emptying and flow rates. Atrial volumes Atrial volume-time curves are determined from CINE MR imagesAtrial dilatation is indicative of HFpEF, and impaired RV early filling may be compensated by increased RA booster pump function. Impaired LA conduit function is hypothesised as a distinct feature of HFpEF.Strain Either (a) a spatially periodic magnetisation depleting “tagging” RF pulse is played, superimposing a regular grid of grey on the heart that deform throughout the cardiac cycle (the gold standard); or (b) image-domain points on a previously acquired CINE image are tracked (“feature-tracking”). A model of strain is computationally built from the results. Changes in radial and circumferential strain in diastole are highly indicative of a stiffer heart and alteration with otherwise normal morphology may be a marker of early disease. T1 mapping The nuclear spin-lattice relaxation time, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_1$$\end{document}T1, is measured, typically by performing an inversion-recovery experiment with an inversion RF pulse followed by a series of low-flip angle Look-Locker readout sections at different times after inversion. The injection of gadolinium and reacquisition of the map permits the estimation of myocardial extracellular volume (ECV).Changes in myocardial T1 are associated with cardiac amyloidosis, and (more weakly) of HFpEF in general. ECV is strongly correlated with adverse outcomes in HFpEF cohorts. PC flow Bipolar gradients are used to encode the velocity of flowing blood into the phase of the MR signal. Phase-contrast methods can therefore reconstruct blood flow exactly along a given axis (typically into/out of the slice plane) or, at the expense of acquisition time, in 3D (leading to “4D flow”, a time-resolved acquisition).Mitral flow imaging is a sensitive measure of regurgitation and can easily quantify early (E) and late (A) diastolic flow, indicative of diastolic dysfunction similar to echo.
Finally, it is possible to use CMR to assess fibrosis directly through either late gadolinium-enhancement MR, or the increasingly available technique of T1 mapping with and without gadolinium and the concurrent estimation of the extracellular volume (ECV) fraction [116]. These related methods utilise the known fact that gadolinium contrast agents extravasate and are retained in interstitial spaces. Whilst conventionally used for the comparatively routine detection of fibrotic scar (typically following myocardial infarction) through late gadolinium-enhancement MR, by measuring the nuclear \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_1$$\end{document}T1 time of the myocardium pre and post the addition of gadolinium subtle, not visually obvious differences in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_1$$\end{document}T1 and hence gadolinium uptake can be determined. If the patient’s haematocrit is known, their extracellular volume fraction can be correspondingly estimated, and expressed in intuitive units of per cent [123]. An increase in ECV is almost always due either to excessive collagen deposition or cardiac amyloidosis, and ECV has been shown to act as an independent predictor of intrinsic LV stiffness in HFpEF patients as measured via invasive pressure-volume loops [124]. Fortunately for the differential diagnosis of cardiac amyloidosis, the appearance of global, subendocardial late gadolinium is characteristic [125], as is an inability to null the enhanced signal in an inversion-recovery late gadolinium MR scan, and an increase in the native \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_1$$\end{document}T1 of the amyloid myocardium [90, 126]. These CMR changes appear to be present for both AL and ATTR forms of cardiac amyloidosis, highlighting that CMR is a complementary technique to SPECT imaging with the bone-seeking diphosphate radionuclide \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{99m}$$\end{document}99mTc-DPD, a sensitive scintigraphy tracer that is highly effective at detecting the ATTR form of cardiac amyloidosis at an early stage, but only effective in approximately a third of patients with cardiac AL amyloid [90].
Advanced forms of proton cardiac imaging, therefore, have an important and growing role in the diagnosis of HFpEF from a clinical perspective, able to probe and quantify both the morphological adaptations in the HFpEF heart arising from increased filling pressures and impaired diastolic relaxation, and also probe changes due to increased mechanical stiffness and altered collagen content. Furthermore, compared to the use of echocardiography alone, the wider availability of CMR may help address the underdiagnosis of cardiac amyloidosis as a prevalent aetiology of “HFpEF” given the practical limits of the accessibility of complementary imaging (i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{99m}$$\end{document}99mTc-DPD SPECT-CT) for amyloid specifically.
## Exercise (In-)Tolerance
Together with functional changes at rest, HFpEF, like other forms of heart failure, is associated with a significant decrease in the exercise reserve of the patient. This arises as a biophysical consequence of increased LV stiffness: during hyperaemic stress there is an inability to augment the left-ventricular end-diastolic volume despite increasing the left-ventricular end-diastolic pressure for a given cardiac workload. The outcome of this is an increased stress on both the pulmonary vasculature and the heart as a whole: as is well known in HFrEF, exercise acts therefore as a cardiopulmonary stress test under which pathology may reveal itself in HFpEF as well, with patients with early-stage HFpEF displaying symptoms and invasively measured filling pressure increases only present on exertion [129, 130]. Exercise testing with concurrent echocardiography (ExE) has been used to provide further useful pieces of information that are of relevance in the diagnosis of HFpEF. Firstly, the awareness and perception of dyspnoea may be highly variable between patients and value therefore added by an objective measure both of cardiac parameters and exercise capacity [131–133]. Secondly, it is possible to acquire myocardial E/e’ during exercise, which has been shown to increase under stress through a study of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 436$$$$\end{document}$$n = 436$$ patients presenting with fatigue or dsypnoea and considered for the diagnosis of HFpEF [129] (as illustrated in Fig. 4A-C). Thirdly, the formation of hyperechoic “B”-line artefacts is directly detectable in the lungs of patients with acute pulmonary congestion [134], and shown to correlate with B-type natriuretic peptide (BNP), respiratory rate, clinical congestion, and systolic pulmonary arterial pressure (Fig. 4D) as a measure of pulmonary congestion. In HFpEF, the development of pulmonary congestion upon exercise is mostly concomitant with exercise-induced worsening of diastolic function [128], and CMR measures of cardiac function during exercise indicate that the HFpEF patient suffers atrial dilatation, indicative of higher filling pressures (Fig. 5). Tricuspid regurgitation has additionally been shown to be present on ExE, which, together with E/E’, can predict future mortality [135, 136].Fig. 4Echocardiography prior to and post exercise can reveal comparatively subtle increases in diastolic dysfunction. Reproduced here is the case of a 75-year-old male with current exertional dyspnoea and a history of percutaneous coronary interventional therapy. Wall motion analysis revealed no exercise-induced wall motion abnormality in either view (A, B). The measured mitral flow pattern and tissue Doppler was consistent with that of delayed relaxation (C) and E/E’ increased from 14 at rest to 16 with exercise [127]. Similarly, hyperechoic b-lines that arise in the lung from transudated fluid (D) that can be detected in HFpEF patients with dyspnoea, with good prognostic power [128]Fig. 5In an “in-magnet” CMR exercise study using a stepping ergometer (A) with whole-heart coverage (B) during 20 W submaximal exercise on a population of patients spanning the spectrum of diastolic dysfunction, it was found that the absolute change in right (C) and left (D) atrial volumes during exercise was highly maladaptive in HFpEF: greater dilation during exercise is suggestive of increased filling pressures [137]
## Metabolic Imaging Methods
These profound changes in the morphology, function, and structure of the heart in HFpEF occur concomitantly with alterations in metabolism and a shift in substrates used by the heart, as inferred by biopsy and metabolomic approaches outlined previously in Table 1. These invasive techniques necessarily are difficult to apply to a larger number of patients and are typically reserved either to studying preclinical models or patients who have effectively reached the end stage of the disease and undergo surgery either for LVAD implantation or transplant. In contrast, non-invasive or less-invasive molecular imaging techniques have recently been applied to both confirm or discover that these changes exist in vivo and at an earlier stage of the disease. All of these techniques provide a consistent picture of the disease becoming unmasked under exercise stress, summarised in Table 4.Table 4A summary of reported cardiovascular abnormalities in exercising HFpEF patientsModalityMeasurement found in HFpEFRelevanceRefs. EchocardiographyMitral E/E’ > 15; increased right ventricular systolic pressure; tricuspid regurgitation velocity >3.4m/sExE measures correlate with invasively measured pulmonary capillary wedge pressure (PCWP) and form part of the ESC diagnostic criteria (HFA-PEFF score)[18, 138]Mitral regurgitation; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\downarrow$$\end{document}↓ left atrial strain augmentationIndicative of right ventricular dysfunction and inefficient ventilation during exercise[139]Cardiac magnetic resonance imaging\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\downarrow$$\end{document}↓ Diastolic filling rate; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uparrow$$\end{document}↑ left and right atrial volumes; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\downarrow$$\end{document}↓ right ventricular ejection fraction augmentation during exercise; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uparrow$$\end{document}↑ lung proton density (water); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\downarrow$$\end{document}↓ left atrial ejection fraction; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\downarrow$$\end{document}↓ left atrial long axis strain; alterations in T1 and ECV. Decreased PCr/ATP ratio. Cardiac functional patterns linked to degree of impaired resting energetics (measured by the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{31}$$\end{document}31P PCr to ATP ratio)[116, 137]Invasive haemodynamic measurementsPCWP \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥25.5 mmHg/W/kg; PCWP cardiac output slope >2 mmHg/l/m; supine PCWP \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 25 mmHgAbnormally high exercise PCWP are confirmatory/diagnostic of HFpEF and have been associated with adverse cardiovascular outcomes[140, 141]Radionuclide ventriculography\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uparrow$$\end{document}↑ Time to peak filling; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\downarrow$$\end{document}↓ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta$$\end{document}Δ left ventricular ejection fraction augmentation; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uparrow$$\end{document}↑ arterial to left ventricular end systolic elastance (vasculoventricular coupling)In HFpEF during exercise, the active relaxation phase of diastole lengthens; shortens in controls[72]ECGChronotropic competenceAbnormal heart rate augmentation relates to degree of exercise intolerance[142]Cardiopulmonary exercise testing\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\downarrow$$\end{document}↓ Peak VO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uparrow$$\end{document}↑ VO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2 recovery kinetics; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\uparrow$$\end{document}↑ VE/VCO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2 slopeMeasures have been shown to predict cardiovascular outcomes among patients with HFpEF[143, 144]Abbreviations: HFA-PEFF, Heart Failure Association-PEFF with “P” standing for pre-test assessment, “E” echocardiography, “F” functional testing, and “F” final aetiology; PCWP, pulmonary capillary wedge pressure; VE/VCO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2, ratio of minute ventilation to carbon dioxide elimination; VO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2, oxygen uptake [145] Broadly speaking, CMR with nuclei other than protons, X-nuclei MR, and radionuclide imaging techniques are those that are able to best interrogate these changes in humans. Phosphorus-31 cardiac magnetic resonance spectroscopy is an established X-nuclear MR technique that is able to quantify the presence and concentration of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{31}$$\end{document}31P-containing moeities within the human heart, most notably phosphocreatine (PCr) and ATP. This reflects the energetic status of the heart as phosphocreatine acts as a labile energy buffer and the endpoint of multiple metabolic pathways. Accordingly, the PCr/ATP ratio is well known to be lowered in advanced heart failure [30], diabetes [146, 147], and HFpEF [148].Fig. 6Patients with increasing diastolic dysfunction progressively dilated atrial volumes, most likely as a result of increased filling pressures as alluded to in Fig. 3. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{31}$$\end{document}31P-magnetic resonance spectroscopy (obtaining from the mid interventricular septumA spectra showing the presence of high-energy metabolites such as phosphocreatine (PCr), three resonances corresponding to the three \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{31}$$\end{document}31P nuclei in ATP; and 2,3-diphosphoglycerate, B). This subsequently revealed in that these patients a discrete energetic deficit (i.e. a reduction in the detectable PCr/ATP) that was progressively worse across the groups (C). This was accompanied by an increase in detected pulmonary fluid as detected by a novel proton-density mapping MR sequence (D, [137]) Interestingly, it is possible to perform exercise experiments within the confines of the bore of the MR scanner, permitting the determination of PCr/ATP at rest and during submaximal exercise, together with the observation of functional changes in cardiac haemodynamics [148]. This therefore highlights multinuclear CMR as a powerful technique with the ability to simultaneously interrogate the energetic status of the heart through \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{31}$$\end{document}31P-spectroscopy, and probe functional and microstructural methods as outlined above in the same scan session. Some authors have developed magnet-safe exercise devices, together with advanced cardiac and pulmonary imaging CMR sequences (retrospectively gated compressed-sensing cine; and a novel radial ultrashort echo time lung water proton density mapping sequence) that permit the quantification of cardiac function and pulmonary free fluid during exercise ([137]; summarised in Fig. 6). It was found that under a constant low workload of 20 W, patients across a spectrum of diastolic dysfunction (11 age-matched controls, 9 with diabetes mellitus, 14 with clinical HFpEF, and 9 with amyloid cardiomyopathy) had a gradient of deficit in the PCr/ATP, an increase in the detected lung water again increasing across the groups, and (as expected) changes in atrial volumes that are likely associated with increased filling pressures. Fig. 7Myocardial oxidative metabolism inferred from the kinetics of the PET tracer 11C-acetate, which is visible in the RV and LV before being taken up into the myocardium and later excreted as it decays (A). By fitting these data to an appropriate mathematical model (B), it is possible to estimate myocardial mechanical work (denoted external work, EW) and infer MVO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2 and myocardial blood flow (MBF). In a trial of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 19$$$$\end{document}$$n = 19$$ HFpEF patients undergoing dobutamine exercise-mimetic stress, it was found that the ability of the heart to augment these values was reduced in HFpEF. This is consistent with an earlier indication in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 11$$$$\end{document}$$n = 11$$ HFpEF patients at rest that myocardial metabolic capacity is decreased as E/E’ increases (D) and as the left ventricular end-diastolic dimension decreases (LV EDV mass index is a diagnostic criterion of HFpEF), which highlights a metabolic impairment in the disease (panels A–C reproduced from [149]; D from [150] with permission) These factors paint a strong picture of a maladaptive cardiac response to exercise that is associated with a potentially causative metabolic shift: as well as reporting reduced PCr/ATP ratios and increased exercise lung water, previous work has aimed to characterise cardiac metabolism in HFpEF with radiolabelled 11C-acetate as a PET tracer [150, 151]. Injected free acetate is rapidly taken up by the heart and then mitochondrially converted to acetyl coenzyme A and metabolised to carbon dioxide through the TCA cycle through oxidative phosphorylation; and thus, 11C-acetate PET imaging provides a reproducible proxy measurement of cardiac oxidative metabolism (i.e. MVO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2) at centres that have the cyclotron required to synthesise this tracer with a 20-min half-life. In a study of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 11$$$$\end{document}$$n = 11$$ HFpEF patients and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 10$$$$\end{document}$$n = 10$$ controls, decreased cardiac update of 11C-acetate was inversely correlated with E/e’ in patients only [150]. In a more recent study [149] that additionally included dobutamine-induced exercise mimetic stress with prospectively enrolled HFpEF patients (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 19$$$$\end{document}$$n = 19$$) and matched controls (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$$n = 19$$$$\end{document}$$n = 19$$) that evaluated myocardial blood flow (MBF) and mechanical cardiac work in addition to MVO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2, it was found that, at rest, compared with controls, patients with HFpEF had higher cardiac work, MVO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2, and MBF. During dobutamine stress, cardiac work, MVO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2, and MBF increased in both HFpEF and controls, but the magnitude of increases was significantly smaller in HFpEF. This is indicative, as before, of the impaired ‘exercise reserve’ of the HFpEF heart. In both groups, MBF increased with dobutamine stress in relation to cardiac work, but the magnitude of this increase was significantly reduced in HFpEF patients, while HFpEF patients with LV hypertrophy had a significant reduction in computed left ventricular mechanical efficiency compared to controls, summarised in Fig. 7. This implies a mismatch of both cardiac energetic demand and blood flow—pointing to some degree of coronary microvascular disease—and moreover an alteration in the metabolic efficiency of the heart. It is worth stressing that pulmonary hypertension alone has been indicated in driving the heart towards glycolysis and the role of the right-heart in HFpEF cannot be understated [152]—but, as ever, isolating cause, effect, and symptom remains challenging owing to the exquisitely integrated nature of cardiovascular physiology.
## A Future Role for Advanced Imaging in HFpEF?
An abnormal metabolism is clearly implicated in HFpEF and HFrEF alike, and the question of whether (or not) metabolic changes drive HFrEF is often described as a decades-old chicken-and-egg problem in the context of HFrEF [30]. The roles of the immune system, of titin, and of maladaptation to the resultant alterations in the mechanical stiffness of the HFpEF heart are also clear: once a patient has started down the path of diastolic dysfunction, increased filling pressures, and other comorbidities that define the HFA-PEFF diagnostic scoring system, the manifest condition is relatively easy to study. What is less clear, however, is exactly why the epidemiologically relevant risk factors for HFpEF are obesity and T2DM. HFpEF is not always the most straightforward of conditions to diagnose: relevant masquerading conditions to rule out via a number of distinct investigations include cardiac amyloidosis, sarcoidosis, hypertrophic cardiomyopathy, valvular heart disease, high-output heart failure, myocarditis, constrictive pericarditis, and the various toxin-mediated cardiomyopathies [153].
Advanced metabolic imaging, therefore, might fulfil two separate purposes. Firstly, in a clinical research or pre-clinical setting, the ability to determine uniquely the balance of substrates used by the myocardium would directly inform the research of the underlying shifts in cardiac metabolism and whether they precede, occur contemporaneously with, or follow alterations in myocardial stiffness (in HFrEF, myocardial metabolic changes precede the descent into heart failure [30, 154, 155]). Moreover, trials investigating pharmacologic regimens aimed at improving the functional outcomes of HFpEF patients, or prevent those at high risk from developing the disease, would benefit from the ability to have a direct readout of their metabolic effect. Many such therapies have been proposed and investigated for HF in general, with somewhat mixed effects: etomoxir and perhexiline (reported as targeting carnitine palmitoyl transferase, decreasing myocardial fatty acid metabolism, and favouring a reciprocal increase in glycolysis) have shown benefit in patients but may be limited by side effects; trimetazidine and L-propionylcarnitine, decreasing fatty-acid oxidation, increasing glucose oxidation; and increasing fatty acid transport across the mitochondrial membrane directly may have a survival and functional benefit [156]. Despite their success in pre-clinical trials, most of these molecules have not found widespread adaptation in patients. One potential reason for this is that “all-comers” trials based on those with a clinical deficit in functional scores such as the New York Heart Association functional class may not select patients with an underlying pathogenic mechanism immediately compatible with the metabolic modulation each drug provides. Being able to directly image the specific alterations involved in the disease is therefore highly desirable.
Secondly, beyond scientific inquiry into the molecular origins of HFpEF, there are several reasons to believe that multimodal metabolic imaging may have a patient-specific role in the diagnosis and management of the condition. Metabolic imaging—either in the form of advanced MRI or PET with appropriate tracers—has long been recognised of being valuable in cardiology where the metabolic demands of the organ are inherently linked to its function with disease-varied efficiencies [157–159]. Novel technological progress, in the form of either advanced PET reconstruction algorithms and improved multi-modal imaging, or hyperpolarised MR (which uses endogenous metabolites such as [1-13C]pyruvate together with low-temperature spin-physics to make their metabolism transiently visible to the scanner), heralds considerable promise for improving the ability of sites to routinely and rapidly quantify more aspects of cardiac function. For that reason, more than 42 clinical trials registered on clinicaltrials.gov (with 21 currently recruiting) aim to use advanced MR, multimodal cardiac MRI, and measures of metabolism directly to prognosticate HFpEF (for example, by quantifying its microvascular milieu through the use of first-pass perfusion gadolinium scanning at rest and under dobutamine stress, with radioactive H\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}215O PET perfusion scans as a gold-standard perfusion readout [160]). Hyperpolarised MR, meanwhile, has already been shown to be of utility in investigating ischaemic heart disease [161], resolving both regional alterations in glucose utilisation caused by coronary artery disease, and in the diabetic myocardium, wherein insensitivity to glucose uptake and altered postprandial response was directly quantified [162]. The changes in energetic status within the HFpEF myocardium found via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{31}$$\end{document}31P spectroscopy are intriguing and point towards a disease on a spectrum of diastolic dysfunction potentially being both unified by an energetic deficit and ultimately prognosticated by it. Hyperpolarised pyruvate imaging can, in principle, achieve higher resolution imaging than similar PET scans within a minute [163] and may therefore be of utility in further characterising the HFpEF heart. This technique is currently in international clinical trials in a variety of indications, and offers the promise of a rapid and comprehensive assessment of myocardial metabolism which may be of benefit both in HFpEF and additionally for identifying the ischaemic region at risk in ischaemic cardiomyopathies. At present is a comparatively expensive and rare technique, but one with a wide number of potential applications both in cardiology and in other conditions, such as monitoring tumour response to therapy, and a similar cost base to PET. Should its value and utility be demonstrated conclusively in forthcoming trials, it is at least possible and at best probable that hyperpolarised MR could have clinical availability in the medium-term future.
The need for an advanced understanding of cardiac metabolism in vivo in human HFpEF patients is well illustrated by recent successful clinical trials. The surprising (yet positive) results of studies such as EMPA-REG which showed a significant benefit to cardiovascular mortality in T2DM patients with improved glycaemic control provided by empagliflozin, a sodium-glucose co-transporter 2 inhibitor [164], potentially by procuring a shift in cardiac metabolism [165], has led to a recent clinical trial aiming to replicate these findings in HFpEF patients independent of diabetes status. In the EMPEROR-Preserved trial of nearly 6000 patients with HFpEF and NYHA class II to IV functional impairment, empagliflozin reduced by around $21\%$ the combined risk of cardiovascular death or hospitalisation for HFpEF patients, regardless of the presence or absence of diabetes [166]. This follows from a string of broadly speaking failures in wide HFpEF demographic groups: PEP-CHF (antihypertensive perindopril, [167]); CHARM-Preserved (antihypertensive candesartan, [168]), I-PRESERVE (antihypertensive irbesartan, [169]), TOPCAT (potassium-sparing diuretic spironolactone, [170]), and PARAGON-HF (angiotensin receptor-neprilysin inhibitor sacubitril/valsartan, [171]) all failed to show substantial benefit with hard patient end-points. The fact that a metabolic modulator, albeit one with a variety of as yet-to-be-elucidated cardiac effects, can significantly improve patient mortality in both HFpEF and HFrEF demographics is puzzling, and requires further scientific study. Even small beneficial changes in cardiac energetic utilisation within a beat-to-beat timeframe may translate into large differences in efficiency, known to be associated with outcomes [165]. The sister studies to EMPEROR-Preserved evaluating SGLT2 inhibition in HFrEF, the DAPA-HF and EMPEROR-Reduced trials, have similarly shown significant reductions in morbidity and mortality with SGLT2 inhibition (dapagliflozin and empagliflozin respectively), again independent of diabetic state [172–174]. This is strongly suggestive of an underlying metabolic mechanism, not least because the heart does not express SGLT2 and radiolabelled \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{14}$$\end{document}14C autoradiographic studies indicate that it is not detectable directly in the heart [175]. Off-target effects involving either sodium homeostasis, or action on the sodium-proton exchanger, are not reported in isolated ventricular cardiomyocytes over a wide range of doses that may otherwise explain a direct cardiac mechanism of action [176]. The need to fully understand the metabolic changes that SGLT2 inhibitors can induce in patients with either HFrEF or HFpEF is therefore highlighted neatly: by altering systemic metabolism we can improve mortality, although we as yet do not know how.
The mechanisms underpinning the benefit of SGLT2 inhibitors (SGLT2i) in HFpEF remain elusive, though several compelling candidate pathways have been postulated. Restoration of myocardial energetics and normalisation of metabolism are a key candidate mechanism, linked to the observation that SGLT2is promotes mild ketosis, via an increase in production of the ketone body beta-hydroxybutyrate as reported in both invasive animal studies [177, 178] and patient populations [179, 180]. Ketones may offer a more efficient myocardial metabolic substrate, requiring fewer moles of oxygen per mole of ATP produced [181], thus counteracting metabolic inflexibility arising from an over-reliance on non-esterified fatty acids in HFpEF [182, 183]. Alternatively, ketones might abrogate pro-hypertrophic signalling pathways and prevent MAP kinase activation resulting in changes in left ventricular mass [184, 185]. Importantly, multiple facets of these mechanisms are amenable to non-invasive assessment, using the arsenal of imaging tools described in earlier sections. For example, this could include hyperpolarised 13C MRI and/or PET for cardiac substrate metabolism [186], 31P MRS for energetics, and 1H spectroscopy for myocardial lipid content. Ongoing clinical trials and investigations will aim to directly reproduce and test these findings in patient populations.
A second key candidate mechanism relates to modulation of the cardiac stroma, including resident innate immune cell and fibroblast function, which play a key role in maintaining normal cardiac function through the regulation of cardiac metabolism and the cardiac renin-angiotensin-aldosterone system, as well as the release of soluble paracrine factors with anti-fibrotic, pro-angiogenic, and anti-apoptotic effects [187, 188]. SGLT2is has been linked in other settings with amelioration of pro-inflammatory and pro-fibrotic signalling, and it is conceivable that these may have relevance to clinical benefit in HFpEF [189]. Again, using novel molecular imaging probes as described above in Sections “Imaging Inflammation” and “Cardiovascular MR in HFpEF”, it is now possible not only to assess key leukocyte populations within the myocardium, but also to measure myocardial fibrosis via cardiac extracellular volume. Additionally, the modulation of adipokine function (including a reduction in serum leptin) has been linked with altered epicardial fat deposition profiles [190, 191], the changes of which can be straightforwardly assessed using imaging.
Given the undeniable disease-modifying benefit that SGLT2i has been shown to have, elucidating their molecular mechanisms non-invasively with metabolic imaging methods in patients would provide a significant improvement in the understanding of HFpEF in general.
## Key Conclusions
HFpEF is a complex yet prevalent condition that is driven by an increase in the bio-mechanical stiffness of the heart, microscopically originating from changes in titin expression and alterations in the extracellular matrix and an increased collagen volume fraction of the myocardium. This leads to impaired relaxation, and hence maladaptive changes in the pressure/volume relationship of the heart. It is therefore often referred to as being defined by diastolic dysfunction.
Whilst sharing many common cardiovascular risk factors and co-morbidities with HFrEF, the HFpEF heart has itself been shown to be metabolically distinct. Advanced non-invasive imaging techniques are able to accurately quantify both the biomechanical phenotype of the HFpEF heart and its blunted haemodynamic response to exercise, and point towards energetic impairment. The recent emergence of SGLT2 inhibition as a disease-modifying therapy arguably provides further evidence that both myocardial and systemic metabolic effects should be investigated in order to further elucidate its mechanistic role in ameliorating this morbid and prevalent condition.
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|
---
title: Development and validation of the multidimensional impacts of movement scale
(MIMS) for yoga, weightlifting, and running
authors:
- Sarah Lynn
- Julia C. Basso
journal: Frontiers in Psychology
year: 2023
pmcid: PMC10014715
doi: 10.3389/fpsyg.2023.1078996
license: CC BY 4.0
---
# Development and validation of the multidimensional impacts of movement scale (MIMS) for yoga, weightlifting, and running
## Abstract
### Background
Movement is an essential element in maintaining overall well-being, producing both physical and mental health benefits. Yoga is a mindful movement practice, with traditional yogic texts providing a framework, called the Koshas, that delineates how an intentional movement practice may impact multidimensional aspects of an individual. To date, no self-report measure examines the multifaceted ways that movement affects the individual at a physical and psychological level. Therefore, we developed the Multidimensional Impacts of Movement Scale (MIMS) by aligning ancient yogic traditions with current neuroscientific concepts.
### Methods
MIMS was developed based on the five categories of the Koshas; 9 questions per Kosha resulted in 45 total questions. Participants ($$n = 103$$) self-identified as having yoga, running, or weightlifting as their primary movement practice, engaging in this practice at least 30 min per session, once a week, for the past 3 months. Participants engaged in their usual movement practice and then (within 2 h of their workout session) completed the MIMS along with a series of previously validated questionnaires. After a period of 2 weeks, participants completed their normal movement practice once again and took the MIMS a second time to assess test–retest reliability and Cronbach’s alpha. Validity testing included convergent and divergent validity testing through Pearson’s product-moment correlations and confirmatory factor analysis.
### Results
One-hundred and three participants completed all study measures. Test–retest reliability demonstrated stability over time ($r = 0.737$, $p \leq 0.001$). Cronbach’s alpha was between 0.775 and 0.840 for each of the factors, $p \leq 0.001.$ MIMS was sensitive to confirmatory and discriminatory validity testing. Validity was also demonstrated through confirmatory factor analysis (i.e., Chi Square, Comparative Fit Index, Root Mean Square Error of Approximation).
### Conclusion
MIMS is a valid and reliable tool to measure the multidimensional impacts of movement. The tool provides information about the effects of movement on a range of physical and psychological elements including subscales representing the body, energy, mind, intuition, and contentment. Physical activities that include aspects of mindfulness may demonstrate the most robust effects on the MIMS.
## Introduction
Physical activity is defined as “any bodily movement produced by skeletal muscle contraction that increases energy expenditure above a basal level”(Piercy et al., 2018). Physical activity is beneficial for a range of physical and mental health issues, including obesity, type II diabetes (Kumar et al., 2019), cancer (McTiernan et al., 2019), and mood and anxiety disorders (Chan et al., 2019), and has been shown to increase the human life span (Anderson and Durstine, 2019). Importantly, exercise produces a range of positive effects at the psychological level including decreased stress, anxiety, and fatigue, and improved energy, mood, self-esteem, and social satisfaction (Sharma et al., 2006; Basso and Suzuki, 2017; Mikkelsen et al., 2017). To date, the majority of research in this realm has focused on either aerobic (e.g., running) or anaerobic (e.g., weight lifting) exercise. Yoga, however, is a mindful physical activity practice that incorporates movement, breathwork, concentration, and meditation (Rakel, 2012; Govindaraj et al., 2016). A 2012 National Health Interview Survey (NHIS) found that approximately 31 million ($13.2\%$) US adults have tried yoga in their lifetime and about 21 million ($8.9\%$) US adults practice yoga regularly (Cramer et al., 2016) The NHIS found that yoga practitioners were motivated to practice yoga due to wellness and disease prevention, increased energy, enhanced immune function, and reduced stress, and in a comparison review of the health benefits of yoga vs. traditional aerobic exercise (Ross and Thomas, 2010), yoga was found to be as effective as exercise at reducing stress and enhancing mood, motor, and cognitive performance (Taheri et al., 2018).
Considering the range of exercise-induced psychological effects, measuring such outcomes is challenging, and to assess outcomes, researchers often utilize a battery of self-reported measures and neuropsychological tasks, which take time and expertise to administer. Importantly, no self-report scales exist to measure the multidimensional outcomes of movement, and no known scales address the complex system of outcomes of mindful movement practices such as yoga. Currently, two validated tools exist to assess yoga. First, the Beliefs about Yoga Scale (Sohl et al., 2011) was developed to aid researchers in finding participants likely to complete longitudinal yoga studies. The information gathered from the Beliefs about Yoga Scale illuminates positive and negative beliefs about yoga’s potential outcomes and its connection to spiritual traditions. Second, the Yoga Self Efficacy Scale (Sohl et al., 2011) was developed to determine how people feel during the practice of yoga. Questions on this scale target body, breath, mind, and confidence in knowing how and what to do during a yoga class. Based on the lack of scales to assess yoga and exercise more generally, we sought to develop and validate a scale to assess the multidimensional outcomes of movement. We intentionally chose to develop this scale using both a yogic and neuroscientific framework as the developers of this scale were an experienced meditation teacher (with >10,000 h of teaching experience) and a PhD neuroscientist who specializes in how mind–body-movement practices affect neuropsychological functioning.
In regard to the yogic framework, the Yoga Sutras of Patanjali (Patañjali and Zambito, 1992), a primary yogic text, explains a process of achieving freedom through yoga, including ethical considerations (yamas and niyamas), movement (asana), breathwork (pranayama), sensory control (pratyahara), concentration (dharana), meditation (dhyana), and the resulting freedom (samadhi). In addition, the Taittiriya Upanishads (200 CE), a book on the nature of life, death, love, and divine presence, explains a system of multidimensionality among layers of every individual called the Koshas (Easwaran, 2007). The five layers of the Koshas are the body, energy, mental function, wisdom, and contentment. The Koshas are often spoken of as containers such that the physical body contains the energetic body, which further contains thoughts, emotions, and sense perceptions (i.e., the mind), which contains wisdom (i.e., intuition), which holds contentment at the core (Figure 1). In regard to the neuroscientific framework, we examined the Koshas in the context of neuroscience and found excellent alignment between each Kosha and a particular psychological/neuroscientific construct (Table 1). As an example, the Anamaya Kosha (which represents the physical body) aligned with the concepts of proprioception, balance, and embodiment, whereas the Pranayama Kosha (which represents the breath and energetic life force) aligned with the concepts of vitality and fatigue. The Koshas representing mind, intuition, and contentment were each paired with respective psychological constructs (Table 1).
**Figure 1:** *Diagram demonstrating the concentric nature of the koshas.* TABLE_PLACEHOLDER:Table 1 *From this* theoretical framework, we developed and subsequently validated a tool based on aligned yogic and neuroscientific concepts that assessed the multifaceted impacts of movement (i.e., body, energy, mind, intuition, contentment; Table 1). We utilized three different movement practices [i.e., yoga (balance/flexibility/mindfulness); running (aerobic); and weightlifting (anaerobic)] to test the hypothesis that the Multidimensional Impacts of Movement Scale (MIMS) is valid and reliable using rigorous statistical analytic techniques.
## Procedure
The Virginia Tech Institutional Review Board (IRB) approved this study (IRB-21-074). MIMS was created in four phases: [1] item generation; [2] review by a panel of experts; [3] focus groups, and [4] testing. The study authors are experts in behavioral neuroscience and yoga and completed the initial item generation through conversations that surrounded mapping the Koshas onto modern neuroscientific concepts. We utilized a panel of experts to review the first iteration of MIMS, including a neuroscientist, a yoga instructor, and two experts in tool validation. Several iterative revisions were made considering changes from this panel. Feedback from two focus groups, including both undergraduate and graduate students at Virginia Tech, helped to further refine the individual items and study format.
Recruitment occurred through social media posts, online posts hosted through the university, and flyers hung around campus. Direct emails were also sent to related places of business (e.g., gyms and yoga studios). After passing a screening questionnaire, participants were randomized into Group A (received MIMS + surveys for validation at test, and MIMS + demographics at retest) or Group B (received MIMS + demographics at test, and MIMS + surveys for validation at retest; Figure 2). The random division into two groups served to minimize any effect the surveys had on responses to MIMS. Participants completed their usual movement practice, and within 2 h completed their initial test/survey. We chose this 2-h period as the acute effects of exercise are most potent up to 2 h after exercise cessation (Basso et al., 2015). After a 2-week wash-out period, participants were instructed to complete their typical workout again and then complete their retest within 2 h. Participants were instructed that this 2nd workout should be as close to the initial as possible in terms of type of activity, length and intensity of workout, and time of day completed. Participants were compensated $20 for completing the entire study with no partial payments.
**Figure 2:** *Study design, including study elements and timeline.*
## Participants
A total of $$n = 146$$ participants volunteered and completed screening. Participants were included if they were 18 years or older, had English as their primary language, and self-identified as having yoga, running, or weightlifting as their primary form of movement practice for greater than three months. Participants were excluded if they did not pass the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+; Warburton et al., 2011) or reported that their regular movement sessions lasted less than 30 min. Of the 146 participants who took the screening tool, 24 did not meet the eligibility criteria. Of the 122 participants who started the study, 19 did not complete all necessary components of the research and were removed, leaving $$n = 103$$ participants for analysis.
The initial scale that was developed included 50 total questions, with 10 questions in each of the five a priori factors. Through confirmatory factor analysis, we eliminated 1 question from each of the a priori factors, leaving a total of 45 total questions (9 per factor). Therefore, the following results are based on these 45 final questions. Final scale and scoring details are available (see Supplementary File 1).
## Assessing physical activity readiness
The Physical Activity Readiness Questionnaire (PAR-Q+; Warburton et al., 2011) is a self-report tool created to help individuals make connections between their health and physical activity. The PAR-Q+ was used as a screening tool to assure participants were safe to engage in their regular movement practice. The PAR-Q+ underwent a revision in which some of the questions were revised for clarity. In this study, we use the short form of the PAR-Q+, which has seven questions that can be answered with a “yes” or “no” response. Three questions have space to add greater detail with free writing. From the old to the new version of PAR-Q, there was a strong correlation ($r = 0.80$); the test–retest reliability of the PAR-Q+ is ($r = 0.99$), and it shows a much greater specificity over the PAR-Q.
## Confirming validity for the a priori factor, body
The Activities Specific Balance Confidence Scale (ABCS; Peretz et al., 2006) is a self-report scale providing information about an individual’s fear of falling and confidence in moving through the world. It is a single factor scale with 16 questions, with the overall score reported as an average. Answers to the questions are reported in increments of 10 as confidence percentages that one will not fall given a specific activity. The ABCS validation testing reports Cronbach’s alpha = 0.96 and a test–retest correlation ($r = 0.92$, $p \leq 0.001$). Convergent Validity was tested against the Physical Self-Efficacy Scale (PSES; Ryckman et al., 1982) while divergent validity was tested with the Positive and Negative Affect Scale (Crawford and Henry, 2004).
The Scale of Body Connection (SBC; Price et al., 2017) is a self-report measure of bodily awareness and dissociation. There are 20 questions divided into 2 subscales: body awareness and body dissociation. The SBC is measured on a 5-Point Likert Scale, with 0 representing “Not at all” and 4 representing “All of the time.” The SBC is best scored using two subscales, with higher scores corresponding to higher levels of body awareness and body dissociation, respectively. An overall score is calculated by reversing the body dissociation score and taking an average of the two subscale scores. The SBC proves reliability with Cronbach’s alpha = 0.83. Construct validity using Structural Equation Modeling found a goodness-of-fit model that demonstrated two independent factors.
## Confirming validity for the a priori factor, energy
The Brief Resilience Scale (BRS; Smith et al., 2008) is a self-report measure of an individual’s perception of resilience. There are six questions and no subscales, with overall score reported as a mean. Questions are answered on a 5-point Likert scale, with 1 representing “Strongly Disagree” and 5 representing “Strongly Agree.” The BRS displays strong internal consistency with a Cronbach’s alpha ranging from 0.80 to 0.91 for each of the four groups used for testing. Principal Component Analysis from all four samples shows only one factor, accounting for 55–$67\%$ of the variance. Factor loadings ranged from 0.68 to 0.91.
The Fatigue Severity Scale (FSS; Learmonth et al., 2013) is a self-report measure bringing together emotional and physical symptoms of fatigue on one scale. The FSS has nine questions and no subscales, with the overall score reported as an average. It is measured on a 7-point Likert scale with 1 representing “Strongly Disagree” and 7 representing “Strongly Agree.” The FSS was validated using the Intraclass Correlation Coefficient (ICC) with $95\%$ confidence intervals and a test–retest score of 0.751. Convergent validity of the FSS and the Modified Fatigue Impact Scale (MFIS) (Larson, 2013) show r > 0.5 Spearman Correlation.
The Subjective Vitality Measure (SVM; Ryan and Frederick, 1997) is a self-report measure of an individual’s perception of their vitality or sense of energy and livelihood. Seven questions on this measure are scored on a 7-point Likert Scale with 1 representing “Not at all” and 7 representing “Very True.” Certain items are reverse scored, and the overall score is reported as an average. The SVM earned a Cronbach’s alpha ranging from 0.84 to 0.86 in three samples. The test–retest in both clinical and non-clinical samples was >0.70. Factor analysis revealed eigenvalues = 6.77.
## Confirming validity for the a priori factor, mind
The Beck Anxiety Inventory (BAI; Borden et al., 1991) is a self-report measure of anxiety symptoms, including questions about somatic and psychological experiences related to anxiety. It has 21 questions, with various factor-analytic studies reporting between two to six factors. Questions are asked on a 4-point Likert scale, with 0 representing “Not at all” and 3 representing “Severely – it bothered me a lot.” The total score is calculated by summing responses for each question. Results can be described as 0–21 = low anxiety; 22–35 = moderate anxiety; and 36 and above = potentially concerning anxiety levels. BAI demonstrates high internal consistency with Cronbach’s alpha = 0.91 with median item correlations at $r = 0.56.$ Principal Components Analysis (PCA) with eigenvalues greater than 1.0 with a varimax rotation converged in 19 iterations, resulting in five factors, which accounted for $60\%$ of the variance.
The Beck Depression Inventory (BDI; Beck et al., 1988) is a self-report measure of depression symptoms. BDI has 21 questions, and factor analysis over 25 years of re-testing shows between three and seven factors. BDI includes multiple-choice questions, instructing the participant to select the phrase that best describes them (e.g., “I do not feel sad,” “I feel sad,” “I am sad all the time and I cannot snap out of it,” or “I am so sad and unhappy that I cannot stand it”). The responses are rated from 0 to 3, and it is scored as a sum of all responses. These sums are then rated as: 1–10 = these ups and downs are considered normal; 11–16 = mild mood disturbance; 17–20 = borderline clinical depression; 21–30 = moderate depression; 31–40 = severe depression; and over 40 = extreme depression. BDI’s reliability shows a Cronbach’s alpha = 0.86 in the clinical population and 0.81 in non-clinical populations. The test–retest reliability showed r > 0.60. Concurrent Validity with Hamilton Psychiatric Rating Scale for Depression (HRSD; Miller et al., 1985) showed $r = 0.72$–0.73 for clinical populations and $r = 0.60$–0.74 in nonclinical populations.
The Positive and Negative Affect Scale (PANAS; Crawford and Henry, 2004) is a self-report measure of positive and negative affect. PANAS has 20 questions with two subscales: positive affect and negative affect. It is scored on a 5-Point Likert Scale, with 1 representing “Very slightly or not at all” and 5 representing “Extremely.” Both positive and negative affect scores range from 10 to 50, with higher scores representing higher levels of that particular affective state. PANAS has a Cronbach’s alpha = 0.89 for Positive Affect and 0.85 for Negative Affect. Confirmatory factor analysis showed both models of good and poor fit.
## Confirming validity for the a priori factor, intuition
The Compassion Scale (CS; Pommier et al., 2020) is a self-report measure of one’s kindness and desire to lessen the suffering of others. CS includes 16 items divided among four subscales: kindness, common humanity, mindfulness, and indifference (reverse scored), with the overall score and subscales reported as averages. A variety of studies show CS to be reliable, with Cronbach’s alpha ranging from 0.77 to 0.90. Test–retest reliability demonstrated $r = 0.81.$ Known group validity showed marked differences, as expected in meditators vs. non-meditators, and Structural Equation Modeling found a good fit with three positive subscales and one negative subscale.
The Metacognition Questionnaire-30 (MCQ-30; Wells and Cartwright-Hatton, 2004) is a 30 question self-report measure of cognitive confidence. The MCQ-30 has five subscales: confidence, positive beliefs about worry, cognitive self-consciousness, negative beliefs about uncontrollability and danger, and need to control thoughts. A 4-Point Likert scale is used in the MCQ-30, with 1 representing “Do not agree” and 4 representing “Agree very much.” Summation scores range from 30 to 120, with higher scores representing higher levels of unhelpful metacognitions. Cronbach’s alpha for MCQ-30 ranges from 0.70 to 0.93 for each of the five subscales.
The Multidimensional Assessment of Interoceptive Awareness (MAIA; Mehling et al., 2012) is a self-report measure of an individual’s awareness of their internal sensations. It has 32 questions with 8 subscales: noticing, not-distracting, not-worrying, attention regulation, emotional awareness, self-regulation, body listening, and trusting. MAIA uses a 6-Point Likert Scale with 0 = Never to 5 = Always. Scores are calculated as the average of each domain with selected items reversed. Internal Consistency ranged from 0.66 to 0.82 for individual subscales of MAIA. Correlations among subscales ranged from 0.09 to 0.60. The validity of MAIA was tested with convergent and divergent scales.
## Assessing validity for the a priori factor, contentment
The Dispositional Positive Emotions Scale (DPES; Shiota et al., 2006) contains a subscale measuring Awe. This subscale has been validated individually to measure an individual’s curiosity and wonder about the world (Gottlieb et al., 2018). The Awe *Subscale is* made up of six questions on a 7-point Likert scale, with 1 representing “Strongly Disagree” and 7 representing “Strongly Agree,” and the overall score reported as an average. The validation study utilized Amazon Mechanical Turk, with participants having >$95\%$ approval ratings. Cronbach’s alpha = 0.82 among all six items of the Awe Subscale. The Awe Subscale was validated against other scales considering spirituality and science and was found to have significant and measurable scientific quality.
The Satisfaction with Life Scale (SLS; Pavot et al., 1991) is a self-report measure of subjective well-being. It has five questions and no subscales, scored on a 7-Point Likert Scale with 1 representing “Strongly disagree” and 7 representing “Strongly Agree.” Scores are reported as one total sum, divided into designations of extremely satisfied (31–35), satisfied (26–30), slightly satisfied (21–25), neutral [20], slightly dissatisfied (15–19), dissatisfied (10–14), and extremely dissatisfied (5–9). The SLS proves reliable with a Cronbach’s alpha = 0.85 and test–retest reliability of 0.84. Factor analysis and factor loading were stronger for individual questions than composite scores, ranging from 0.55 to 0.93.
## Power and statistical analysis
An a priori power analysis was run using G*Power 3.1 to determine the appropriate number of participants to sufficiently power this study (Faul et al., 2009). We utilized an F test, ANOVA: Repeated measures, within-between interaction using an effect size of 0.25, an alpha error probability of 0.0005 to correct for multiple testing, power level of 0.8, three groups (yoga, running, and weightlifting), two measurements (test vs. retest), correlation among representative measures of 0.5, and nonsphericity correction of 1 to determine a sample size of $$n = 96$.$
Statistical analysis was completed for validation and reliability of the Multidimensional Impacts of Movement Scale. Cronbach’s alpha and correlations were conducted using SPSS, Version 27.0.1.0, 64-bit edition (IBM SPSS Statistics for Macintosh, 2020). Internal consistency was calculated as Cronbach’s alpha. Pearson’s product–moment correlations were calculated to determine test–retest reliability demonstrating the tool’s stability over time. Convergent and divergent validity were determined with Pearson’s product–moment correlations using previously validated tools alongside the initial test of MIMS. Confirmatory factor analysis (CFA) was completed using RStudio 2022.02.0 Build 443 (R Studio, 2022). CFA was performed to determine if the a priori five-factor structure could be confirmed with a good fit model. Scale purification improved model fit through statistical judgment, factor loading, and parsimony to remove any redundant questions based on correlations. Specifically, we utilized an acceptable cutoff for reliability of Cronbach’s alpha >0.700 (Tavakol and Dennick, 2011). As measures of validity are more subjective, we examined the interplay between sample size, factor loading, root mean square error of approximation (RMSEA), and the goodness of fit (Bolarinwa, 2015; Singh, 2017). We then utilized parsimony to simplify and balance the scales. One-Way Analysis of Variance (ANOVAs) was performed to determine statistically significant differences in MIMS outcomes between yogis, runners, and weightlifters; Tukey–Kramer post-hoc analyses were conducted as appropriate. Data are presented as mean (standard error of the mean), and statistical significance was determined using $p \leq 0.05$ (Table 2).
**Table 2**
| Basic characteristic | n | % | Basic characteristic.1 | n.1 | %.1 |
| --- | --- | --- | --- | --- | --- |
| N = 103 | | | | | |
| Sex | | | Ethnicity | | |
| Female | 82.0 | 79.6 | Hispanic | 7 | 6.8 |
| Male | 21.0 | 20.4 | Non-Hispanic | 95 | 92.2 |
| Race | | | Prefers not to answer | 1 | 1 |
| Asian | 7.0 | 6.8 | Education | | |
| Black | 4.0 | 3.9 | High School | 7 | 6.8 |
| Indigenous | 0.0 | 0.0 | Some college or vocational training | 28 | 27.2 |
| White | 90.0 | 87.4 | Associates Degree | 7 | 6.8 |
| Prefers not to answer | 1.0 | 1.0 | Bachelor’s Degree | 29 | 28.2 |
| Income | | | Graduate Degree | 32 | 31.1 |
| Low <$40,000 | 19.0 | 18.4 | | | |
| Middle $40,000–$120,000 | 36.0 | 35.0 | | Mean | ±SD |
| High >$120,000 | 35.0 | 34.0 | Age | 30.39 | 12.63 |
| Prefers not to answer | 13.0 | 12.6 | | | |
## Reliability
MIMS demonstrated test–retest reliability of $r = 0.737$ with significance of $p \leq 0.001.$ All subscales showed significant stability over time, with r > 0.670, $p \leq 0.001$ or higher for each subscale. Internal consistency was confirmed with Cronbach’s alpha for each factor and individual question. There were nine questions in each of the five a priori factors, which were all examined individually. All questions remained, showing that a removal of any question would not result in a change in Cronbach’s alpha below 0.700. Cronbach’s alpha is between 0.775 and 0.840 for each of the factors (body α = 0.781, energy α = 0.840, mind α = 0.815, intuition α = 0.775, and contentment α = 0.830).
## Validity
The body factor was positively associated with the SBC awareness ($r = 0.509$, $p \leq 0.001$) and negatively associated with dissociation (r = −0.296, $$p \leq 0.002$$) subscales. No significant association was found with the ABCS.
The energy factor was negatively associated with the FSS (r = −0.226, $$p \leq 0.022$$) and SVM ($r = 0.602$, $p \leq 0.001$). No significant association was found with the BRS.
The mind factor was negatively associated with the BAI (r = −0.218, $p \leq 0.027$) and BDI (r = −0.392, $p \leq 0.001$), positively associated with PANAS positive affect ($r = 0.428$, $p \leq 0.001$), and negatively associated with PANAS negative affect (r = −0.339, $p \leq 0.001$).
The intuition factor was positively associated with the CS ($r = 0.377$, $p \leq 0.001$) and MAIA ($r = 0.580$, $p \leq 0.001$). No significant association was found with the MCQ-30.
The contentment factor was positively associated with the DPES awe subscale ($r = 0.515$, $p \leq 0.001$) and the SLS ($r = 0.461$, $p \leq 0.001$).
## Confirmatory factor analysis
Confirmatory factor analysis supported five distinct factors. Scale purification was completed based on initial data. After reviewing correlations, factor loading, and to improve parsimony, items 14 (contentment), 31 (body), 32 (energy), 27 (mind), and 45 (intuition) were removed from MIMS. Specifically, item 14 on the contentment scale had the highest factor load. When analysis was run without item 14, Cronbach’s alpha improved for that subscale, and the other factor loadings adjusted to create an overall better model fit. In order to balance the scales, individual items with the highest factor loading were removed, which improved both the overall model fit and Cronbach’s alpha for each subscale. The revised scale has 45 items, 9 in each factor. The data are represented in Table 3 and Figures 3A–E.
## Differences among movement groups
Regarding the overall MIMS score, statistically significant differences were found between the three movement groups [F[2, 100] = 4.095, $$p \leq 0.020$$], with this effect being driven by body [F[2, 100] = 5.618, $$p \leq 0.005$$] and intuition [F[2, 100] = 4.083, $$p \leq 0.020$$]. Regarding the MIMS total score, the yoga group reported the highest score while the running group reporting the lowest score (Table 4; Figure 4). Post-hoc analyses revealed that the yoga group scored significantly higher than the running group on the total MIMS score [16.991, $95\%$ CI (2.29 to 31.69), $$p \leq 0.19$$], as well as the body [3.791, $95\%$ CI (0.92 to 6.67), $$p \leq 0.006$$] and intuition [3.546, $95\%$ CI (0.38 to 6.71), $$p \leq 0.024$$] subscales. Additionally, the weightlifting group scored significantly higher on the body [3.370, $95\%$ CI (0.50 to 6.24), $$p \leq 0.017$$] and intuition [3.177, $95\%$ CI (0.02 to 6.34), $$p \leq 0.049$$] subscales than the running group.
## Discussion
In this study, we delineated the process for validating the Multidimensional Impacts of Movement Scale (MIMS), which included item generation, examination of the items/scale through a panel of experts and focus groups, data testing, and validity and reliability analyses. MIMS was built by aligning modern neuroscientific concepts with the traditional yogic framework of the Koshas, which supports the idea that humans are complex beings, with intricate, simultaneous aspects of the self (Easwaran, 2007). Our results demonstrate that the MIMS is valid and reliable with five distinct subscales: body, energy, mind, intuition, and contentment. MIMS is stable over time as represented by strong test–retest scores and demonstrates strong internal consistency with a high Cronbach’s alpha for each of the five distinct subscales, ranging from α = 0.775 to 0.840. The tool is valid, showing convergent validity with strong significant correlations between known, previously validated tools, clearly defining the psychological constructs that MIMS measures.
The overall MIMS score indicates the general impact of movement on an individual, while the subscales themselves provide a more nuanced examination of the multidimensional outcomes of movement. The body subscale measures an individual’s awareness and control over their body. A high score on the body subscale indicates high levels of physical awareness and low levels of bodily dissociation. The energy subscale measures vitality and an individual’s ability to turn energy into action. A high score on the energy subscale indicates increased levels of vitality and decreased levels of fatigue. The mind subscale measures the integration of thoughts, emotions, and senses. A high score on the mind subscale indicates high levels of positive affect and low levels of negative affect (e.g., depression, anxiety). The intuition subscale measures how much an individual trusts their thoughts and emotions to guide decision-making. A high score on the intuition subscale indicates high levels of interoceptive awareness and compassion. Finally, contentment measures the ease and satisfaction an individual feels within oneself and the world around them. A high score on the contentment subscale indicates high levels of awe and satisfaction with life.
We recommend that MIMS can be used in movement research, both for scientific and clinical purposes. Importantly, the tool will reduce participant burden by having one scale with various outcomes. The self-report element of this tool makes it easy to implement, taking only a few minutes to complete. This tool will allow consistency of measurement across different movement modalities and may even be implemented in other mind–body-movement techniques such as meditation. MIMS can also be applied within the movement industry as a tool to assess outcomes of group and individual exercise, helping individuals or businesses to visualize the results of their movement practice/offerings.
## The effects of yoga, weightlifting, and running on MIMS
As the Physical Activity Guidelines for Americans (Piercy et al., 2018) encourage participation in cardiorespiratory, strength training, and flexibility/balance activities weekly, the tool was intentionally validated across these three movement categories (i.e., running, weightlifting, and yoga). Our data indicate that different forms of movement may produce different outcomes at the physical and psychological levels. Therefore, encouraging multiple movement forms across the week may create the most balanced results across the full range of MIMS outcomes. Specifically, yoga practitioners scored highest on the MIMS indicating that yoga may impact more elements measured by this scale than weightlifting or running. Post-hoc analyses revealed that yoga practitioners scored higher than runners on total MIMS as well as body and intuition subscales, and weightlifters scored higher than runners on body and intuition subscales. We hypothesize that these findings may be because yoga is a mindfulness-based technique that incorporates aspects of the physical body (asana) as well as breathwork (pranayama) and meditation (dhyana). Additionally, weightlifting has been considered as a contemplative practice and may incorporate mindful aspects as intense focus and concentration are needed to safely lift heavy weights (Vernon, 2018). These types of physical activities that incorporate multiple aspects of physical and mental wellbeing may be optimal to enhance overall wellness.
Similar to our results, others have demonstrated that yoga may provide additional benefits beyond aerobic exercise. Specifically, a systematic review and meta-analysis of 22 randomized controlled trials found that compared to active controls, yoga improved lower limb strength, lower body flexibility, and depression levels (Sivaramakrishnan et al., 2019). Another integrative review found that yoga is more beneficial than aerobic exercise for reduction of anxiety symptoms (Cole et al., 2022). Others have shown that yoga may be more helpful than aerobic exercise in terms of executive functioning (e.g., attention, working memory; Moore et al., 2019), though some studies have demonstrated equivalent results (Telles et al., 2013; Vhavle et al., 2019). Conversely, other work revealed that physical exercise is more beneficial at improving social self-esteem compared to yoga (Telles et al., 2013). In regard to comparisons between aerobic and anaerobic training, a recent study during the COVID-19 pandemic found that individuals practicing aerobic exercise had lower levels of depression and anxiety than those practicing anaerobic exercise (i.e., strength training), but individuals who practiced both had better levels of health perception than either group (da Costa et al., 2022). Such discrepancies in the literature may be due to the fact that yoga and other physical activities are not standardized, with the protocols significantly varying between studies (e.g., acute vs. long-term; different lengths of the intervention; different assessment tools). MIMS will allow future studies to have a standardized assessment tool to determine the multidimensional outcomes of movement including aspects of body, energy, mind, intuition, and contentment.
## Limitations and future directions
While the study shows strong reliability and validity, there are some limitations to this research. First, we utilized a convenience sample with the population being mostly white ($87.4\%$), female ($79.6\%$), and young (mean age 30.4 years). Therefore, outcomes would benefit from sampling a more diverse population. Second, participants engaged in diverse workout experiences. Controlling for the same time of day, duration, and intensity of workouts may further refine outcomes. Third, this research was conducted during the COVID-19 pandemic. We did not control for pandemic-based variables such as wearing a mask during workouts, previous or current COVID-19 status, or other aspects of the pandemic. Closer consideration to pandemic variables may be warranted in future studies.
Future research with the MIMS is needed to investigate the influence of a range of movement practices including dance, tai chi, qi gong, swimming, or cross-training. Additionally, researchers may be interested in utilizing MIMS for team sports such as soccer, football, basketball, baseball, lacrosse, or rugby. Future research may also seek to investigate the relationship between the MIMS outcomes and brain-based effects using tools such as electroencephalography or magnetic resonance imaging. Researchers may also consider investigating the influences of exercise duration, exercise habits, age, and COVID-19 considerations on MIMS outcomes. Finally, cultural considerations should be made through culturally sensitive translations into other major languages, allowing the tool to be used more broadly.
MIMS should be used as a standard tool when investigating the outcomes of movement practices, particularly when investigating mind–body impacts. As the original framework of this scale is rooted in yogic texts designed to explore and explain the multidimensional aspects of any individual, MIMS may help explain varied outcomes of movement among individuals. MIMS can also help individuals find their desired results and motivations for movement as the scale may help identify unexpected positive effects of movement. Professionals may use MIMS to help guide individuals to their most needed movement practice.
## Conclusion
MIMS is a valid and reliable tool that measures the multidimensional impacts of movement. Test–retest reliability confirms stability over time ($r = 0.737$). Cronbach’s alpha is between 0.775 and 0.840 for all five factors. Confirmatory Factor Analysis demonstrates a good model fit for each factor along with convergent and divergent validity creating specificity in what the tool measures. MIMS can be used in research, the fitness industry, or by individuals.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Virginia Tech Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JB and SL conceptualized the study. SL ran all study procedures and analyzed all data and wrote the first version of the manuscript and created all figures and tables. JB edited the manuscript to produce the final version. All authors contributed to the article and approved the submitted version.
## Funding
JB is an Integrated Translational Health Research Institute at Virginia (iTHRIV) Scholar, supported by the National Center for Advancing Translational Science of the National Institutes of Health Award UL1TR003015/KL2TR003016.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1078996/full#supplementary-material
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|
---
title: Developing a non-invasive diagnostic model for pediatric Crohn’s disease using
RNA-seq analysis
authors:
- Bin He
- Fang Wang
- Junhua Shu
- Ying Cheng
- Xiaoqing Zhou
- Tao Huang
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10014721
doi: 10.3389/fgene.2023.1142326
license: CC BY 4.0
---
# Developing a non-invasive diagnostic model for pediatric Crohn’s disease using RNA-seq analysis
## Abstract
Introduction: Pediatric Crohn’s disease is a chronic inflammatory condition that affects the digestive system in children and adolescents. It is characterized by symptoms such as abdominal pain, diarrhea, weight loss, and malnutrition, and can also cause complications like growth delays and delayed puberty. However, diagnosing pediatric Crohn’s disease can be difficult, especially when it comes to non-invasive methods.
Methods: *In this* study, we developed a diagnostic model using RNA-seq to analyze gene expression in ileal biopsy samples from children with Crohn’s disease and non-pediatric Crohn’s controls.
Results: Our results showed that pediatric Crohn’s disease is associated with altered expression of genes involved in immune response, inflammation, and tissue repair. We validated our findings using two independent datasets from the Gene Expression Omnibus (GEO) database, as well as through one prospective independent dataset, and found that our model had a high accuracy rate.
Discussion: These findings suggest the possibility of non-invasive diagnosis for pediatric Crohn’s disease and may inform the development of targeted therapies for this condition.
## Introduction
Pediatric Crohn’s disease is a chronic inflammatory condition that affects the digestive system, specifically the small intestine and colon. It is a type of inflammatory bowel disease (IBD) that affects the digestive system in children and adolescents, and it is often diagnosed between the ages of 15 and 25. Symptoms of pediatric Crohn’s disease may include abdominal pain, diarrhea, weight loss, and malnutrition. The disease can also cause complications such as growth delays and delayed puberty in children (Diefenbach and Breuer, 2006). Treatment for pediatric Crohn’s disease may involve medications to reduce inflammation, as well as dietary changes and surgery in some cases. It is important for children with Crohn’s disease to receive regular medical care and follow their treatment plan to manage the disease and prevent complications. ( Abraham et al., 2012) Medications used to treat pediatric Crohn’s disease include anti-inflammatory drugs, immune system suppressors, and biologic therapies. Anti-inflammatory drugs, such as corticosteroids, can help reduce inflammation in the digestive tract. Immune system suppressors, such as azathioprine and 6-mercaptopurine, can help prevent the immune system from attacking the digestive tract. Biologic therapies, such as infliximab, work by targeting specific proteins involved in the inflammatory process. The diagnosis of pediatric Crohn’s disease is based on a combination of medical history, physical examination, and test results (Hommel et al., 2013).
To diagnose pediatric Crohn’s disease, a healthcare provider may ask about the child’s symptoms and medical history, including any family history of inflammatory bowel disease. The provider may also perform a physical examination, including a rectal exam, to check for signs of inflammation (de Bie et al., 2013). The accuracy of the diagnosis of pediatric Crohn’s disease can vary depending on the symptoms and test results. *In* general, the diagnosis of Crohn’s disease is made based on a combination of medical history, physical examination, and test results. Diagnostic tests that may be used to confirm the diagnosis of pediatric Crohn’s disease include blood tests, stool sample analysis, endoscopy, imaging tests, and biopsy. These tests can help identify the presence of inflammation and other signs of Crohn’s disease. However, it is important to note that the accuracy of the diagnosis can sometimes be limited by the variability of the disease and the fact that the symptoms and test results of Crohn’s disease can be similar to those of other conditions. In some cases, a definitive diagnosis of Crohn’s disease may not be possible until the child has had symptoms for a longer period of time and more tests have been performed. Overall, the accuracy of the diagnosis of pediatric Crohn’s disease can be high, but it is important to work closely with a healthcare provider to ensure that the diagnosis is as accurate as possible (Gupta et al., 2008).
In the context of pediatric Crohn’s disease, RNA-seq can be used to identify changes in gene expression that are specific to the disease. For example, studies have shown that pediatric Crohn’s disease is characterized by altered expression of genes involved in immune response, inflammation, and tissue repair. By analyzing the transcriptome of intestinal tissue samples from children with Crohn’s disease, it may be possible to identify specific gene expression patterns that are associated with the disease.
## Multi-RNA-seq dataset
To compile the gene expression dataset for this study, we used paired-end RNA-seq data from 304 ileal biopsy samples (GSE101794). These samples included both pediatric Crohn’s patient samples and non-pediatric Crohn’s controls. To further validate our findings, we also accessed two independent datasets from the Gene Expression Omnibus (GEO) database, which is maintained by the National Center for Biotechnology Information (NCBI). The datasets, GSE57945 and GSE93624, comprised 322 and 245 ileal biopsy samples, respectively, and were used to validate our findings. The GEO database can be accessed at https://www.ncbi.nlm.nih.gov/geo/. In addition to the ileal biopsy samples, we also collected blood samples from 13 patients, including 6 pediatric Crohn’s patients and 7 non-pediatric Crohn’s controls. More details and and quantify of expression levels of specific transcripts can be found in the Supplementary Tables S1,S2.
## RNA-seq read mapping
To quantify gene expression levels in the RNA-seq data, we used the nf-core/rnaseq pipeline. This pipeline is an open-source, community-driven pipeline for the analysis of RNA-seq data and is designed to be both reproducible and scalable (Lataretu and Hölzer, 2020). It includes a range of quality control checks, mapping of reads to a reference genome, and quantification of gene expression levels using count-based methods such as featureCounts. The pipeline also includes options for downstream differential expression analysis and visualization of results. To run the pipeline, we first processed the raw RNA-seq data to remove low-quality reads and adaptors using Trimmomatic (Bolger et al., 2014). We then mapped the cleaned reads to the reference genome using Hisat2 and quantified gene expression levels using featureCounts. Finally, we used the nf-core/rnaseq pipeline to perform differential expression analysis and generate a range of visualizations to aid in the interpretation of the results.
## Statistical analysis
In this study, we utilized the R programming language (version 4.2.2) for statistical analysis. To determine significant gene expression differences between pediatric Crohn’s patients and non-pediatric Crohn’s controls, we employed T-tests using the t. test function in the stats package. We also employed principal component analysis (PCA) to visualize the data using the prcomp function in the stats package. In addition, we used an enhanced volcano plot to visualize the results, which is a modified version of the traditional volcano plot that includes additional information such as fold change and p-value on the plot. We used the EnhancedVolcano package in R to generate the plots. To identify the most relevant gene set for distinguishing between the two groups, we utilized the forward search function in the MetaIntegrator R package.
## Gene expression profiles differentiate pediatric Crohn’s disease from non-pediatric Crohn’s disease and identify differentially expressed genes
Gene expression profiling was used to identify differentially expressed genes in 304 ileal biopsy samples from pediatric and non-pediatric Crohn’s disease patients and normal controls. A total of 68 genes were found to be significantly upregulated or downregulated in pediatric Crohn’s disease ($p \leq 0.001$), and their distribution and fold change values were visualized using a volcano plot (Figure 1A). Principal component analysis (PCA, Figure 1B) showed that there was no significant separation between the pediatric Crohn’s disease and control group, but there was a trend towards separation, suggesting that these differentially expressed genes may be useful for differentiating pediatric from non-pediatric Crohn’s disease and identifying potential biomarkers for this condition (Supplementary Table S3).
**FIGURE 1:** *Differential gene expression in Pediatric Crohn’s Disease and controls. (A) Enhanced volcano plot showing the log2 fold change and -log10 p-value for each gene in the dataset. Genes with a significant difference in expression (p < 0.05) are highlighted in red, with those showing an increased expression in Pediatric Crohn’s Disease in blue and decreased expression in green. (B) Principal component analysis (PCA) plot illustrating the separation of Pediatric Crohn’s Disease and control samples based on gene expression levels. Each point represents a sample, with Pediatric Crohn’s Disease samples shown in red and controls in blue. The first two principal components (PC1 and PC2) are plotted, representing the majority of the variation in the data.*
## Machine learning-based gene selection and diagnostic model construction
In order to further improve the diagnostic power of our model, we employed machine learning techniques using the forward search function in MetaIntegrator to identify a smaller set of highly informative genes. Through this process, we identified four differentially expressed genes that were most informative for differentiating pediatric Crohn’s disease from normal/non-pediatric Crohn’s disease (shown in Figure 2A–D). *These* genes included two upregulated genes (FCGR3A and CBR3) and two downregulated genes (CHST13 and FZD7).
**FIGURE 2:** *Gene expression of FCGR3A, CBR3, CHST13, and FZD7 in three datasets. (A) Boxplot of FCGR3A gene expression in GSE101794, GSE57945 and GSE93624 datasets, representing the pediatric Crohn’s Disease (PD) and control samples. The center line of the box represents the median expression level, the top and bottom edges of the box represent the 75th and 25th percentiles, respectively, and the whiskers extend to the most extreme data points within 1.5 times the interquartile range. The red dots represent outliers. (B) Boxplot of CBR3 gene expression in GSE101794, GSE57945 and GSE93624 datasets. (C) Boxplot of CHST13 gene expression in GSE101794, GSE57945 and GSE93624 datasets. (D) Boxplot of FZD7 gene expression in GSE101794, GSE57945 and GSE93624 datasets.*
We trained and tested various classifiers using these genes as input features and selected the classifier that achieved the highest performance in terms of accuracy, sensitivity, and specificity through cross-validation. Using this optimized classifier, we constructed a diagnostic model that was able to accurately classify samples as pediatric Crohn’s disease or normal/non-pediatric Crohn’s disease, as shown by the high AUC of 0.97 on the ROC curve in Figure 3A.
**FIGURE 3:** *Receiver Operating Characteristic (ROC) curves of gene expression-based classification of Pediatric Crohn’s Disease in three datasets. (A) ROC curve of GSE101794 dataset, showing the relationship between the true positive rate (sensitivity) and false positive rate (1-specificity) for different threshold values of gene expression levels for Pediatric Crohn’s Disease classification. The area under the curve (AUC) is indicated. (B) ROC curve of GSE57945 dataset, showing the relationship between the true positive rate (sensitivity) and false positive rate (1-specificity) for different threshold values of gene expression levels for Pediatric Crohn’s Disease classification. The area under the curve (AUC) is indicated. (C) ROC curve of GSE93624 dataset, showing the relationship between the true positive rate (sensitivity) and false positive rate (1-specificity) for different threshold values of gene expression levels for Pediatric Crohn’s Disease classification. The area under the curve (AUC) is indicated.*
## Validation of gene signature using public datasets and independent validation set
To validate the diagnostic power of our gene signature, we used two independent approaches. First, we tested the performance of our model on publicly available datasets. We obtained gene expression data from GSE57945 and GSE93624 datasets, and applied our diagnostic model to classify samples as pediatric Crohn’s disease or normal/non-pediatric Crohn’s disease. The results showed that our model achieved high accuracy, as demonstrated by the AUC of 0.97 and 0.9 on the ROC curve for these two datasets, respectively (Figure 3A–C). This indicates that our model was able to generalize to independent samples. Second, we also tested the performance of our model on an independent validation set that was not used in the training or testing of the model. This validation set consisted of 6 samples from pediatric Crohn’s disease patients and 7 samples from normal controls. The results showed that our model was able to accurately classify samples as pediatric Crohn’s disease or normal/non-pediatric Crohn’s disease with high accuracy (AUC = 0.88, Figure 4). Supplementary Table S4 provides ROC curve analysis results for four data sets, including information on accuracy, sensitivity, and specificity. Overall, these results demonstrate the robustness and generalizability of our gene signature for differentiating pediatric Crohn’s disease from normal/non-pediatric Crohn’s disease. The high accuracy of our model on both public datasets and independent validation set suggests that our gene signature has the potential to be used as a diagnostic tool for pediatric Crohn’s disease.
**FIGURE 4:** *Gene expression and classier performance in an prospective independent validation dataset. (A) Boxplots of gene expression for FCGR3A, CBR3, CHST13, and FZD7 in the prospective independent validation set. The center line of the box represents the median expression level, the top and bottom edges of the box represent the 75th and 25th percentiles, respectively, and the whiskers extend to the most extreme data points within 1.5 times the interquartile range. The red dots represent outliers. The boxplots are split into pediatric Crohn’s Disease (PD) and control groups. (B) Receiver Operating Characteristic (ROC) curve of gene expression-based classification of Pediatric Crohn’s Disease in the prospective independent validation set. The curve shows the relationship between the true positive rate (sensitivity) and false positive rate (1-specificity) for different threshold values of gene expression levels for Pediatric Crohn’s Disease classification. The area under the curve (AUC) is indicated.*
## Discussion
Our study identified four differentially expressed genes that were most informative for differentiating pediatric Crohn’s disease from normal/non-pediatric Crohn’s disease. We utilized GSE101794 as the discovery set to identify signature genes and establish a model. Through DEseq2 method, we identified p-values, Fold change, and FDR values for each gene, resulting in volcano plot and PCA analysis. The PCA analysis revealed that the case and control groups were not well separated. Therefore, we employed MetaIntegrator’s machine learning algorithm and identified four differentially expressed genes. The diagnostic model constructed using these four differentially expressed genes demonstrated excellent diagnostic performance on three publicly available datasets. We subsequently validated the model in an prospective independent validation dataset. *These* genes included two upregulated genes (FCGR3A and CBR3) and two downregulated genes (CHST13 and FZD7). FCGR3A is a gene encoding the Fc fragment of IgG, receptor IIIa, which is a surface receptor expressed on various immune cells, including monocytes, macrophages, and neutrophils. Previous studies have shown that FCGR3A is involved in immune responses and inflammation, and its expression is upregulated in various autoimmune and inflammatory diseases, such as rheumatoid arthritis and lupus erythematosus (Breunis et al., 2009). Our findings suggest that FCGR3A may play a role in the pathogenesis of pediatric Crohn’s disease, potentially through its involvement in immune responses and inflammation. CBR3 is a gene encoding carbonyl reductase 3, which is a member of the aldo-keto reductase family and involved in the metabolism of various endogenous and exogenous compounds. Previous studies have shown that CBR3 is involved in the metabolism of xenobiotics, such as drugs and environmental toxins, and its expression is upregulated in various cancer types (Blanco et al., 2008). Our findings suggest that CBR3 may be involved in the pathogenesis of pediatric Crohn’s disease. CHST13 is a gene encoding carbohydrate sulfotransferase 13, which is a member of the sulfotransferase family and involved in the sulfation of various carbohydrates. Previous studies have shown that CHST13 is involved in the synthesis of proteoglycans and glycosaminoglycans, which are important components of the extracellular matrix and involved in tissue development and repair. Our findings suggest that CHST13 may be involved in the pathogenesis of pediatric Crohn’s disease, potentially through its role in extracellular matrix homeostasis and tissue repair. FZD7 is a gene encoding Frizzled 7, which is a member of the Frizzled family and a receptor for Wnt signaling. Previous studies have shown that FZD7 is involved in various developmental and physiological processes, such as cell proliferation, differentiation, and migration (Guo et al., 2019). Our findings suggest that FZD7 may be involved in the pathogenesis of pediatric Crohn’s disease, potentially through its role in the regulation of these processes.
As we all known, RNA-seq can not only be used to identify differentially expressed encoding genes but also be used to explore non-coding genes in patient samples. There is a growing body of evidence suggesting that long non-coding RNAs (lncRNAs) may play a role in the development of Crohn’s disease and colorectal cancer (Yarani et al., 2018; Chen and Shen, 2020). For example, lncRNA THOR expression was significantly increased in colorectal cancer tissue compared to normal prostate tissue associating with poor patient outcomes (Chu et al., 2020). Other studies have also shown that lncRNA THOR is overexpressed in other types of cancer, including endometrial cancer and ovarian cancer (Ge et al., 2020; Zhang et al., 2022). While some studies have identified lncRNAs that are differentially expressed in pediatric Crohn’s disease patients and have suggested their potential as therapeutic targets or diagnostic biomarkers, more research is needed to fully understand their role in the development and progression of this condition (Haberman et al., 2018; Lucafò et al., 2019). Additionally, further studies are needed to determine the relationship between lncRNA expression and patient outcomes in pediatric Crohn’s disease, as this information could potentially be used to guide treatment decisions and improve patient outcomes. Therefore, we will continue to investigate the profiles of lncRNAs in pediatric Crohn’s disease in our future research.
Overall, our findings provide new insights into the molecular mechanisms underlying pediatric Crohn’s disease and identify potential therapeutic targets for this disease. Further studies are needed to confirm the roles of these genes in pediatric Crohn’s disease and to investigate their therapeutic potential.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
## Ethics statement
The studies involving human participants were reviewed and approved by Tongji Medical College, Huazhong University of Science and Technology. Written informed consent to participate in this study was provided by the participants’legal guardian/next of kin.
## Author contributions
TH initiated the investigation and performed the data analysis. TH also wrote the original draft of the manuscript. FW contributed to the investigation and assisted with the data analysis. Both TH and FW reviewed and approved the final version of the manuscript. All authors contributed to the article.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1142326/full#supplementary-material
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|
---
title: The effects of camelina sativa oil and high-intensity interval training on
liver function and metabolic outcomes in male type 2 diabetic rats
authors:
- Zeynab Kavyani
- Parvin Dehghan
- Mostafa Khani
- Mousa Khalafi
- Sara K. Rosenkranz
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10014722
doi: 10.3389/fnut.2023.1102862
license: CC BY 4.0
---
# The effects of camelina sativa oil and high-intensity interval training on liver function and metabolic outcomes in male type 2 diabetic rats
## Abstract
### Objectives
The purpose of this study was to evaluate the independent and combined effects of camelina sativa oil and high-intensity interval training (HIIT) on liver function, and metabolic outcomes in streptozotocin-induced diabetic rats.
### Methods
Forty male Wistar rats were randomly assigned to five equal groups (8 per group): Normal control (NC), diabetic control (DC), diabetic + camelina sativa oil (300 mg/kg by oral gavage per day; D + CSO), diabetic + HIIT (running on a treadmill 5 days/week for 8 weeks; D + HIIT), diabetic + camelina sativa oil + HIIT (D + CSO + HIIT).
### Results
In all three intervention groups (D + CSO, D + HIIT, and D + CSO + HIIT) compared to the DC, hepatic TNF-α, MDA, and histopathology markers, decreased and hepatic PGC-1α, and PPAR-γ increased ($p \leq 0.05$). However, the effect of D + CSO was greater than D + HIIT alone. Hepatic TG decreased significantly in D + HIIT and D + CSO + HIIT compared to other groups ($p \leq 0.001$). Fasting plasma glucose in all three intervention groups (D + CSO, D + HIIT, and D + CSO + HIIT) and HOMA-IR in D + CSO and D + CSO + HIIT were decreased compared to DC ($p \leq 0.001$). Only hepatic TAC and fasting plasma insulin remained unaffected in the three diabetic groups ($p \leq 0.001$). Overall, D + CSO + HIIT had the largest effect on all outcomes.
### Conclusions
At the doses and treatment duration used in the current study, combination of CSO and HIIT was beneficial for reducing liver function and metabolic outcomes other than CSO and HIIT alone.
## Introduction
Type 2 diabetes mellitus (T2DM) is a long-term endocrine disease characterized by hyperglycemia and is associated with inflammation, oxidative stress; insulin resistance (IR), and hepatic steatosis [1, 2]. There is emerging evidence that chronic hyperglycemia via dysregulated production of tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6), and C-reactive protein (CRP), along with excess free radical production and oxidative stress, plays a critical role in the development of IR and T2DM [3]. Furthermore, diabetic patients have an increased risk of liver disease and liver failure, which is one of the most common causes of death in diabetic patients [4]. Impaired liver function is caused by IR, oxidative stress, and inflammation in the tissue organ, and in patients with T2DM, is partly due to elevated blood glucose levels [5, 6]. In addition, previous research has indicated a strong association between liver fat accumulation and T2DM, indicating an increased risk for nonalcoholic fatty liver disease [7]. Therefore, the prevention of inflammation and oxidative stress leading to liver fat accumulation are therapeutic targets in patients with T2DM. Numerous studies have confirmed that consumption of omega-3 fatty acids improves both macro and micro-vascular complications of T2DM by modifying the gut microbiota [6, 8] and controlling IR (9–12), oxidative stress [13], inflammation [14, 15], lipid metabolism [16, 17], and hepatic fat deposition [18]. Recently, due to current concerns about heavy metal-contaminated fish oil supplements and their adverse effects, switching omega-3 fatty acids sources from animal to plant sources has been considered [19]. Camelina sativa, known as false flax, is one of the richest food sources of omega-3 fatty acids, with polyunsaturated fatty acid (PUFA) values of more than $50\%$, alpha-linolenic acid (ALA) $40\%$–$45\%$, as well as high content of phytosterols (331–442 mg/100 g), carotenoids (103–198 mg/of carotene/kg), and tocopherols (55.8–76.1 mg/100 g) [20]. According to the evidence provided by the FDA and the study conducted by Mousazadeh et al. [ 21], side effects have not been reported, but due to the high dose of omega-3 (more than 3 g/day), it may have gastrointestinal effects, so caution should be used in the prescription of high doses of camelina oil [22]. Allong with nutrient intake [5], exercise training is an effective intervention for the treatment and prevention of metabolic disorders such as T2DM [23, 24]. The effects of exercise training are associated with increased expression or activity of proteins involved in insulin signaling, subsequently modulating glycogen synthase activity, glucose transporter expression in the muscle, and improving IR, inflammation, and oxidative stress in T2DM patients [25]. In a study, the benefits of strength exercise have been shown in reducing hepatic triglyceride content among T2DM rats [26]. Traditionally, moderate-intensity continuous training has been considered an effective method of training for improving health outcomes in T2DM patients; however, high-intensity interval training (HIIT) is a well-accepted alternative strategy that may serve as a way for some individuals to save time [27].
Peroxisome proliferator-activated receptor gamma (PPAR-γ) controls fatty acid, glucose, and inflammatory processes [28, 29]. PPAR-γ agonists directly activate liver glucose-sensing genes, improving glucose homeostasis and insulin sensitivity in T2DM patients [30]. Omega-3 fatty acids and exercise upregulate PPAR-γ coactivator 1α (PGC-1α), which regulates mitochondrial biogenesis and activates PPAR-γ [31, 32]. HIIT is an effective approach for reducing lipogenesis [33] and improving inflammation [34], IR, postprandial glycemia [35] fat loss [36], visceral fat and liver fat [37] which are all important treatment targets for patients with T2DM. To the best of our knowledge, there is no study investigating the simultaneous effects of CSO intake and HIIT on liver function, the status of inflammation, oxidative stress, and lipogenesis. The current intervention study aimed to examine to determine the independent and combined effects of camelina sativa oil and HIIT on liver function, and metabolic outcomes in male T2DM rats. We hypothesized that the combination of both would provide superior benefits for reducing inflammation, oxidative stress and lipogenesis, as well as liver triglycerides.
## Ethics statement
All animal experiments were carried out in accordance with the National Institutes of Health’s ethical standards for the care and use of laboratory animals (NIH; Publication No. 85-23, revised 1985), which were examined and confirmed by the Veterinary Ethics Committee of Tabriz University of Medical Sciences (Approval No.: IR.TBZMED.AEC.1401.040).
The animal study was reviewed and approved by the Veterinary Ethics Committee of Tabriz University of Medical Sciences (approval no.: IR.TBZMED.AEC.1401.040). Written informed consent was obtained from the owners for the participation of their animals in this study.
## Experimental design
Forty (3-month-old) adult male Wistar rats (225–300 g), were obtained from the Central Animal House, Tehran University of Medical Sciences, and adapted to the experimental conditions in standard polypropylene cages (4 rats/cage) under controlled humidity (50 ± $5\%$) and temperature (20 ± 2°C) with a 12 h light/dark cycle for 2 weeks. T2DM was induced through a combination of a high-fat diet (HFD) and a single dose of Streptozotocin (STZ; 35 mg/kg, intraperitoneal (ip) 0.1 M citrate buffer, pH 4.5) after rats have fasted for 5 h. Rats were fed with a high-fat diet ($45\%$ fat, $34\%$ carbohydrate, and $21\%$ protein) prepared from animal tail oil (450 g per 100 g standard pellet) and cholesterol gel for an initial period of 2 weeks and then injected intraperitoneally with a single dose of streptozotocin (STZ, 35 mg/kg of body weight), which was freshly prepared by dissolving in 0.1 M citrate buffer (pH 4.5). A week after induction of T2DM, the rats with blood glucose levels of 250 mg/dL or greater were considered diabetic [38]. Blood glucose levels were measured by a glucometer from the tail vein of animals after a 12 h fast following the 2-week high-fat diet. Animals had access to ad libitum water and standard chow ($54\%$ carbohydrate, $26\%$ protein, $13\%$ fat, $5\%$ fiber, and $3\%$ vitamins, and minerals). Rats were randomly allocated into five groups (8 per group, calculated using G*Power) including 1-Normal control (NC) was given normal saline by oral gavage; 2-Diabetic control (DC) was given normal saline by oral gavage; 3-Diabetic + camelina sativa oil (300 mg/kg) by oral gavage (D + CSO), 4-Diabetic + HIIT (D + HIIT) were given normal saline by oral gavage, and 5-Diabetic + camelina sativa oil (300 mg/kg) by oral gavage + HIIT (D + CSO + HIIT) (Figure 1).
**Figure 1:** *Experimental design. HIIT, High-intensity interval training.*
## Camelina oil supplementation
We used a gas chromatograph to analyze the fatty acid composition of CSO (Bistun Shafa Co, Kermanshah, Iran). The study’s CSO analysis showed that the highest fatty acids were linolenic acid ($29.70\%$), linoleic acid ($21.03\%$), and oleic acid ($16.41\%$; Table 1; 39). According to the evidence provided by the FDA, CSO has been deemed to be generally safe, and is registered as food oil in many European nations. Rats in the CSO conditions were fed by oral gavage based on weight at a dose of 300 mg/kg per day for 8 weeks. Rats in the non-CSO groups were given saline, % 0.9 NaCl, via oral gavage (1 mL per day). Oral gavages were performed before exercise in the HIIT conditions [40].
**Table 1**
| Fat | % | Fatty acid | Name | Camelina oil (%) |
| --- | --- | --- | --- | --- |
| SFA | 13.82 | C12:0 | Lauric acid | 0.0 |
| SFA | 13.82 | C14:0 | Myristic acid | 0.09 |
| SFA | 13.82 | C16:0 | Palmitic acid | 6.45 |
| SFA | 13.82 | C18:0 | Stearic acid | 2.56 |
| SFA | 13.82 | C20:0 | Arashidic acid | 1.89 |
| SFA | 13.82 | C21:0 | Heneicosanoic acid | 1.66 |
| SFA | 13.82 | C22:0 | Behenic acid | 1.0 |
| SFA | 13.82 | C24:0 | Lignoseric acid | 0.17 |
| MUFA | 34.36 | C16:0 | Palmitoleic acid | 0.17 |
| MUFA | 34.36 | C18:0 | Oleic acid | 16.41 |
| MUFA | 34.36 | C20:0 | Elcosenoic acid | 14.09 |
| MUFA | 34.36 | C22:0 | Erucic acid | 3.21 |
| MUFA | 34.36 | C24:0 | Nervonic acid | 0.47 |
| PUFA | 51.83 | C18:0 | Linoleic acid | 21.03 |
| PUFA | 51.83 | C18:0 | Linolenic acid | 29.7 |
| PUFA | 51.83 | C20:0 | Elcosadienoic acid | 0.68 |
| PUFA | 51.83 | C20:0 | Elcosatrienoic acid | 0.41 |
## Exercise training protocol
Before the interventions, all rats were familiarized with treadmill running for 1 week (10 min per day) at a speed of 8–10 m per min with a $0\%$ incline. Afterward, HIIT was performed 5 days per week for 8 weeks on a treadmill at 6 p.m. (lights off). The HIIT program involved 8 sets of 3 min of high-intensity running at $85\%$–$90\%$ of one’s maximum speed, followed by 2 min of active rest at $30\%$–$40\%$ of one’s maximum speed. The HIIT protocol comprised 5-min warm-up and cool-down intervals that were low-intensity ($30\%$–$40\%$ of maximum speed) before and after each session. To determine the maximal speed at the time of maximum oxygen consumption (VO2 max), rats ran 5 m on a treadmill at a speed of 6 m/min with a zero-degree gradient for 5 min (warm-up), and then the treadmill speed increased to 3 m/min every 3 min until the animals reached the point of extinction and could no longer continue. The incapacity of the rats to continue the workout program with increasing speed and collision three times in 1 min to the end of the treadmill was the criterion for reaching VO2 max, hence VO2 max was assessed using speed. Every 2 weeks, the animals were assessed, and the training intensity was determined based on the new test values.
## Experimental procedures
After fasting for 12–14 h, and 48 h after the previous training session, all rats were sedated with a painless intraperitoneal injection of ketamine (90 mg/kg) and zailazin (10 mg/kg). Blood was collected from the tail vein; plasma was separated via centrifuging at 3,500 rpm for 5 min. After blood sampling, the animals were sacrificed and the livers were removed. The liver tissue samples and serum samples were flash-frozen and stored at −70°C, and the remaining livers were used for histopathological study and were homogenized in appropriate buffers for analysis of biochemical parameters like inflammatory and oxidative stress indices of the liver. Based on the aim of present study, markers of liver function, inflammation and oxidative stress were the main outcomes and glycemia markers and hepatic histopathology were secondary outcomes.
## Measurement of hepatic TNF-α
TNF-α levels were measured using an ELISA kit (catalog no. DY510-05, R&D System) after liver tissue aliquots were homogenized in accordance with the manufacturer’s instructions. All TNF-α analysis were carried out in duplicate serial dilutions.
## Measurement of hepatic MDA and TAC
The presence of malondialdehyde (MDA), a sign of lipid peroxidation, was measured. In a nutshell, livers were treated as previously reported after being homogenized in a solution of $1.15\%$ KCl [26]. By comparing the OD550 of the reference solutions of 1,1,3,3-tetramethoxypropan $99\%$ malondialdehyde bis (dymethyl acetal) $99\%$ (Sigma), the sample absorbance was determined by spectrophotometry, and MDA values were derived [41]. A decrease in the production of thiobarbituric acid reactive compounds served as the basis for measuring the hepatic total antioxidant capacity (TAC) [42]. A commercially available colorimetric kit (Bioquochem FRAP Assay Kit, KF-01-003, R&D System) was used to measure the hepatic TAC levels in accordance with the manufacturer’s recommendations. Results were adjusted for protein levels [43].
## Measurement of hepatic TG
Hepatic triglyceride (TG) concentrations were measured using commercially available colorimetric kits (Triglyceride G-Test kit, Wako Pure Chemical Industries) according to manufacturer instructions.
## Hepatic PGC-1α and PPAR-γ
Western Blotting methods were used to messure protein levels of hepatic PGC-1α and PPAR-γ. Protein lysates were isolated from 500 mg of liver tissue in lysis buffer (500 μL Tris, PH = 8, 150 mM sodium chloride, $1\%$ NP-40, $0.5\%$ sodium deoxycholate, $0.1\%$ SDS, and 0.1 mM EDTA) supplemented with a complete protease inhibitor cocktail and centrifuged at 12,000g for 10 min at 4°C. The Bradford method was used to determine the protein concentration in the supernatant [44]. Proteins were separated using SDS-polyacrylamide gel electrophoresis with $8\%$–$12\%$ denatured ready gel (Bio-Rad, Hercules, CA, United States) and transferred to a PVDF membrane (Roche, West Sussex, United Kingdom). To prevent nonspecific bindings, the membrane was blocked for 1 h in $5\%$ BSA in tris-buffered saline and $0.1\%$ Tween 20 (TBST). Blots were then incubated overnight at 4° C with the following primary antibodies: β-actin (sc-47,778, 1: 300), PPAR-γ (ab20935), and PGC-1α (ab54481), all purchased from Cell Signaling Technology. The membrane was then washed three times and incubated for 1 h at room temperature in $5\%$ milk in TBST with the appropriate secondary antibody (m-IgG BP-HRP: sc-516,102, and mouse anti-rabbit IgG-HRP: sc-2,357) [44]. Protein bands were visualized using an enhanced chemiluminescence (ECL) reagent and quantified using densitometry analysis with Image J software.
Hepatic PGC-1α and PPAR-γ decreased in the DC group compared to NC. In contrast, both PGC-1α and PPAR-γ increased in all three intervention groups as compared to DC. For PGC-1α, D + CSO + HIIT significantly increased hepatic PGC-1α as compared to D + HIIT alone ($p \leq 0.001$) and D + CSO alone ($p \leq 0.001$). In addition, D + CSO significantly increased hepatic PGC-1α as compared to D + HIIT alone ($p \leq 0.001$). For PPAR-γ, D + CSO + HIIT and D + CSO alone significantly increased hepatic PPAR-γ as compared to D + HIIT alone ($p \leq 0.001$ and $p \leq 0.05$, respectively). But there was not a significant difference between D + CSO + HIIT and D + CSO alone ($p \leq 0.05$; Figure 3).
**Figure 3:** *Western bloting analysis of protein expression of β-actin, PGC-1α, and PPARy. The effect of D, D + CSO, D + HIIT, and D + CSO + HIIT on PGC-1α, and PPARy. NC, Normal control; DC, Diabetic control; D + CSO, Diabetic + camelina sativa oil; D + HIIT, Diabetic + HIIT; D + CSO + HIIT, Diabetic + camelina sativa oil + HIIT.*
## Liver histopathological study
After being cleaned with normal saline, liver tissues were fixed in $10\%$ buffered formalin for 48 h. For the purpose of the histological evaluation, samples were embedded in paraffin, divided into 5-lm pieces, stained with hematoxylin and eosin (H and E), and examined under a light microscope. Scores were made in 10 fields of each H and E-stained slide, which were then examined under a light microscope to determine the liver’s histological, hydropic degeneration, microvesicular and macrovesicular vacuoles, sinusoidal congestion, and cell necrosis findings [40]. Scores for the histopathological results were none (−), mild (+), moderate (++), and severe damage (+++) [45, 46].
## Measurement of fasting blood glucose, insulin, HOMA-IR
Fasting blood glucose was measured by using commercially available colorimetric diagnostic kits (Pars Azmoon kit, Iran) according to the instructions. The level of insulin was determined using the rat Insulin ELISA Kit (ALPCO, Catalog no: 80-INSRTH-E01). HOMA-IR was employed to assess the IR via the following formula [47]:
## Statistical analysis
The Shapiro–Wilk test was used to assess the distribution’s normality. The variances were then shown to be homogenous by a Leven test. The mean differences between the groups were examined using a one-way analysis of variance (ANOVA). Using Tukey’s Test, differences between two groups were measured. Means and SEM were used to express the data. Statistical significance was defined as a value of $p \leq 0.05.$ Pearson correlation coefficients were used to ascertain the relationship between the variables. The statistical software SPSS was used for all calculations (Version 20.00).
## Hepatic TNF-α
Hepatic TNF-α was significantly ($p \leq 0.001$, Figure 2A) increased in the DC group compared to NC. In contrast, TNF-α decreased in all three intervention groups as compared to DC, an effect that was greater in D + CSO and D + CSO + HIIT as compared to D + HIIT alone.
**Figure 2:** *The effect of D, D + CSO, D + HIIT, and D + CSO + HIIT on hepatic (A) TNF-α, (B) MDA, (C) TAC, and (D) TG. One-way ANOVA followed by Tukey post-test was used. Data are represented as means ± SEM and significant differences between groups are indicated by *p < 0.05. NC, Normal control; DC, Diabetic control; D + CSO, Diabetic + camelina sativa oil; D + HIIT, Diabetic + HIIT; D + CSO + HIIT, Diabetic + camelina sativa oil + HIIT.*
## Hepatic MDA and TAC
Hepatic MDA increased in the DC group compared to NC ($p \leq 0.001$, Figure 2B). Hepatic MDA decreased in all three intervention groups compared to DC ($p \leq 0.001$). These decreases were greater with D + CSO and D + CSO + HIIT compared to D + HIIT. In addition, D + CSO + HIIT significantly decreased hepatic MDA compared to D + CSO alone. Hepatic TAC decreased in the DC group compared to NC; however, it did not change significantly ($p \leq 0.05$, Figure 2C) with the three interventions.
## Hepatic TG
Hepatic TG increased in the DC group compared to NC ($p \leq 0.001$, Figure 2D), and decreased significantly in D + HIIT and D + CSO + HIIT compared to DC. In addition, Hepatic TG decreased in D + CSO + HIIT compared to D + CSO alone. Changes in D + CSO alone were not statistically significant as compared with DC ($p \leq 0.05$).
## Hepatic histopathology
Hepatic histopathology markers including hydropic degeneration, micro-vesicular vacuoles, macro-vesicular vacuoles, and sinusoidal congestion were increased in the DC group compared to NC, whereas these markers were decreased in all three intervention groups compared to DC (Table 2). These decreases were more significant in D + CSO + HIIT as compared with D + CSO and D+ HIIT alone (Figure 4).
**Figure 4:** *The effect of DC, D + CSO, D + HIIT, and D + CSO + HIIT on hepatic histopathology, 400 × magnification, DC, Diabetic control; D + CSO, Diabetic + camelina sativa oil; D + HIIT, Diabetic + HIIT; D + CSO + HIIT, Diabetic + camelina sativa oil + HIIT.* TABLE_PLACEHOLDER:Table 2
## Glycaemia markers
Fasting plasma glucose and HOMA-IR were significantly ($p \leq 0.001$, Figure 5A; $p \leq 0.001$, Figure 5C) increased in diabetic rats compared to NC. In contrast, fasting plasma glucose in all three interventions (D + CSO, D + HIIT, and D + CSO + HIIT) and HOMA-IR in D + CSO and D + CSO + HIIT were decreased compared to DC. In addition, D + CSO+ HIIT significantly ($p \leq 0.001$) decreased fasting glucose and HOMA-IR compared to D + HIIT alone ($p \leq 0.001$). Also, D+CSO significantly decreased fasting plasma glucose and HOMA-IR compared to D+HIIT ($p \leq 0.001$). However, fasting plasma insulin changes were not significantly ($p \leq 0.05$, Figure 5B) different between groups.
**Figure 5:** *The effect of D, D + CSO, D + HIIT, and D + CSO + HIIT on glycemia markers including (A) glucose, (B) insulin, and (C) HOMA-IR. One-way ANOVA followed by Tukey post-test was used. Data are represented as means ± SEM and significant differences between groups are indicated by *p < 0.05. NC, Normal control; DC, Diabetic control; D + CSO, Diabetic + camelina sativa oil; D + HIIT, Diabetic + HIIT; D + c + HIIT, Diabetic + camelina sativa oil + HIIT.*
## Discussion
There has been some previous research examining the independent effects of CSO and HIIT on glycemic control [5, 48, 49]; however, there is no previously published study on the combined effects of CSO and HIIT, in particular in a T2DM model or in patients with T2DM. Therefore, in the present study, the combined effects of CSO and HIIT on glycemic indices, inflammatory and oxidative stress markers in hepatic cells, hepatic triglyceride content, and liver histopathological findings were investigated in male T2DM rats. According to our findings, there were improving synergistic effects of CSO and HIIT for 8 weeks on glucose, HOMA-IR, hepatic MDA, TNF-α, TG, PPAR-γ, PGC-1α and histopathology markers; however, insulin and TAC did not change significantly in three intervention groups.
Our results suggest that CSO, as a rich source of omega-3 fatty acids exerted positive effects on glycemic and insulin resistance markers, in agreement with previous research in patients with NAFLD [5, 6] and impaired glucose metabolism [48]. The proposed anti-hyperglycemic mechanisms of action by which CSO may influence insulin resistance are mostly related to its omega-3 fatty acids contents. Omega-3 fatty acids are thought to improve insulin resistance by modulating mitochondrial bioenergetics and endoplasmic reticulum stress, and through upregulation of PPAR-γ, one of the main regulators of glucose homeostasis [50, 51]. Moreover, HIIT improves insulin resistance by increasing mitochondrial biogenesis, GLUT-4 translocation, and PGC-1α [52]. Therefore, the synergistic effects of CSO and HIIT on glycemic parameters might be related to their shared effects on mitochondrial bioenergetics, PPAR-γ activity, and GLUT-4 translocation. Our findings showed that CSO increased PPAR-γ protein expression. In line with our findings, Taranu et al. and Tejera et al., using in-vivo models, reported that ω-3 PUFA rich CSO increased PPAR-γ expression [53, 54]. Moreover, ω-3 PUFAs have been recognized as the natural agonists of PPAR-γ [55]. The current study, for the first time, evaluated the effects of CSO plus HIIT on PPAR-γ protein expression in an animal model. However, our study did not show a beneficial effect for HIIT on PPAR-γ. Similar result were obtained from another in-vivo investigation [56]. However, another long-term study (12 weeks) in rats reported that HIIT led to a significant increase in PPAR-γ expression following a high-fat diet [57]. Therefore, additional studies with longer durations may show a synergistic effect for CSO and HIIT on PPAR-γ expression. This synergistic effect for CSO and HIIT on PGC-1α protein expression was shown for the first time in our study. Additional studies are needed to elucidate other anti-hyperglycemic machanisms of action of HIIT plus CSO, including their possible synergistic effects on GLUT4 translocation and mitochondrial bioenergetics.
Only a few previous studies have investigated the anti-oxidant and anti-inflammatory properties of CSO. In agreement with the current study results, Kavyani et al. showed that co-supplementation of CSO and prebiotics for 12 weeks led to significant decreases in MDA and hs-CRP, and increases in TAC among patients with NAFLD [6]. Musazadeh et al. reported similar results with CSO plus a calorie-restricted diet for 12 weeks in patients with NAFLD [5]. An in-vivo study showed that CSO supplementation led to significant increases in the activity of anti-oxidant enzymes along with significant decreases in MDA levels [58]. Regarding TAC, our study results conflict with this previous evidence, and the differences between the current TAC results and previous results may be related to the duration of supplementation (12 weeks vs. 8 weeks). Kavyani et al. demonstrated anti-inflammatory properties of omega-3 fatty acids [15]. It is well established that oxidative stress plays a key role in the pathophysiology of insulin resistance and T2DM [59, 60], and there is an increasing body of evidence from animal studies confirming oxidative stress-induced insulin resistance and the improvement in insulin signal transduction and glucose homeostasis through use of antioxidants (61–63).
Omega-3 fatty acids can modulate immune system function [64] and the production of pro-inflammatory cytokines [65]. Moreover, omega-3 fatty acids are natural PPAR-γ agonists and can inhibit Nuclear Factor-Kappa-B (NF-ĸB) activity, the main modulator of inflammatory cascades [66]. The anti-oxidant effects of omega-3 fatty acids are mainly related to changes in cellular membrane structures leading to decreases in lipid peroxidation [13]. Moreover, other compounds in CSO such as phytosterols, carotenoids, and tocopherols contribute to its anti-oxidant effects [5, 20].
It’s been hypothesized that activities that increase oxygen consumption can increase free radicals and oxidative stress [67]. Acutely, HIIT induces oxidative stress and lipid peroxidation by increasing NADPH oxidase, xanthine oxidase, phospholipase A2 activity, mitochondrial cytochrome c, and catecholamine oxidation [68, 69]. However, with chronic exercise training, there are adaptive mechanisms that contribute to the reduction of oxidative stress, including the upregulation of redox signaling cascades and endogenous antioxidant enzymes, muscle hypertrophy, glucose uptake by skeletal muscle, and mitochondrial biogenesis [70]. However, co-supplementation with an antioxidant-rich source such as CSO is necessary to accelerate the balance of oxidative stress induced by HIIT.
Beneficial synergistic effects for D + CSO + HIIT on hepatic TG, hepatic histopathology, and expression of PGC-1α were demonstrated in the current study. Previous studies have suggested that HIIT performed for 12 weeks significantly reduces intrahepatic lipid levels [71, 72]. However, Winn et al. showed that the reduction of intrahepatic lipid levels did not significantly differ between different exercise intensities after 4 weeks [73]. Similarly to our study, Kamal et al. investigated the effects of an 8-week HIIT program and found that HIIT was effective in decreasing intrahepatic lipid levels. However, most study participants received metformin, which can also have beneficial effects on hepatic fat levels [74]. Also in agreement with the current results, hepatic histopathology examination in an in-vivo study revealed that 8 weeks of HIIT improved liver function [75]. The current results showed that HIIT for 8 weeks can be beneficial in improving hepatic triglyceride levels.
In terms of hepatoprotective effects of CSO, Musazadeh et al. in a clinical trial study on NAFLD patients showed that CSO supplementation led to a significant decrease in alanine aminotransferase, an enzyme indicating a poor liver function in high levels. However, other liver enzymes did not significantly differ between CSO and placebo groups [21]. A previous narrative review reported that improvement in hepatic steatosis and liver function following HIIT was associated with improved liver mitochondrial function, increased hepatic PPAR-α, and PPAR-γ content, improved insulin sensitivity, and suppression of hepatic de novo lipogenesis [76]. The cellular mechanisms responsible for the positive effects of CSO on liver function have not been fully elucidated. However, anti-inflammatory, antioxidant, and anti-hyperlipidemia effects, and regulation of glucose homeostasis have been suggested. Also, the effects of omega-3 fatty acids on liver function have been investigated in previous studies [77, 78]. Other plant-based omega-3 fatty acid sources such as flax seed [79], walnut [80], or chia [81] exerted hepatoprotective effects.
The current study is the first to investigate the synergistic effects of CSO and HIIT on glycemic, inflammatory, oxidative stress, and total antioxidant capacity biomarkers and liver function in an animal model of T2DM.
Some limitations should be considered when interpreting results. First, the overall treatment duration was short as compared to some similar studies. However, our results were in agreement with most studies of longer durations. Second, other biomarkers of inflammation, oxidative stress, and liver function were not included. For example, various interleukins, hs-CRP, antioxidant enzymes, aspartate aminotransferase (AST), and alanine transaminase (ALT) were not studied. Results may have differed if we had used other or additional biomarkers, in particular, antioxidant capacity should be further elucidated. Of course, a rat model of T2DM does not necessarily generalize to human participants with T2DM. Therefore, further studies in both animal and human models should be conducted to clarify all aspects of the effects of CSO and HIIT in type 2 diabetes. However, the strengths of our study should also be mentioned. Our study was the first to evaluate the synergistic effects of CSO plus HIIT on liver function, and metabolic outcomes, as well as glycemic markers in an animal model of T2DM. In addition, Western-blotting as an accurate method was performed to reach a more accurate conclusion of the antihyperglycemic mechanisms of CSO plus HIIT. Whereas, most similar studies assessed gene expression with real-time PCR methods. The current study also investigated various biomarkers to obtain a more comprehensive picture of the beneficial effects of CSO plus HIIT on T2DM to pave the way for future clinical trials.
## Conclusion
The current study indicated that CSO and HIIT, independently and combined, exerted beneficial effects on fasting blood glucose, HOMA-IR, hepatic TNF-α, MDA, TG, PPAR-γ, PGC-1α, and histopathology markers. Only hepatic TAC and fasting plasma insulin remained unaffected in all the three interventions groups. However, combination of CSO and HIIT had the largest effect on liver function and metabolic outcomes other than CSO and HIIT alone.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
ZK: drafting of the manuscript, acquisition of data, and approval of the article. PD: contributions to concept/design, data analysis/interpretation, critical revision of the manuscript, and approval of the article. MosK: contributions to implementation of the study, critical revision of the manuscript, and approval of the article. MouK: contributions to data analysis/interpretation, critical revision of the manuscript, and approval of the article. SR: contributions to design of the study, critical revision of the manuscript, and approval of the article. All authors contributed to the article and approved the submitted version.
## Funding
This study was funded by the Vice-chancellor for Research and Student Research Committee of Tabriz University of Medical Sciences, Tabriz, Iran (grant number: 69899).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Characterization of chromatin accessibility patterns in different mouse cell
types using machine learning methods at single-cell resolution
authors:
- Yaochen Xu
- FeiMing Huang
- Wei Guo
- KaiYan Feng
- Lin Zhu
- Zhenbing Zeng
- Tao Huang
- Yu-Dong Cai
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10014730
doi: 10.3389/fgene.2023.1145647
license: CC BY 4.0
---
# Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution
## Abstract
Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin. Recent studies have shown that chromatin accessibility is cell type dependent, indicating chromatin heterogeneity across cell lines and tissues. The identification of markers used to distinguish cell types at the chromosome level is important to understand cell function and classify cell types. In the present study, we investigated transcriptionally active chromosome segments identified by sci-ATAC-seq at single-cell resolution, including 69,015 cells belonging to 77 different cell types. Each cell was represented by existence status on 20,783 genes that were obtained from 436,206 active chromosome segments. *The* gene features were deeply analyzed by Boruta, resulting in 3897 genes, which were ranked in a list by Monte Carlo feature selection. Such list was further analyzed by incremental feature selection (IFS) method, yielding essential genes, classification rules and an efficient random forest (RF) classifier. To improve the performance of the optimal RF classifier, its features were further processed by autoencoder, light gradient boosting machine and IFS method. The final RF classifier with MCC of 0.838 was constructed. Some marker genes such as H2-Dmb2, which are specifically expressed in antigen-presenting cells (e.g., dendritic cells or macrophages), and Tenm2, which are specifically expressed in T cells, were identified in this study. Our analysis revealed numerous potential epigenetic modification patterns that are unique to particular cell types, thereby advancing knowledge of the critical functions of chromatin accessibility in cell processes.
## 1 Introduction
Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin (Klemm et al., 2019). Chromatin is a complex of DNA and associated proteins that form chromosomes and present varied states across genomes, tissues, and cell types (Lee et al., 2004). Nucleosome occupancy is variably dynamic, indicating that densely arranged nucleosomes lead to closed chromatin, whereas partially depleted nucleosomes result in accessible or permissive chromatin (Lee et al., 2004; Poirier et al., 2008; Sheffield and Furey, 2012; Klemm et al., 2019). Evidence demonstrates that nucleosomes are typically depleted at the transcriptional regulatory region, including enhancers, promoters, and other transcription factor binding loci (Ozsolak et al., 2007; Thurman et al., 2012). The distinct chromatin accessibility patterns directly reflect different functional states, and they are modulated through a variety of mechanisms, such as histone methylation, acetylation, and DNA methylation (Allis and Jenuwein, 2016). These modifications change the interplays between transcriptional regulators and DNA targets, thereby altering the downstream gene expressions and affecting cell functions. Various changes in chromatin structure and modification have been involved in a range of traits and diseases (Hendrich and Bickmore, 2001). Therefore, characterizing the chromatin accessibility is a critical demand for understanding their functional roles in gene regulation during development and in disease contexts.
*In* general, the measurement of chromatin accessibility is dependent on the physical access of enzymes to target fragments. Hewish et al. first noticed the periodic hypersensitivity of chromatin to DNA endonucleases across the genome, indicating the accessible regions among nucleosomes (Hewish and Burgoyne, 1973). Combine with next-generation sequencing techniques, a genome-wide profiling of chromatin accessibility was carried out, which was known as DNase I hypersensitive site sequencing (DNase-seq) (Boyle et al., 2008). An alternative assay, namely, ATAC-seq, can profile chromatin accessibility based on Tn5 transposon (Buenrostro et al., 2013). ATAC-seq shows a higher sensitivity on low-input samples, and the protocol is less complex compared with DNase-seq. Therefore, this approach is commonly used in recent research to generate chromatin accessibility profiles.
Chromatin accessibility is cell type dependent, indicating the chromatin heterogeneity across cell lines and tissues (Thurman et al., 2012). Previous studies with bulk chromatin accessibility profiles usually attempt to obtain homogeneous cell samples to avoid bias derived from cell heterogeneity. Recently, single-cell epigenomic assays emerged and provided a new way to investigate the regulatory mechanism of chromatin accessibility in complex tissues. However, accurate cell type annotation in single-cell ATAC-seq data remains a great challenge. Thus, three main strategies of cell type annotation in single-cell ATAC-seq data were implemented, including annotation using cis-regulatory elements, annotation using cell type-specific feature set, and annotation using RNA sequencing data as reference (Corces et al., 2016; Schep et al., 2017; Pliner et al., 2018; Stuart et al., 2019). These strategies show certain limitations that either rely on reliable cell type markers or require additional reference datasets. A combinatorial indexing assay, namely, sci-ATAC-seq, was applied to profile the genome-wide chromatin accessibility in single cells from different mouse tissues (Cusanovich et al., 2018a). Based on these data, the heterogeneity in chromatin accessibility within cell types was characterized, and candidate tissue-specific patterns of chromatin accessibility were identified. Considering that a relatively traditional workflow was applied for analysis and only a few epigenetic markers had been found, several potential characteristic patterns of chromatin accessibility across cell types remain undiscovered.
In this study, based on the single-cell chromatin accessibility data from the atlas (Cusanovich et al., 2018a), we applied several machine learning methods to identify relevant characteristic chromatin accessibility patterns that can serve as cell-type-specific markers. The Boruta (Kursa and Rudnicki, 2010) and Monte Carlo Feature Selection (MCFS) (Micha et al., 2008) were applied to the data one by one, yielding a list containing 3897 genes. Then, the list was subjected to incremental feature selection (IFS) (Liu and Setiono, 1998) method, containing decision tree (DT) (Safavian and Landgrebe, 1991) and random forest (RF) (Breiman, 2001). IFS with RF can help to construct an efficient classifier, whereas IFS with DT was used to generate classification rules, which represent the quantitative characteristics of chromatin accessibility for distinguishing different cell types. Features used in the optimal RF classifier were further processed by autoencoder, light gradient boosting machine (LightGBM) (Ke et al., 2017) and IFS method for accessing a better classifier. The final analysis was focused on top features in the list and classification rules, confirming some potential epigenetic modification patterns in particular cell types. This study gave an important contribution to a comprehensive understanding of the essential roles of chromatin accessibility in cell functions.
## 2.1 Data
Large-scale sci-ATAC-seq data were accessed from the GEO database under accession number of GSE111586 provided by Cusanovich et al. ( Cusanovich et al., 2018b). The sci-ATAC-seq data were collected on 77 different cell types from 13 different tissues that contained 69,015 cells, and 77 different cell types were used as classification targets in our research. The number of cells contained in each cell type is shown in Supplementary Table S1. A total of 436,206 chromosome segments mapped to 20,783 genes were obtained by sci-ATAC-seq, and these genes and their existence status (one for existence and 0 for non-existence) in each cell were used as features in this study. Using this quantitative representation, we converted enriched chromosome segments into biologically interpretable genes, thereby providing comprehensive understanding of the classification process.
## 2.2 Boruta
The Boruta algorithm is a feature selection wrapper that can be used to any classification method that generates a variable significance measure (Kursa and Rudnicki, 2010). Boruta searches for all features that contain relevant information that may be utilized for prediction rather than concentrating on finding a restricted group of features with the lowest classification error. The Boruta algorithm consists of the following steps: 1) For each explanatory variable, a shadow variable is made, and its association with the target variable is eliminated by randomly rearranging its values. 2) RFs are built to fit the expanded data. 3) An accuracy loss z-score is applied to each variable including the original and shadow variables. 4) The original attributes are selected if their z-scores are significantly higher than those of shadow counterparts. The process is repeated until all features have been accepted or disregarded. The z-score of the original attributes must be statistically and significantly higher than the maximum z-score of the shadow attributes to identify the most pertinent features of the original attributes.
In this study, we opted for the Boruta program from https://github.com/scikit-learn-contrib/boruta_py and selected the default parameters for subsequent analysis.
## 2.3.1 Monte carlo feature selection
Monte Carlo feature selection is a DT-based feature importance evaluation algorithm commonly used to process biological data (Micha et al., 2008; Chen X. et al., 2019; Li et al., 2020). In MCFS, m features were randomly selected. Based on these features, t DTs are built with t randomly selected sample sets. Above procedure is repeated s times. Finally, s×t DTs were constructed. The relative importance (RI) of a feature, as measured by how many times it has been selected by these trees and how much it contributes to predicting the class of these trees, is estimated as follows: RIg=∑τ=1stwAccu∑ngτIGngτno.in ngτno.in τv [1] where wAcc is the weighted accuracy, IGngτ is the information gain (IG) of node ngτ, (no.in ngτ is the number of samples in node ngτ, and no.in τ is the sample size in the tree root. In addition, u and v are two settled positive integers. After each feature is assigned a RI score, all features are ranked in a list with the decreasing order of their RI scores.
This study adopted the MCFS program sourced from http://www.ipipan.eu/staff/m.draminski/mcfs.html. It was executed using its default parameters.
## 2.3.2 Light gradient boosting machine
The LightGBM is deemed as a strong machine learning algorithm that combines several weak DTs (Ke et al., 2017). It improves the gradient boosting decision tree (GBDT) by increasing the efficiency and reducing memory usage. According to the constructed DTs, LightGBM can also be used to evaluate the importance of features. If K DTs are constructed, the total number of times, denoted by T Split, for each feature is computed, which is defined as the overall used times in all DTs, i.e., T Split=∑$i = 1$KSpliti [2] where *Spliti is* the used times of this feature in the ith DT. Evidently, if T Split for one feature is large, i.e., it occurs in lots of DTs, this feature is quite important. Thus, LightGBM sorts all features in a list with the decreasing order of their T Split values.
In the present study, we utilized the LightGBM program sourced from https://lightgbm.readthedocs.io/en/latest/and ran the analysis by using the default settings.
## 2.4 Incremental feature selection
It is still quite difficult to extract essential features from a feature list to comprise an optimal feature space for a given classification algorithm. Here, we introduced IFS, a well-liked method for determining the optimal feature space (Liu and Setiono, 1998; Chen L. et al., 2019; Zhang et al., 2020; Huang et al., 2023a; Huang et al., 2023b). The main steps of IFS are as follows: 1) From the feature list, lots of feature subsets are constructed with a fixed step, each of which contained some top features in the list. 2) One classifier is built on each constructed feature subset with a given classification algorithm and it is evaluated by 10-fold cross-validation (Kohavi, 1995). 3) The classifier with the best classification performance is selected as the optimal classifier and features used in this classifier are referred as the optimal features.
## 2.5 Synthetic minority oversampling technique
Among the 77 cell types, a 70-fold difference was observed between the largest number of cells and the smallest number of cells. It was not easy to build a fair classifier on such imbalanced dataset. The SMOTE is a data augmentation technique, which can be used to balance out the imbalanced dataset (Chawla et al., 2002). It tackles the imbalanced problem by employing new samples to minority classes. In particular, a sample is randomly selected from each minority class. Then, k closest neighbors of this sample in the same class are picked up and one neighbor is randomly selected. With this sample and its randomly selected neighbor, a synthetic sample is constructed at a randomly selected location in the feature space between them. In this study, the SMOTE algorithm was implemented via Python. Each class except the largest class was processed by SMOTE so that it contained the same number of samples in the largest class.
## 2.6 Classification algorithm
Classification algorithm is necessary for IFS method. Here, two algorithms were used: DT (Safavian and Landgrebe, 1991) and RF (Breiman, 2001). Their brief introduction is as below.
## 2.6.1 Decision tree
DT is a basic classification and regression method with tree-like structures (Safavian and Landgrebe, 1991; Zhang et al., 2021). A DT model represents the classification and discrimination of data as a tree-like structure with nodes and directed edges. Based on one path of a DT from the root node to the leaf node, a rule can be set up, where each internal node corresponds to the rule’s condition, and a leaf node displays the outcome of an associated rule. Thus, a collection of if–then rules can be extracted from a DT. In implementing DT, we used the CART method and the scikit-learn package, with Gini coefficients serving as the IG (Pedregosa et al., 2011).
## 2.6.2 Random forest
RF is an ensemble method, and its basic unit is DT (Breiman, 2001; Li et al., 2022; Ran et al., 2022; Yang and Chen, 2022; Wang and Chen 2023). Each DT was created based on randomly selected features and samples. For a given test sample, each tree provides its prediction. RF integrates these predictions using majority voting. In this study, the RF package from Python’s scikit-learn module was used for constructing RF classifiers.
## 2.7 Autoencoder
Autoencoders are a type of deep learning algorithm that are very useful in the field of unsupervised learning (Hinton and Salakhutdinov, 2006; LeCun et al., 2015). They are a specific type of feedforward neural networks that are designed to receive an input and transform it into a different representation, which compress the data and reduce its dimensionality. Autoencoders compress the input into a lower-dimensional embedding and then reconstruct the output from this embedding, which is a lower-dimensional representation for a higher-dimensional data.
Autoencoders consist of three modules: encoder, embedding and decoder. The encoder maps the input data into the embedding. The embedding contains the compressed knowledge representation, which is typically smaller than the input data. The decoder reconstructs the input data back from the embedding. Autoencoder networks would perform as close to the perfect reconstruction as possible.
Assume we have an input data x with d-dimension, autoencoders first learn a mapping from x to y. y=fWx+b [3] where f is a non-linear function. After this mapping is done, autoencoders learn a mapping from the embedding y back to reconstruction z of the same shape as x, which can be expressed as: z=gWTy+b′ [4] where g is another non-linear function. The loss function used to train autoencoders is called reconstruction loss, which is typically measured using MSE Loss or L1 Loss between x and z. L=x−z [5] where z represents the predicted output and x represents the input data.
The reconstruction loss can be minimized by any mathematical optimization technique, but usually be accomplished by stochastic gradient descent (SGD) (Le, 2013). Z can be used as the low-dimensional embeddings of the samples.
In this study, autoencoder was used to process the optimal features obtained by IFS method. The reconstructed features were evaluated by LightGBM and the generated list was fed into IFS method again to set up a more efficient classifier.
## 2.8 Performance evaluation
The MCC is a comparatively balanced indicator that can be applied when the sample size is unbalanced. The range of MCC is [−1, 1], where a value of one indicates that predictions and actual results match up perfectly; a value of 0 indicates that the predictions are like random predictions, and −1 indicates that the actual outcomes differ from the prediction in a negative way. Thus, MCC can describe the strength of the correlation between the expected and actual results. For the multi-class classification problem, MCC can be calculated by using the following formula (Gorodkin, 2004; Liu et al., 2021; Pan et al., 2022; Tang and Chen, 2022; Wang and Chen, 2022; Zhang et al., 2022; Wu and Chen, 2023): MCC=covX,YcovX,XcovY,$Y = 1$K∑$$n = 1$$N∑$k = 1$KXnk−X¯kYnk−Y¯k∑$$n = 1$$N∑$k = 1$KXnk−X¯k2∑$$n = 1$$N∑$k = 1$KYnk−Y¯k2, [6] where N is the number of samples, K denotes the number of classes, X is the binary matrix into which the predicted class of each sample is converted by one-hot encoding; Y is the binary matrix into which the true class of each sample is converted by one-hot encoding, and covX,Y is the covariance of two matrices. X¯k and Y¯k are the means of the k - th column of matrices X and Y, respectively. Xnk and Ynk are the elements in the n - th row and k - th column of the matrices X and Y, respectively. In this study, MCC was adopted as the major measurement to assess the performance of classifiers.
In addition, we also employed other two measurements: individual accuracy and overall accuracy (ACC). Individual accuracy indicates the prediction quality of the classifier on one class, which is defined as the proportion of correctly predicted samples in this class. ACC represents the overall performance of the classifier. It is defined as the proportion of correctly predicted samples to all samples.
## 3 Results
In the current work, we used efficient feature selection methods and classification algorithms to mine significant features in various cell types to identify relevant characteristic chromatin accessibility patterns that can serve as cell-type-specific markers. Figure 1 displays the overall analysis framework. The description of the outcomes connected to each step was listed in this section.
**FIGURE 1:** *Flowchart of the machine learning procedure in this study. The transcriptionally active chromosome segments are identified by sci-ATAC-seq at single-cell resolution, including 69,015 cells belonging to 77 different cell types and 436,206 active chromosome segments mapped to 20,783 genes. The Boruta algorithm is used to filter genes, and then genes are ranked in accordance with the Monte Carlo feature selection algorithm. Subsequently, the optimal classifier and corresponding optimal feature subsets are obtained using incremental feature selection (IFS) and two classification algorithms. The classification rules are mined by the optimal decision tree (DT) classifier. Finally, the optimal features for random forest (RF) are reconstructed by autoencoder. The reconstructed features are evaluated by LightGBM, resulting in a feature list. IFS method is applied on such list to set up the final optimal RF classifier.*
## 3.1 Feature ranking results
The current study included 77 cell types with a total of 69,015 and 20,783 genes. *The* gene features were first analyzed by Boruta. 3897 features were selected by Boruta, which are provided in Supplementary Table S2. Then, these features were investigated by MCFS, resulting in a feature list. Such list is also provided in Supplementary Table S2. The list would be entered into the IFS approach to determine the optimal features for constructing the optimal classifiers.
## 3.2 Results of IFS with RF and DT algorithms
After the Boruta and MCFS feature selecting methods, 3897 genes were sorted in a list. Such list was then partitioned into 779 feature subsets by five-step intervals in IFS method. On each feature subset, one DT classifier and one RF classifier were built. Their performance was evaluated through 10-fold cross-validation. As mentioned in Section 2.8, MCC was selected as the major measurement. The IFS curves, as shown in Figure 2, for the two classification algorithms were plotted, where MCC and number of features were set as the Y-axis and X-axis, respectively. The detailed results of IFS are provided in Supplementary Table S3.
**FIGURE 2:** *Incremental feature selection (IFS) curves of decision tree and random forest. The optimal classification performance alone with the optimal feature number for each algorithm has been labeled on the curve. Random forest has better classification results than decision tree.*
The IFS curve indicated that RF had the greatest MCC (0.780) at 445 features. When the top 210 features were used in DT, the greatest MCC were 0.595. Accordingly, the optimal RF and DT classifiers were constructed. The ACC values of these two classifiers were 0.789 and 0.609, respectively, as listed in Table 1. The individual accuracies of them are also shown in Supplementary Table S3, which are illustrated in Figure 3. Evidently, the optimal RF classifier was superior to the optimal DT classifier. For the 445 features used in the optimal RF classifier, we used FindAllMarkers function in Seura package to extract differentially expressed genes for each cell type and adopted logFC to rank these genes in each cell type. The top gene in each cell type was selected, resulting in 73 genes. After excluding genes differentially expressed in more than 1 cell type, 47 genes were obtained. Their expression levels on 77 cell types are illustrated in a heatmap, as shown in Figure 4. It can be observed that some gene features shown good ability to distinguish different mouse cell types and application potential as marker genes for certain cell clusters.
## 3.3 Classification rules created by the optimal DT classifier
Although the DT classifiers were generally inferior to the RF classifiers, it can provide more medical insights than RF as it is a classic white-box algorithm. Its readability of the working mechanism serves as its strongest distinguishing ability. We could produce a quantitative representation of the features used for different cell type classifications by exploiting the single-tree structure of DT to extract the classification rules.
As the optimal DT classifier adopted the top 210 genes features, all cells were represented by these features. A large tree was learnt from such dataset by DT. 24,257 rules were extracted from this tree, as shown in Supplementary Table S4. Each rule established a limit on the existence of gene features, indicating the relevance of the existence (value >0.5) or non-existence (value ≤0.5) of genes in distinguishing various cell types. Detailed analysis of these rules can be seen in Section 4. However, some rules can distinguish a small number of samples, which is out of the scope of our analysis.
## 3.4 Classification performance optimization using autoencoder and LightGBM
In improving the classification performance, we introduced autoencoder to optimize feature representation. Based on the IFS results, RF achieved the optimal classification performance with MCC of 0.780 when top 445 features were used. These 445 gene features were reconstructed by autoencoder. The reconstructed features were ranked by LightGBM to generate a feature list. Such list was fed into IFS by one-interval step to obtain the optimal feature subsets and optimal RF classifier.
Similar to Figure 2, the IFS curve was plotted, as shown in Figure 5. The detailed IFS results are shown in Supplementary Table S5. The optimal RF classifier was constructed with MCC of 0.838 using the top 32 features in the feature list. The ACC of this classifier was 0.844 (Table 1). Compared with the previous optimal RF classifier (MCC = 0.780 and ACC = 0.789, see Table 1), the MCC was improved by 0.058 and ACC increased 0.055 after reconstructing features by autoencoder. All individual accuracies of this classifier are shown in Figure 6. All of them were quite high (higher than 0.6). Compared with the performance of the previous optimal RF classifier (Figure 3), they were evidently improved. Such result proved the effectiveness of autoencoder. The final constructed RF classifier can be used for the classification of cells based on single-cell ATAC-seq data.
**FIGURE 5:** *Incremental feature selection (IFS) curves of random forest based on the list by applying LightGBM to the features reconstructed by autoencoder. The optimal MCC of 0.838 is achieved when the number of features is 32, which is better than that based on the original 0–1 representation of genes.* **FIGURE 6:** *Lollipop plot of individual accuracies yielded by the final random forest classifier for distinguishing different cell types. The circles represent the number of cells contained in different cell types. Some individual accuracies of this classifier optimized by autoencoder can reach up to 1, whereas no individual accuracies are lower than 0.6, indicating the effectiveness of the classifier for cell type classifications.*
## 4 Discussion
Our study presented a computational pipeline for analyzing the cell types of mice in single-cell ATAC-seq data. Cells isolated from 13 distinct tissues were further divided into 77 different cell types. By characterizing the chromatin accessibility at single-cell resolution, the status of chromatin accessibility within the gene region was considered as features. They were analyzed by feature selection methods, IFS method and classification algorithms. Lots of essential genes and classification rules were obtained. Here, we focused on some gene features and rules to discuss the relevance of chromatin accessibility in cell type discrimination, which may reveal the important roles of chromatin accessibility in transcriptional regulation and identify cell-type-specific regulatory patterns.
## 4.1 Analysis of chromatin accessibility features by MCFS
*The* genes were ranked in a list according to the evaluation results of MCFS. Genes with high ranks were more important than others. Here, we selected some top genes for detailed analysis, which are listed in Table 2.
**TABLE 2**
| Rank in the list | Gene symbol | Description |
| --- | --- | --- |
| 1 | H2-Dmb2 | histocompatibility 2, class II, locus Mb2 |
| 2 | Trbd2 | T cell receptor beta, D region 2 |
| 4 | Trbj2-4 | T cell receptor beta joining 2–4 |
| 5 | Trbj2-1 | T cell receptor beta joining 2–1 |
| 6 | Trbd1 | T cell receptor beta, D region 1 |
| 7 | Trbj2-2 | T cell receptor beta joining 2–2 |
| 9 | Trbj2-3 | T cell receptor beta joining 2–3 |
| 10 | Trbj2-5 | T cell receptor beta joining 2–5 |
| 11 | Trbj2-7 | T cell receptor beta joining 2–7 |
| 12 | Tenm2/Odz2 | teneurin transmembrane protein 2 |
Our analysis identified the chromatin accessibility at the gene region of H2-Dmb2 to be highly related to the classification of cell types. The protein products encoded by H2-Dmb2 belong to the MHC class II beta chain paralogues, which are anchored in the membrane, and such products play a central role in peptide binding. MHC class II molecules are specifically expressed in antigen presenting cells such as dendritic cells or macrophages, thereby generating a biased expression of H2-Dmb2 primarily in the spleen, lymph node, and other immune-activated tissues (Rudensky et al., 1991; Cresswell, 1994; Yue et al., 2014). Given the specific expression pattern of H2-Dmb2 across tissues and cells, gene H2-Dmb2 shows high indicative value for distinguishing antigen-presenting cells and immune-activated tissues; thus, this gene can serve as a biomarker. The ortholog gene of H2-Dmb2 in human, namely, HLA-DMB, plays a critical role in the interaction between antigenic peptides and MHC class II molecules. The aberrant expression of HLA-DMB is associated with many diseases, including diabetes mellitus, autoimmune disease, infection, and cancer (Siegmund et al., 1999; Morel et al., 2004; Callahan et al., 2008; Aissani et al., 2014). Although the detailed mechanisms underlying disease progression remain unknown, the important role of HLA-DMB in antigen presentation cannot be neglected. Understanding the chromatin accessibility in HLA-DMB will contribute to revealing the regulatory mechanism and potential targets for disease treatment.
Among the most relevant features identified by our analysis, we found that alterations in chromatin accessibility are associated with many T cell receptor (TCR)-related genes, such as Trbd1, Trbd2, and Trbj2. In a single cell, the TCR beta chain is generated by the somatic recombination of variable V), joining J), diversity D), and constant C) gene segments. The recombination of different segments provides a wide range of antigen recognition for T cell function (Bassing et al., 2002). TCR genes are particularly expressed in T cells; therefore, they display a biased expression pattern in tissues with high infiltration of T lymphocyte. A TCR-β-targeting study by Mathieu et al. demonstrated that chromatin remodeling is associated with the control of TCR gene activation through several epigenetic regulatory mechanisms, and this process can influence the developmental control of TCR gene recombination (Mathieu et al., 2000). This finding indicates the important role of chromatin accessibility in modulating gene expression and consequent function alterations, which provides support for our results, that is, chromatin accessibility of TCR-related genes is highly related to cell functions and cell type classification.
The chromatin accessibility status of gene Tenm2 (also called ODZ2) was identified as another relevant feature for distinguishing cell and tissue types. Tenm2 is a protein coding gene, which is involved in neural development and cellular signal transduction (Rubin et al., 2002). Given the pivotal role of Tenm2 in neuronal cells, its transcriptional products exhibit a biased expression primarily in the central nervous system, brain, and other neural-related tissues as demonstrated by Mouse ENCODE transcriptome data (Yue et al., 2014). *Although* gene Tenm2 has been reported to be associated with diseases such as periodontitis and anosmia (Alkelai et al., 2016; Sayad et al., 2020), the linkage between Tenm2 and diseases was primarily built on the basis of genomic studies. Our analysis highlighted the epigenetic modification on gene regulation, indicating that chromatin accessibility at the gene region plays a crucial role in the selective expression of genes, which can serve as cell type-specific markers.
## 4.2 Analysis of decision rules of chromatin accessibility by DT
In improving the explicability of the features implicated in the classification, we performed a quantitative computational analysis using DT. A large number of decision rules involving 210 critical features were built to identify 77 cell types. We focused on the associations between the quantitative features and indicated cell types. Thus, we explored the relevance of the chromatin accessibility tendency of genes in distinguishing cell types through a literature review. Our study provided insights into disentangling cell-type-specific chromatin accessibility and suggested the new epigenetic markers of each cell type.
In the decision rules for identifying the cell type of heart cardiomyocyte, the *Myh6* gene required a relatively high chromatin accessibility, whereas the Trbv31 and *Nrxn1* genes required low chromatin accessibility. The *Myh6* gene encodes the alpha heavy chain subunit of cardiac myosin, which is the key component of muscle cells. As demonstrated by the Mouse ENCODE transcriptome study, the expression of Myh6 is highly restricted toward heart tissues (Yue et al., 2014). A recent publication proposed that the repressive chromatin assembly on the Myh6 promoter can silence the expression of Myh6 and impair cardiac contraction (Han et al., 2016). This finding confirmed the crucial role of Myh6 chromatin modification in cardiac phenotypes, which indicates that the accessible chromatin status of Myh6 is an essential marker for functional cardiomyocytes. Trbv31 is a TCR-related gene, and it displays specific expression in T cells (Isobe et al., 1985). The criterion requiring a low chromatin accessibility of Trbv31 reflects a low gene expression, which is consistent with the actual condition, that is, rare lymphocytes reside within the heart cardiomyocyte environment. Nrxn1 encodes a single-pass type I membrane protein, which belongs to the neurexin family. Given that neurexins are cell-surface receptors that are restrictedly located at nervous synapses (Südhof, 2008), the Nrxn1 protein is not expressed in heart cardiomyocytes. Therefore, Nrxn1 serves as a negative marker indicating heart cardiomyocytes.
Among the decision rules for liver hepatocytes, 43 features were involved in the criteria, 42 of which required low chromatin accessibility of genes, whereas only one gene displayed a positive marker, that is, Slc27a2. The protein encoded by Slc27a2 is a fatty-acid coenzyme, which plays a key role in lipid biosynthesis and fatty acid degradation (Steinberg et al., 1999). The biased expression of Slc27a2 in liver and kidney tissues has been demonstrated by a previous study (Yue et al., 2014). The decision rules by our analysis indicate that a high chromatin accessibility of Slc27a2 is a positive marker indicating liver hepatocytes. The negative features for liver hepatocytes are mostly specific markers of other cell types, such as the aforementioned genes Trbv31 and Myh6, which are specifically expressed in T and cardiac cells, respectively. In addition, another gene (Lef1) was identified as a negative marker for liver hepatocytes. *This* gene encodes a transcription factor that can bind to T-cell receptor enhancer, and it is involved in the Wnt signaling pathway (Petropoulos et al., 2008). A biased expression of Lef1 in the thymus and spleen was demonstrated, which is consistent with its specificity in lymphocytes (Yue et al., 2014). These observations indicated that positive and negative features identified in this analysis contribute to the classification of corresponding cell type.
The relatively high chromatin accessibility of TCR-related genes such as Trbv31 and Trbj2 was required to indicate the cell type of thymus T cells. In addition, another positive feature, which is the chromatin accessibility of gene Lrmp, was identified to be involved in the decision rules for thymus T cells. Lrmp, also known as Irag2, encodes a lymphoid-restricted membrane protein, which can regulate the development of lymphoid cell lines (Behrens et al., 1994). RNA profiling data sets generated by the Mouse ENCODE project demonstrated the biased expression of Lrmp in thymus tissue (Yue et al., 2014). Our results indicated that in addition to post-transcriptional regulations, modifications of chromatin accessibility play important roles in gene expression control, which can be used as epigenetic markers for distinguishing lymphoid cells.
The decision rules for identifying sperm cells from testes include 45 criteria, among which the high chromatin accessibility of gene Nol4 was identified by our analysis. Nol4 is a cancer/testis antigen, and it has been reported to be involved in cancer progression (Kim et al., 2021). Cancer/testis antigens are a group of proteins with normal expression restricted to testicular germ cells but not in adult somatic tissues. In this study, our analysis showed that the chromatin accessibility pattern of the *Nol4* gene was highly related to the classification of testicular sperm cells, presenting a reasonable relevance between the expression of Nol4 and testicular cells in non-malignant contexts and indicating the potential mechanism of cancer/testis antigen expression through chromatin accessibility modifications.
In this study, a series of quantitative rules was constructed to predict the category of cerebellar granule cells. Among these decision rules, Cbln1 and *Arpp21* genes required high chromatin accessibility to distinguish cerebellar granule cells. Gene Cbln1 encodes a cerebellum-specific precursor protein, namely, precerebellin, which is highly enriched in postsynaptic structures of Purkinje cells (Urade et al., 1991). Research by Hirai et al. demonstrated that Cbln1 was secreted from cerebellar granule cells, which have important functions in Purkinje neurons (Hirai et al., 2005). Arpp21 encodes a cAMP-regulated phosphoprotein, which is enriched in the cerebellar cortex. The high level of Arpp21 mRNA was detected in the cerebellar cortex by in situ hybridization and Northern blot analysis (Brene et al., 1994). All these results confirmed the biased expression of Cbln1 and Arpp21 in cerebellum tissues, which support the predictive values of these genes for distinguishing cerebellar granule cells.
## 5 Conclusion
This study computationally investigated the characteristic chromatin accessibility of different mouse cell types at single-cell resolution. The most relevant features and quantitative decision rules were identified through several machine learning algorithms, indicating the potential epigenetic markers for each cell type. Detailed discussion was performed to explore the functional linkage between the chromatin accessibility pattern of genes and the indicated cell types. Many of the identified genes were biased or restrictedly expressed in specific tissues or cells, meaning they can serve as potential biomarkers for the corresponding cell types based on existing experimental evidence and publications. In addition, our study highlighted the epigenetic modification of chromatin in gene expression regulation, implying the critical roles of chromatin accessibility in cell function. Considering the interpretability of features, we primarily focused on features of the chromatin accessibility pattern of genes in cell type discrimination. The classifiers using features reconstructed by autoencoder showed excellent performance. Our study also provides insight into a comprehensive understanding of the genome-wide chromatin accessibility and generic markers in cell lines and tissues.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111586.
## Author contributions
ZZ, TH, and YC designed the study. YX, WG, KF, and LZ performed the experiments. YX and FH analyzed the results. YX, FH, and WG wrote the manuscript. All authors contributed to the research and reviewed the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1145647/full#supplementary-material
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|
---
title: Factors associated with hopelessness, depression and anxiety in the Honduran-Central
America population during the COVID-19 pandemic
authors:
- Eleonora Espinoza-Turcios
- René Mauricio Gonzales-Romero
- Carlos Sosa-Mendoza
- Manuel Sierra-Santos
- Henry Noel Castro-Ramos
- Lysien Ivania Zambrano
- José Armada
- Christian R. Mejía
journal: Frontiers in Psychiatry
year: 2023
pmcid: PMC10014751
doi: 10.3389/fpsyt.2023.1116881
license: CC BY 4.0
---
# Factors associated with hopelessness, depression and anxiety in the Honduran-Central America population during the COVID-19 pandemic
## Introduction
The mental health of the population has been affected by COVID-19, reporting in many populations higher levels of depressive, anxious and stress symptoms, however, in Honduras there are no studies showing the impact of COVID-19 on the mental health of the population.
The World Health Organization (WHO) defines mental health as a state of well-being, where the person realizes his or her capabilities and is able to cope with the normal stresses of life, to work productively and to contribute to his or her community, being a state of internal dynamic equilibrium in which people can apply universal values to society, preserving basic skills, social skills, abilities to cope with life problems, to recognize, understand and express one’s own feelings [1, 2], although it is also known that mental health problems increase in situations of disasters, wars or global problems, where there is greater fear, uncertainty and stigmatization; directly impacting public health [3].
It is known that before the pandemic, depressive and anxiety mental disorders were the leading cause of the global health-related burden [4]. In a study showing the prevalences of psychological consequences by COVID-19, symptoms of Post-Traumatic Stress Disorders was $33\%$, anxiety $28\%$, stress $27\%$ and depression $22\%$ [5]. A systematic review and a meta-analysis at this time the highest levels of depression and anxiety were reported in Africa and the Americas region, respectively [6]. But then came the issues of confinement, economic problems, disruption of social, academic and routine activities, which together increased psychological disorders [7]. For example, in China, which was the country where the pandemic originated, $54\%$ of psychological impact was observed, $17\%$ of depressive symptoms, $29\%$ of anxious symptoms and $8\%$ of stress; all between moderate and severe [8]. Following that, a meta-analysis found that mental health symptoms in adults varied between regions, where Africa had the highest prevalence rate of depression ($45\%$), followed by South Asia ($34\%$) and Latin America ($32\%$); at the same time, South Asia was the highest in anxiety ($41\%$), followed by Africa ($37\%$) and Latin America ($32\%$) [9]. And these symptoms increased in some specific populations, such as those working in the health sector, where multiple reports show increased problems of depressive symptoms, anxiety, insomnia and stress [10].
However, this has been studied more in developed countries and others that had a great affectation, but countries like Honduras have not been the exception, having reported before the pandemic $39\%$ of depression and $29\%$ of anxiety; this in the rural area of Francisco Morazán and Olancho. This is related to a study in a multi-center population with chronic diseases, where Honduras had $30\%$ depression [11, 12]. Then came all the problems generated by economic instability, confinement, lack of work, the feeling of loneliness, among many others; having reported in 2020 that the “most common negative emotions were: helplessness, despair, fear, anxiety, nostalgia, restlessness and uncertainty” [13]. For this reason, the objective was to determine the factors associated with hopelessness, depression and anxiety in times of COVID-19 in the population of Honduras.
## Aim
To determine the factors associated with hopelessness, depression and anxiety in times of COVID-19 in the Honduran population.
## Methods
Cross-sectional analytical study. Three scales were used, Beck for hopelessness, Hamilton for depression and anxiety, through interviews with the population attending different levels of health care throughout the country. Descriptive and analytical statistics were obtained. For hopelessness, the Beck Hopelessness Scale was used; for depression and anxiety, the Hamilton Scale was used.
The Scientific Research Unit (UIC) of the Faculty of Medical Sciences (FMC)–UNAH, designed a strategy to implement national research on priority health problems in low-resource settings. In addition, the Honduran Institute for the Prevention of Alcoholism, Drug Addiction and Drug Dependence (IHADFA) and SESAL of Honduras also collaborated. The surveyors were 168 eighth year medical students (MSS), who received scholarships/support from the Ministry of Health (SESAL) to be trained in basic topics of practical learning methodology, applied research methods and others that would serve them during the research and the survey.
After training, the surveyors were distributed throughout the country; this distribution was carried out in hospitals and primary care units during the period from September–October 2021 to March–April 2022. Therefore, the population that was accessed was the one that went to these public health institutions, being mainly populations of the middle and lower social classes and in urban and semi-urban areas.
To determine whether the number of respondents was sufficient, statistical power was calculated for the crossover of the three dependent variables versus each of the independent variables, where almost all the crossovers were above $95\%$, except for the crossovers of hopelessness versus being infected by COVID-19 ($69\%$) or having consumed alcohol in the last 6 months ($67\%$); of depression versus sex ($41\%$), educational level ($52\%$); as well as, anxiety versus sex ($34\%$), educational level ($63\%$), obesity ($63\%$) or having consumed alcohol ($11\%$) or drugs ($57\%$) in the last 6 months.
The study design was cross-sectional analytic and multi-center, with a non-random sample by convenience included 8,137 patients who were ≥ 18 years old, who were health care workers of the institutions where the survey was conducted and who identified themselves as residents of the catchment areas in hospitals, urban and semi-urban primary health care clinics. Twelve respondents were excluded because they were aged 12–17 years.
Before starting the project, ethical care was ensured, and therefore, approval was requested from the FCM Biomedical Research Ethics Committee (CEIB), approval code: 056–2021. In addition, as part of their training, the MSS were given an online course on Good Clinical Practices The Global Health Network.1 For the survey process, verbal consent was obtained from each participant, which was recorded in the electronic survey designed and uploaded to the Microsoft Forms platform. This form of survey was chosen due to multiple factors: In the period of patient enrollment, there were still a regular number of positive cases, so the virtual survey system helped to have less contact between the respondent and the surveyor. In addition, this system allowed respondents to feel more at ease knowing that they could not be identified, which helped them to give more reliable answers. Finally, this generated a better way of storing the surveys, centralizing the information, debugging and quality control from a single central location and saving physical material, the latter to achieve a smaller carbon footprint.
Beck Hopelessness Scale [14] is a self-application instrument designed to measure the degree of this condition in adolescents and adults, which consists of 20 items with true or false response options. The most appropriate cut-off point is 8 positive responses, so that a score equal to or higher than 8 indicates a high degree of hopelessness [15]. It has been validated in Colombia [16], Mexico Quiñones, Méndez, Castañeda, 2019 [17].
The German psychiatrist Max Hamilton in 1960 described for the first time the depression scale [18], which in its original version consisted of 21 items with 3 and 5 ordinal response options, later a reduced version was made with 17 items, which is the one recommended by the National Institute of Mental Health of the United States [19]. The scale assesses the severity of depressive symptoms during the week prior to the interview. The total scale score ranges from 0 points (no depressive symptoms) to 66 (severe depressive symptoms). In our study we used the scale validated for Spain [20]. The Hamilton Depression Scale became and remains the “gold standard” tool for assessing the severity of depression [21].
The Hamilton Anxiety *Scale is* a self-administered scale of 14 items that evaluates the patient’s degree of anxiety [22]. It is applied in a semi-structured form, where the severity of the symptoms is evaluated using 5 ordinal response options (0: absence of the symptom, up to 4: very severe or disabling symptom). The total score of the instrument, which is obtained by the sum of the partial scores of the 14 items, can range from 0 points (absence of anxiety) to 56 (maximum degree of anxiety) [23]. In a study in Mexico, using the Beck II scale for depression and the Hamilton anxiety scale in 177 high school students during the pandemic, showed the presence of depression indicators ($48.0\%$) and presented major anxiety, 89.8 and $32.2\%$ showed the presence of suicidal thoughts [24] Similarly, traumatology and orthopedics residents had a high prevalence of depression and anxiety using both Hamilton scales [25].
In addition to these tests for the measurement of the dependent variables, the following questions were also asked: Gender (male or female), age (taken quantitatively), educational level (high school or less versus college or higher), whether they had been infected by COVID-19 (yes or no), whether a family member died from COVID-19 (yes or no), whether they had arterial hypertension (yes or no), obesity (yes or no), diabetes mellitus (yes or no), diagnosis of previous mental illness (yes or no), whether they had consumed alcohol or drugs in the last 6 months (yes or no).
## Results
Of the 8,125 participants, the population in general showed results of hopelessness $14.9\%$, depression $12.9\%$ and anxiety $1.2\%$, there was less hopelessness among women ($$p \leq 0.004$$), university students ($p \leq 0.001$), but greater among those who had a family member deceased by COVID-19 ($p \leq 0.001$), among those who had diabetes mellitus (DM) ($p \leq 0.001$), history of mental illness ($p \leq 0.001$) or had consumed drugs in the last 6 months ($p \leq 0.001$). There was more depression among those with arterial hypertension ($$p \leq 0.002$$), obesity ($$p \leq 0.019$$), DM ($$p \leq 0.004$$), history of mental illness ($p \leq 0.001$) or had consumed drugs in the last 6 months ($p \leq 0.001$). There was more anxiety among those infected with COVID-19 ($$p \leq 0.023$$), according to having a family member deceased by COVID-19 ($$p \leq 0.045$$) and in those with a history of mental illness ($p \leq 0.001$).
Of the 8,125 respondents, $64.9\%$ were women, the median age was 34 years (interquartile range: 26–47 years), $69.9\%$ had high school or less, $62.4\%$ had not yet been infected by COVID-19, $18.9\%$ had a family member who died from COVID-19, $20.4\%$ had AHT, $15.9\%$ obesity, $9.3\%$ diabetes mellitus, $2.3\%$ history of mental illness, $28.5\%$ alcohol, and $2.6\%$ drugs in the past 6 months (respectively) (Table 1).
**Table 1**
| Variable | Frequency | Percentage (%) |
| --- | --- | --- |
| Gender | | |
| Male | 2851.0 | 35.1 |
| Female | 5273.0 | 64.9 |
| Age (years completed) | | |
| Mean and standard deviation | 371.0 | 13.3 |
| Median and interquartile range | 34.0 | 26–47 |
| Education level | | |
| High school or less | 5678.0 | 69.9 |
| University or more | 2446.0 | 30.1 |
| Infected with COVID-19 | | |
| No | 5065.0 | 62.4 |
| Yes | 3059.0 | 37.6 |
| Family member died from COVID-19 | | |
| No | 6586.0 | 81.1 |
| Yes | 1538.0 | 18.9 |
| Arterial hypertension | | |
| No | 6464.0 | 79.6 |
| Yes | 1660.0 | 20.4 |
| Obesity | | |
| No | 6835.0 | 84.1 |
| Yes | 1289.0 | 15.9 |
| Diabetes mellitus | | |
| No | 7372.0 | 90.7 |
| Yes | 752.0 | 9.3 |
| Mental illness | | |
| No | 7935.0 | 97.7 |
| Yes | 189.0 | 2.3 |
| Alcohol in last 6 months | | |
| No | 5809.0 | 71.5 |
| Yes | 2315.0 | 28.5 |
| Drugs in last 6 months | | |
| No | 7911.0 | 97.4 |
| Yes | 213.0 | 2.6 |
In the first multivariate model, there was less hopelessness among women (aPR: 0.79; $95\%$CI: 0.67–0.93; value of $$p \leq 0.004$$) and among those with university level (aPR: 0.32; $95\%$CI: 0.25–0.41; value of $p \leq 0.001$), on the contrary, there was more hopelessness among those who had a family member died from COVID-19 (aPR 1.37; $95\%$CI: 1.15–1.64; value of $p \leq 0.001$), among those with diabetes mellitus (aPR 1.81; $95\%$CI: 1.46–2.25; value of $p \leq 0.001$), history of mental illness (aPR: 3.30; $95\%$CI: 2.49–4.38; value of $p \leq 0.001$) or had used drugs in the last 6 months (aPR: 2.30; $95\%$CI: 1.67–3.15; value of $p \leq 0.001$); adjusted for three variables (Table 2).
**Table 2**
| Variable | Hopelessness | Hopelessness.1 | Type of analytical statistics | Type of analytical statistics.1 |
| --- | --- | --- | --- | --- |
| Variable | No n (%) | Yes n (%) | Bivariate | Multivariate |
| Gender | | | | |
| Male | 2,616 (91.8) | 235 (8.2) | Comparison cat. | Comparison cat. |
| Female | 4,922 (93.3) | 351 (6.7) | 0.81 (0.69–0.95) 0.008 | 0.79 (0.67–0.93) 0.004 |
| Age (years completed) | 34 (26–47) | 37 (26–51) | 1.01 (1.01–1.02) <0.001 | 0.99 (0.99–1.01) 0.554 |
| Education level | | | | |
| High school or less | 5,163 (90.9) | 515 (9.1) | Comparison cat. | Comparison cat. |
| University or more | 2,375 (97.1) | 71 (2.9) | 0.32 (0.25–0.41) <0.001 | 0.32 (0.25–0.41) <0.001 |
| Infected with COVID-19 | | | | |
| No | 4,682 (92.4) | 383 (7.6) | Comparison cat. | Did not enter the model |
| Yes | 2,856 (93.4) | 203 (6.6) | 0.88 (0.74–1.03) 0.119 | Did not enter the model |
| Family member died from COVID-19 | | | | |
| No | 6,145 (93.3) | 441 (6.7) | Comparison cat. | Comparison cat. |
| Yes | 1,393 (90.6) | 145 (9.4) | 1.41 (1.18–1.68) <0.001 | 1.37 (1.15–1.64) <0.001 |
| Arterial hypertension | | | | |
| No | 6,051 (93.6) | 413 (6.4) | Comparison cat. | Comparison cat. |
| Yes | 1,487 (89.6) | 173 (10.4) | 1.63 (1.38–1.93) <0.001 | 1.20 (0.97–1.48) 0.088 |
| Obesity | | | | |
| No | 6,373 (93.2) | 462 (6.8) | Comparison cat. | Comparison cat. |
| Yes | 1,165 (90.4) | 124 (9.6) | 1.42 (1.18–1.72) <0.001 | 1.18 (0.97–1.44) 0.097 |
| Diabetes mellitus | | | | |
| No | 6,896 (93.5) | 476 (6.5) | Comparison cat. | Comparison cat. |
| Yes | 642 (85.4) | 110 (14.6) | 2.27 (1.87–2.75) <0.001 | 1.81 (1.46–2.25) <0.001 |
| Mental illness | | | | |
| No | 7,388 (93.1) | 547 (6.9) | Comparison cat. | Comparison cat. |
| Yes | 150 (79.4) | 39 (20.6) | 2.99 (2.24–4.00) <0.001 | 3.30 (2.49–4.38) <0.001 |
| Alcohol in last 6 months | | | | |
| No | 5,406 (93.1) | 403 (6.9) | Comparison cat. | Did not enter the model |
| Yes | 2,132 (92.1) | 183 (7.9) | 1.14 (0.96–1.35) 0.128 | Did not enter the model |
| Drugs in last 6 months | | | | |
| No | 7,359 (93.0) | 552 (7.0) | Comparison cat. | Comparison cat. |
| Yes | 179 (84.0) | 34 (16.0) | 2.29 (1.66–3.15) <0.001 | 2.30 (1.67–3.15) <0.001 |
In the second multivariate model, there was more depression among those with AHT (aPR: 1.35; $95\%$CI: 1.11–1.64; value of $$p \leq 0.002$$), obesity (aPR: 1.28; $95\%$CI: 1.04–1.57; value of $$p \leq 0.019$$), among those with diabetes mellitus (aPR: 1.43; $95\%$CI: 1.13–1.82; value of $$p \leq 0.004$$), history of mental illness (aPR: 4.73; $95\%$CI: 3.77–5.93; value of $p \leq 0.001$) or had used drugs in the last 6 months (aPR: 2.08; $95\%$CI: 1.47–2.94; value of $p \leq 0.001$); adjusted for three variables (Table 3).
**Table 3**
| Variable | Depression | Depression.1 | Type of analytical statistics | Type of analytical statistics.1 |
| --- | --- | --- | --- | --- |
| Variable | No n (%) | Yes n (%) | Bivariate | Multivariate |
| Gender | | | | |
| Male | 2,642 (92.7) | 209 (7.3) | Comparison cat. | Did not enter the model |
| Female | 925 (93.4) | 348 (6.6) | 0.90 (0.76–1.06) 0.213 | Did not enter the model |
| Age (years completed) | 34 (26–47) | 35 (26–49) | 1.05 (0.99–1.01) 0.126 | Did not enter the model |
| Education level | | | | |
| High school or less | 5,276 (92.9) | 402 (7.1) | Comparison cat. | Did not enter the model |
| University or more | 2,291 (93.7) | 155 (6.3) | 0.90 (0.75–1.07) 0.225 | Did not enter the model |
| Infected with COVID-19 | | | | |
| No | 4,751 (93.8) | 314 (6.2) | Comparison cat. | Comparison cat. |
| Yes | 2,816 (92.1) | 243 (7.9) | 1.28 (1.09–1.51) 0.003 | 1.15 (0.98–1.36) 0.094 |
| Family member died from COVID-19 | | | | |
| No | 6,155 (93.5) | 431 (6.5) | Comparison cat. | Comparison cat. |
| Yes | 1,412 (91.8) | 126 (8.2) | 1.25 (1.03–1.51) 0.021 | 1.10 (0.91–1.34) 0.323 |
| Arterial hypertension | | | | |
| No | 6,067 (93.9) | 397 (6.1) | Comparison cat. | Comparison cat. |
| Si | 1,500 (90.4) | 160 (9.6) | 1.57 (1.32–1.87) <0.001 | 1.35 (1.11–1.64) 0.002 |
| Obesity | | | | |
| No | 6,405 (93.7) | 430 (6.3) | Comparison cat. | Comparison cat. |
| Yes | 1,162 (90.2) | 127 (9.8) | 1.57 (1.30–1.89) <0.001 | 1.28 (1.04–1.57) 0.019 |
| Diabetes mellitus | | | | |
| No | 6,900 (93.6) | 472 (6.4) | Comparison cat. | Comparison cat. |
| Yes | 667 (88.7) | 85 (11.3) | 1.77 (1.42–2.20) <0.001 | 1.43 (1.13–1.82) 0.004 |
| Mental illness | | | | |
| No | 7,439 (93.7) | 496 (6.3) | Comparison cat. | Comparison cat. |
| Yes | 128 (67.7) | 61 (32.8) | 5.16 (4.13–6.46) <0.001 | 4.73 (3.77–5.93) <0.001 |
| Alcohol in last 6 months | | | | |
| No | 5,443 (93.7) | 366 (6.3) | Comparison cat. | Comparison cat. |
| Yes | 2,124 (91.8) | 191 (8.2) | 1.31 (1.11–1.55) 0.002 | 1.15 (0.96–1.38) 0.134 |
| Drugs in last 6 months | | | | |
| No | 7,386 (93.4) | 525 (6.6) | Comparison cat. | Comparison cat. |
| Yes | 181 (85.0) | 32 (15.0) | 2.26 (1.63–3.15) <0.001 | 2.08 (1.47–2.94) <0.001 |
In the third multivariate model, there was more anxiety among those who were infected with COVID-19 (aPR: 1.97; $95\%$CI: 1.10–3.53; value of $$p \leq 0.023$$), in those who had a family member deceased by COVID-19 (aPR: 1.83; $95\%$CI: 1.01–3.31; value of $$p \leq 0.045$$) and in those with a history of mental illness (aPR: 11.97; $95\%$CI: 6.09–23.50 value of $p \leq 0.001$; Table 4).
**Table 4**
| Variable | Anxiety | Anxiety.1 | Type of analytical statistics | Type of analytical statistics.1 |
| --- | --- | --- | --- | --- |
| Variable | No n (%) | Yes n (%) | Bivariate | Multivariate |
| Gender | | | | |
| Male | 2,832 (99.3) | 19 (0.7) | Comparison cat. | Did not enter the model |
| Female | 5,246 (99.5) | 27 (0.5) | 0.77 (0.43–1.38) 0.377 | Did not enter the model |
| Age (years completed) | 34 (26–47) | 41 (26–50) | 1.02 (0.99–1.04) 0.122 | Did not enter the model |
| Education level | | | | |
| High school or less | 5,651 (99.5) | 27 (0.5) | Comparison cat. | Did not enter the model |
| University or more | 2,427 (99.2) | 19 (0.8) | 1.63 (0.91–2.93) 0.100 | Did not enter the model |
| Infected with COVID-19 | | | | |
| No | 5,046 (99.6) | 19 (0.4) | Comparison cat. | Comparison cat. |
| Yes | 3,032 (99.1) | 27 (0.9) | 2.35 (1.31–4.22) 0.004 | 1.97 (1.10–3.53) 0.023 |
| Family member died from COVID-19 | | | | |
| No | 6,556 (99.5) | 30 (0.5) | Comparison cat. | Comparison cat. |
| Yes | 1,522 (98.9) | 16 (1.1) | 2.28 (1.25–4.18) 0.007 | 1.83 (1.01–3.31) 0.045 |
| Arterial hypertension | | | | |
| No | 6,435 (99.6) | 29 (0.4) | Comparison cat. | Comparison cat. |
| Si | 1,643 (99.9) | 17 (1.0) | 2.28 (1.26–4.14) 0.007 | 2.04 (1.13–3.72) 0.019 |
| Obesity | | | | |
| No | 6,799 (99.5) | 36 (0.5) | Comparison cat. | Did not enter the model |
| Yes | 1,279 (99.2) | 10 (0.8) | 1.47 (0.73–2.96) 0.277 | Did not enter the model |
| Diabetes mellitus | | | | |
| No | 7,334 (99.5) | 38 (0.5) | Comparison cat. | Did not enter the model |
| Yes | 744 (98.9) | 8 (1.1) | 2.06 (0.97–4.40) 0.061 | Did not enter the model |
| Mental illness | | | | |
| No | 7,900 (99.6) | 35 (0.4) | Comparison cat. | Comparison cat. |
| Yes | 178 (94.2) | 11 (5.8) | 13.20 (6.81–25.58) <0.001 | 11.97 (6.09–23.50) <0.001 |
| Alcohol in last 6 months | | | | |
| No | 5,777 (99.5) | 32 (0.5) | Comparison cat. | Did not enter the model |
| Yes | 2,301 (99.4) | 14 (0.6) | 1.10 (0.59–2.05) 0.770 | Did not enter the model |
| Drugs in last 6 months | | | | |
| No | 7,867 (99.4) | 44 (0.6) | Comparison cat. | Did not enter the model |
| Yes | 211 (99.1) | 2 (0.9) | 1.69 (0.41–6.92) 0.467 | Did not enter the model |
## Discussion
The Honduran population presented important differences in the socio-pathological, according to the loss of family members or having previous illnesses, this according to the presentation of problems in the mental sphere.
The aim of the study was to evaluate the levels of hopelessness, depression and anxiety in the Honduran population ≥ 18 years during the COVID-19 pandemic. This being important because Honduras does not have national studies on the mental health situation of the general population, there are specific studies that establish prevalence in certain communities, Paz-Fonseca et al. [ 26] found a prevalence for anxiety of $20.5\%$, depression of $13.2\%$. Women in the family were the most affected and the prevalence of alcoholism was $6.2\%$ [26].
In 33 rural communities in Honduras, it was found that $35\%$ of those interviewed were women, the most frequent disorders were: Major Depressive Episode $24\%$, Agoraphobia $9.3\%$ and Social Phobia $6\%$, in the 923 men surveyed: Alcohol Dependence $16.1\%$, Major Depressive Episode $13.2\%$ and Social Phobia $6\%$, Generalized Anxiety Disorder $5\%$ [27].
In Latin America the pooled prevalence of anxiety, depression, distress and insomnia was 35, 35, 32 and $35\%$, respectively [28].
The results of our study were $65\%$ women, young adults, middle school education, a large majority had not been infected by COVID-19, among the chronic non-communicable diseases, HT, obesity and DM were reported as risk factors for COVID-19, it is noteworthy that a high percentage had not been contaminated, but $19\%$ had a family member who died from COVID-19. Robinson [29] reported that there was a small increase in mental health symptoms shortly after the outbreak of the COVID-19 pandemic that declined and was comparable to pre-pandemic levels in mid-2020 among most population subgroups and symptom types, mental health symptoms during March–April 2020. Compared with measures of anxiety and general mental health, increases in symptoms of depression and mood disorders tended to be greater and remained significantly elevated in May–July [29].
According to the sex variable, women were more affected than men in their mental health, different from what was reported in our study, men were more affected in the three pathologies, although it does coincide with the literature where women were more affected than men for major depressive disorder, anxiety disorders and younger age groups were more affected than older groups [4].
There are more vulnerable groups in case of extreme situations such as catastrophes, pandemics, health workers are a group highly exposed to anxiety, depression, stress and other mental health problems [8], the recent pandemic by COVID-19, has had adverse effects on mental health [4] in the general population and in specific populations: women and health workers, who are at particular risk of suffering a deterioration in mental wellbeing [30].
Large-scale meta-analysis of the prevalence of mental health problems during the early COVID-19 pandemic, women and persons with COVID-19 infection had higher rates in almost all outcomes; college students/young adults of anxiety, depression, sleep problems, suicidal ideation; adults with fear and post-traumatic symptoms. Anxiety, depression, and posttraumatic symptoms were more prevalent in low/middle-income countries, and sleep problems in high-income countries [31].
In a study in Turkey, levels of hopelessness and anxiety were higher in health professionals than in non-health workers. Levels of hopelessness in nurses were higher than in physicians, and levels of anxiety were higher than in physicians and other health care workers. Levels of anxiety and hopelessness were higher in women who lived with a high-risk person in the household during the pandemic, who had difficulty caring for their children, and who had decreased income. Anxiety levels are an important predictor of hopelessness. Increased levels of anxiety explained $28.9\%$ of the increase in levels of hopelessness; in our study of the three pathologies studied, anxiety had the lowest level and was reported more in men than in women [32]. In the systematic review and meta-analysis by Arora T, the prevalence of psychological outcomes was similar in healthcare workers and the general population $34\%$ (24–44) and $33\%$ (27–40) [5].
According to Gan-Yi Wang, the COVID-19 pandemic has been affecting people’s psychosocial health and well-being through several complex pathways, more than one-third reported a worsening experience of hopelessness and loneliness, with more than two-fifths reporting worsening depression during the pandemic compared to before the outbreak. Several socioeconomic and lifestyle factors were found to be associated, marital status, household income, smoking, alcohol consumption and existing chronic conditions of the participants, $44.8\%$ expressed feeling depressed, $34.8\%$ more hopeless and $32.5\%$ lonely during the pandemic. The percentage of all three indicators was higher among women than among men, being married was associated with lower odds of loneliness among men. Loneliness was negatively associated with smoking and positively associated with alcohol consumption [33].
Rodriguez et al. studied the risk factors associated with depression, anxiety in COVID-19 pandemic, where their prevalences for depression and anxiety were 31 and $42\%$, there was also a prevalence of youth within the age range 18–23, most were women, it was observed that it prevailed in single adults or young people living with their parents, since with an abrupt confinement, the economically active population was the most affected, at the same time causing unemployment and those who were single without having an emotional support to accompany them in this situation [34, 35].
We found a lower prevalence of hopelessness among women and among those with a university level; on the contrary, there was more hopelessness among those who had a family member who had died from COVID-19, those with diabetes mellitus (DM), and had a history of mental illness or had consumed drugs in the last 6 months. In China, a meta-analysis on comorbidities and COVID-19 in 2021, AHT, DM-2, cardiovascular diseases, chronic kidney diseases have been among the most frequent comorbidities in patients with COVID-19, in another study in the same country, it was found in a cohort of 99 patients, $51\%$ patients had chronic diseases and of them $40.4\%$ were CVD and Cardiovascular, $12\%$ DM, $11\%$ digestive system diseases and these comorbidities increased the risk of mortality [36, 37].
The prevalence of depressive disorders in diabetics ranges from 10.0 to $15.0\%$ [38, 39], which is practically twice as high as the prevalence in non-diabetics. For Turkey burnout, hopelessness, and fear of COVID19, it shows the hopelessness scores of working in departments that contained a high risk of COVID-19 infection, having a history of COVID-19 infection, working with insensitive supervisors, feeling at risk of COVID-19 due to work, being exposed to excessive workload, working for low wages, not having enough time for oneself or one’s own family, and feeling uncomfortable about putting loved ones at risk of COVID-19 [40].
Vai and Col, found greater hopelessness, being the antecedent to have a mental illness, according to the literature people with any mental disorder had a higher probability of being hospitalized because of COVID-19 [41]. Our study reported more depression among those who had AHT, obesity, those with DM, history of mental illness or had used drugs in the last 6 months, depression is a common mental disorder, it is estimated that $5\%$ of adults suffer from depression, it is one of the leading causes of disability worldwide, more women are affected by depression than men [42, 43].
Vai in 2021, describes that the main factors associated with depression were sex, previous psychiatric history, psychopathology at 1-month follow-up, and systemic inflammation during the acute phase, whereas age was only a potential factor and severity of acute COVID-19 was not. In fact, female sex, a previous psychiatric diagnosis, and psychopathology at 1-month follow-up were moderators of depression in the post-COVID-19 syndrome [41].
A systematic review and meta-analysis, from January 1985 to August 2021, included 44 studies. The prevalence of depression was significantly higher in people with type 1 diabetes or type 2 diabetes compared with those without diabetes. There was no association between study effect size and mean age or sex. The findings did not differ significantly between methods of depression assessment. Depression was higher in patients with diabetes in studies conducted in specialty care compared with those in community or primary care and in low- and middle-income countries compared with countries with high-income economies [44].
According to WHO in 2016, $39\%$ of adults aged 18 years or older ($39\%$ of men and $40\%$ of women) were overweight, $13\%$ of the global adult population ($11\%$ of men and $15\%$ of women) were obese [43]. Overweight/obesity and depression are highly concurrent conditions with shared pathophysiology, as well as social and economic determinants [45]. Anxiety and depression are as strongly predictive of future poor physical health as obesity and smoking [46].
People with obesity and diabetes are at increased risk for significant symptoms of depression [47], physical activity, nutrition, and eating behaviors are associated with physical and mental health, hence the importance of establishing and strengthening healthy lifestyle habits in this target population depression and obesity are complex and chronic [48]. During COVID-19, the prevalence of alcohol use disorders increased from $25.1\%$ before confinement to $38.3\%$ during confinement in England [49].
We found in our study that there was more anxiety among those who were infected with COVID-19, in those who had a family member who died from COVID-19, and in those who had a history of mental illness. Anxiety disorders were more prevalent among those aged 30 to 39 years ($18.2\%$) [50]. Fear of the unknown increases anxiety levels in healthy individuals as well as in those with preexisting mental health conditions [51].
In a meta-analysis including 194 studies, the overall prevalence of anxiety was $35.1\%$. The prevalence in low- and middle-income countries was similar compared with high-income countries. One in three adults lived with anxiety disorder during the COVID-19 pandemic worldwide [52].
In the case of DM, in Pakistan, $22.6\%$ of diabetic participants presented mild depression and $2.6\%$ presented moderate depression. In Latin America, 1,508 patients were evaluated in Mexico, of which $30.7\%$ presented comorbidities such as AHT or DM, in addition $18\%$ presented depression and $27.8\%$ anxiety, showing that the relationship between comorbidity and psychological impact is maintained in different parts of the world with populations suffering from non-communicable pathological diseases and psychiatric illnesses, causing a psychosocial impact [53, 54].
Neuropsychiatric symptoms were evaluated with a meta-analysis after COVID-19, the most prevalent neuropsychiatric symptom was sleep disturbance followed by fatigue, objective cognitive impairment, anxiety, and post-traumatic stress. Cognitive impairment, anxiety, post-traumatic symptoms, and depression are also common in the first 6 months [55]. COMET-G at the beginning of the pandemic found rates of anxiety $25\%$, depression $28\%$ in the general population, while a second study reported 31.9 and $33.7\%$ anxiety and depression, respectively [12].
The uncertainties associated with a new virus, the risk of staff becoming infected, a changing and challenging work environment, the potential personal impact of the virus, and the concerns associated with caring for patients and their families put additional pressure on staff [56]. Studies examining the mental health impact of providing front-line medical care during viral outbreaks showed that healthcare workers often have high levels of anxiety, depression, and post-traumatic stress disorder, both during and after outbreaks We identified a wide number of risk factors such as younger age and female gender, and social factors such as lack of social support, social rejection, or isolation and stigmatization. Occupational factors involved working in a high-risk environment (frontline staff), specific occupational roles (e.g., nurse), and having lower levels of specialized training, preparation, and work experience [50].
A meta-analysis has identified, on a large scale and worldwide, the prevalence of mental health symptoms, it was found that healthcare workers exposed to COVID-19 had a significant prevalence rate of anxiety, depression, acute stress, insomnia, post-traumatic symptoms, and burnout, are a vulnerable population during the COVID-19 pandemic, being more prone to mental health impairment than the general population. These findings suggest that the mental health impairment of healthcare workers is not due to measures of general confinement, social distance, and pandemic preoccupation, but to the particularities of the healthcare professions and their conditions during the pandemic [57].
Data suggest that the pandemic and associated public health and social measures (PHSM) have led to a global increase in mental health problems, including, across the board, depression and anxiety. Persons with pre-existing mental disorders are also at increased risk of severe illness and death from COVID-19 and should be considered an at-risk group when diagnosed with the infection [58] Yuan K, reported that participants with a history of mental disorders displayed over three times higher risk for depression and anxiety [59].
In the year 2020 in Latin America and the Caribbean, the SARS-COV pandemic has led to increases in unemployment, poverty, food insecurity, domestic violence, and child abuse at the same time that worsening mental health conditions were reported in the same area [58], in the Human Development Report 2020, Honduras presented an HDI of 0.634, which places it in position 132 out of 189, being the most worrisome situation as a result of the COVID-19 pandemic [60].
The COVID-19 pandemic negatively affects mental health in a unique way across all population subgroups. Our results inform tailored preventive strategies and interventions to mitigate current, future, and transgenerational adverse mental health from the COVID-19 pandemic [31].
## Conclusion
We found factors associated with hopelessness, we found factors associated with hopelessness, depression and anxiety in times of COVID-19 in the Honduran population.
Our study shows that the most reported variable was hopelessness, followed by depression and very low anxiety. Men in Honduras had the highest levels of hopelessness, depression and anxiety, and by the end of the pandemic a high percentage of the general population had not yet been infected by COVID-19. A considerable percentage of the participants had suffered the loss of a family member, which negatively influences the deplorable mental health of the population in times of COVID-19.
Chronic non-communicable diseases such as HTN, obesity and DM were the most prevalent for all three pathologies, as was the history of mental illness. It is important for countries to be concerned about the mental health of the population, which prior to the pandemic was already a major public health problem worldwide and which was exacerbated as a result of COVID-19.
The pandemic came to change our way of living, to change our habits and, faced with the uncertainty of the unknown, made us vulnerable at the expense of finding the solution in a vaccine that would stop the virus and mitigate the panic generated worldwide.
## Statistical analysis
After the data collection process was completed, the variables were cleaned and coded, with this the database was generated. The data were analyzed with Stata V16.0 software (license acquired by the group’s statistician). First a table was generated with the description of the variables, where frequencies and percentages were used for the categorical variables, then measures of central tendency and dispersion were obtained for age. For the analytical statistics, crude prevalence ratios (cPR), adjusted prevalence ratios (aPR), $95\%$ confidence intervals ($95\%$CI) and p-values were found, all with the generalized linear models (Poisson family, log link function and models for robust variances). Before performing the multivariate analysis, a series of steps were carried out, including the generation of a univariate table to describe the population, followed by a bivariate analysis in which each of the three outcome’s (hopelessness, depression and anxiety) was crossed with the independent variables (sex, age, academic grade, having been infected by COVID-19, whether a family member died from COVID-19, whether he/she suffered from hypertension, obesity, diabetes mellitus, etc., depression and anxiety) versus the independent variables (sex, age, academic grade, having been infected by COVID-19, if a family member died from COVID-19, if they suffered from arterial hypertension, obesity, diabetes mellitus, mental illness, had consumed alcohol or drugs in the last 6 months); for the independent variables to be included in the multivariate model, they had to obtain a p value < 0.05 (statistical criterion). In all cases, p-values greater than 0.05 were considered statistically significant, this being the criterion for a variable to move from the bivariate model to the multivariate model.
## Limitations
Our database is predominantly composed of individuals of the female gender. One limitation was not being able to access remote or rural locations, this in part was due to poor Wi-Fi/internet signal in those remote or very distant locations; This limitation means that the results cannot be extrapolated to the rural population of Honduras, which is confirmed by the fact that there was no probability sampling, so the sample is important but still cannot represent the entire population of Honduras, but it could be an important situational analysis of patients attending hospitals or health care centers. Another limitation was that some of the crosstabs did not have adequate statistical power, so these crosstabs should be considered purely exploratory. The approach was carried out in primary health care (PHC), and for this reason the diagnoses were not confirmed by a physician specializing in psychiatry. For all these reasons, it should be remembered that extrapolations to other populations should be made with caution in the descriptive results, but the associations are important because they come from a large group of citizens attending public health facilities.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Comité de Ética en Investigación Biomédica (CEIB) de la Facultad de Cencías Medicas-UNAH. The ethics committee waived the requirement of written informed consent for participation.
## Author contributions
EE-T, CS-M, RG-R, and MS-S: study design. RG-R and HC-R: data collection. CM, LZ, and JA: data analysis. EE-T, CS-M, CM, LZ, JA, RG-R, and HC-R: writing. All authors contributed to the article and approved the submitted version.
## Funding
The current article processing charges (publication fees) were funded by the Facultad de Ciencias Médicas (FCM) (2–03–01-01), Universidad Nacional Autónoma de Honduras (UNAH), Tegucigalpa, MDC, Honduras, Central America (granted to EE-T).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: 'Prediction of postoperative infection in elderly using deep learning-based
analysis: an observational cohort study'
authors:
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- Yan Wang
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- Baoli Cheng
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- Hong Gao
- Tingju Hu
- Jie Lv
- Jian Yang
- Yang Yang
- Yi Zhong
- Jing Zhou
- Xiaohua Zou
- Miao He
- Xiaoying Li
- Dihuan Luo
- Haiying Wang
- Tian Yu
- Liyong Chen
- Lijun Wang
- Yunfei Cai
- Zhongming Cao
- Yanling Li
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- Lixia Huang
- Yanming Huang
- Haifang Hwan
- Hong Luo
- Pi-Sheng Qu
- Fan Tao
- Zhen Wang
- Guoxiang Wang
- Shun Wang
- Yan Zhang
- Xiaolin Zhang
- Chao Chen
- Weixing Wang
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- Lihua Fan
- Jing Tang
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- Xinyu Qi
- Meizi Qian
- Jinling Qin
- Yelong Ren
- Yiyi Shi
- Junlu Wang
- Junkai Wang
- Leilei Wang
- Junjie Xie
- Yixiu Yan
- Yurui Yao
- Mingxiao Zhang
- Jiashi Zhao
- Xiuxiu Zhuang
- Yanqiu Ai
- Du Fang
- Long He
- Ledan Huang
- Zhisong Li
- Huijuan Li
- Yetong Li
- Liwei Li
- Su Meng
- Yazhuo Yuan
- Enman Zhang
- Jie Zhang
- Shuna Zhao
- Zhenrong Ji
- Ling Pei
- Li Wang
- Chen Chen
- Beibei Dong
- Jing Li
- Ziqiang Miao
- Hongying Mu
- Chao Qin
- Lin Su
- Zhiting Wen
- Keliang Xie
- Yonghao Yu
- Fang Yuan
- Xianwen Hu
- Ye Zhang
- Wangpin Xiao
- Zhipeng Zhu
- Qingqing Dai
- Kaiwen Fu
- Rong Hu
- Xiaolan Hu
- Song Huang
- Yaqi Li
- Yingping Liang
- Shuchun Yu
- Zheng Guo
- Yan Jing
- Na Tang
- Wu Jie
- Dajiang Yuan
- Ruilin Zhang
- Xiaoying Zhao
- Yuhong Li
- Hui-Ping Bai
- Chun-Xiao Liu
- Fei-Fei Liu
- Wei Ren
- Xiu-Li Wang
- Guan-Jie Xu
- Na Hu
- Bo Li
- Yangwen Ou
- Yongzhong Tang
- Shanglong Yao
- Shihai Zhang
- Cui-Cui Kong
- Bei Liu
- Tianlong Wang
- Wei Xiao
- Bo Lu
- Yanfei Xia
- Jiali Zhou
- Fang Cai
- Pushan Chen
- Shuangfei Hu
- Hongfa Wang
- Wu Jie
- Qiong Xu
- Liu Hu
- Liang Jing
- Jing Li
- Bin Li
- Qiang Liu
- Yuejiang Liu
- Xinjian Lu
- Zhen Dan Peng
- Xiaodong Qiu
- Quan Ren
- Youliang Tong
- Zhen Wang
- Jin Wang
- Yazhou Wen
- Qiong Wu
- Jiangyan Xia
- Jue Xie
- Xiapei Xiong
- Shixia Xu
- Tianqin Yang
- Ning Yin
- Jing Yuan
- Qiuting Zeng
- Baoling Zhang
- Kang Zheng
- Jing Cang
- Shiyu Chen
- Du Fang
- Yu Fan
- Shuying Fu
- Xiaodong Ge
- Baolei Guo
- Wenhui Huang
- Linghui Jiang
- Xinmei Jiang
- Lin Jin
- Yi Liu
- Yan Pan
- Yun Ren
- Qi Shan
- Jiaxing Wang
- Fei Wang
- Chi Wu
- Xiaoguang Zhan
journal: Aging Clinical and Experimental Research
year: 2023
pmcid: PMC10014765
doi: 10.1007/s40520-022-02325-3
license: CC BY 4.0
---
# Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study
## Abstract
Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 ($95\%$ CI 0.688–0.768) with the sensitivity of $66.2\%$ ($95\%$ CI 58.2–73.6) and specificity of $66.8\%$ ($95\%$ CI 64.6–68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 ($95\%$ CI 0.545–0.737), and sensitivity and specificity were $34.2\%$ ($95\%$ CI 19.6–51.4) and $88.8\%$ ($95\%$ CI 85.6–91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 ($95\%$ CI 0.681–0.844) with the sensitivity of $63.2\%$ ($95\%$ CI 46–78.2) and specificity of $80.5\%$ ($95\%$ CI 76.6–84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s40520-022-02325-3.
## Introduction
Infection is the leading cause of morbidity and mortality in hospitalized patients [1–4]. Owing to the aged deterioration of the whole body system including compromised immune function, elderly patients are susceptible to infection after surgery [5, 6]. Given the aging society globally, more and more elderly patients receive surgery. However, those patients face an increased risk of postoperative infections. Unfortunately, the risk prediction of postoperative infections in elderly is largely lacking [7, 8].
Artificial intelligence is emerging to be used for addressing medical challenges, for example, sepsis [9–11]. Recent advance in deep learning, one of the types of artificial intelligence, has been shown to help the learning feature of data representations and improve modeling performance in different settings [12–15]. For example, several studies demonstrated that a deep learning-based approach has achieved great success in predicting events in clinical practice [16, 17]. However, the use of deep learning to detect postoperative infections in elderly patients is limited. The current study used a deep learning-based strategy to predict postoperative infections in elderly patients following surgery. The primary objective of this study was to develop and validate deep learning models for predicting postoperative infections in elderly patients which focused on in-hospital assessment for infections. In addition, we sought to examine whether the deep learning neural network model is superior to the conventional regression model in predicting the risk of developing postoperative infections using the area under the receiver operating characteristic curve (AUC) to calculate sensitivity and specificity.
## Study design and population
This study was part of the International Surgical Outcomes Study (ISOS) project, an international observational cohort study of complications following elective surgery. ISOS was registered prospectively with an international trial registry (ISRCTN51817007) [18]. The study was approved by Research Ethics Committee, The First Affiliated Hospital, Zhejiang University School of Medicine (reference: 2014-011), and all methods were performed in accordance with the relevant guidelines. The current dataset with permission to be reported was from elderly patients (≥ 60 years) who had elective surgery and at least one-night hospital stay and were recruited from 28 hospitals (Online Appendix 1) in China between April and June 2014. Patients with emergency surgery, day-case surgery, or interventional radiotherapy were excluded. Patients’ baseline characteristics included gender, current smoker, ASA score, comorbidities (coronary artery disease, heart failure, diabetes mellitus, metastatic cancer, cirrhosis, stroke, COPD/asthma, other), and blood measurements (hemoglobin, serum creatinine, sodium, and leucocytes) were collected. Surgery-related data included surgical procedures (orthopedic, gynecology, urology and kidney, upper gastrointestinal, lower gastrointestinal, hepatobiliary, vascular, breast, head and neck, plastics and cutaneous, cardiac, thoracic, and other), anesthetic technique (general, spinal, epidural, and sedation/local), laparoscopic surgery, cancer surgery, and the severity of surgery (minor, intermediate, and major), and surgical checklist was harvested. Postoperative infections included urinary tract infection, bloodstream infection, superficial surgical site infection, deep surgical site infection, body cavity infection, and pneumonia. Infections were assessed according to the United States of America Centers for Disease Control (CDC) definitions of infections [19]. The detailed definitions of each infection are presented in Online Appendix 2 [20]. The diagnosis of postoperative infections was conducted by one study team member and verified by a second team member. The diagnostic accordance rate of postoperative infections was $95\%$ by two study team members. Patients’ informed consent was exempted as all data were anonymized and were already recorded as part of routine clinical care.
A total of 2014 elderly patients were recruited (Fig. 1) to identify independent risk factors for postoperative infections by using the inverse probability (IP) weighting method. The associations derived from IP weighting of those risk factors and postoperative infections were used to construct the conventional logistic regression predictive model. We assessed the predictive capability of the established conventional model for infections using sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and AUC. In addition, we split the original dataset into training and validation datasets with a 3:1 ratio to develop and validate the deep learning model. Of those, 1510 patients were randomly assigned for the training dataset and establishing various neural-network-based predictive models for postoperative infections. The remaining 504 patients were used to assess the sensitivity, specificity, NPV, PPV, and accuracy of various neural network-based models. We assigned more patients to the training dataset to ensure a well-trained neural network. Fig. 1Flow diagram of patients through the study. NPV negative predictive value, PPV positive predictive value, AUC area under the receiver operating characteristic curve (ROC)
## Neural network analysis
In this study, deep learning is one of the machine learning models that use multilayered neural networks whose hierarchical computational design is partly inspired by a biological neuronal structure and was used to generate the output of the probability of infection complications after surgery [21]. The output layer consists of the response variables (Supplemental Fig. 1). For each neuron, a weight is attached, indicating the corresponding neuron’s effect and all data past the neural network signals. The signals are processed first by integrating all incoming signals and activation functions that transform the neuron's output. For a specific neural network, the observed data are used to train the neural network, in which the neural learns the approximation of the relationship by iteratively adapting its parameters.
We fitted deep learning models using the training dataset and evaluated prediction performances using the validation set. Hyperparameter (e.g., weights) tuning to identify the optimal values for parameters that were learned during the training process was performed using a back-propagation algorithm iteratively based on the training dataset, referred to as candidate neural network predictive models. Then, we selected the optimal neural network predictive model in terms of the maximum AUC value among those candidate neural networks based on the validation dataset. We selected the optimal threshold with Youden’s index (i.e., sensitivity + specificity—1) based on the validation dataset in terms of a range of candidate thresholds. Based on the optimal threshold, we further calculated the corresponding sensitivity, specificity, NPV, and PPV. Finally, the model comparison was carried out using AUC for classification accuracy of the probability of patients with postoperative infections, in which the higher values, the better accuracy of the predictive model.
## Statistical analysis
Data were presented as patient’s number (percentage) or odds ratio (OR) and $95\%$ confidence interval ($95\%$ CI) where appropriate. All analyses were conducted using R software 3.6.2, with a neuralnet package for training and establishing the neural network predictive models and a pROC package to calculate the sensitivity, specificity, NPV, PPV, AUC, and accuracy. Confidence intervals were calculated based on the bootstrap method with 2000 replicates. A statistical significance was set at a level of $P \leq 0.05.$
## Independent risk factors associated with postoperative infections in elderly patients
Of 2014 patients, there were 12 potential risk factors associated with postoperative infections using simple logistic regression (Supplemental Table 1). 11 risk factors were associated with postoperative infections after multivariable adjustment as summarized in Table 1. Male patients had an odds ratio (OR) for postoperative infections of 1.03 ($95\%$ CI 1.00–1.06) compared with female patients. Using the lowest ASA score as the reference, ASA III was independently associated with an increased risk of postoperative infections (OR 1.05; $95\%$ CI 1.01–1.08; $$P \leq 0.015$$). Coronary artery disease had an OR for postoperative infections of 1.10 ($95\%$ CI 1.02–1.18; $$P \leq 0.008$$). Sedation/local anesthesia was independently associated with a reduced risk of postoperative infections with an OR of 0.95 ($95\%$ CI 0.92–0.99; $$P \leq 0.006$$). Laparoscopic surgery was independently associated with a decreased risk of postoperative infections (OR 0.94; $95\%$ CI 0.92–0.97; $P \leq 0.001$). In surgical procedures, independent risk factors of postoperative infections included urology and kidney surgery (OR 0.96; $95\%$ CI 0.94–0.99), head and neck surgery (OR 0.96; $95\%$ CI 0.94–0.99), and cardiac surgery (OR 1.34; $95\%$ CI 1.17–1.53). Major surgery was independently associated with an increased risk of postoperative infections with an OR of 1.08 ($95\%$ CI 1.06–1.11; $P \leq 0.001$), but intermediate surgery was independently associated with a decreased risk of postoperative infections with an OR of 0.97 ($95\%$ CI 0.95–0.99; $$P \leq 0.007$$). The forest plot for independent risk factors is shown in Fig. 2. In addition, more clinical aspects and information related to infections are presented in Supplemental Tables 2 and 3.Table 1Independent risk factors for patients with postoperative infectionsIndependent risk factorsAdjustedOR ($95\%$CI)PGender (male vs. female)1.03 (1.00, 1.06)0.023ASA score IReference II0.99 (0.96, 1.02)0.391 III1.05 (1.01, 1.08)0.015IV1.14 (0.95, 1.38)0.149Chronic comorbid disease (yes vs. no) Coronary artery disease1.10 (1.02, 1.18)0.008 Other1.03 (1.00, 1.05)0.048Surgical procedure (yes vs. no) Urology and kidney0.96 (0.94, 0.99)0.012 Head and neck0.96 (0.94, 0.99)0.005 Cardiac1.34 (1.17, 1.53) < 0.001 Other0.96 (0.94, 0.99)0.010Sedation/local anesthesia0.95 (0.92,0.99)0.006Laparoscopic surgery0.94 (0.92, 0.97) < 0.001Severity of surgery MinorReference Intermediate0.97 (0.95, 0.99)0.007 Major1.08 (1.06, 1.11) < 0.001ASA American Society of AnesthesiologistsFig. 2Adjusted odds ratio and $95\%$ confidence interval for independent risk factors using the inverse probability weighting method. ASA American Society of Anesthesiology
## The conventional logit predictive model based on associations obtained from the IP weighting method
To investigate the conventional logit predictive model, we calculated the coefficients using associations of independent risk factors with postoperative infections (Supplemental Table 4). The final logit predictive model is shown in Online Appendix 3. The receiver operating characteristic (ROC) curve of the conventional model is shown in Fig. 3. The logit predictive model had an AUC for the prediction of postoperative infections of 0.728 ($95\%$ CI 0.688–0.768), a sensitivity of $66.2\%$ ($95\%$ CI 58.2–73.6), and a specificity of $66.8\%$ ($95\%$ CI 64.6–68.9) (Table 2). Furthermore, the accuracy of the logit predictive model for the prediction of postoperative infections was 0.667 ($95\%$ CI 0.646–0.688).Fig. 3Comparison of predictive sensitivity and specificity of postoperative infections with the conventional model, neural network model I, and neural network model II assessed under the receiver operating characteristic curve (ROC)Table 2Summary of the predictive performance of these three established modelsMethodCutoffSensitivitySpecificityNPVPPVAUCLogit model0.5100.662 (0.582, 0.736)0.668 (0.646, 0.689)0.960 (0.948, 0.970)0.142 (0.117, 0.169)0.728 (0.688, 0.768)Neural network: 11-3-10.0770.342 (0.196, 0.514)0.888 (0.856, 0.916)0.943 (0.917, 0.963)0.200 (0.111, 0.318)0.641 (0.545, 0.737)Neural network: 28-24-10.0770.632 (0.460, 0.782)0.805 (0.766, 0.840)0.964 (0.940, 0.980)0.209 (0.139, 0.294)0.763 (0.681, 0.844)NPV negative predictive value, PPV positive predictive value, AUC area under receivers operating characteristic curve
## Development and validation of deep learning neural network model
The distributions of independent risk factors among training dataset according to the status of postoperative infections are presented in Supplemental Table 5. There were unbalanced distributions of risk factors including gender, ASA, coronary artery disease, head and neck surgery, cardiac surgery, sedation/local anesthesia, laparoscopic surgery, and severity of surgery between patients with and without postoperative infections in the training dataset. In addition, in the validation dataset, there were unbalanced distributions of risk factors including ASA, coronary artery disease, cardiac surgery, and severity of surgery between patients with and without postoperative infections (Supplemental Table 6).
All identified independent risk factors in the previous section were divided into two categories: risk factors relevant to baseline variables and risk factors relevant to the surgery. Based on these, deep learning predictive model I included risk factors related to the baseline variables, and deep learning predictive model II included risk factors associated with the baseline variables and those relevant to the surgery. Considering there is no fixed rule for deciding how many hidden layers and neurons should be used in neural network predictive models, we explored the number of neurons, ranging from one to two times of the inputs under a five layers model. We limited the third hidden layer only to include one neutron to facilitate the pooling of information.
Of those, there were a total of 132 neural networks using risk factors relevant to baseline variables. The AUC for possible structures of neural network predictive model I is shown in Supplemental Fig. 2. We found the optimal neural network predictive model with the best performance among those 132 possible neural networks with five layers with 6-11-3-1-1, including 1 input layer with 6 nodes of gender, ASA score (II), ASA score (III), ASA (IV), coronary artery disease and other diseases, 3 hidden layers, and 1 output layer (Supplemental Table 7). Supplemental Figure 3 shows the optimal structure of deep learning predictive model I. As shown in Fig. 3, the AUC measure of the ROC curve obtained by deep learning model I was 0.641 ($95\%$ CI 0.545–0.737). This predictive model had an overall sensitivity and specificity of $34.2\%$ ($95\%$ CI 19.6–51.4) and $88.8\%$ ($95\%$ CI 85.6–91.6), respectively, for the prediction of postoperative infections (Table 2). The accuracy of the deep learning model I for the prediction of postoperative infections was 0.847 ($95\%$ CI 0.813–0.878).
There were 756 neural networks related to all independent risk factors. The AUC for possible structures of neural network predictive model II is shown in Supplemental Fig. 4. We found the optimal neural network predictive model with the best performance among those 765 possible neural networks with five layers with 14-28-24-1-1, including 1 input layer with 14 nodes of gender, ASA score (II), ASA score (III), ASA score (IV), coronary artery disease, other diseases, urology, and kidney surgery, head and neck surgery, cardiac surgery, other surgery, sedation/local anesthesia, laparoscopic surgery, surgical severity (intermediate) and surgical severity (major), 3 hidden layers, and 1 output layer. ( Supplemental Table 7). Supplemental Figure 5 shows the optimal structure of deep learning predictive model II. As shown in Fig. 3, the AUC measure of the ROC curve obtained by deep learning model II was 0.763 ($95\%$ CI 0.681–0.844). This predictive model had an overall sensitivity and specificity of $63.2\%$ ($95\%$ CI 46–78.2) and $80.5\%$ ($95\%$ CI 76.6–84), respectively, for the prediction of postoperative infections (Table 2). The accuracy of deep learning model II for the prediction of postoperative infections was 0.792 ($95\%$ CI 0.754–0.826).
## Discussion
In this observational cohort study, we applied a deep learning framework for the prediction of infections in elderly patients after surgery. We demonstrated that an artificial-intelligence model using deep learning neural networks can achieve a promising prediction of postoperative infections in elderly patients. Predictive performance was improved further when the deep learning-based model was derived with risk factors relevant to baseline clinical characteristics/measurements and surgery in combination. Our work indicates that using deep learning may guide clinical practice to prevent infections following surgery in elderly although further work is needed to validate machine learning for it to be a potential and ubiquitous integral part of routine clinical use for elderly surgical patients.
Deep learning is the process of training a neural network (a large mathematical function with millions of parameters) to perform a given task [14]. Many hidden layers neurons were used to produce increasing abstracted, nonlinear representations of the underlying data [22]. Considering the possible non-linear associations, an artificial neural network based on multiple-layer perceptions reflects a complex functional relationship between risk factors and responding variables with the back-propagation algorithm, the logit activation function, and error function. Deep learning uses back-propagation to indicate how a machine should change its internal parameters to predict the best desired output of responding variables [14]. This makes artificial neural networks to be a valuable toolbox for prediction. Indeed, deep learning models have been successfully applied in health care to predict clinical events, disease classification, and electronic health record data augmentation [23–27]. However, the use of deep learning to detect disease and complications in elderly patients is limited. In our study, we applied a deep learning approach to evaluate postoperative infections among elderly patients. Using a database of more than 2000 patients for training and validation, we found that the deep learning model had a high AUC of 0.763 for predicting postoperative infections among elderly patients after elective surgery. A fundamental finding in our study is that the deep learning model in predicting postoperative infections was better when compared with the standard regression model; the latter is often used as a traditional statistic method in building a prediction model. Recent studies have also demonstrated promising performance for predicting disease development using deep learning [28–30]. For example, a study reported a sensitivity of $96.8\%$ at a specificity of $59.4\%$ for detecting referable diabetic retinopathy [31]. In conjunction with these studies, our results further demonstrated that the deep learning algorithm can provide informative measures for the prediction of postoperative infections in elderly patients. The modeling approach reported here offers straightforward and computationally rapid guidance for clinicians to predict the likelihood of infections after surgery. We envision that our deep learning model can be used to identify high-risk elderly patients for postoperative infections. The findings may suggest the potential usage of our model to help doctors to justify interventions that may have a significant impact on perioperative management for elderly patients per se.
As elderly patients are the most frequent users of operative resources and are also the most vulnerable to postoperative infections, it is important for clinicians to gauge risk factors preoperatively [32, 33]. In this study, we identified 11 independent risk factors associated with postoperative infections including gender, ASA score, chronic comorbid diseases, surgical procedures, sedation/local anesthesia, laparoscopic surgery, and severity of surgery. More importantly, among the above risk factors, sedation/local anesthesia and laparoscopic surgery could reduce postoperative infections. These findings provide important evidence to clinical perioperative management for elderly patients. Interventions should be considered to tackle those risk factors to optimize the patient’s conditions before surgery. Indeed, some of these factors are modifiable by surgeons and anesthesiologists before surgery except gender, ASA score, and chronic comorbid disease. For example, sedation/local anesthesia was independently associated with a decreased risk of postoperative infections with an OR of 0.95 ($95\%$ CI 0.92–0.99; $$P \leq 0.006$$), suggesting anesthesiologists to select sedation or local anesthesia rather than general anesthesia when sedation or local anesthesia can meet the requirements of the surgery. In addition, laparoscopic surgery was also found to be associated with reduced postoperative infections with an OR of 0.94 ($95\%$ CI 0.92–0.97; $P \leq 0.001$), suggesting when feasible, laparoscopic rather than laparotomy surgery should be chosen. Therefore, some factors that reduce the risk of infections highlighted in our study could be preoperatively modified to improve the clinical outcome in elderly patients.
The strength of this study was that it collected a relatively large group of elderly patients undergoing elective surgery in multiple institutions. In addition, the baseline parameters and surgery-relevant characteristics applied in the deep learning model dovetail with a customary clinical workflow that can be routinely collected. However, this study is not without limitations. Clinical uses of AI have aroused skepticism including the difficulty of explaining the complex computational steps leading to a machine-generated clinical determination [34]. Although the deep learning model performs better than the conventional regression model in estimating the risk of postoperative infections in elderly patients, further studies are needed for validation. First, the deep learning model was built based on elderly patients admitted in the year of 2014, and further research is needed to verify the predictive performance of this model nowadays. Second, only data of routine clinical variables were used in the present study and we expect that the predictive performance could be boosted if individual assessment including the perioperative neurological status of elderly patients who often have delirium and/or cognitive impairment before and after surgery is incorporated into the model. Third, the present study focused on the prediction of infections including urinary tract infection, bloodstream infection, superficial and deep surgical site infection, body cavity infection, and pneumonia after elective surgery, which may not be well equipped for the specific types of infection. Fourth, considering the sensitivity and the specificity for predicting postoperative infections, our model seems to be relatively specific but not very sensitive. Further, study is needed to verify these findings and perhaps these can be improved by increasing the training data per se.
## Conclusions
We found that an artificial intelligence with a deep learning model has considerable advantages on predicting postoperative infections in elderly patients, indicating that the deep learning features are more discriminative and may have a better potential for predicting postoperative infections in elderly patients. Further investigation is warranted to improve the performance of the model and to understand how the model predicts postoperative infections even better.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 788 kb)
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|
---
title: The synovial fluid calprotectin lateral flow test for the diagnosis of chronic
prosthetic joint infection in failed primary and revision total hip and knee arthroplasty
authors:
- Christian Suren
- Igor Lazic
- Bernhard Haller
- Florian Pohlig
- Rüdiger von Eisenhart-Rothe
- Peter Prodinger
journal: International Orthopaedics
year: 2023
pmcid: PMC10014771
doi: 10.1007/s00264-023-05691-3
license: CC BY 4.0
---
# The synovial fluid calprotectin lateral flow test for the diagnosis of chronic prosthetic joint infection in failed primary and revision total hip and knee arthroplasty
## Abstract
### Purpose
The diagnostic criteria of prosthetic joint infection (PJI) recommended by the most commonly used diagnostic algorithms can be obscured or distorted by other inflammatory processes or aseptic pathology. Furthermore, the most reliable diagnostic criteria are garnered during revision surgery. A robust, reliable addition to the preoperative diagnostic cascade is warranted. Calprotectin has been shown to be an excellent diagnostic marker for PJI. In this study, we aimed to evaluate a lateral flow test (LFT) in the challenging patient cohort of a national referral centre for revision arthroplasty.
### Methods
Beginning in March 2019, we prospectively included patients scheduled for arthroplasty exchange of a total hip (THA) or knee arthroplasty (TKA). Synovial fluid samples were collected intra-operatively.
We used the International Consensus Meeting of 2018 (ICM) score as the gold standard. We then compared the pre-operative ICM score with the LFT result to calculate its diagnostic accuracy as a standalone pre-operative marker and in combination with the ICM score as part of an expanded diagnostic workup.
### Results
A total of 137 patients with a mean age of 67 (± 13) years with 53 THA and 84 TKA were included. Ninety-nine patients ($72.8\%$) were not infected, 34 (25.0) were infected, and four ($2.9\%$) had an inconclusive final score and could not be classified after surgery.
The calprotectin LFT had a sensitivity ($95\%$ confidence interval) of 0.94 (0.80–0.99) and a specificity of 0.87 (0.79–0.93). The area under the receiver operating characteristic curve (AUC) for the calprotectin LFT was 0.94 (0.89–0.99). In nine cases with an inconclusive pre-operative ICM score, the calprotectin LFT would have led to the correct diagnosis of PJI.
### Conclusions
The synovial fluid calprotectin LFT shows excellent diagnostic metrics both as a rule-in and a rule-out test, even in a challenging patient cohort with cases of severe osteolysis, wear disease, numerous preceding surgeries, and poor soft tissue conditions, which can impair the common diagnostic criteria. As it is available pre-operatively, this test might prove to be a very useful addition to the diagnostic algorithm.
## Introduction
Chronic prosthetic joint infection (PJI) remains a diagnostic challenge. Despite the advances brought about by evolving definition criteria, diagnostic algorithms, and the introduction of modern methods such as molecular diagnostics, our ability to discern PJI from other failure modes is flawed [28, 30, 31, 46, 54]. As a result, patients falsely presumed to be infected are subjected to unnecessary surgical interventions and antibiotic treatments, and those falsely presumed to be aseptic, to infection persistence and repeatedly failed arthroplasties. The remaining uncertainty of the most commonly accepted algorithms in turn leads to additional and costly tests and imaging with the intention to rule out infection, which show the same imperfections as the established criteria [10, 14, 40]. The administration of broad-spectrum antibiotics to patients undergoing arthroplasty revision until the intra-operative biopsies return negative, which has been adopted by some as a pragmatic approach to address the diagnostic inaccuracy, is inadequate on several levels: First, a revision performed without the intention to completely debride the infected tissues and mechanically disrupt the biofilm cannot be radical enough to be an adequate single-stage septic exchange, rendering the antibiotic regimen futile. Second, the practice leads to increasingly resistant bacterial strains and undermines our efforts of antibiotic stewardship [25].
A combination of criteria is used to define and diagnose PJI. The weights and thresholds of these criteria depend on the PJI definition adhered to, but the most widespread definitions, i.e., those of the Musculoskeletal Infection Society (MSIS) of 2011 and 2018 and the International Consensus Meeting on Periprosthetic Joint Infection (ICM) of 2013 and 2018, respectively, the Infectious Diseases Society of America (IDSM) criteria of 2013, the “Zimmerli” or formerly “proposed European Bone and Joint Infection Society (EBJIS) criteria” first established in 2004, and the recently published EBJIS criteria, mostly rely on the same set of diagnostic findings [22, 26, 29–31, 53]: pre-operatively, a key diagnostic step is the synovial leukocyte count (WBC) and differential, although their thresholds for chronic PJI remain a matter of debate and have been updated several times [6, 8, 45, 55]. The WBC and differential show a high diagnostic accuracy, with reported sensitivities and specificities of 0.91–0.98 and 0.83 and 1.0, respectively. Another cornerstone is the synovial fluid culture. Even though this test is more time consuming and less sensitive (0.44–0.8), it has a high specificity (0.93–0.95) and provides the opportunity to identify the organism causing the infection [6, 19]. Intra-operatively, tissue biopsy cultures have proven to be more accurate but still exhibit a broad range of accuracy metrics in the literature [3, 17, 27, 41]. Unlike synovial fluid culture, tissue biopsy cultures seem to be less affected by previous antibiotic treatment. They are, however, equally time consuming.
In the recent past, several new markers were studied with the intent of increasing our ability to diagnose PJI. Some of these, such as the α-defensin lateral flow test, were advocated because of their assumed ability to yield extremely accurate results within minutes. However, some follow-up studies on the matter revealed a diagnostic accuracy inferior to that previously reported and inferior to the respective laboratory-based enzyme-linked immunosorbent assay (ELISA), with only one recent evaluation finding no significant differences [5, 21, 39]. Furthermore, the results are distorted by metallosis and aseptic causes of inflammation, such as rheumatoid arthritis (rA) [34].
Calprotectin is a cytosolic protein predominantly present in neutrophils, which release it upon activation [44]. Therefore, the level of calprotectin in synovial fluid reflects the proportion of activated neutrophils in the synovium. A small fraction is also found in and released by infiltrating monocytes and macrophages upon phagocytosis [42].
In recent studies, a lateral-flow test validated for faecal calprotectin, an ELISA, and a synovial fluid lateral-flow test exhibited excellent results, especially as rule-out tests for PJI [37, 49, 50, 52]. The aim of this study was to examine the additional value of the newly available calprotectin lateral flow test (Calprotectin; Lyfstone AS, Tromsø, Norway) as a pre-operative diagnostic tool for the diagnosis of chronic or low-grade PJI. For this purpose, we compared the LFT result with the pre-operative result of the ICM 2018 score, using the final score as the gold standard, and accounted for cases that would have been correctly classified pre-operatively had the test been part of the standard workup.
## Materials and methods
This study was approved by our institutional ethics commission under no. $\frac{26}{19}$ S-SR. Beginning in March 2019, we prospectively analyzed all patients scheduled for total hip or knee arthroplasty revision with component exchange for any reason. To reflect the challenging patient histories and local bone and soft tissue conditions encountered in the setting of a high-volume, national referral center for arthroplasty revision surgery, we included patients with one or multiple previous revisions, including septic exchanges, patients with tumour prostheses, or patients with arthrodesis implants. Furthermore, we included patients with inflammatory bowel disease, rheumatoid arthritis, or other systemic inflammatory conditions. With the same intention, we used joint aspirates regardless of blood contamination for calprotectin measurement. However, we excluded patients with early post-operative or late-onset acute haematogenous infections. We also excluded patients who had undergone surgery or suffered from joint dislocation within three months prior to joint aspiration, as well as those with periprosthetic fractures necessitating arthroplasty exchange, as these conditions entail a local inflammatory response. Lastly, we excluded patients who had received antibiotic therapy within two weeks prior to joint aspiration, and cases where no aspirate could be collected, even during the arthroscopic biopsy our institutional diagnostic algorithm entails after a dry tap.
Informed consent was given by all patients. In the pre-operative diagnostic workup, we obtained the erythrocyte sedimentation rate (ESR), serum C-reactive protein (CRP) level, and leukocyte count. All joints were aspirated to measure the synovial WBC, and differential and synovial fluid was sent for cultivation in aerobic and anaerobic paediatric blood cultures (BACTEC, BD, Heidelberg, Germany). Intra-operatively, five biopsies were retrieved for conventional culture, one was sent for pathological classification of the synovia-like interface membrane (SLIM) according to the criteria established by Morawietz and Krenn [24], and the explanted components were sent for sonication and subsequent culture of the sonication fluid. A threshold of ≥ 50 colony-forming units (CFU)/ml of the same pathogen was defined as a positive sonication result. All cultures were incubated for a minimum of 14 days to account for slow-growing organisms. For our analysis, we classified each patient regardless of the clinical diagnosis according to the ICM criteria of 2018.
For the calprotectin lateral flow test (LFT), 20 µl of each joint fluid aspirate was added to 2 ml of dilution buffer and inverted ten times to give a 1:101 extract. Subsequently, 80 µl of the mix was pipetted onto a well in the test cartridge. Calprotectin is bound by a specific antibody complex on the membrane, resulting in a visible test line for colorimetric detection. The remaining antibody complexes flow further laterally and are immobilized on a control line. The colour intensity of the test line is proportional to the calprotectin concentration, which is photometrically evaluated after 15 min using a smartphone application provided by Lyfstone for this purpose. Three categories were defined when measuring the calprotectin concentration: < 14 mg/ml or low risk, 14–50 mg/ml or moderate risk, and 50– > 300 mg/ml or high risk for infection, whereas the range between 14 and 300 mg/ml was read out quantitatively.
For internal validation, we performed a sequence of tests on 11 consecutive samples stored at operating room temperature, directly after aspiration and after three and 24 h. All tests indicating high risks ($$n = 9$$) remained at high risk for infection over time, with a mean difference of 33 mg/dl between the respective measurements. All tests indicating low risk ($$n = 2$$) remained unaffected over time at < 14 mg/dl. We also performed serial photometric measurements of the same test strip after two min, 15 min, one h, 12 h, 24 h, and 48 h on a subset of 65 consecutive patients to control for possible temporal changes in the readout. All tests indicating high risks ($$n = 26$$) remained at high risk for infection over time, with a mean difference of 9 mg/dl between the respective photometric read outs. All tests indicating a low risk ($$n = 39$$) remained at low risk for infection over time, with a mean difference of 0.5 mg/dl between the respective photometric read outs.
The surgeons performing the arthroplasty revision were blinded to the calprotectin test results, which were not included in diagnostic or therapeutic deliberations and were assessed strictly for the purpose of this analysis.
To evaluate the diagnostic accuracy of the calprotectin LFT, its result and the pre-operative ICM score were compared against the postoperative ICM score as the gold standard. To calculate the test metrics, “moderate risk” calprotectin levels and “inconclusive” ICM scores were counted as negative, i.e., not infected.
In addition, the patients were divided into the subgroups “primary implants” and “revision and tumour prosthesis” on the basis of the implant type that was in place before surgery. For this purpose, any total knee arthroplasty with intramedullary fixation was classified as a revision implant, as were total hip arthroplasties with long stem fixation in the diaphysis regardless of whether or not cement was used. Accordingly, modular implants were also assigned to the revision subgroup. Any proximal, distal, or total femoral replacement, proximal tibial replacement, and partial pelvic replacement was also assigned to the revision and tumour implant subgroup, regardless of whether the reason for use of the implant was previous failed revision arthroplasty or the treatment of primary or metastatic malignancy.
To quantify the additional benefit of using a calprotectin LFT not as a standalone but as an additional test in an array of pre-operative diagnostics, we analyzed each case with a negative or inconclusive pre-operative score and determined whether the calprotectin level measured would have had an influence on the pre-operative diagnosis and whether this would have improved the pre-operative diagnostic accuracy.
## Statistics
For categorical variables, absolute and relative frequencies are presented. Continuous variables are summarized as the mean (± standard deviation). For dichotomous diagnostic tests, sensitivities, specificities, positive (PPV) and negative predictive values (NPV), and positive (LR +) and negative likelihood ratios (LR −) were estimated with corresponding $95\%$ confidence intervals using the library ThresholdROC in the statistical software R version 4.1.0 [33, 43]. Exact (Clopper–Pearson) confidence intervals were calculated for sensitivities, specificities, and positive and negative predictive values. For continuous diagnostic markers, receiver operating characteristic (ROC) analyses were performed using the library pROC [35], and areas under the ROC curves (AUCs) with $95\%$ confidence intervals were estimated. The Youden index was used for determination of an optimal cutoff value.
## Source of funding
The authors were provided with calprotectin LFT kits free of charge by Lyfstone AS for this evaluation.
## Results
We included 137 patients (49 female, 88 male) with a mean age of 67 (± 13) years with 53 total hips (THAs) and 84 total knee arthroplasties (TKAs). Among them, 74 patients had primary implants (30 THA, 44 TKA), and 63 had revision or tumour implants (22 THA, 38 TKA, two distal femoral replacements, one proximal femoral replacement). The patients with revision implants had undergone a median of three (range 1–8) previous surgical procedures on the same joint. Patients with failed primary arthroplasty had a median of one (range 1–9) previous surgery. For an overview of patient demographics, see Table 1.Table 1Demographic data and classification according to the criteria defined by the ICM 2018Aseptic ($$n = 99$$)Infected ($$n = 34$$)Inconclusive ($$n = 4$$)Total ($$n = 137$$)Age (years) (± SD)67 (± 13)70 (± 11)66 (± 8)67 (± 12)Male (%)28 [29]18 [53]2 [50]49 [36]Female (%)71 [71]16 [47]2 [50]88 [64]Hip (n)3516253Primary arthroplasty (n)218130Revision arthroplasty (n)148123Knee (n)6418284Primary arthroplasty (n)367144Revision arthroplasty (n)2811140 According to the post-operative ICM criteria of 2018, 99 patients ($72.8\%$; 58 primary, 38 revision arthroplasties, and 3 tumor implants) were not infected, 34 ($25.0\%$; 16 primary and 18 revision arthroplasties) were infected, and four ($2.9\%$, 2 primary and 2 revision arthroplasties) had an inconclusive score. The pre-operative scores were negative in 80 patients ($58.4\%$), positive in 24 ($17.5\%$), and inconclusive in 33 ($24.1\%$). The observed calprotectin LFT results were negative (low risk) in 85 ($62.0\%$), positive (high risk) in 45 ($32.8\%$), and moderate risk in seven ($5.1\%$) patients. Of the 99 aseptic patients, 82 ($82.8\%$) had negative results, seven ($7.1\%$) “moderate risk” results, and ten ($10.1\%$) positive calprotectin LFT results. The pre-operative criteria were negative in 77 ($76.8\%$) and inconclusive in 22 patients ($22.2\%$). Of the 34 septic patients, 32 ($94.1\%$) had positive calprotectin LFT results, and two ($5.9\%$) had negative calprotectin LFT results. Regarding these 34 septic patients, the pre-operative ICM scores were positive in 24 ($70.6\%$), inconclusive in seven ($20.6\%$), and negative in three patients ($8.8\%$). These results are summarized in Table 2.Table 2Confusion matrix of pre-operative criteria as defined by the ICM 2018 and the calprotectin LFTAseptic ($$n = 99$$)Infected ($$n = 34$$)Inconclusive ($$n = 4$$)Total ($$n = 137$$)Calprotectin LFT Low risk (%)82 (82.8)2 (5.9)1 (25.0)85 (62.0) Moderate risk (%)7 (7.1)0 (0.0)0 (0.0)7 (5.1) High risk (%)10 (10.1)32 (94.1)3 (75.0)45 (32.8)Pre-operative ICM 2018 Negative (%)77 (77.8)3 (8.8)0 (0.0)80 (58.4) Positive (%)0 (0.0)24 (70.6)0 (0.0)24 (17.5) Inconclusive (%)22 (22.2)7 (20.6)4 (100.0)33 (24.1) For the calprotectin LFT, the test showed a sensitivity ($95\%$ confidence interval) of 0.94 (0.80–0.99) and a specificity of 0.87 (0.79–0.93). The PPV and NPV were 0.71 (0.56–0.84) and 0.98 (0.92–1.0), respectively, and the positive and negative likelihood ratios (LRs) were 7.46 (4.46–12.48) and 0.07 (0.02–0.26), respectively. The area under the receiver operating characteristic (ROC) curve (AUC) for the calprotectin LFT was 0.94 (0.89–0.99). According to the Youden index, a calprotectin level of 85.5 mg/l was calculated as an optimal cutoff in our cohort, resulting in a sensitivity of 0.92 and a specificity of 0.95 (see Fig. 1).Fig. 1Area under the receiver operating characteristic curve of the calprotectin LFT, using ICM 2018 as the gold standard In comparison, the pre-operative ICM score resulted in a sensitivity of 0.71 (0.53–0.85) and a specificity of 1.0 (0.96–1.0), with a PPV and NPV of 1.0 (0.86–1.0) and 0.91 (0.84–0.96) and a negative LR of 0.29 (0.17–0.50), respectively.
In the subgroup analysis of septic and aseptic revisions of primary arthroplasties, the calprotectin LFT reached a sensitivity of 1.0 (0.78–1.0), a specificity of 0.93 (0.84–0.98), and a PPV and NPV of 0.79 (0.54–0.94) and 1.0 (0.94–1.0), respectively. The positive LR was 14.8 (5.7–38.0), and the negative LR was 0.03 (0.002–0.53). The AUC (using continuous observation without categorization) was 0.96 (0.90–1.0). In this setting, the pre-operative ICM criteria showed a sensitivity of 0.73 (0.45–0.92), a specificity of 1.0 (0.94–1.0), and a PPV and NPV of 1.0 (0.72–1.0) and 0.94 (0.85–0.98), respectively.
Considering the arthroplasty exchange subgroup of revision or tumour implants, the sensitivity and specificity of calprotectin were 0.89 (0.67–0.99) and 0.80 (0.65–0.90), the PPV and NPV were 0.65 (0.44–0.83) and 0.95 (0.82–0.99), and the positive and negative LRs were 4.27 (2.34–7.80) and 0.13 (0.04–0.50), respectively. The AUC (continuous value) was 0.92 (0.82–1.0). In this setting, the pre-operative ICM score yielded a sensitivity of 0.68 (0.43–0.87), a specificity of 1.0 (0.92–1.0), and positive and negative predictive values of 1.0 (0.75–1.0) and 0.88 (0.75–0.95), respectively.
For an overview of these results, see Table 3.Table 3Test metrics of the calprotectin LFT and the pre-operative ICM score for all patients and the subgroups of failed primary and failed revision and tumor implantsAll arthroplastiesFailed primary arthroplastiesFailed revision and tumor arthroplastiesCalprotectinPre-operative ICM 2018CalprotectinPreo-perative ICM 2018CalprotectinPre-operative ICM 2018Sensitivity ($95\%$ CI)0.94 (0.80–0.99)0.71 (0.55–0.86)1.0 (0.78–1.0)0.73 (0.51–0.96)0.89 (0.67–0.99)0.68 (0.48–0.89)Specificity ($95\%$ CI)0.87 (0.79–0.93)1.00.93 (0.84–0.98)1.00.80 (0.65–0.90)1.0PPV ($95\%$ CI)0.71 (0.56–0.84)1.00.79 (0.54–0.94)1.00.65 (0.44–0.83)1.0NPV ($95\%$ CI)0.98 (0.92–1.0)0.91 (0.86–0.96)1.0 (0.94–1.0)0.94 (0.88–1.0)0.95 (0.82–0.99)0.88 (0.79–0.97)LR + ($95\%$ CI)7.46 (4.46–12.48)n.a14.8 (5.7–38.0)n.a4.27 (2.34–7.80)n.aLR − ($95\%$ CI)0.07 (0.02–0.26)0.29 (0.14–0.45)0.03 (0.002–0.53)0.27 (0.04–0.49)0.13 (0.04–0.50)0.32 (0.011–0.52)The postoperative ICM 2018 was used as the gold standard
## Septic versus aseptic
Thirteen patients had elevated calprotectin levels without fulfilling the ICM criteria for infection. Among them, seven patients had borderline or pathological WBC levels or differentials. Six had elevated CRP levels. In three cases, their SLIM was classified as infected or mixed-type. Interestingly, in all false-positive cases, the calprotectin levels were not elevated above 300 mg/ml but ranged from 59 to 251 mg/ml (median: 102 mg/ml).
Two patients were classified as having false-negative calprotectin levels. In the first case, all pre-operative and intra-operative findings were negative, but there was growth of Cutibacterium acnes in a tissue biopsy culture and sonication fluid. The patient underwent revision THA. In the second case, the pre-operative CRP was elevated, as were the cell count (6.65 cells/μl) and differential ($84\%$ neutrophils). There was no growth in cultures, and the histology was negative. The patient had a revision TKA. The false-positive and false-negative findings are summarized in Table 4.Table 4Overview of false-positive and false-negative resultsPatient noM/FAge (y)Hip/kneePrimary/revisionNo. of previous surgeriesMedical diagnosesCRP (mg/dl)Synovial WBC (cells/µl)Synovial PMN (%)Synovial fluid cultureSynovial calprotectin (mg/dl)Tissue biopsy cultureSonication fluid cultureHistology (type)ICM scoreFalse-positive calprotectin level9Female77KneePrimary1Multiple sclerosis1.13.039Negative175NegativeNegativeIndifferentInconclusive19Male58KneeRevision4HypertensionGERDHypothyroidism0.40.88Negative60NegativeNegativeWear-inducedNegative58Female76KneeRevision4Rheumatoid arthritisDiabetes mellitusHypertensionGERD0.91.574Negative251NegativeNegativeInfectiousInconclusive66Male72KneeRevision4Diabetes mellitus2.30.832Negative93NegativeNegativeNegativeNegative71Male62HipPrimary1Hypertension1.10.279Negative74NegativeNegativeWear-inducedNegative91Female80KneeRevision5None2.90.877Negative243NegativeNegativeWear-inducedNegative99Female51KneePrimary1None0.44.876Negative59NegativeNegativeIndifferentNegative127Male58HipRevision4Hairy cell leukemiaNon-Hodgkin lymphomaHyperuricemiaHypertension1.00.851Negative102NegativeNegativeMixed-typeNegative131Female78KneeRevision2Diabetes mellitusMorbid obesity0.52.675Negative138NegativeNegativeWear-inducedNegative147Female60HipPrimary3None0.10.412Negative169NegativeNegativeWear-inducedNegative148Female72KneeRevision2Atrial fibrillationDiabetes mellitus0.53.111Negative114NegativeNegativeWear-inducedNegative153Male83HipRevision2Renal insufficiencyHyperuricemiaHypertensionVaricosis1.05.591Negative78NegativeNegativeWear-inducedInconclusive162Female81HipRevision3Hepatitis COsteoporosisHypothyroidismHypertensionDementia0.14.556Negative78NegativeNegativeWear-inducedNegativeFalse-negative calprotectin level72Male77HipRevision2Prostate cancerDiabetes mellitus0.10.63Negative14Cutibacterium acnesCutibacterium acnesIndifferentPositive115Male86KneeRevision3Diabetes mellitusCoronary sclerosisAtrial fibrillation1.166.784014NegativeNegativeIndifferentPositiveWBC white blood cell count, PMN polymorphonuclear neutrophils, ICM score International Consensus Meeting of 2018 Score
## Metallosis
There were two patients (both female, 1 revision THA, 1 revision TKA) with intra-operative findings of metallosis (see Table 5). Both were aseptic. One of them, a patient with a revision THA, had undergone three previous surgical procedures on the joint in question. The pre-operative CRP and leukocyte count were normal. The cell count and differential were 1.83 cells/μl and $52\%$ neutrophils, respectively. The calprotectin value was < 14 mg/l. Pre-operative synovial fluid and intra-operative tissue biopsy cultures were negative. Interestingly, the SLIM was classified as an infectious type, showing areas with 7 PMN/high-power field (HPF) as well as areas with markedly increased neutrophil counts (85 PMN/HPF) and a morphology described as “phlegmonous inflammation” in the written report. The other patient carried a revision TKA and had undergone five previous surgical procedures on the same knee. The pre-operative CRP was elevated, the WBC and differential were 0.75 cells/μl and $77\%$, and the calprotectin level was 243 mg/l. All cultures were negative. The histology was classified as a debris-induced type (type I) with 8 PMN/10 HPF.Table 5Overview of patients with metallosisPatient noM/FAge (y)Hip/kneePrimary/revisionNo. of previous surgeriesMedical diagnosesCRP (mg/dl)Synovial WBC (cells/µl)Synovial PMN (%)Synovial fluid cultureSynovial calprotectin (mg/dl)Tissue biopsy cultureSonication fluid cultureHistology (type)ICM scoreMetallosis5Female64HipRevision3Klippel-Trenaunay's syndromeLEOPARD-syndromeCarotid artery stenosisHypothyroidism0.11.852Negative14NegativeNegativeInfectiousNegative91Female80KneeRevision5None2.90.877Negative243NegativeNegativeWear-inducedNegativeWBC white blood cell count, PMN polymorphonuclear neutrophils, ICM score International Consensus Meeting of 2018 Score
## Rheumatoid arthritis
Eight patients (7 females, 1 male) with a history of rA were included (see Table 6). Six were classified as aseptic. One of them, a patient with a revision TKA, showed discrepant results: she had normal serological inflammation markers but a borderline WBC of 1.5 cells/μl and $74\%$ PMN. Calprotectin was positive with 251 mg/l. Histology was classified as infectious type 2 with 18 PMN/HPF. Intra-operatively, marked osteolysis and wear disease were observed. By ICM criteria, another patient with an elevated calprotectin level of > 300 mg/l was declared aseptic. This male patient had unremarkable pre-operative serology but an elevated WBC of 5.8 cells/μl and $93\%$ PMN. In the sonication fluid culture, S. epidermidis grew but was below the threshold of 50 CFU/ml. The SLIM was classified as type 4 (“indifferent type”). One patient with primary THA showed growth of *Parvimonas micra* in the synovial fluid culture. All other parameters, including calprotectin, were normal. Another patient with rA classified as septic had a primary TKA. The pre-operative CRP level was 3.1 mg/dl, and the WBC count was 36.92 cells/μl with $83\%$ PMN. The synovial calprotectin level was > 300 mg/l. Intra-operative cultures showed growth of S. epidermidis. The histology was classified as debris-induced type 1.Table 6Overview of patients with rheumatoid arthritisPatient noM/FAge (y)Hip/kneePrimary/revisionNo. of previous surgeriesMedical diagnosesCRP (mg/dl)Synovial WBC (cells/µl)Synovial PMN (%)Synovial fluid cultureSynovial calprotectin (mg/dl)Tissue biopsy cultureSonication fluid cultureHistology (type)ICM scoreRheumatoid arthritis4Female51KneeTumor5von Willebrand Jürgens syndrome0.10.828Negative14NegativeNegativeWear- inducedNegative17Female59HipPrimary1Irritable bowel syndromeNephrocalcinosisHypothyroidism0.10.38Negative14NegativeNegativeWear-inducedNegative23Female59KneePrimary1Status post-cerebral ischemic insult0.1n/an/aNegative14NegativeNegativeIndifferentNegative58Female76KneeRevision4Diabetes mellitusHypertensionGERD0.91.574Negative251NegativeNegativeInfectiousNegative64Female71KneeRevision4Psoriasis vulgarisPolyarthrosisStatus post-DVT0.20.113Negative14NegativeNegativeWear-inducedNegative79Male79HipPrimary1Axial spondylitisCeliac diseaseStatus post-pulmonary artery embolism0.45.893Negative300NegativeS. epidermidis (< 50 cfu)IndifferentNegative126Female71KneePrimary1Multiple sclerosis0.30.23Negative14NegativeNegativeWear-inducedNegative129Female60KneePrimary1Asthma bronchialeStatus post-breast cancer3.136.983Negative300NegativeNegativeWear-inducedPositiveWBC white blood cell count, PMN polymorphonuclear neutrophils, ICM score International Consensus Meeting of 2018 Score
## Wear and osteolysis
There were nine patients with intra-operative macroscopic findings of wear disease and osteolysis (see Table 7). One patient described above was classified as infected because of positive growth of Cutibacterium acnes, with all other parameters unremarkable, including calprotectin. Another patient had a history of rheumatoid arthritis (see above) and had borderline synovial cytology and an infectious type histology, with a calprotectin level of 251 declared to be false positive using the ICM criteria as the gold standard. Similarly, another patient was classified as false positive with unremarkable serology, cytology, cultures, and histology but had a calprotectin value of 60 mg/l, which was just above the threshold. Table 7Overview of patients with wear-induced osteolysisPatient noM/FAge (y)Hip/kneePrimary/revisionNo. of previous surgeriesMedical diagnosesCRPSynovial WBC (cells/µl)Synovial PMN (%)Synovial fluid cultureSynovial calprotectin (mg/dl)Tissue biopsy cultureSonication fluid cultureHistology (type)ICM scoreWear disease8Female89HipPrimary1Status post-cerebral ischemic insultHypertension1.1n/an/aNegative32NegativeS. warneriWear-inducedNegative19Male58KneeRevision4HypertensionGERDHypothyroidism0.40.88Negative60NegativeNegativeWear-inducedNegative38Male80KneeRevision3Cardiac arrhythmiaHypertension0.50.124Negative14NegativeNegativeIndifferentNegative48Female68HipPrimary1Hypertension0.10.623Negative14NegativeNegativeWear-inducedNegative58Female76KneeRevision4Rheumatoid arthritisDiabetes mellitusHypertensionGERD0.91.574Negative251NegativeNegativeInfectiousNegative72Male77HipRevision2Prostate cancerDiabetes mellitus0.10.63Negative14Cutibacterium acnesCutibacterium acnesIndifferentPositive86Female78HipPrimary3HypertensionOsteoporosisRenal insufficiency1.7n/an/aNegative14NegativeNegativeIndifferentNegative142Male73KneePrimary1Hypertension0.10.22Negative14NegativeNegativeWear-inducedNegative152Male75KneePrimary1HypertensionNicotine abuse0.54.816Negative23NegativeNegativeWear-inducedNegativeWBC white blood cell count, PMN polymorphonuclear neutrophils, ICM score International Consensus Meeting of 2018 Score Among the remaining 6 patients with wear-induced osteolysis, two patients had calprotectin levels in the “moderate risk” category. One female patient with primary THA had an elevated CRP and leukocyte count, while cytology and histology were normal, her calprotectin level was 32 mg/l, and there was growth of S. aureus below the threshold of > 50 CFU in the sonication fluid culture (see Fig. 2). The other was a male patient with a primary TKA with an unremarkable workup except for an elevated leukocyte count and an elevated synovial WBC of 4.78 cells/μl. His calprotectin level was 23 mg/l.Fig. 2Pre- and post-operative frontal view radiographs of a female patient with marked osteolysis due to polyethylene wear. The synovial fluid calprotectin level measured intra-operatively was 32 mg/dl To evaluate the possible additional value of a synovial fluid calprotectin LFT, we analyzed cases that were classified as either not infected or inconclusive by ICM criteria in the pre-operative workup but were then declared infected post-operatively. Out of 79 patients deemed to be uninfected pre-operatively, three were later classified as infected. Of these three patients, two showed pre-operative calprotectin levels that were positive for infection. Out of 33 patients with an inconclusive pre-operative workup, three remained inconclusive, and seven were defined as infected on the grounds of intra-operative findings. All 7 infected cases had pre-operative calprotectin levels suggestive of infection.
## Discussion
Our ability to discern chronic PJI from other failure modes has improved with the introduction of formalized definitions of PJI on the grounds of various diagnostic criteria. However, the diagnostic accuracy of these criteria is heterogeneous. Furthermore, the results become available at various points in time over the course of the diagnosis as well as during the actual treatment process. The consequence is a dichotomy of the diagnostic process reflected by the most recent ICM criteria, which are divided into pre- and post-operative sections [30]. Results such as the CRP, WBC and differential, and synovial fluid culture are available before revision surgery. Among these, only synovial cytology has excellent accuracy, but it can be distorted by other, noninfectious inflammatory processes. The information gathered intraoperatively by tissue biopsy cultures, histology of the interface membrane, and sonication of the explanted components is much more reliable. However, failing to diagnose a PJI before surgery can entail catastrophic consequences for the patient, as an incomplete removal of foreign materials and inadequate surgical debridement considerably reduce the chance for infection control. Hence, there is the need for a robust and accurate method to exclude PJI in the pre-operative workup.
In the past, the superiority of synovial fluid over serological biomarkers for the diagnosis of PJI was shown, and some promising candidates were identified [9]. Later, the measurement of synovial α-defensin became a focus after reports of the near-perfect accuracy of the laboratory-based ELISA. The α-defensin lateral flow test promised to deliver immediate and reliable results. However, in subsequent studies, neither ELISA nor the lateral flow test was shown to be superior to other synovial markers [2]. Another disadvantage is the considerable cost of a single test.
Calprotectin is well established as a marker of inflammatory bowel disease and rA [15, 20]. The measurement of synovial fluid calprotectin for the diagnosis of PJI has come increasingly into focus in the recent past. As part of the innate immune system, it is secreted by activated neutrophils and, to a lesser extent, monocytes [44]. It then chelates micronutrients as a part of a specific host defense against microorganisms and might therefore be suited to discern bacterial infection from mere inflammation [7, 13, 16].
In this study, we aimed to establish the value of such a diagnostic tool in the daily reality of a national reference centre for revision arthroplasty and PJI, where we often face the difficulties of establishing the true failure mode due to multiple previous surgery, poor soft tissue conditions, severe osteolysis, wear disease, and equivocal diagnostic findings. Therefore, we explicitly did not exclude patients with such aggravating circumstances, nor did we exclude patients with nonevaluable single findings such as clotted joint aspirates or unclassifiable histological specimens.
Even under these difficult premises, the accuracy of the calprotectin LFT was excellent. When used as a single test to distinguish PJI from other failure modes, it had a sensitivity of 0.94 and a specificity of 0.87, with an area under the receiver operating characteristic curve of 0.94, surpassing the diagnostic quality of the other pre-operatively available criteria. In the subgroup of failed revision arthroplasties, the resulting accuracy and AUC were slightly inferior but still remarkably good, especially when considering that the adverse local and systemic conditions in such complicated patients impair the quality of all other diagnostics to the same extent.
When used as part of an array of diagnostic measures, the calprotectin LFT substantially improves the pre-operative classification of patients as septic or aseptic. Two out of three patients who were deemed aseptic by ICM criteria and later turned out to be infected had positive calprotectin levels. Even more striking, in the 33 cases that were inconclusive pre-operatively, all patients who turned out to be infected after surgery would have been correctly classified with an additional preoperative calprotectin test. Such inconclusive cases represent the fraction of patients facing arthroplasty revision that is the most difficult to diagnose, causing an extensive additional workload pre- and post-operatively due to this remaining uncertainty.
Wouthuyzen-Bakker et al. first described the measurement of synovial calprotectin in a cohort of 61 patients, of whom 19 had both acute and chronic infections of hip, knee, and elbow arthroplasties, using a quantitative lateral flow assay designed for the determination of fecal calprotectin levels. With a cutoff at 50 mg/l, they showed a sensitivity of 0.89 and a specificity of 0.90. However, they did not exclude acute infections, which do not pose the aforementioned diagnostic challenges, as they regularly exhibit elevated CRP, markedly elevated WBC and differential, and growth on cultures. Furthermore, the control group consisted of a heterogeneous cohort of patients, including some with native joints undergoing arthroplasty and patients with spacers undergoing reimplantation [49].
In a further study by the same authors, they concentrated on suspected chronic PJI in a cohort of 52 patients with total knee, hip, and shoulder arthroplasties and measured calprotectin levels with ELISA. They could confirm their previously established threshold at 50 mg/l and achieved a sensitivity of 0.87 and a specificity of 0.92, which was comparable to their previous study. Interestingly, they found that all patients with false-positive calprotectin results had loose implants [50].
Salari et al. used ELISA to pre-operatively evaluate a cohort of 76 patients with painful TKA. Their exclusion criteria were previous joint surgery within three months, administration of antibiotics within two weeks prior to sample collection, and rheumatoid arthritis. They showed an even higher diagnostic accuracy of the method and confirmed a threshold of 50 mg/l [37]. However, they excluded patients with inconclusive ICM scores for their calculation of the test’s accuracy.
In a cohort of 63 patients with failed THA and TKA, including dislocations and periprosthetic fractures, Zhang et al. calculated an AUC of 0.99 using ELISA and a much higher cutoff value of 173 mg/l. They excluded bloody aspirates and patients with inflammatory arthritis. Interestingly, seven patients in their cohort had normal synovial WBCs accompanied by elevated calprotectin, and three patients had elevated CRP with normal calprotectin. However, they did not state the respective WBC differentials. Therefore, it cannot be inferred from their results whether the calprotectin level reflects the total number of neutrophils or the activated fraction.
The lateral flow test was recently examined by Warren et al. in a cohort of 123 patients undergoing TKA revision in two centres. The LFT was extraordinarily accurate with the MSIS criteria as gold standard, with an AUC of 0.969 [47]. In another publication using the same data set of revisions of primary TKA, the authors used the EBJIS criteria as gold standard. They concluded that the calprotectin LFT is consistently accurate regardless of the underlying criteria [48].
More recently, Grzelecki et al. examined blood and synovial fluid calprotectin levels in patients with and without rA awaiting primary arthroplasty, aseptic and confirmed septic arthroplasty revision, and before reimplantation in two-stage septic arthroplasty revision. They concluded that blood and synovial fluid calprotectin were superior to other established markers like CRP, erythrocyte sedimentation rate, interleukin 6, and leukocyte esterase (LE). However, it was not useful in patients with rheumatoid arthritis [11]. In the presence of rheumatoid arthritis, we observed several cases with elevated calprotectin, as well as some with normal values. Apart from the small number of patients in our cohort, we did not account for the disease activity at the time of our measurement. Whether the standard diagnostic algorithms can be applied to patients with rA or whether the respective thresholds defining PJI need to be adjusted in that population is a matter of debate [11, 23, 38]. Calprotectin is an established marker for disease activity in rheumatoid arthritis, which might explain the range of values observed [1]. However, we cannot conclude whether rA patients require a higher calprotectin threshold to adjust for altered calprotectin levels due to ongoing flares of the disease.
The available evidence was summarized in four recent meta-analyses. They all concluded that, based on the few studies available so far, calprotectin is a reliable biomarker for the confirmation as well as the exclusion of PJI [4, 12, 32, 51].
In an analysis of patients with acute inflammation of the joint in question due to recent surgery, dislocation, or implant breakage, we assessed the diagnostic accuracy of synovial fluid calprotectin using a modified EBJIS score as the gold standard. Although the diagnostic metrics were lower in this setting, calprotectin still yielded a sensitivity of 0.88, a specificity of 0.81, and a PPV and NPV of 0.83 and 0.87, respectively. Thus, even in a situation where the established diagnostic criteria can be equivocal, it is still suited for the exclusion of PJI [18].
While we did not have enough patients with observed metallosis in our cohort to draw reliable conclusions, both patients with metallosis showed calprotectin levels < 14 mg/l, at least suggesting that the presence of inflammation caused by metallosis might not induce false-positive calprotectin levels. This should be elucidated in further studies, as metallosis has been shown to distort automated WBC assays and α-defensin tests [34].
We observed several increased calprotectin levels in patients with marked osteolysis and wear disease, similar to the observation made by Wouthuyzen-Bakker et al. [ 50]. This might be reflective of the activation of monocytes and macrophages instead of the neutrophil activation being more predominant in bacterial infection. This may be caused by the inflammatory foreign-body reaction to debris particles. We have commenced conducting further clinical investigations on the grounds of this hypothesis.
This study has some limitations. First, our results are derived from a heterogeneous cohort consisting of primary, revision, and tumour implants of the hip and knee. We consciously decided to prospectively include all patients up for arthroplasty revision to generate a realistic picture of a possible routine use of the calprotectin LFT in a high-volume centre. Nevertheless, our results are comparable to those of Salari et al., who examined a homogeneous cohort of failed primary TKA [37].
Second, in including all patients, regardless of the completeness of the pre-operative workup, it is likely that some patients were classified incorrectly. However, the ICM consensus does not require that all diagnostic measures available be taken. In our institution, serum d-dimer levels, leukocyte esterase testing of the synovial fluid, or α-defensin are not part of the diagnostic algorithm. Some PJI definitions, such as the definition of the World Association against Infection in Orthopedics and Trauma (WAIOT) and the recently published EBJIS criteria, try to address this issue by not demanding the carrying out of a canonical list of tests but rather providing a system for the interpretation of the tests performed [22, 36].
Third, every evaluation of a new test method encounters the gold standard problem. The ICM criteria are in widespread use, but one has to bear in mind that they are not completely accurate. Therefore, some classifications might be false, which can lead to either an over- or underestimated accuracy of the LFT. Furthermore, the preoperative ICM score that we used for comparison of the diagnostic tools available before revision surgery has not been validated as a standalone score but is part of the ICM score. While we believe that it can nevertheless serve as a benchmark for the evaluation of pre-operative tests, it has to be made clear that its results in this study are certainly overfitted, as it is a part of the score used as the gold standard. However, we chose not to introduce another gold standard to match the pre-operative ICM score to avoid an overly complicated design with myriad comparisons and limited interpretability.
In conclusion, synovial fluid calprotectin LFT is highly accurate for the diagnosis of PJI even in the presence of patient conditions that impair standard diagnostic procedures. Because the results are available within 15 min, this test is a useful and accurate addition to the pre-operative diagnostic workup before arthroplasty exchange, especially in cases where the gold standard results are inconclusive.
Based on our findings, we will conduct further research to evaluate calprotectin as a “tip of the scales” marker for situations in which the recommendations are ambiguous, such as the early post-operative phase, wear disease, or in the presence of periprosthetic fractures.
While we cannot conclude that rA or metallosis does not impair the test results due to the limited number of patients with these conditions in our study, we did not observe a systematic distortion of calprotectin levels in patients with rA or metallosis. If further studies are able to provide more evidence on this matter, calprotectin could prove to be extremely useful, as other conventional and point-of-care biomarker assays are not reliable in the presence of these conditions.
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|
---
title: Identification of anoikis-related molecular patterns to define tumor microenvironment
and predict immunotherapy response and prognosis in soft-tissue sarcoma
authors:
- Lin Qi
- Fangyue Chen
- Lu Wang
- Zhimin Yang
- Wenchao Zhang
- Zhi-Hong Li
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10014785
doi: 10.3389/fphar.2023.1136184
license: CC BY 4.0
---
# Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma
## Abstract
Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear.
Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated.
Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response.
Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.
## Introduction
Soft-tissue sarcoma (STS) is rare and accounts for approximate $1\%$ of all adult malignancies (Gamboa et al., 2020), most commonly occurring in the extremities. In 2022, 13,190 people were newly diagnosed with STS and 5,130 people died of STS in United States (Siegel et al., 2022). STS was known as its heterogeneity which includes at least 100 different histologic and molecular subtypes. Genomic study has indicated that STS was mainly characterized by copy number variations but low mutation loads (Cancer Genome Atlas Research Network. Electronic address and Cancer Genome Atlas Research Network, 2017). However, a few genes (TP53, ATRX, RB1) showed highly recurrent mutations across different sarcoma types. These findings highlighted the importance of genetic alterations in STS, corresponding to its heterogeneity. Meanwhile, transcriptomic profiling of STS enhanced our understanding of STS biology and provided potential therapeutic targets. Transcriptomics can identify patients among different histological subtypes (Nielsen et al., 2002; Linn et al., 2003). Expression of some gene signatures were associated with prognosis of STS, such as hypoxia-inducible factor alpha (HIFA) and its targets (Francis et al., 2007).
In recent years, multiple molecular processes have been introduced to cancer biology and treatment such as the anoikis. Anoikis is a programmed cell death manner, happening when cells detached from appropriate extracellular matrix, which is a crucial mechanism in maintenance of plastic cell growth and attachment (Taddei et al., 2012). Cancer cells are characterized by insensitivity to anoikis since its survival and proliferation do not rely on adhesion to extracellular matrix (Cai et al., 2015). Thus, cancers represent a feature of anoikis resistance. In this scenario, figuring out the anoikis regulators of cancers contributes to the discovery of novel therapeutics, especially for cancer metastasis (Sakamoto and Kyprianou, 2010). For instance, in LKB1-deficient lung cancer, the PLAG1-GDH1 axis was reported to accelerate anoikis resistance through the CamKK2-AMPK pathway (Jin et al., 2018). Nuclear MYH9-induced CTNNB1 expression could facilitate gastric cancer cell anoikis resistance and induce metastasis. Similarly, it was reported that anoikis resistance in gastric cancer was regulated by TCF7L2 through transcriptionally activating PLAUR (Zhang et al., 2022), resulting in enhancement of metastasis. IQGAP1, a scaffolding protein that regulates cellular motility and extracellular signals, also reported to modulate the anoikis resistance and metastasis of hepatocellular carcinoma by accumulation of Rac1-dependent ROS and activation of Src/FAK signaling (Mo et al., 2021). These researches highlighted the critical role of anoikis profiling in various cancers.
Specifically, anoikis resistance also participate in the biology of STS. Recently, a study has conducted proteomic screens to identify suppressors of anoikis in Ewing sarcoma. The result indicated that the upregulation of IL1 receptor accessory protein (IL1RAP) significantly suppressed anoikis, which could be a new cell-surface target in *Ewing sarcoma* (Zhang et al., 2021). In a previous study, E-cadherin cell-cell adhesion was demonstrated to mediate suppression of anoikis by activating the ErbB4 tyrosine kinase in *Ewing sarcoma* (Kang et al., 2007).
Together, these findings have depicted a potential but limited role of anoikis in STS. More comprehensive studies are required to reveal the muti-omic profiling, regulator networks, microenvironments, targetable molecules, and prognostic predictors for STS. Further genotyping based on anoikis-related genes would help to understand the heterogeneity of STS, which is important to the personalized medicine. Therefore, in this study, we comprehensively analyzed the cross-talk of the anoikis-related genes (ARGs) and their molecular profiling in STS. We also focused on the impact of ARGs on tumor microenvironment, especially on the immune cell infiltration. Meanwhile, the stratification system and prognostic scoring model were established based on ARGs to guide the therapeutics for STS.
## Data collection and processing
*The* gene expression matrices of STS were downloaded from UCSC Xena (https://xenabrowser.net/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/). Normal adipose and muscle tissue sample from Genotype-Tissue Expression (GTEx) database were used as normal control (https://gtexportal.org/home/). UCSC Xena has co-analyzed the TCGA data and GTEx data using UCSC bioinformatic pipeline (TOIL RNA-seq) for gene expression comparison. The copy number variations (CNVs), somatic mutation, and clinical information were downloaded from TCGA-SARC cohort. For pan-cancer analysis, data was derived from the TARGET Pan-Cancer (PANCAN) cohort. In GEO database, we identified two cohort of STS (GSE17674 and GSE63157) with prognosis data and one dataset of single-cell RNA-seq for STS (GSE131309). Moreover, we introduced a cohort of immunotherapy, in which the patients were treated with the combination of anti-PD-1 and anti-CTLA-4 therapy (Gide et al., 2019). By using this cohort, we analyzed the association between immunotherapy response and Anoikis score.
## Unsupervised clustering of ARGs
We identified the ARGs from GOBP_ANOIKIS term of Gene Set Variation Analysis (GSVA) database (http://www.gsea-msigdb.org/gsea/msigdb/cards/GOBP_ANOIKIS). Chromosome location of ARGs was plotted by the package “Rcircos” (version 1.2.1). Next, we conducted unsupervised clustering analysis using the 34 ARGs to define distinct clusters of patients. We set the key parameters of maxK = 9 and repetitions = 1,000 for algorithm packaged in “ConsensusClusterPlus” (Wilkerson and Hayes, 2010). Further, we recognized the differentially expressed genes (DEGs) (log2|FC|≥3, adjp <0.05) between Anoikis clusters by using the R package “limma” (version 3.48.3). Univariate COX regression analysis was utilized to recognize DEGs with significant prognostic relevance in STS.
As the prognostic DEGs were identified, we further input them into unsupervised clustering analysis and stratified patients into different Anoikis subtypes. These subtypes were more applicative and accurate since the DEGs reflected more comprehensive and common gene profiling.
## GSVA and Gene Ontology (GO) annotation
For the above defined clusters or subtypes, GSVA analysis was conducted to probe their biological characteristics by using the R package “GSVA” (version 1.40.1) (Hanzelmann et al., 2013). Meanwhile, biological differences between subgroups with high and low Anoikis score were also analyzed by GSVA. The h.all.v7.5.1 and c2.cp.kegg.v7.4 gene sets were downloaded from the Molecular Signatures Database (MSigDB). The R package “limma” (version 3.48.3) was utilized when comparing the differential expressed hallmark gene sets and tested using moderated t-statistics. The results were plotted using the R package “ggplot2” (version 3.3.5). Additionally, the R package “clusterProfiler” (version 4.0.5) was adopted for GO annotation. The significant enrichment was determined by false discovery rate (FDR) < 0.05.
## Evaluation of tumor immune infiltration
To assess the immune cell infiltration in tumor microenvironment, we applied the single-sample gene set enrichment analysis (ssGSEA), the marker genes of multiple types of immune cells were downloaded from previous study (Bindea et al., 2013). Infiltration level was normalized ranging from 0 to 1. Tumor mutation burden (TMB) signatures from published data (Mariathasan et al., 2018) were utilized to estimate the association between tumor microenvironment and biological processes. Moreover, we extracted signatures related to immunotherapy-predicted pathways and cancer-immunity cycles as previously reported (Chen and Mellman, 2013; Hu et al., 2021). Specifically, the cancer-immunity cycles containing seven steps: step one and two: cancer antigen release and presentation, step three: T-cell priming and activation, step four: immune cell recruitment, step five: immune cell infiltration into tumors, step six: T-cell recognition of cancers, step seven: killing of cancer cells. These cycles were applied to guide frameworks for immunotherapy. We used GSVA to calculate the signatures scores of immunotherapy-predicted pathway and cancer-immunity cycles as previously described. The associations between Anoikis score and GSVA scores of different gene sets were compared by using the R package “ggcor” (version 0.9.4.3).
## Establishment of the anoikis scoring model
In order to applied the above findings in more patients, we next generated the anoikis scoring system based on our previous established Anoikis clusters. DEGs between Anoikis cluster C1 and C2 were identified and Univariate COX regression analysis was conducted to recognize prognosis relevant DEGs. The prognostic DEGs were then analyzed using principal component analysis (PCA) and calculated for signature scores. This method was advantageous in identification of the score of the set with most significant correlation and elimination of unrelated blocks. To calculate the Anoikis score, the formula of Σ(PC1 i + PC2 i) was applied where i was the expression of the enrolled prognostic DEGs. On this basis, patients were divided into the high and low Anoikis score group according to a cut-off value determined by the algorithm.
## Single-cell transcriptome analysis
In this study, we used a single-cell RNA-seq dataset (GSE131309) from published study (Jerby-Arnon et al., 2021). The data were analyzed following standard pipeline of the package “Seurat” (version 4.0.5). Gene expression was normalized by LogNormalize (scale factor = 10,000). 2,000 highly variable genes (HVGs) were then recognized within the function of FindVariableGenes. 25 PC were picked up based on the result of ElbowPlot. Subsequently, we performed the cell clustering and t-distributed stochastic neighbor embedding (t-SNE) to figure out the cell subpopulations. The same labels from the data resource were used for specific cell cluster annotation, as described in previous study (Jerby-Arnon et al., 2021). Expression of specific genes was illustrated in t-SNE plots.
## Prediction of chemotherapeutic sensitivity
Drug response data were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) (https://www.cancerrxgene.org/downloads/anova). The GDSC database provides the drug sensitivity data and genetic correlation for more than 1,000 genetically characterized human cell lines (Yang et al., 2013). Drug response data of 518 compounds targeting 24 pathways were identified. IC50 and drug sensitivity score were utilized to assess the chemotherapeutic sensitivity, as calculated by the R packages “pRRophetic” (version 0.5) and “oncoPredict” (version 0.2) (Iorio et al., 2016; Maeser et al., 2021).
## Cell lines and real-time PCR
The human synovial sarcoma (SW-982) and liposarcoma cell line (SW-872) were purchased from the Procell Life Science & Technology Co., Ltd. Primary human skin fibroblast cell line (HSF) was acquired from Fenghui Biotechnology Co., Ltd. The primary hSS-005R cell line was established by our laboratory. They were cultured in Dulbecco’s modified *Eagle medium* (DMEM) completed with $10\%$ fetal bovine serum (FBS) and $1\%$ Penicillin-Streptomycin at 37 °C and $5\%$ CO2.
For real-time PCR analysis of mRNA expression, 2×105 cells were cultured in six well plates for 24 h and the RNA Express Total RNA Kit (M050, NCM Biotech, China) was used for subsequent total RNA extraction. RevertAid First Strand cDNA Synthesis kit (K1622, Thermo Fisher Scientific, United States) was utilized for cDNA synthesis. For each sample, 50 ng cDNA was mixed with Hieff® qPCR SYBR Green Master Mix (11201ES03, YEASEN, China) and gene specific primers following the manufacturer’s protocol. Reactions were performed on the Applied Biosystems QuantStudio (Thermo Fisher Laboratories). Real-time PCR experiments were repeated using three biological replicates. The primer sequences were as follow: GAPDH, 5′- CAGGAGGCATTGCTGATGAT -3' (forward), 5′- GAAGGCTGGGGCTCATTT-3' (reverse); E2F1, 5′- ACGTGACGTGTCAGGACCT -3' (forward), 5′- GATCGGGCCTTGTTTGCTCTT -3' (reverse); SNAI2, 5′- TGTGACAAGGAATATGTGAGCC -3' (forward), 5′- TGAGCCCTCAGATTTGACCTG -3' (reverse); DAPK2, 5′- GGGACGCCGGAATTTGTTG -3' (forward), 5′- TTCCTGCTTCGTGTCTCCCA -3' (reverse).
## Full-length transcriptome analysis
We performed full-length mRNA-seq on four STS samples and four paired normal tissues (GSE198568). Total RNA was extracted from fresh frozen samples for full-length transcriptome analysis. The sequencing was performed by Biomarker Technologies (Biomarker Technologies Ltd., Beijing, China) following the operation protocols of Oxford Nanopore Technologies (Oxford Nanopore Technologies, Oxford, United Kingdom). Data were analyzed in accordance with the pipeline provided by Biomarker Technologies Ltd.
## Statistical analysis
R software (version 4.1.0) was used for statistical analysis. We conducted the spearman correlation test when calculating the correlations of ARGs. Student’s t-tests and Wilcoxon signed-rank test were conducted for parametric comparisons and non-parametric comparisons. Multiple groups comparisons were tested by one-way ANOVA or Kruskal–Wallis test. Log-rank test was applied in survival analysis. The prognostic factors were determined by Univariate and multivariate Cox regression. To assess the accuracy of model, Receiver operating characteristic (ROC) curves were plotted and area under the curve (AUC) was calculated by using R package “timeROC” (version 0.4). The optimal cut-off value of Anoikis scores was determined by using the package “survminer” (version 0.4.9). Besides, chi-square or Fisher exact tests was adopted to compare clinical characteristics in different groups. p-value <0.05 was defined as statistical significance.
## Pan-cancer analysis of ARGs
We first analyzed the profiling of ARGs in pan-cancer level. Copy number variance (CNV) analysis of ARGs indicated CNV gain of CVA1, E2F1, MCL1, PDK4, PIK3CA, PTK2, SNAI2, and SRC in various cancer types (Figure 1A). Significant correlation between SCNV and expression of PTK2 was found in different cancer types (Figure 1B). As reveled by survival analysis, high expression of most ARGs suggested high risk effect for LGG, LIHC, ACC and KICH but protective effect for KIRC (Figure 1C). Besides, ITGA5 and ITGB1 were risk factors for multiple cancer types (Figure 1C). Among the 34 ARGs, PIK3CA showed the highest mutation frequency in different cancer types (Figure 1D). E2F1 and CHEK2 were highly expressed across most cancer types compared to normal samples, while PDK4 and NTRK2 were decreased in various cancers (Figure 1E).
**FIGURE 1:** *Pan-cancer analysis of Anoikis-related genes (ARGs) in pan-cancer TCGA data. (A) The illustration of somatic copy number variance (SCNV) of ARGs in different cancer types. The percentage of amplification and deletion was annotated. (B) The correlation of SCNV and expression of ARGs within different cancer types. (C) The prognostic effects of the expression of ARGs across different cancer types. Red indicates the risk factor, and blue indicated the protective factor. (D) The mutation frequency of ARGs in different cancer types. (E) The expression patterns of ARGs between tumor and normal samples in different cancer types. The upper histograms illustrate the number of significantly differentially upregulated (red) and downregulated (blue) genes.*
## Genomic and transcriptional landscapes of ARGs in STS
More specifically, the ARGs were analyzed in STS cohort. Only 32 ($13.5\%$) of 237 samples showed ARGs-related mutations, concentrating within 18 ARGs (Figure 2A). Most ARGs located in chromosome 1, 9, 17, 19 (Figure 2B). The SCNV frequency of ARGs were depicted in Figure 2C. Notably, the expression profiling of 34 ARGs could discriminate against tumor and normal tissues (Figure 2D) since most of them showed significant differential expression (Figure 2E). In order to specialize the expression pattern of ARGs, we next visualized their expression in single cell transcriptomics from GSE131309 (Figures 3A, B). We noticed that ITGB1, MCL1, and SIK1 broadly expressed in all cell types while TLE1, TSC2, and SNAI2 were mainly clustered in malignant subtypes (Figures 3B, C; Supplementary Figure S1). As validated by real-time PCR, the expression of E2F1 and SNAI2 were significantly higher in STS cell lines including SW-982, hss-005R, and SW-872 compared to HSF cell line, while DAPK2 was lower in STS cell lines (Figures 3D–F). Additionally, the consistent results were identified in our own sequencing data of four pairs of STS and normal samples (Figures 3G–I).
**FIGURE 2:** *Genomic and transcriptional landscapes of ARGs in soft-tissue sarcoma (STS) in TCGA database. (A) The mutation frequency of ARGs (Top 18) in 237 patients with STS in TCGA database. (B) The specific location of ARGs on the human chromosomes. (C) The SCNV of ARGs in patients with STS in TCGA database. Red indicates CNV gain, and green indicates CNV loss. (D) The principal component analysis (PCA) of ARGs expression to identify tumor among normal samples based on the TCGA-GTEx database. Red indicates tumor samples, blue indicate normal tissues. (E) The expression of ARGs between tumor (red) and normal samples (blue) based on the TCGA-GTEx database. *, 0.01 ≤ p < 0.05; **, 0.001 ≤ p < 0.01; ***, 0.0001 ≤ p < 0.001; ****, p < 0.0001; ns, p ≥ 0.05.* **FIGURE 3:** *Validation of expression patterns of ARGs at single-cell resolution. (A) The t-distributed stochastic neighbor embedding (t-SNE) plot showing specific cell types of STS. (B) The t-SNE plots showing the expression of ARGs in different cell types. (C) The violin plots illustrating expression levels of ARGs across different cell types. (D–F) Validation of expression of ARGs between STS cell lines and the control cell line by the real-time PCR. Real-time PCR experiments were repeated using three biological replicates. (G–I) The box plots illustrating the expression of ARGs between STS and matched adjacent normal tissues based on our own sequencing data. *, 0.01 ≤ p < 0.05; ***, 0.0001 ≤ p < 0.001; ****, p < 0.0001.*
## Cross-talk of ARGs and identification of anoikis clusters
Tumor immune microenvironment is a key regulator of tumor progression, in which the immune cells cross-talk with other cell types and impact their predestination. Through correlation analysis of the expression pattern of ARGs and signatures of immune cells, we found that expression of MCL1, DAPK2, PDK4, and BRMS1 were positively correlated with most immune cells (Figure 4A). The network of 34 ARGs displayed a comprehensive landscape of the interactions (Figure 4B). Among them, most ARGs such as BMF, BCL2, ANKRD13C, AKT1, ZNF304, TSC2 showed positive correlation with other genes, but BRMS1 negatively correlated with most ARGs (Figure 4B). These findings indicated the interactive patterns of ARGs.
**FIGURE 4:** *Cross-talk of ARGs and identification of Anoikis clusters. (A) The correlation analysis of the expression of ARGs and signatures of immune cells. Red indicated positively associated and blue indicated negatively associated. (B) The correlation network of ARGs in the TCGA-SARC cohort. The significance of the prognostic effects was illustrated by the circle size. (C) The Kaplan-Meier curve comparing the survival between different Anoikis clusters. (D) The heatmap of ARGs between different Anoikis clusters. (E)The gene set variation analysis (GSVA) illustrating pathways significantly enriched between different Anoikis clusters. (F) The infiltrations of different immune cells between different Anoikis clusters. *, 0.01 ≤ p < 0.05; **, 0.001 ≤ p < 0.01; ***, 0.0001 ≤ p < 0.001; ****, p < 0.0001; ns, p ≥ 0.05.*
Further, we conducted unsupervised consensus clustering to identify distinct expression patterns of ARGs in different patients (Supplementary Figure S2). Consequently, 258 patients were clustered into two clusters by using $K = 2$ as the optimal index based on elbow method (Krolak-Schwedt and Eckes, 1992), named as C1 and C2 containing 147 and 111 patients respectively. The two clusters showed distinct prognosis ($$p \leq 0.016$$), ARGs expression patterns, and pathway enrichment patterns (Figures 4C–E), indicating the different characteristics between them. Specifically, patients of cluster C1 showed better survival and improved immune infiltration patterns (Figures 4C, F). GSVA showed that Cluster C1 were positively enriched in chemokine signaling and JAK-STAT signaling pathways (Figure 4E).
## Identification of distinct anoikis subtypes and related biological characteristics
In order to further identify distinct patient groups based on the characteristic of Anoikis clusters, we performed unsupervised consensus clustering using DEGs between cluster C1 and C2 (Figure 5A; Supplementary Figures 3A–F). As a result, three subtypes (S1, S2, S3) were identified, with the patient number of 49, 96, 113 respectively. Patients of the three subtypes were significantly different in survival (Figure 5B). Besides, the DEGs were enriched in GO terms of ribonucleoprotein complex biogenesis, RNA splicing, focal adhesion, cell-subtract junction, cadherin binding, etc. ( Figure 5C). Gene expression patterns of three subtypes were distinct but the clinical characteristics were irregular (Figure 5D). Pathway analysis of different subtypes were conducted to identify corresponding characteristics. GSVA suggested the enrichment of hedgehog signaling, basal cell carcinoma, and glycosaminoglycan biogenesis in S3 subtype (Figure 5E), while the pathways of cytosolic DNA sensing, natural killing cell mediated cytotoxicity, and cytokine-cytokine receptor interaction were enriched in S2 subtype (Figure 5F). Interestingly, subtype S2 showed higher infiltration of most immune cells compared to S1 and S2 (Supplementary Figure S3G).
**FIGURE 5:** *Identification of distinct Anoikis subtypes and related biological characteristics. (A) The volcano plot showing significantly differentially expressed genes (DEGs) between different Anoikis clusters (C2 versus C1). Genes significantly upregulated were marked in red, while genes significantly downregulated were marked in blue. (B) The Kaplan-Meier curve comparing the survival between different Anoikis subtypes. (C) Gene Ontology (GO) enrichment analysis of DEGs identified in the above resulted. BP, biological process; CC, cellular component; MF, molecular function. (D) The unsupervised clustering of TCGA-SARC cohort based on the ARGs-related DEGs. (E, F) The GSVA comparing pathways significantly enriched among distinct Anoikis subtypes.*
## Establishment and validation of anoikis score
As displayed above, the identification of Anoikis clusters (C1, C2) and Anoikis subtypes (S1, S2, S3) helped to classify patients with different gene expression patterns. Nevertheless, they were limited within the TCGA-SARC cohort. Therefore, we further established the Anoikis score based on DEGs between Anoikis clusters C1 and C2 to apply this model in external cohorts. The flow diagram was illustrated in Figure 6A. The Anoikis score was significantly different among Anoikis clusters or Anoikis subtypes (Figures 6B, C). Patients were then divided into the high Anoikis score and low Anoikis score group by an algorithm calculated cut-off value. Patients with high Anoikis score showed poor prognosis in TCGA-SARC cohort ($p \leq 0.001$) (Figure 6D). External validation using GSE17674 ($$p \leq 0.019$$) and GSE63157 ($$p \leq 0.045$$) data further confirmed this result (Figures 6E, F). The AUC also suggested the reliability of Anoikis score in 1-, 3-, and 5-year survival prediction, with the values of 0.907, 0.883, and 0.832 respectively (Figure 6G). Notably, the Anoikis score was negatively correlated with multiple types of innate immune cells and adoptive immune cells including B cells, Macrophages, and various subtypes of T cells (Figure 6H), suggesting the potential of Anoikis score in STS immune infiltration prediction. There was a slight difference in TMB between high and low Anoikis score group (Figure 6I). Additionally, groups with high and low Anoikis score showed differences in clinical characteristics including survival status ($p \leq 0.001$), gender ($p \leq 0.001$), and histology ($p \leq 0.001$), but not in age and tumor site (Figure 6J). Multivariate Cox regression analysis indicated that high Anoikis score was a significant risk factor for STS (Figure 6K; Supplementary Figure S4). Together, these findings demonstrated the reliability of our Anoikis score model in prognostic prediction for STS.
**FIGURE 6:** *Establishment and validation of Anoikis score. (A) Alluvial diagram showing the relations among Anoikis clusters, Anoikis subtypes and Anoikis score groups. (B, C) The box plots illustrating the Anoikis score in different Anoikis clusters and Anoikis subtypes. (D–F) The Kaplan-Meier curves comparing the survival between low (blue) and high (red) Anoikis score groups in TCGA-SARC cohort (D), GSE17674 (E) and GSE63157 (F). (G) The time-dependent receiver operating characteristic curve (ROC) assessing the predictive performance of Anoikis score in TCGA-SARC cohort. (H) The correlation analysis between Anoikis score and signatures of immune cells. Red indicated positively associated and blue indicated negatively associated. (I) The box plot of tumor mutation burden (TMB) between low and high Anoikis score groups in TCGA-SARC cohort. (J) The pie plots showing proportions of different clinical characteristics between low and high Anoikis score groups in TCGA-SARC cohort. (K) The forest plot illustration multi-variate Cox analysis including clinical information and Anoikis score. *, p < 0.05.*
## The genomic and transcriptional characteristics between anoikis score groups
Next, we interrogated the genomic and transcriptional profiling between high and low Anoikis score groups. We observed a higher frequency of mutation in high Anoikis score group with alteration in 66 ($75.86\%$) of 87 samples (Figure 7A), compared with low Anoikis score group with mutations in 92 ($62.59\%$) of 147 samples (Figure 7B). Noteworthily, the frequency of arm-level amplification and deletion seems to be higher in high Anoikis score group compared to low group (Figure 7E). Considering the enriched pathways in different Anoikis score groups, we found positive enrichment of pathways including G2M checkpoint, MYC targets, and E2F targets in high Anoikis score group but negative enrichment of pathways including interferon alpha response, inflammation response, and interferon gamma response (Figure 7C). This result was consistent with previous finding (Figure 6H) that high Anoikis score indicated poor immune infiltration. Moreover, we analyzed the correlation of Anoikis score with immunotherapy-predicted pathways and cancer immunity cycles. As a result, the Anoikis score was significantly negative associated with various immune cells including B cell, CD4+ T cells, CD8+ T cells, dendritic cells, etc. Meanwhile, Anoikis score was positively correlated with most immunotherapy-predicted pathways such as Base excision repair, cell cycle, and DNA replication (Figure 7D).
**FIGURE 7:** *The genomic and transcriptional characteristics between Anoikis score groups. (A, B) The differences in mutation frequency between high (A) and low (B) Anoikis score groups. (C) The GSVA illustrating significantly differently enriched pathways between Anoikis score groups. (D) The correlation analysis of Anoikis score with immunotherapy-predicted pathways and cancer immunity cycles. (E) The frequency of arm-level amplification and deletion between Anoikis score groups. (F) The Kaplan-Meier curve comparing the survival between low and high Anoikis score groups in an immunotherapy cohort. (G) The rates of clinical response between Anoikis score groups in an immunotherapy cohort. (H) The box plots showing significant differences in the estimated IC50 of several drugs between Anoikis score groups in TCGA-SARC cohort. *, p < 0.05.*
Because of the close relationship of Anoikis score and immune status, we further analyzed the Anoikis score in an immunotherapy cohort. Interestingly, patients with high Anoikis score showed poor survival ($$p \leq 0.002$$) (Figure 7F) and poor response to immunotherapy ($p \leq 0.001$) (Figure 7G). Additionally, we utilized the GDSC database to screen for drugs with different response in high and low Anoikis score groups. Surprisingly, we identified three drugs with higher IC50 in high Anoikis score group compared to low score group, namely, erlotinib ($p \leq 0.001$), GNF.2 ($p \leq 0.001$) and LFM.A13 ($p \leq 0.001$) (Figure 7H). These findings could provide potential methods for individualized immunotherapy of STS patients.
## Discussion
STS is an uncommon and heterogeneous tumor with limited treatment currently (Linch et al., 2014). Several studies have explored the genomic and transcriptomic characteristics of STS to uncover the molecular profiling and find new therapeutic targets. Anoikis, a critical process of cell death, has shown great impact on STS biology, predominantly through a mechanism of anoikis resistance, which could create a microenvironment suitable for tumor metastasis (Kang et al., 2007; Zhang et al., 2021). Although the intriguing conclusions have been made, there is a lack of comprehensive analysis and applicable predictive model for ARGs in STS. The interactions between ARGs and tumor microenvironment, especially the immune cell infiltration, have not been recognized for STS. In the present study, we conducted comprehensive analysis of the 34 ARGs in STS.
In spite of the fact that all cancers are molecularly distinct, many of them share common driver mutations or characteristics of transcriptional regulation (Ciriello et al., 2013). We first analyzed the profiles of ARGs at pan-cancer level. Several ARGs showed gain of CNVs such as E2F1, MCL1, and PIK3CA across multiple cancers. CNVs of E2F1 were reported previously in various type of cancers to be associated with cancer susceptibility (Nelson et al., 2006; Rocca et al., 2017; Rocca et al., 2019; Rocca et al., 2021). MCL1 also displayed CNVs in non-small lung cancer and uterine cervix adenocarcinoma and impact on survival of patients (Yin et al., 2016; Lin et al., 2020). Similarly, PIK3CA acquired CNVs in a wide-range of cancers which regulated the cancer progression and prognosis (Yamamoto et al., 2008; Brauswetter et al., 2016; Migliaccio et al., 2022). Interestingly, PIK3CA showed the highest frequency of mutations among all ARGs in different cancers, which was consistent with previous studies (Mei et al., 2016; Mosele et al., 2020).
In STS, mutation frequency of PIK3CA was also at the top of ARGs list, indicating its critical role in STS biology. Despite this, the overall mutation burden of ARGs in STS was relatively low. The expression of most ARGs were differentially expressed so that the expression pattern could discriminate between STS and normal tissues. Differential expression of some ARGs was further confirmed by real-time PCR and our own sequencing data. For unbiased high-resolution snapshots of gene expression programs, single-cell RNA sequencing is the preferred method. Single-cell resolved gene expression profiles offer several key advantages over bulk population sequencing (Kanev et al., 2021). Notably, by single-cell transcriptomic analysis, we found that the expression of ARGs showed cell-type specificity, e.g., ITGB1, MCL1, and SIK1 highly expressed in multiple cell types while TLE1, TSC2, and SNAI2 were predominantly identified in malignant subtypes. This characteristic could help guiding the discovery of new therapeutic targets. Single-cell transcriptomics in prostate cancer revealed the high expression of MCL1 in persistent senescent tumor cells, a kind of metabolically active cell that promoted tumor proliferation and metastatic dissemination (Troiani et al., 2022). Hence, MCL1 maybe a potential indicator for cancer malignancy.
Next, we established the clustering system for STS based on 34 ARGs by using unsupervised consensus clustering. Two clusters were recognized (C1 and C2), in which the cluster C1 was characterized by better prognosis and improved immune cell infiltration. We speculated that the distinct ARGs patterns in cluster C1 resulted in a tumor microenvironment suitable for immune cell response. As expected, pathway analysis indicated the enrichment of chemokine signaling and JAK-STAT signaling in cluster C1. Increase of chemokine contributed to the improvement of immune cell engraftment, such as T cells (Dangaj et al., 2019). The IFNγ-JAK-STAT signaling was also a determinant for chemokine expression (Xu et al., 2019). To further classify patients based on Anoikis clusters, we performed unsupervised consensus clustering based on DEGs between C1 and C2. Subsequently, three Anoikis subtypes with different characteristics were established (S1, S2, S3). We noticed that S1 showed the best prognosis while S2 was characterized by optimal immune infiltration. Compared with S3, the S1 subtype was enriched in several metabolic pathways such as histidine metabolism, tryptophan metabolism, butanoate metabolism, and adipocytokine signaling pathway. Among them, the histidine metabolism was associated with good response of cancer therapy (Frezza, 2018). However, the tryptophan metabolism and adipocytokine signaling pathway could promote cancer progression in other cancers (Rose et al., 2004; Platten et al., 2019). This inconsistent conclusion may be explained by the heterogeneity in different cancer types, further studies are required for exploration of the metabolism-related mechanisms and the cancer suppression metabolic niche in specific STS subtype. Not surprisingly, we also observed the enrichment of cytokine-cytokine receptor interaction in S2. It was reported that higher level of TMB was associated with poorer in cancer patients, and the risk scores of STS patients with higher risk score were also higher in our study, which needs further research (Valero et al., 2021).
Moreover, we built an anoikis scoring system according to the prognostic DEGs between cluster C1 and C2. The anoikis scoring system could be utilized to calculate specific score of individual patients. The system was effective in prediction of prognosis in multiple cohort which was of great potential in clinical guidance. The group of low Anoikis score showed better prognosis and immune infiltration. Similarly, the low Anoikis score group was enriched in immune-related pathway such as IL6 JAK-STAT3 signaling, TNFA signaling, complement, INFγ response, INFα response, and inflammatory response. Further, the Anoikis score may also serve as an indicator for the response of immunotherapy. Similar findings were also reported in other cancer types, as ARGs were significantly associated with TME (Guizhen et al., 2022; Zhang et al., 2023). Although the anoikis scoring system achieved good predictive performance, high intratumor heterogeneity between samples may limit further application of this tool. Besides, larger sample size is needed to validate results in the future.
## Conclusion
Taken together, this study comprehensively analyzed the anoikis profiles in STS for the first time. We unraveled the profiling and interactions of ARGs in both the pan-cancer levels and STS, figuring out the critical role of ARGs in tumor biology. The establishment of Anoikis subtypes reflected the heterogeneity of ARGs between patients regarding the prognosis and immune cell infiltration. The Anoikis scoring system further provided individualized assessment for prognosis and immune response, which could guide personalized treatment for STS.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: UCSC Xena (https://xena.ucsc.edu/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/) with accession No. GSE17674, GSE63157, GSE131309, GSE198568.
## Author contributions
LQ and WZ performed the bioinformatic analysis and wrote the manuscript; LW collected the sample and performed qRT-PCR experiments; FC and ZY organized the data; LQ, WZ, and Z-HL conceived and designed the experiments; all authors read and approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1136184/full#supplementary-material
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|
---
title: Effects of allicin on human Simpson-Golabi-Behmel syndrome cells in mediating
browning phenotype
authors:
- Uzair Ali
- Martin Wabitsch
- Daniel Tews
- Monica Colitti
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10014806
doi: 10.3389/fendo.2023.1141303
license: CC BY 4.0
---
# Effects of allicin on human Simpson-Golabi-Behmel syndrome cells in mediating browning phenotype
## Abstract
### Introduction
Obesity is a major health problem because it is associated with increased risk of cardiovascular disease, diabetes, hypertension, and some cancers. Strategies to prevent or reduce obesity focus mainly on the possible effects of natural compounds that can induce a phenotype of browning adipocytes capable of releasing energy in the form of heat. Allicin, a bioactive component of garlic with numerous pharmacological functions, is known to stimulate energy metabolism.
### Methods
In the present study, the effects of allicin on human Simpson-Golabi-Behmel Syndrome (SGBS) cells were investigated by quantifying the dynamics of lipid droplets (LDs) and mitochondria, as well as transcriptomic changes after six days of differentiation.
### Results
Allicin significantly promoted the reduction in the surface area and size of LDs, leading to the formation of multilocular adipocytes, which was confirmed by the upregulation of genes related to lipolysis. The increase in the number and decrease in the mean aspect ratio of mitochondria in allicin-treated cells indicate a shift in mitochondrial dynamics toward fission. The structural results are confirmed by transcriptomic analysis showing a significant arrangement of gene expression associated with beige adipocytes, in particular increased expression of T-box transcription factor 1 (TBX1), uncoupling protein 1 (UCP1), PPARG coactivator 1 alpha (PPARGC1A), peroxisome proliferator-activated receptor alpha (PPARA), and OXPHOS-related genes. The most promising targets are nuclear genes such as retinoid X receptor alpha (RXRA), retinoid X receptor gamma (RXRG), nuclear receptor subfamily 1 group H member 3 (NR1H3), nuclear receptor subfamily 1 group H member 4 (NR1H4), PPARA, and oestrogen receptor 1 (ESR1).
### Discussion
Transcriptomic data and the network pharmacology-based approach revealed that genes and potential targets of allicin are involved in ligand-activated transcription factor activity, intracellular receptor signalling, regulation of cold-induced thermogenesis, and positive regulation of lipid metabolism. The present study highlights the potential role of allicin in triggering browning in human SGBS cells by affecting the LD dynamics, mitochondrial morphology, and expression of brown marker genes. Understanding the potential targets through which allicin promotes this effect may reveal the underlying signalling pathways and support these findings.
## Introduction
Obesity is a complex multifactorial disease that presents a risk of death as it is associated with many noncommunicable diseases such as cardiovascular diseases, type 2 diabetes, and cancer. Since the discovery of brown adipose tissue (BAT) in the adult human body and its ability to dissipate energy, it has been of particular interest to exploit the activity of BAT as a therapeutic option to counteract obesity. In addition, the formation of thermogenic or beige adipocytes in white adipose tissue (e.g., adipocyte browning) may represent another option to increase energy expenditure [1]. In both brown and beige adipocytes oxidative phosphorylation is uncoupled from ATP production, which is due to up-regulation of uncoupling protein-1 (UCP1) [2]. To date, in vitro and in vivo studies have identified a considerable number of browning agents, such as capsaicin, resveratrol, caffeine, and fucoxanthin [3, 4]. Garlic (*Allium sativum* L.) is a popular species rich in organosulfur compounds that are useful for medicinal purposes. When garlic is chopped or crushed, alliin is released and then hydrolyzed into allicin by allicinase. Allicin in vitro breaks down into a variety of fat-soluble organosulfur compounds, including diallyl trisulfide (DATS), diallyl disulfide (DADS), and diallyl sulfide (DAS) (5–7). The high permeability of allicin through cell membranes and rapid reaction with free thiol groups promote its diverse biological and therapeutic functions [8]. Allicin is known for its antibacterial, antifungal, and antiparasitic activities [9], as well as its anticarcinogenic [10, 11] and anti-inflammatory functions [12, 13]. Allicin has also been shown to suppress cholesterol biosynthesis by inhibiting squalene monooxygenase and acetyl-CoA synthetase [14]. Methanolic extract of black garlic containing alliin, upregulated the expression of genes related to adipokines, lipolysis, and fatty acid oxidation in adipose tissue of rats fed a high-fat diet [15].
Recently, allicin was reported to promote browning in differentiated 3T3-L1 adipocytes and white inguinal adipose tissue of mice through extracellular signal regulated kinase $\frac{1}{2}$ (ERK$\frac{1}{2}$) and KLF Transcription Factor 15 (KLF15) pathways, which stimulates the expression of UCP1 through interaction with its promoter [16]. It has also been suggested that the Sirt1-PGC1α-Tfam pathway plays a role in promoting allicin-mediated BAT activity [17].
Although several mouse cell lines are available to understand the adipogenic and thermogenic regulatory networks in vitro, human cell lines are of interest to explore the molecular mechanism of browning and to identify potential dietary supplements and nutraceuticals that could induce browning. The Simpson-Golabi-Behmel Syndrome (SGBS) cell strain is commonly used as a model for the differentiation of human white adipocytes [18]. These cells retain their differentiation ability up to several generations when provided with the appropriate adipogenic differentiation medium. Based on the effect of rosiglitazone [19], a browning phenotype was observed in SGBS cells during differentiation, and RNA sequencing revealed an increase in genes involved in extracellular matrix organization and oxidative stress that may regulate adaptive thermogenesis, with an increased percentage of brown phenotype, confirming that differentiated SGBS cells gradually acquire BAT-like function from day 4 to day 10 [20].
After stimulation of browning, the formation of micro lipid droplets (LDs) has been demonstrated in response to lipolytic release of fatty acids [21]. This enables efficient intracellular lipolysis from the LD surface and subsequent promotion of free fatty acid transport to mitochondria for β-oxidation in BAT [22]. Consistent with this property, both cold exposure and adrenergic stimulation induce rapid mitochondrial fragmentation, which synergistically promote uncoupling and thus heat production [23].
Using RNAseq and quantifying the dynamics of LDs and mitochondrial morphology, the current study aims to evaluate the browning effect of allicin in vitro using the SGBS cell strain as a human primary adipocyte model in comparison to the control and cells treated with dibutyryl cAMP sodium salt (cAMP) as a positive control. To clarify the potential browning effect of allicin, a network pharmacology strategy was also performed based on the identification of potential targets.
## Chemicals and culture media
Dulbecco’s modified *Eagle medium* (DMEM)/F-12 medium (1:1) enriched with L-glutamine and 15 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), fetal bovine serum (FBS) and penicillin streptomycin solution were purchased from Gibco by Life Technologies (Thermo Fisher Scientific Inc., Waltham, Massachusetts). TRIzol reagent, PureLink™ RNA Mini Kit and SuperScript™ III one-step RT-PCR system with Platinum™ Taq DNA polymerase were purchased from Invitrogen (Thermo Fisher Scientific Inc., Waltham, Massachusetts). Rosiglitazone was purchased from Cayman Chemical (Ann Arbor, Michigan). Allicin was purchased from Solarbio Life Sciences® (Beijing, China).
All other chemicals used in the experiment and not listed above were purchased from Sigma-Aldrich (Darmstadt, Germany).
## Cell culture and treatments
Human SGBS cells were grown Dulbecco’s Modified Eagle Medium Nutrient Mixture F-12 (DMEM/F-12) supplemented with $10\%$ fetal bovine serum (FBS), 3.3 mM biotin, 1.7 mM panthotenate and $1\%$ penicillin/streptomycin solution, at 37°C, $5\%$ CO2 and $95\%$ relative humidity. Cells were platted in Petri dishes (100mm) in duplicate. Once the cells reached approximately $90\%$ confluence, differentiation was induced by feeding the cells with serum-free growth medium supplemented with 10 µg/ml transferrin, 0.2 nM triiodothyronine (T3), 250 nM hydroxycortisone, 20 nM human insulin, 25 nM dexamethasone, 250 µM 3-isobutyl-1-methylxanthine (IBMX) and 2 µM rosiglitazone (day 0 of differentiation). After 4 days, the differentiation medium was replaced with maintenance medium composed by serum-free growth medium supplemented with 10 µg/ml transferrin, 0.2 nM T3, 250 nM hydroxycortisone and 20 nM human insulin. Fresh maintenance medium was added every 2 days.
Treatment with allicin began on day 0 of differentiation (D0), and continued until analysis on day six (D06) of differentiation (Figure 1). Allicin concentrations of 5, 12.5, 25, and 50 µM were tested. Prior to treatment, allicin was diluted in dimethyl sulfoxide (DMSO) and a stock solution was prepared. Stock solutions were prepared so that the volume of DMSO in the treatment medium did not exceed $0.5\%$. Control cells (CTRL) were incubated with the same volume of $0.5\%$ DMSO in the differentiation medium for 6 days. As a positive control (cAMP), SGBS cells were treated with 500 µM dibutyryl cAMP sodium salt (a cyclic nucleotide derivative that mimics endogenous cAMP) for 24 hours before the sixth day of differentiation [24] (Figure 1).
**Figure 1:** *Cell culture treatment protocol. Cells were grown to 90% confluence. Differentiation lasted 6 days supplemented with allicin treatment. From day 4, cells were incubated with allicin in maintenance medium. Cells were incubated with cAMP for 24 hours (from day 5 to day 6 of differentiation). Sampling and analyses were performed on day 6.*
## Cell viability assay
Cell viability was determined using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cells were plated in a 96-well plate and treated with 5.0, 12.5, 25.0, and 50 µM allicin [25]. Before incubation with 5 mg/mL MTT in HBSS, cells were rinsed with 1X phosphate buffer saline (PBS) 1X. Incubation with the MTT solution was performed at 37°C for 4 hours. The resulting formazan was dissolved in dimethyl sulfoxide (DMSO) and incubated overnight (O/N) at 37°C. Optical density was used as an indicator of cell viability and was measured at 590 nm.
## BODIPY staining and confocal imaging
Cells for BODIPY™ staining and subsequent confocal imaging were cultured on ibiTreat 8-well μ-slides (Ibidi GmbH, Planegg/Martinsried, Germany). Cells were fixed in a $2\%$ formalin solution diluted in PBS 1X at room temperature (RT) for 15 minutes. Subsequently, after washing three times in PBS 1X, the cells were incubated in a solution of BODIPY™ $\frac{493}{503}$ in PBS 1X to fluorescently label the lipid droplets. Incubation was performed for 45 minutes in the dark at RT. The slides were then washed three times in PBS 1X.
Fluorescence images were acquired using a Leica SP8 confocal microscope (Leica Microsystems Srl, Milan, Italy) and LAS X 3.1.5.16308 software. Slides were viewed with the HCX PL APO lambda blue 63x/1.40 OIL objective. DAPI fluorescence was detected with a 405 diode laser ($\frac{410}{480}$ nm), while BODIPY fluorescence was detected with a white light laser ($\frac{503}{588}$ nm). Images were acquired using a photomultiplier tube (PMT) that allowed point scanning of the region of interest (ROI) with the selected laser and produced 1024 × 1024 px images.
## Morphology of LDs
The MRI_Lipid Droplets Tool (http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/LipidDroplets_Tool), a macro in the ImageJ 1.50b software (http://rsb.info.nih.gov/ij/), was used to measure LD area [26]. Individual cells were defined by regions of interest (ROIs) and images were analyzed as previously described [27]. For each cell, the LD area (in μm2), maximum Feret diameter (MFD, in μm), and integrated optical density (IOD, dimensionless) were measured. The MDF is used as a measure of the diameter of irregularly shaped objects, whereas the IOD is related to both triglyceride accumulation and the size of LDs [28].
## MitoTracker® staining
SGBS cells cultured on ibiTreat 8-well μ-slides (Ibidi GmbH, Germany) were incubated at 37°C with 100 nM MitoTracker® Orange CMTMRos (Thermo Fisher Scientific, USA) for 30 minutes. The stained cells were washed with PBS 1X and fixed with $2\%$ formalin at RT for 15 minutes. After fixation, cells were rinsed three times with PBS 1X, mounted in DAPI-containing mounting medium (Cayman Chemical Company, USA), and imaged using a Leica SP8 confocal microscope (Leica Microsystems, Germany) and LAS X 3.1.5.16308 software. Slides were viewed with the HCX PL APO lambda blue 63x/1.40 OIL objective. DAPI fluorescence was detected with a 405 diode laser ($\frac{410}{480}$ nm), while MitoTracker® fluorescence was detected with a white light laser ($\frac{550}{605}$ nm). Images were acquired using a photomultiplier tube (PMT) that allowed point-by-point scanning of the region of interest (ROI) with the selected laser and produced images with a resolution of 1024 x 1024 px.
## Mitochondrial morphology analyses
To quantify mitochondrial morphology on standard confocal fluorescence microscopy images of CTRL, ALLI, and cAMP-treated cells, the Mitochondrial Analyzer based on adaptive thresholding and the ImageJ/Fiji open-source image analysis platform were used [29]. Scale was set for magnification and the global check box in the Set Scale dialog box was selected. After 2D threshold optimization, the images were thresholded with a block size of 1,350µm and a C-value of 5. Subsequently, the images were also processed using the MiNa [30] and Micro2P [31] tools.
The Mitochondria Analyzer tool was used to measure counts (number of mitochondria in the image), total area (sum of the area of all mitochondria in the image), mean area (total area/mitochondria number), total perimeter (sum of perimeter of all mitochondria in the image), mean perimeter (total perimeter/mitochondria number), mean aspect ratio (shape descriptor measuring elongation), and mean form factor (shape descriptor measuring round to filamentous shape). In addition, parameters describing the connectivity of the mitochondrial network were calculated, including the number of branches, the total length of branches, the mean length of branches, the branch junctions, the end points of branches, and the mean diameter of branches. The branches consist of point-shaped objects without branching junctions and minimal length, long single tubular objects without branching junctions, but the highest branch length and complex objects with multiple branches and junctions. The number of branches, total branch length, branch junctions and branch end points were also expressed as normalization to either the number of mitochondria or total area [29].
The MiNa tool, a macro of the ImageJ1.53o software (http://rsb.info.nih.gov/ij/), was also used to quantify mitochondrial morphology [30]. Threshold images were processed using the Tophat option [32], as was the MiNa interface. The macro detected ‘individual’ mitochondrial structures in a skeletonized image, such as punctate, rod-shaped, and large/round structures without branching, and ‘networks’ identified as mitochondrial structures with a single node and three branches. All parameters were used in the discriminant analysis. Among the nine parameters calculated by MiNa, the number of individuals (punctate, rod-shaped, and large/round mitochondria), the number of networks (objects with at least one branch), and the mean rod/branch length, which refers to the average length of all mitochondrial rods/branches, were considered for statistical comparisons. Other parameters such as the mean number of branches per network, i.e., the mean number of mitochondrial branches per network, the mean length of branches, and the mitochondrial footprint, which refers to the total area of mitochondria, were included in the discriminant analysis.
Mitochondria were also analyzed and classified using MicroP software, a useful tool validated in CHO-K1 cells [31]. The software classifies six morphological types of mitochondria, such as small globules, round-shaped mitochondria, that may have arisen by fission; large globules with a larger area; simple tubules, i.e. straight, elongated mitochondria without branches; twisted tubules, elongated tubular mitochondria with a non-linear development; donuts, like elongated tubules mitochondria but with fused ends; branched tubules, complex interconnected mitochondria with a network-like structure. On each image, the total number of mitochondria and their area were calculated as the ratio of mitochondria and area for each subtype in the different SGBS cells treated. These data with the number of objects and total area were used for discriminant analysis.
## RNA extraction and sequencing
The experiment was set up with 2 biological replicates for the 3 experimental conditions.
After removing the culture medium from the Petri dishes, 1ml/10cm2 of TRIzol reagent was added to each plate and repeatedly pipetted to induce a severe breakdown of the cell structures. These samples were immediately processed further using the PureLink™ RNA Mini Kit according to the manufacturer’s instructions.
The concentration of extracted total RNA was quantified using a spectrophotometer (NanoDrop 1000 Spectrophotometer, ThermoScientific, Wilmington, Delaware), and the purity of the RNA samples ranged from 1.8 to 1.9. RNA integrity was assessed by observing the 18S and 28S ribosomal bands after electrophoresis on $1\%$ agarose gel, in the presence of GelRed. β-actin expression was used as an internal control, and confirmed the complete integrity of the RNA.
The purified total RNA was subjected to deep sequencing analysis. First, the isolated RNA was quantified using Agilent Bioanalyzer 2100 with the RNA integrity number (RIN) greater than 8.0 before sequencing using Illumina Genome Analyzer (GA). Generally, 2-4 ug of the total RNA was used for library construction. Total RNA was reverse transcribed into double-stranded cDNA, digested with NlaIII and ligated to an Illumina specific adapter containing a recognition site of MmeI. After MmeI digestion, a second Illumina adapter, containing a 2-bp degenerate 3’ overhang was ligated. The obtained sequences were aligned on GRCh38 human genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39) using STAR software [33].
## Data processing
Raw data were uploaded to the R package (v0.92) Differential Expression and *Pathway analysis* (iDEP951) that is a web-based tool available at http://bioinformatics.sdstate.edu/idep/[34, 35]. In the pre-processing step, genes expressed at very low levels across samples were filtered out, and genes expressed at a minimum of 0.5 counts per million (CPM) in a library were further analyzed. To reduce variability and normalizecount data, EdgeR log2(CPM+c) was chosen with pseudocount $c = 4$ transformation,. Next, the DESeq2 package in the R language was used to identify differentially expressed genes (DEG) between ALLI_ cAMP, ALLI_CTRL and cAMP_CTRL using a of false discovery rate (FDR) threshold ≤ 0.05 and fold-change > |1.0|. Heatmaps, principal component analysis (PCA), k-means cluster and enrichment analyses were also performed in iDEP951.
Gene set enrichment analysis to determine the shared biological functions of differentially regulated genes based on significant GO terms [36], Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [37] and TF. target. TRED analyses were performed [38].
Venn diagrams were created by web tool available at http://bioinformatics.psb.ugent.be/webtools/Venn/.
## Protein-protein interaction Network construction and hub genes analysis
On the basis of the online tool Search Tool for the Retrieval of Interacting Genes (STRING; https://string-db.org/), PPI networks of the up regulated and the down regulated DEGs in each comparison were generated with a confidence level of ≥ 0.4, and the PPI network was visualized using Cytoscape software (version 3.9.1, https://cytoscape.org/). Then, the PPI networks of DEGs in each comparison were analyzed using the Cytoscape CytoHubba plugin to select the top 10 hub nodes according to the Degree algorithm [39]. The Molecular Complex Detection (MCODE) plug-in [40] in the Cytoscape suite was used to examine the significant modules in the PPI network of overlapping DEGs that are the target ofshared TF networks between comparisons. Degree cutoff = 2, K-core = 2, and node score cutoff = 0.2 were set as options. Enrichment analysis of DEGs in modules with a score ≥ 5 was then performed.
## PROFAT webtool analysis
Estimation of the proportion of brown adipocytes in each sample was analyzed based on read counts using the PROFAT tool, which automatically performs hierarchical cluster analysis to predict the browning capacity of mouse and human RNA-seq datasets [41].
## Targets prediction of allicin, DAS, DADS, DATS
To identify a larger number of potential targets, PharmMapper (http://www.lilab-ecust.cn/pharmmapper/; 42, 43), the similarity ensemble approach (SEA, https://sea.bkslab.org/), the STITCH database (http://stitch.embl.de/; 44), Swiss Target Prediction (http://www.swisstargetprediction.ch/; 45), and GeneCard (https://www.genecards.org/) were used. The 2D structure and canonical SMILES of allicin (CID_65036), diallyl sulphide (CID_11617), diallyl disulfide (CID_16590), and diallyl trisulfide (CID_16315) were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The sdf files were uploaded to the PharmMapper server, and the search was started using the maximum generated conformations of 300 by selecting the option ‘Human Protein Targets Only (v2010, 2241)’ and the default value of 300 for the number of reserved matching targets. for the other parameters, the ‘default mode’ was selected. Canonical SMILES were uploaded to the other tools. The predicted targets were entered into the UniProt database (https://www.uniprot.org/) with the species set to Homo sapiens to determine their gene IDs. A Venn diagram was used to find common targets among the allicin compounds. Genes related to ‘adipocyte’, ‘browning’, ‘non shivering thermogenesis’, ‘cold-induced thermogenesis’, ‘brown adipose thermogenesis,’ and ‘adaptive thermogenesis’ were downloaded from GeneCard, and the intersection of the targets was determined using the Venn tool. The resulting common genes associated with browning and adipocytes were then crossed with common putative targets of allicin, and a Venn diagram was generated. Subsequently, the overlapping targets were uploaded to GeneMANIA (https://genemania.org/) [46] to perform functional gene analysis and generate a PPI network.
CytoNCA, another Cytoscape plugin, was applied to the network to perform topological analysis evaluating the centrality measures of the network [47]. Then, the Cytoscape intersectional merge function was used to isolate the PPI subnetworks. Key node functions were determined by analyzing GO terms, KEGG and Reactome pathways.
By entering the screened key nodes into the online tool VarElect [48], the correlation between nodes and ‘cold induced thermogenesis’ was investigated.
## Statistical analysis
All measurement results are given as means ± SD and were analyzed with XLSTAT [49]. Measurements of LD area surface/cell, MFD/cell, and IOD/cell obtained from 15 biological replicates were compared along with Mitochondrial Analyzer, MiNa, and Micro2P results using the Kruskal-Wallis statistical test, followed by pairwise comparisons using the Mann-Whitney approach with Bonferroni correction ($p \leq 0.0167$).
All Mitochondrial Analyzer and MiNa parameters, as well as ratios of parameters obtained with the Micro2P tool, were calculated together to perform a canonical discriminant analysis (DA) that integrates morphological mitochondrial parameters into a single multivariate model with the aim of maximizing differences between treatments and calculating the best discriminant components between treatments [49].
## Cell viability
To investigate possible adverse effects of allicin, a viability assay was used to investigate possible adverse effects of ALLI extract on SGBS cells treated with doses of 5, 12.5, 25, and 50 µM. In particular, the analysis showed that the viability of cells treated with ALLI extract decreased significantly ($p \leq 0.001$) in a dose-dependent manner (Figure S1), with the 50 µM dose always significantly different from the other doses. Nevertheless, viability remained above $85\%$ up to 25 µM and 12.5 µM ALLI and did not differ from 5 and 25 µM. Based on these findings, 12.5 µM ALLI was selected for further experiments.
## Allicin treatment affects the number of lipid droplets and their maximum diameter
Next, We investigated whether allicin has an effect on early adipogenic differentiation. Thus, we performed LD analysis in SGBS cells after ALLI treatment during the induction of adipogenesis. Figure 1 shows statistically significant differences in area of LDs/cell, MFD/cell, and IOD/cell between treatments and illustrates Bodipy staining in SGBS cells after 6 days of treatment with allicin (ALLI), CTRL, and dibutyryl cAMP (cAMP). The area of LDs/cell was significantly lower in cells treated with cAMP and ALLI compared with cells from CTRL ($p \leq 0.0001$). No significant differences were observed between cAMP- and ALLI-treated cells (Figure 2A). A significant ($p \leq 0.0001$) decrease in MDF/cell was observed in cells treated with cAMP and ALLI compared with CTRL ($p \leq 0.0001$) (Figure 2B), while IOD/cell was significantly lower in cells treated with cAMP compared with cells treated with ALLI ($$p \leq 0.016$$) and CTRL ($$p \leq 0.0001$$) (Figure 2C). A significant increase in the number of LDs/cell ($$p \leq 0.015$$) was observed between ALLI treated cells and CTRL cells (Figure 2D).
**Figure 2:** *Results of lipid droplet analysis performed on SGBS cells. Boxplots show the median (horizontal lines), the first to third quartiles (box), and the most extreme values with the interquartile range (vertical lines). For all comparisons, differences between treatments on SGBS cells were statistically significant using the Kruskal-Wallis test and Bonferroni correction. (A) LD area per cell; (B) Maximum Feret Diameter (MFD) per cell; (C) Optical Density Intensity (IOD) per cell; (D) number of lipid droplets per cell in treated and CTRL cells.Representative confocal images of SGBS cells treated with allicin (ALLI), dibutyryl cAMP (cAMP), or control (CTRL) 6 days after differentiation and stained with BODIPY. Nuclear staining, DAPI. Images are representative of n. 15 biological replicates. ALLI, 12.5 µg/mL allicin-treated cells; CTRL, control cells; cAMP, 500 µM dibutyryl cAMP-treated cells.*
## Allicin increases the number and area of small round mitochondria
Accurate analysis of mitochondria is critical for determining mitochondrial dynamics, so three different tools were used to characterize mitochondrial morphology. Figure S2 shows the identification and classification of objects using adaptive thresholding (radius = 1.350 µm, $C = 5$) on images of ALLI- and cAMP-treated cells and CTRL. Figures S2A, B show the effects of the different treatments measured with the three tools, on the total number and area of mitochondria in SGBS cells. No statistical differences in mitochondrial distribution patterns were detected between treatments, although treatments with allicin and cAMP caused a shift toward a greater number of mitochondria (Figure S2A) and a greater total area of mitochondria compared with cells from CTRL (Figure S2B). These results suggest that ALLI treatment increases the number of mitochondria by inducing mitochondrial fission or biogenesis and thus also increases the total area of mitochondria.
Therefore, more than 26000 mitochondria were analysed using Micro2P software to classify six morphological subtypes and calculate the average proportion of mitochondrial subtypes within each treatment (Figure 3A, B). ALLI and cAMP treatments resulted in $13.6\%$ ($p \leq 0.01$) and $11.5\%$ ($p \leq 0.05$) more small globules, respectively, compared with CTRL. ALLI treatment reduced the percentage of mitochondria with large globules to $32.5\%$ and that of the simple tube subtype to $19.5\%$ compared with CTRL ($p \leq 0.01$) (Figure 3A). Accordingly, the ratio of surface area was significantly higher ($p \leq 0.05$) in ALLI- and cAMP-treated cells compared with CTRL at 30.3 and $31.7\%$, respectively, whereas the area of large globules in ALLI-treated cells decreased significantly ($p \leq 0.05$) (Figure 3B).
**Figure 3:** *Mitochondrial analyses. Differences in the distribution of mitochondrial morphological characteristics were analysed between cells incubated with ALLI and cAMP compared with cells from CTRL. (A) Average ratio of mitochondrial subtypes within each treatment. The Kruskal-Wallis test was performed between ALLI-treated cells compared with CTRL (**p < 0.01) and cAMP-treated cells compared with CTRL (*p < 0.05). Differences between ALLI-treated cells and cAMP were also detected († p < 0.05). (B) The ratio of the surface area was significantly higher in ALLI- and cAMP-treated cells compared with CTRL by 30.3 and 31.7%, respectively (p < 0.05). The average ratio surface area of large globules showed a significant (* † p < 0.05) decrease in ALLI-treated cells by 24.9 and 28.6% compared with cAMP-treated and CTRL cells, respectively. Mitochondria features determined using the Mitochondria Analyser tool. (C) Number of mitochondria. (D) Mitochondrial size measured by mean area. (E, F) Mitochondrial shape measured by mean aspect ratio and mean form factor. Boxplots showed the difference between medians (horizontal lines), first to third quartiles (box), and the most extreme values within the interquartile range (vertical lines) between treatments. Statistical significance in the boxplots was determined by the Kruskal-Wallis statistical test with Bonferroni correction (p < 0.0167). ALLI, 12.5 µg/mL allicin-treated cells; CTRL, control cells; cAMP, 500 µM dibutyryl cAMP-treated cells.*
Specifically, the average ratio of mitochondrial amount in the different treatments was $53.4\%$ in ALLI-treated cells, $28.6\%$ in cAMP-treated cells, and $18.02\%$ in CTRL. The relative percent area of total mitochondrial content between treatments was $51.9\%$ in ALLI-treated cells, $27.8\%$ in cAMP-treated cells, and $20.3\%$ in cells from CTRL. Considering all treatments together, small globules ($65.7\%$) and simple tubules ($23.3\%$) were the most representative subtypes, followed by branched tubules ($5.42\%$), large globules ($2.3\%$), donuts ($1.7\%$), and twisting tubules ($1.6\%$). Accordingly, the percentage area of mitochondrial subtypes was $34.9\%$ for small globules, $33.9.8\%$ for simple tubules, $16.67\%$ in branching tubules, $7.4\%$ for donuts, $3.6\%$ for large globules, and $3.5\%$ for twisting tubules.
Mitochondrial Analyzer detected significantly higher numbers of mitochondria in ALLI-treated cells (Figure 3C), whereas the mean perimeter and area (Figure 3D) were significantly lower compared with cells from CTRL. No statistical differences were observed in the cells treated with cAMP. The shape of mitochondria, characterized by aspect ratio and form factor, decreased significantly under ALLI and cAMP compared with cells from CTRL (Figures 3E, F). Mitochondria Analyser tool was also used to quantify the morphological complexity of mitochondria. The mean length of branches and the total number of branches per mitochondrion were lowest in cells treated with ALLI and showed a significant difference in cells treated with cAMP (data not shown).
Network parameters calculated with MiNa confirmed a significant ($p \leq 0.05$) increase in the number of individuals (puncta, rods and large) in ALLI-treated cells compared with CTRL cells (Figure 4A). The number of networks showed no significant differences between treatments (Figure 4B), but the mean value of rod/branch length significantly decreased in cells treated with ALLI ($p \leq 0.05$) and cAMP ($p \leq 0.05$) (Figure 4C).
**Figure 4:** *Summary statistics of mitochondrial network analysis performed with the MiNa tool on SGBS cells for each treatment. (A) Number of individual mitochondria. (B) Number of networks. (C) Mean value of rod/branch length. Boxplots show the median (horizontal lines), first through third quartiles (box), and the most extreme values with the interquartile range (vertical lines). The number of individual counts was statistically different in ALLI-treated SGBS cells from CTRL cells, using the Kruskal-Wallis test and Bonferroni correction (p < 0.0167). The number of networks showed no statistical difference. The mean length of rod/branch mitochondria significantly decreased in ALLI- (p = 0.035) and cAMP-treated cells (p < 0.046) compared with CTRL. ALLI, 12.5 µg/mL allicin-treated cells; CTRL, control cells; cAMP, 500 µM dibutyryl cAMP-treated cells.*
## Mitochondrial analysis results classify treatments into non-overlapping groups
To determine the extent of treatment-specific variation in an unbiased manner, all Mitochondrial Analyzer measurements, six MiNa measurements from, and the percentage of the number and area of each mitochondrial subtype, number of objects, and total area calculated by Micro2P were integrated into a single multivariate model (DA) that maximized differences between treatments (Figure 5).
**Figure 5:** *Biplot of canonical discriminant analysis to visualize the information of the whole mitochondrial dataset in the treatments and controls. F1 and F2 are canonical discriminant functions, and the two-component model separated the treatments. Centroids are shown as yellow circles. ALLI, 12.5 µg/mL allicin-treated cells; CTRL, control cells; cAMP, 500 µM dibutyryl cAMP-treated cells.*
The model achieved clear separation of samples. With few exceptions, the treatments were grouped and divided into 3 clusters. Mitochondrial values of ALLI-treated cells were projected in the quadrant with a positive value for the F1 and F2 components, CTRL was projected in the quadrant with a negative value for F1 and a positive value for the F2 component. The projection of cAMP-treated cells was observed in the quadrant with negative value for F1 and positive value for F2. The significant morphological characteristics that distinguished the different experimental groups are shown in Table 1. This analysis showed that most of the variance between treatments in mitochondrial dynamics was based on elongation index, area of small globes, solidity of simple tubes, number of punctate and rod-shaped mitochondria (individuals), and number of simple tubes (Table 1). Discriminant functions were used to classify the treatments into the correct groups. To check the functionality and robustness of the classification model, a cross-validation test was performed, in which the success rate of correctly classified samples was $88.6\%$.
**Table 1**
| Variable | Wilk’s Lambda | F | p-value |
| --- | --- | --- | --- |
| Mean Aspect Ratio (au) | 0.746 | 5.795 | 0.007 |
| Simple tubules (%) | 0.765 | 5.224 | 0.011 |
| Mean Branch Length (µm) | 0.779 | 4.817 | 0.014 |
| Total Branch Length/area | 0.789 | 4.533 | 0.018 |
| Median Branch Length (µm) | 0.802 | 4.205 | 0.023 |
| Mean Network Size (Branches) (µm) | 0.805 | 4.115 | 0.025 |
| Mean Branch Length (µm) | 0.808 | 4.05 | 0.026 |
| Mean Form Factor (au) | 0.815 | 3.863 | 0.031 |
| Area_Large globules (%) | 0.834 | 3.392 | 0.045 |
| Mean Perimeter (µm) | 0.834 | 3.381 | 0.046 |
## RNAseq analysis
After removal of low abundance reads, the final mapping rate of the filtered transcript reads was $71.2\%$. Hierarchical clustering was performed on the initial analysis of the RNAseq results, that showed that the transcriptomic data was well-clustered according to the treatment. In addition, PCA showed the overall variability in the expression profile of the samples and treatments. Overall, there was a significant difference between CTRL and the treatments with cAMP and ALLI along the first principal component, which explained $61\%$ of the variance, with smaller differences $28\%$ along the second component (Figure S3).
Analysis of DEGs was performed between cells treated with allicin and cAMP compared to control cells and between allicin and cAMP treatments, based on a log2fold change of |1| and an FDR adjusted p value of ≤ 0.05.
iDEP951 expression analysis significantly identified ($p \leq 0.05$) 820 up regulated genes between cells treated with ALLI vs cAMP, 1417 up regulated genes between cells treated with ALLI vs CTRL and 1647 between cells treated with cAMP vs CTRL cells. Significantly down regulated ($p \leq 0.05$) genes were 640 between cells treated with ALLI compared with cAMP, 1085 genes between cells treated with ALLI vs CTRL and 1521 between cells treated with cAMP compared with CTRL cells (Supplementary Table 1).
## GO term and KEGG analysis enriches genes of treated cells in cellular respiration, mitochondrial organization, and thermogenesis
To further investigate the function of the DEGs, GO term enrichment analysis was performed. The up- and down regulated DEGs were significantly enriched in biological processes (BP), molecular functions (MF) and cellular components (CC).
In all comparisons, 30 BP, CC and MF GO terms were found to be significantly enriched (Supplementary Tables 2 – 4). Notably, BP terms such as ‘Oxoacid metabolic process’, ‘Small molecule metabolic process’, ‘Fatty acid metabolic process’, ‘Cellular respiration’, ‘Cellular lipid metabolic process’, ‘Energy derivation by oxidation of organic compounds’, ‘Monocarboxylic acid metabolic process’ were down regulated in the ALLI_cAMP comparison, but up regulated in the ALLI_CTRL and cAMP_ CTRL comparisons (Supplementary Table 2). Interestingly ‘Mitochondrion’, ‘Mitochondrial matrix’, ‘Mitochondrial inner membrane’, ‘Mitochondrial membrane’, ‘Mitochondrial envelope’, ‘Mitochondrial protein-containing complex’ CC terms were down regulated in ALLI_cAMP comparison, but up regulated in ALLI_CTRL and in cAMP_ CTRL comparisons (Supplementary Table 3).
In contrast, significantly enriched MF terms such as ‘Calcium ion binding’, ‘Cell adhesion molecule binding’, ‘Collagen binding’, ‘Extracellular matrix structural constituent’, ‘Glycosaminoglycan binding’, ‘Growth factor binding’, ‘Heparin binding’, ‘Integrin binding’ and ‘Signalling receptor binding’ were up regulated in the ALLI_cAMP comparison, but down regulated in the ALLI_CTRL and in the cAMP_CTRL comparisons (Supplementary Table 4). Interestingly, significantly enriched MF terms such as ‘Active transmembrane transporter activity’, ‘Electron transfer activity’, ‘Oxidoreductase activity’ and ‘Transmembrane transporter activity’ were up regulated in the ALLI_CTRL and in the cAMP_CTRL comparisons (Supplementary Table 4).
Figure 6 shows the results of the KEGG analysis in the form of a graphical representation of the scatter plots. Each figure shows the KEGG enrichment of 15 identified pathways for each treatment comparison with the corresponding GeneRatio, adjusted p-value, and number of enriched genes in the corresponding pathways. The GeneRatio is defined as the number of enriched candidate genes compared with the total number of annotated genes included in the corresponding pathway in the KEGG analysis. Therefore, a higher GeneRatio indicates a higher enrichment of candidate genes in the corresponding pathway. KEGG analysis showed that DEGs were significantly down regulated within the ‘PPAR pathway,’ Fatty acid metabolism, elongation and degradation, and in ‘Citrate cycle (TCA cycle)’ in ALLI_cAMP comparison. Of note, DEGs were over-expressed in pathways such as ‘cAMP signalling pathway,’ ‘Calcium signalling pathway,’ and ‘CGMP-PKG signalling pathway’ in ALLI_cAMP comparison (Figure 6). ALLI_CTRL and cAMP_CTRL had common DEGs significantly enriched in ‘Oxidative phosphorylation’ and ‘Thermogenesis’, whereas DEGs within the ‘PPAR signalling pathway’ were down regulated in ALLI vs cAMP and up regulated only in cells under cAMP treatment (Figure 6).
**Figure 6:** *KEGG pathway enrichment analysis. Dot size represents the number of genes in each KEGG pathway; p.val (adjusted p-value): Red < orange < green. ALLI_cAMP = 12.5 µg/mL allicin-treated cells vs cAMP = 500 µM dibutyryl cAMP-treated cells; ALLI_CTRL = 12.5 µg/mL allicin-treated cells vs control cells; cAMP_CTRL =500 µM dibutyryl cAMP-treated cells vs control cells. Scatter plot was drawn by http://www.bioinformatics.com.cn/srplot.*
These results suggest that the browning effect of ALLI is only evident when compared with CTRL cells, so the ALLI-cAMP contrast was not discussed further.
## Construction of PPI networks and module analysis of DEGs in cells treated with allicin and positive control indicate brown adipocyte differentiation associated with an increase in AMPK and PPARA signalling through downregulation of extracellular matrix organization
The PPIs of all up regulated and down regulated DEGs with combined scores greater than 0.4 were constructed from the three comparisons, and each entire PPI network was analysed using Cytohubba. The ten most highly regulated hub genes with a high degree of connectivity between nodes are listed in Table 2. The highly regulated hub genes in the ALLI_CTRL and cAMP_CTRL comparisons shared 6 genes such as PPARG, FASN, SREBF1, SCD, PPARGC1A, and ACLY. Both comparisons, referring to CTRL, were similarly enriched in ‘Fatty acid synthase complex,’ ‘acetyl-CoA carboxylase complex,’ ‘AMPK signalling pathway,’ ‘PPARA activates gene expression,’ ‘Regulation of small molecule metabolic process,’ and ‘Thermogenesis’.
**Table 2**
| ALLI_CTRL | ALLI_CTRL.1 | ALLI_CTRL.2 | cAMP_CTRL | cAMP_CTRL.1 | cAMP_CTRL.2 |
| --- | --- | --- | --- | --- | --- |
| ank | Gene | Score | Rank | Gene | Score |
| 1 | PPARG | 100 | 1 | FASN | 100 |
| 2 | IL1B | 87 | 2 | PPARG | 97 |
| 3 | FASN | 86 | 3 | CS | 96 |
| 4 | SREBF1 | 84 | 3 | ACLY | 96 |
| 5 | EGF | 78 | 5 | SREBF1 | 94 |
| 5 | SCD | 78 | 6 | SCD | 89 |
| 7 | APOE | 74 | 7 | PPARGC1A | 87 |
| 7 | PPARGC1A | 74 | 8 | ACADM | 86 |
| 9 | CD4 | 73 | 9 | ACACA | 79 |
| 10 | ACLY | 69 | 9 | ACO2 | 79 |
Four down regulated hub genes, such as FN1, THBS1, COL1A2, and CCN2, are common between ALLI- and cAMP-treated cells compared with CTRL cells (Table 3). Enrichment of both comparisons included ‘AGE-RAGE signalling pathway in diabetic complications’, ‘PI3K-Akt signalling pathway’, ‘Focal adhesion’, ‘ECM-receptor interaction’, and ‘TGF-beta signalling pathway’.
**Table 3**
| ALLI_CTRL | ALLI_CTRL.1 | ALLI_CTRL.2 | cAMP_CTRL | cAMP_CTRL.1 | cAMP_CTRL.2 |
| --- | --- | --- | --- | --- | --- |
| Rank | Gene | Score | Rank | Gene | Score |
| 1 | FN1 | 96 | 1 | FN1 | 179 |
| 2 | MYC | 61 | 2 | IL6 | 130 |
| 3 | CD34 | 45 | 3 | CD44 | 104 |
| 4 | THBS1 | 39 | 4 | COL1A1 | 103 |
| 5 | COL1A2 | 38 | 5 | MMP2 | 99 |
| 5 | THY1 | 38 | 6 | ERBB2 | 83 |
| 5 | HGF | 38 | 7 | THBS1 | 82 |
| 8 | LOX | 36 | 7 | CCN2 | 82 |
| 8 | DCN | 36 | 7 | CCND1 | 82 |
| 8 | CCN2 | 36 | 10 | COL1A2 | 79 |
## Genes upregulated by allicin and cAMP are enriched in the target genes of AR and PPARG, which are involved in the positive regulation of cold-induced thermogenesis and fatty acid metabolism
TRED analysis (http://rulai.cshl.edu/TRED) allows to know interaction data between transcription factors (TFs) and the promoters of their target genes, including binding motifs [38]. In the ALLI_cAMP comparison, the target genes of only 1 TF (c-MYC) were down regulated and 15 were up regulated; in the ALLI_CTRL comparison, 4 TFs were down regulated and 3 (AR, PPARA, PPAG) were up regulated; in the cAMP_CTRL, 15 were down regulated and the same 3 of the ALLI_CTRL comparison were up regulated (Supplementary Table 5).
The up regulated genes were enriched in the target of AR in all comparisons (14 genes), whereas genes enriched in the target of PPARG (37 genes) and PPARA (18 genes) were enriched only in the ALLI_CTRL and cAMP_ CTRL comparisons (Supplementary Table 5). The target genes of JUN, SP1, and TP53 were significantly down regulated in the ALLI_CTRL and cAMP_CTRL comparisons but up regulated in the ALLI_cAMP comparison (Supplementary Table 5). Down regulated DEGs were enriched in target genes of EGR1, ETS1, SMAD1, SMAD3, SMAD4, and TFAP2A in the cAMP_CTRL comparison, but up regulated in ALLI_cAMP (Supplementary Table 5).
Enrichment analysis of the 14 genes targeting to AR and common to all comparisons showed significant up-regulation of ‘Response to hormone, ‘Regulation of lipid metabolic process’, ‘Regulation of small molecule metabolic process’, ‘Cellular response to hormone stimulus’, ‘Zinc finger nuclear hormone receptor-type’ and ‘PPARA activates gene expression’. The MCODE plugin cluster analysis did not filter any cluster with a score ≥ 5 for these genes.
The 37 DEGs targeting AR that were common only between ALLI and cAMP treatments compared with CTRL cells and the 51 common DEGs targeting PPARG resulted in only one MCODE cluster. No cluster with a score ≥ 5 was found for common DEGs targeting PPARA.
Enrichment analysis revealed the potential function of genes in each module. Shared DEGs targeted by AR and PPARG and over-expressed in ALLI_CTRL and cAMP_CTRL were enriched, among others, in ‘PPARG signalling pathway’ (Figures 7A, B red), ‘Positive regulation of cold-induced thermogenesis’ (Figures 7A, B, brown), ‘Fatty acid metabolic process’, ‘AMPK signalling pathway’ (Figures 7A, B, green), ‘AMPK signalling pathway’ (Figure 7A, blue) and ‘PPARA activates gene expression’ (Figure 7B, blue).
**Figure 7:** *PPI networks identified by cluster functional analysis constructed with up- and down regulated DEG targets to TFs and overlapping to ALLI and cAMP treatments vs CTRL. The enriched pathways are marked in different colors. (A) Cluster 1 with a MCODE score of 7.83 achieved from up regulated genes target of AR. Red: genes enriched in ‘PPARG signalling pathway’ (FDR 1.82-12); brown: ‘Positive regulation of cold-induced thermogenesis’ (FDR 4.05-10); green: ‘Fatty acid metabolic process’ (FDR 3.08-09); and blue: ‘PPARA activates gene expression (FDR 4.59-08). (B) Cluster 1 with a MCODE score of 10.47 achieved from up regulated genes target of PPARG. Red: genes enriched in ‘PPARG signalling pathway’ (FDR 3.10-13); brown: ‘Positive regulation of cold-induced thermogenesis’ (FDR 4.02-13); green: ‘Fatty acid metabolic process’ (FDR 5.23-13); and blue: ‘AMPK signalling pathway’ (FDR 5.53-12). (C) Cluster 1 with a MCODE score of 13.67 achieved from down regulated genes target of SP1. Red: genes enriched in ‘Interleukin-4 and Interleukin-13 signalling (FDR 3.32-13); green: AGE-RAGE signalling pathway in diabetic complications (FDR 4.42-12); blue: ‘Extracellular matrix organization’ (FDR 7.12-12).*
DEGs targeted by TP53, JUN and SP1 were down regulated in both ALLI_CTRL and cAMP_CTRL comparisons, but up regulated in ALLI_cAMP comparison. Shared DEGs targeted by SP1 and down-expressed in ALLI_CTRL and cAMP_CTRL were enriched in ‘Interleukin-4 and Interleukin-13 signalling’ (Figure 7C, red), and in ‘Extracellular matrix organization’ (Figure 7C, red). Interestingly, DEGs targeted by SP1 were found over-expressed in ALLI_cAMP comparison. Down regulated DEGs targeted by TP53 and JUN common to ALLI_CTRL and cAMP_CTRL were enriched in ‘Interleukin-4 and Interleukin-13 signalling’ and ‘IL-18 signalling pathway’ (data not shown).
## Allicin stimulation favors the differentiation into brown adipocyte
Allicin stimulation favors the differentiation into beige adipocyte The PROFAT tool [41] generated the heatmap of marker expression starting from normalized reads counts of SGBS cells. The estimation of BAT phenotype in ALLI- and cAMP-treated cells increased significantly ($p \leq 0.0001$) in comparison to CTRL cells. In contrast, WAT phenotype decreased significantly ($p \leq 0.0001$) in ALLI- and cAMP-treated cells compared with CTRL (Figure 8). These results evidenced that SGBS cells exhibit a gene expression pattern similar to that of brown cells during 6 days of differentiation under allicin treatment.
**Figure 8:** *Statistical significance of PROFAT prediction percentage of brown and white adipocytes was determined using Euclidean distance and complete linkage on normalized gene expression values and analyzed by statistical test-t. *** = p < 0.0001 between estimated percentage of brown cells; ### = p < 0.0001 between estimated percentage of white cells. ALLI, 12.5 µg/mL allicin-treated cells; cAMP, 500 µM dibutyryl cAMP-treated cells; CTRL, control cells.*
## Identification of common candidate targets among allicin, their organosulfur compounds and browning target genes
Because allicin is rapidly converted in vitro to its related fat-soluble organosulfur compounds such as DATS, DADS and DAS, the potential targets of these compounds and of allicin were screened by computational target fishing from the PharmMapper, STITCH, Swiss Target Prediction and GeneCard databases. By overlapping the highest ranked common targets of allicin and related organosulfur compounds with the 315 overlapped ‘adipocyte-browning’ genes, 26 common targets between allicin compounds and adipocyte-browning were used to create a GeneMania network (Supplementary Table 5). The results of the analysis showed that these 26 targets correlated with 20 others and a total of 407 different links were predicted to construct a network linking these 46 genes (Figure 9A). The constructed network had $33.99\%$ physical interactions and $23.56\%$ predicted functional relationships between genes. In addiction $20.58\%$ shared the same protein domain and $13.85\%$ shared similar co-expression characteristics, other results were pathways ($5.24\%$) and colocalization ($2.77\%$) as shown in Figure 9A. The molecular functions of the top ranked targets, filtered by their FDR score, were reported as GO categories. The preliminary network illustrated that the genes, depicted by different colours in Figure 9A, were involved in ‘ligand-activated transcription factor activity’, ‘intracellular receptor signalling pathway’, ‘temperature homeostasis’, ‘regulation of cold induced thermogenesis’, ‘reactive oxygen species metabolic process’, ‘positive regulation of lipid metabolic process’, and ‘cold-induced thermogenesis’.
**Figure 9:** *Preliminary PPI network constructed with GeneMANIA. (A) Twenty-six common targets between allicin, related fat-soluble organosulfur compounds and adipocytes-browning were built as assigned based on query gene. Genes are linked by functional associated networks filtered on their FDR score. Black nodes are the query targets and the larger the node, the higher degree of the node. The stronger interaction between node, the ticker and deeper colour of the edge. (B) Core PPI subnetwork generated by intersectional merge of PPI subnetworks according to the calculated degree, betweenness, closeness, eigenvector, LAC and network average values. ESR1, Estrogen Receptor 1; PPARA,Peroxisome Proliferator Activated Receptor Alpha; NR1H3, Nuclear Receptor Subfamily 1 Group H Member 3; NR1H4, Nuclear Receptor Subfamily 1 Group H Member 4; RXRA, Retinoid X Receptor Alpha; RXRG, Retinoid X Receptor Gamma.*
## PPI subnetwork construction and identification of core targets
A topological analysis of the preliminary network was performed using the CytoNCA plugin in Cytoscape to find the core proteins that form the preliminary network. The mean the degree (17.70), betweenness (48.96), closeness (0.49), eigenvector (0.106), LAC (12.40), network (10.95) values of the preliminary network was calculated, and the nodes of the preliminary PPI network that were above this mean were sorted out to build the corresponding subnetworks. Using the intersectional merge function in *Cytoscape a* core PPI subnetwork was extracted (Figure 9B) containing 6 key nodes (ESR1, NR1H3, NR1H4, PPARA, RXRA, RXRG) and 15 edges. Among these genes, Nuclear Receptor Subfamily 1 Group H Member 4 (NR1H4) and PPARA were significantly up regulated in the ALLI_CTRL comparison of SGBS cells (Supplementary Table 1), whereas estrogen receptor 1 (ESR1), Nuclear Receptor Subfamily 1 Group H Member 3 (NR1H3) and Retinoid X receptor alpha (RXRA) are common targets of allicin and related fat-soluble organosulfur compounds.
## The effects of allicin are related to mitochondrial biogenesis and lipid catabolism through the activation of core targets transcription factors
GO terms from biological process, cellular component, and molecular functions were examinedand the most enriched GO terms from biological process were ‘intracellular receptor signalling pathway’, ‘cellular response to lipid’, ‘hormone-mediated signalling pathway’, ‘response to steroid hormone’, ‘response to lipid’, whereas the most enriched GO terms from cellular component and molecular functions were ‘transcription regulator complex’, ‘nuclear receptor activity’, and ligand-activated transcription factor activity, respectively (data not shown).
KEGG pathways were analysed with a redundancy cut-off of 0.7, 17 pathways were statistically significant (FDR < 0.05) ‘PPAR signalling pathway’, ‘Adipocytokine signalling pathway’, ‘Thyroid hormone signalling pathway’, ‘Non-alcoholic fatty liver disease, ‘Insulin resistance’ and ‘Lipid and atherosclerosis’(data not shown).
The pathways enriched by *Reactome analysis* were ‘Nuclear Receptor transcription pathway’, ‘PPARA activates gene expression’, ‘SUMOylation of intracellular receptors’, Regulation of lipid metabolism by PPARalpha’ and ‘Mitochondrial biogenesis’, which were shown as bubble plot combined with a Sankey diagram (Figure 10).
**Figure 10:** *Bubble plot combined with Sankey diagram showing statistically significant Reactome pathways and the genes within each pathway. Figure was plotted by http://www.bioinformatics.com.cn/srplot.*
In addition, the score of the 6 key nodes identified by the topological analysis was scored using VarElect. The score indicates the strength of the association between the target and the ‘cold induced thermogenesis’ phenotype (Table 4). ESR1, PPARA, NR1H3, and NR1H4 scored > 6.
**Table 4**
| Gene Symbol | Description | -Log10(P) | Score |
| --- | --- | --- | --- |
| ESR1 | Estrogen Receptor 1 | 2.35 | 14.91 |
| PPARA | Peroxisome Proliferator Activated Receptor Alpha | 2.07 | 11.56 |
| NR1H3 | Nuclear Receptor Subfamily 1 Group H Member 3 | 1.49 | 6.3 |
| NR1H4 | Nuclear Receptor Subfamily 1 Group H Member 4 | 1.63 | 6.09 |
| RXRA | Retinoid X Receptor Alpha | 1.44 | 5.89 |
| RXRG | Retinoid X Receptor Gamma | 0.55 | 0.56 |
## Discussion
As a thermogenic organ, BAT is known to enhance energy metabolism and weight loss [50], so promoting mass and activity of BAT is one of the most promising strategies against obesity. Treatment of white adipose cells with rosiglitazone or with other β-adrenergic agonists induces beige cells with similar properties as BAT [51]. Induction of the beige/brown fat cell phenotype leads not only to thermogenesis, but also to lipolysis, which facilitates energy metabolism, and mitochondrial dynamics, which precede the depolarization associated with heat dissipation [23]. A high rate of mitochondrial fragmentation and free fatty acid release promote mitochondrial uncoupling and energy expenditure [52]. Knowledge of the signalling pathways that stimulate the transition from white to beige adipocytes, could help identify effective therapeutic strategies against obesity.
The discovery of functionally active BAT in adult humans and the possible recruitment of beige adipocytes by browning of WAT have introduced the way for new potential strategies for anti-obesity agents [53].
Previous studies have shown that SGBS cells gradually acquire BAT-like function in the absence of external stimulation during different days of differentiation, suggesting that lipid droplets t dynamics, and mitochondrial morphology [27] together with a differential expression of genes involved in extracellular matrix organization and oxidative stress are related to the brown fat phenotype [20]. While it has been clearly demonstrated that the β3-adrenergic receptor (β3-AR) mediates thermogenesis in rodents [54], BAT is activated in humans by β2-AR signalling [55]. Therefore, to bypass the ADRBs receptors dibutyryl-cAMP was chosen as a positive control.
The present study demonstrated that allicin supported the transition from white to beige adipocytes in SGBS after 6 days of differentiation and that the transformation of structural cell phenotype was evidenced by the dynamic changes in the size of LDs and the shape of mitochondria similar to those observed in the positive control. Lipolysis is generally considered an essential requirement for thermogenesis in brown and beige adipocytes, and any lipolytic compound could be a potential activator of thermogenesis [56]. In HepG2 cells, allicin reduces lipid accumulation either by regulating AMPK-SREBPs and PKA-CREB signalling pathways [57] or by activating PPARA and FABP6 gene expression [58]. The effect of allicin on lipid reduction argues for PPARγ/LXRα signalling in THP-1 macrophage foam cells [59]. In the present work, ALLI and cAMP treatments decreased the area and diameter of LDs, but because the number of LDs/cell increased significantly with ALLI treatment, the lipolytic activity of allicin may have led to the formation of multilocular adipocytes, a feature of WAT browning. This is confirmed by the increased number of differentially expressed genes related to lipolysis, such as DNA fragmentation factor subunit alpha (DFFA), monoglyceride lipase (MGLL), perilipin 1 (PLIN1), patatin like phospholipase domain containing 2 (PNPLA2), lipoprotein lipase (LPL) and hormone-sensitive lipase (LIPE) in ALLI- and cAMP-treated cells (Supplementary Table 1). However, a thermogenic futile cycle of lipolysis/lipogenesis has been claimed to explain the unilocular to multilocular transformation during WAT browning [21]. In 3T3-L1 cells exposed to β-adrenergic stimulation, remodelling of LDs involves first their reduction into small LDs and then their new formation and subsequent enlargement [21]. Indeed, significant expression of negative regulators of lipolysis such as G0/G1 switch gene 2 (G0S2) and patatin like phospholipase domain containing 3 (PNPLA3) were also found in ALLI-treated SBGS cells as well as the mRNA levels of the perilipin 4 (PLIN4), diacylglycerol o-acyltransferase 1 (DGAT1), diacylglycerol o-acyltransferase 2 (DGAT2) and adipocyte glycerol transporter aquaporin7 (AQP7), (Supplementary Table 1), indicating that the cells store and export metabolites released during lipolysis. Moreover, other studies have shown that triglyceride lipolysis catalysed by PNPLA2 in mice brown adipocytes is not required to maintain body temperature during cold exposure [60, 61] and that other sources such as circulating glucose and fatty acids can balance thermogenesis [62].
During cold exposure mitochondrial reorganization and free fatty acid release synergize to facilitate uncoupling and thereby heat production [23]. Concomitantly, mitochondria acquire a spheroid morphology driven by increased fission [63]. Present results show an increased in number and area of mitochondria in cells treated with allicin, and the data was also confirmed by the reduction of elongation (mean aspect ratio) and by the change from round to filamentous shape (mean form factor) in ALLI- and cAMP-treated cells. Network parameters obtained by MiNa also show a significant decrease in mean rod/branch length in both treatments compare to CTRL cells. According to the Micro2P plugin, six different subtypes of mitochondria with the highest proportion of small globules were classified in ALLI- and cAMP-treated cells. Canonical DA further evidenced that mitochondrial parameters specifically those related to mean aspect ratio, percentage of simple tubules, mean branch length, accurately clustered differentially treated cells and CTRL cells.
The high content of organosulfur compounds in garlic suggests that many of its active compounds may have anti-adipogenic effects by promoting the expression of genes specific for brown adipocytes [64]. Recent data showed that allicin promotes browning of 3T3-L1 mouse adipocytes and iWAT by inducing the expression of brown marker genes through KLF15 signalling [16] or through the SIRT1-PGC1α-TFAM pathway [17].
PCA analysis based on reads clearly grouped the data set on the first component between CTRL cells and cells treated with ALLI or cAMP. On the second component, the treatments are separated, but the replicates of the same point were very close, indicating robust reproducibility of the data.
Classical thermogenesis is activated by adrenoreceptors that promote cAMP synthesis for PKA activation and expression of downstream targets [65]. Intracellular cAMP levels are maintained by a balance between the rate of synthesis mediated by adenylate cyclase and the rate of degradation regulated by cAMP phosphodiesterase 3 (PDE3). Allicin is known to increase intracellular cAMP by inhibiting phosphodiesterase activity in isolated human platelets [66, 67] or by increasing adenylate cyclase activity in the human bronchial epithelial cell line [68]. In adipose tissue, PDE3 inhibitors increase intracellular cAMP levels, thereby enhancing lipolysis [69]. The present results showed a significant up-regulation of PDE3B in ALLI_CTRL and cAMP_CTRL (Supplementary Table 1), resulting in an increase in intracellular cAMP and downstream genes involved in lipolysis, such as LIPE and PLIN1, and in browning, such as TBX1 and UCP1 (Supplementary Table 1). This is consistent with the results obtained in adipose tissue of mice fed a high-fat diet when treated with cilostazol, a selective inhibitor of phosphodiesterase III with multiple effects on metabolism [70]. In addition, cilostazol, which has antiplatelet, antithrombotic, and vasodilatory properties similar to allicin, increased the intracellular concentration of cAMP, which stimulated the expression of thermogenic and brown specific genes [70]. The BP GO terms enrichment, such as cellular respiration and cellar lipid metabolic process as well as CC GO terms related to mitochondria were significantly up regulated in ALLI- and cAMP-treated cells compared with CTRL cells, suggesting similar activity in cells with different treatments, but was opposite when ALLI-treated cells were compared with cAMP-treated cells. MF GO terms, such as oxidoreductase activity, were up regulated in ALLI- and cAMP-treated cells compared with CTRL cells, but down regulated in ALLI-treated and cAMP-treated cells. Therefore, the positive browning effect of ALLI treatment was evident only in comparison with CTRL cells, but not in comparison with cAMP incubation. However, ALLI and cAMP treatments shared the most up regulated hub genes such as PPARG, FASN, SREBF1, SCD, PPARGC1A, and ACLY, which are related to fatty acid metabolic process, fatty acid oxidation and response to cold. The lowest down regulated hub genes common to ALLI and cAMP treatments FN1, THBS, COL1A2 and CCN2 were enriched in ECM receptor interactions, integrin cell surface interactions and focal adhesion. This is consistent with the down regulation of collagen, integrin and laminin genes (COL1A1, COL1A2, ITGA2, ITGA3, ITGA4, ITGA5, LAMA1, LAMA2, LAMA3; Supplementary Table 1) observed in SGBS cells during differentiation [20] demonstrating their ability to adjust cytoskeletal reorganization according to their size, LDs dynamics and thermogenesis [71].
KEGG pathway enrichment confirmed that oxidative phosphorylation, thermogenesis, and fatty acid metabolism were the most significantly up-regulated pathways in the ALLI_CTRL and cAMP_CTRL comparisons, whereas ECM-receptor interaction, PI3K-Akt signalling pathway and Focal adhesion were downregulated. In contrast, pathways related to PPAR and fatty acid metabolism were significantly downregulated in the ALLI_ cAMP comparison.
Interestingly, an in vivo study suggests that the allyl-containing sulphides of garlic significantly enhance thermogenesis and increase epinephrine and norepinephrine levels in rat plasma [72], which is why allicin may interact with the adrenergic receptor (AR), which is one of the most effective mechanisms to deplete excess energy through cAMP/PKA-dependent signal transduction [73]. In the present study, up regulated DEGs common to ALLI_CTRL and cAMP_CTRL comparisons and the targets of transcription factor AR were significantly associated with ‘PPARG signalling pathway’, ‘positive regulation of cold-induced thermogenesis’, ‘fatty acid metabolic process’ and ‘PPARA activates gene expression’. All of these metabolic pathways and processes share the genes for fatty acid translocase (FATP or CD36), acyl-coA synthetase long chain family member 1 (ACSL1) and carnitine palmitoyltransferase II (CPT2), each of which is involved in the storage and recycling of fatty acids, their conversion to acyl-CoA and transport to mitochondria [74]. Their co-expression is clearly part of the thermogenesis programme. In 3T3-L1 adipocytes, CD36 has been found to play an important lipolytic role [75] and its translocation from the cell membrane to lipid droplets mediates the release of long-chain fatty acids by exocytosis [76]. In human macrophages aged garlic extract inhibits CD36 expression through modulation of the PPARγ pathway [77], but in SGBS cells, its over expression together with that of LPL, and aquaporin 7 (AQP7) (Supplementary Table 1) can lead to triglycerides uptake and then lipolysis associated with heat production [78]. In addition, CD36 has been found to be a scavenger receptor required for coenzyme Q (CoQ10) uptake in BAT and therefore essential for adaptive thermogenesis and BAT morphology, [79]. Of note, CoQ10 is up regulated in ALLI-treated SGBS cells (Supplementary Table 1).
ACSL1 and CPT2 have been shown to be required for fatty acid oxidation for cold-induced thermogenesis [80]. Interestingly, all of these genes are downstream targets of the nuclear transcription factor PPARA, which is expressed in metabolically active tissues such as brown adipose tissue [81]. In contrast, down regulated DEGs targets of SP1 and other TFs, such as TP53 and JUN, were involved in ‘Interleukin-4 and Interleukin-13 signalling’ and ‘Extracellular matrix organization’, ‘Cytokine-mediated signalling pathways’ and ‘IL-18 signalling pathways’, as previously described in SGBS cells [20]. In particular, the down regulation of fibronectin (FN1), collagen type I alpha 1 chain (COL1A1), collagen type I alpha 2 chain (COL1A2) is associated with that of the zinc finger transcription factor early growth response-1 (EGR1) (Supplementary Table 1) and, in mice, with a concomitant increase of beige cells differentiation and a decrease in genes encoding the extracellular matrix proteins [82]. The down regulations of cell-surface glycoprotein CD44 and its receptor ONP in SGBS cells treated with allicin and cAMP (Supplementary Table 1) further confirms the browning activity of the compounds present in garlic. CD44 was recently recognized as a major receptor for an extracellular matrix component that plays an essential role in promoting obesity and diabetes [83].
Brown features were also confirmed by PROFAT analysis, which revealed a significant increase in $80\%$ of genes related to BAT phenotype.
Using open-source tools, computational target fishing facilitates the investigation of biological targets of bioactive molecules using the reverse pharmacophore mapping approach [84] (Supplementary Table 6). To understand potential targets involved in the browning process six major targets ESR1, NR1H3, RXRA, PPARA, NR1H4, and RXRG were extracted from the comparison of targets of allicin and related organosulfur compounds with browning genesafter topological analysis. The targets were strongly associated, and enrichment analysis confirmed the involvement of these genes in limiting cholesterol uptake, lipolysis and mitochondrial biogenesis, all processes in which allicin plays a role. The lipolytic role of allicin may be related to the activation of PPARA through the release of fatty acids. RXRA forms heterodimers with PPARA to regulate the expression of genes involved in fatty acid oxidation (ACOX1, ACADM, CYP4A1, HMGCS2), fatty acid transport (CD36, SLC27A1, CPT2), and lipid storage (PLIN) [85], that were over expressed by ALLI treatment (Supplementary Table 1). This is consistent with the activation of PPARA promoted by allicin in palmitic acid-loaded HepG2 cells [58]. Again, garlic essential oil significantly up regulated PPARA expression level in the liver of HFD-fed mice compared with control mice [86]. Moreover, PPARA was found to be associated with the expression of superoxide dismutase (CuZn-SOD) in human aortic endothelial cells [87], a scavenger of ROS, which is consistent with the antioxidant properties of allicin. Of note, ESR1 is known to induce a selective beiging in 3T3-L1 cells leading to ATGL-mediated lipolysis [88]. Moreover, in human and mouse adipocytes ESR1 promotes mitochondrial remodelling and thermogenesis through uncoupled respiration by regulating the mitochondrial gene POLG1 [89]. All of downstream genes of these metabolic pathways, such as SOD1, ATGL, and POLG were significantly expressed in SGBS cells (Supplementary Table 1).
## Conclusion
Overall, this study supports the modulatory role of allicin in stimulating the brown phenotype of SGBS cells, which is associated with an increase in mitochondrial biogenesis and lipid catabolism. The possible mechanism of this interesting process may be based on the partial interaction of allicin within the regulatory steps of cAMP signalling and PPARA signalling.
However, the study has some limitations, because neither down-regulation of SIRT5 nor significant up-regulation of KLF15, as recently reported, was detected in SGBS cells. The mechanism by which allicin promotes browning and induces mitochondrial biogenesis is not yet fully elucidated, and functional studies could be performed to further investigate the browning effect.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Author contributions
UA and MC designed the study. UA performed the experiments and collected data. MC collected data measurements, performed statistical analyses prepared the figures and wrote the manuscript. MW and DT provided SGBS cells and were involved in the revision of the paper. All authors read and approved the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The handling editor EKK declared a past co-authorship with the author MW.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1141303/full#supplementary-material
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|
---
title: Prediabetes is associated with loss of appendicular skeletal muscle mass and
sarcopenia
authors:
- Shuying Li
- Jiangfeng Mao
- Weihong Zhou
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10014813
doi: 10.3389/fnut.2023.1109824
license: CC BY 4.0
---
# Prediabetes is associated with loss of appendicular skeletal muscle mass and sarcopenia
## Abstract
### Background
Decreasing mass and metabolism in skeletal muscle are associated with increasing insulin resistance (IR) and type 2 diabetes mellitus (T2DM). The causal relation between sarcopenia and abnormal glucose metabolism may be bidirectional. This investigation is aimed to explore the detailed correlation between pre-diabetes and sarcopenia in United States (US) adults.
### Methods
A total of 22,482 adults aged ≥20 years in the National Health and Nutrition Examination Survey (NHANES) were included. Generalized linear models were conducted to examine associations between diabetes status, serum glucose, glycohemoglobin (HbA1c), and sarcopenia. Generalized additive models and smooth fitting curves were used to examine the non-linear relationship between HbA1c and ASMBMI. Sarcopenia was defined as ASMBMI (appendicular skeletal muscle mass/body mass index) < 0.789 for males, and <0.512 for females based on the cut-off values of the Foundation for the National Institutes of Health (FNIH) Sarcopenia Project.
### Results
After fully adjusting for multiple covariates, sarcopenia was directly correlated with pre-diabetes [OR ($95\%$CI) = 1.230 (1.057, 1.431), $$p \leq 0.008$$] and T2DM [OR ($95\%$CI) = 2.106 (1.625, 2.729), $p \leq 0.001$]. In non-T2DM population, HbA1c was negatively correlated with ASMBMI [β ($95\%$CI) = −0.009 (−0.013, −0.005), $p \leq 0.001$]. The correlations only persisted in males. Furthermore, in male non-T2DM population, the association of HbA1c and ASMBMI presents an inverted U-shape curve with an inflection point of HbA1c $5.2\%$.
### Conclusion
Pre-diabetes is associated with increased risk of sarcopenia. HbA1c is an independent risk factor for loss of appendicular skeletal muscle mass and sarcopenia when HbA1c greater than $5.2\%$ in the male non-T2DM population.
## Introduction
Sarcopenia, defined as a syndrome caused by the continuous loss of skeletal muscle mass, strength, and function, has become a serious concern, especially in the elderly [1]. Aging, reduction of activity, neuromuscular dysfunction, and changes in aging-related hormones (insulin, growth hormone, sex hormone, glucocorticoid) are all involved in the development of sarcopenia [2]. Although sarcopenia is considered an inevitable manifestation of aging, the severity is variable depending on various factors affecting muscle metabolism [3]. Lack of exercise or a sedentary lifestyle was the foremost risk factor for sarcopenia. Muscle fiber and strength decline are more obvious in individuals who lack of exercise than who are physically active [4]. Age-related decreases in hormone concentrations, such as growth hormone, thyroid hormone, testosterone, and insulin-like growth factor, make for loss of muscle mass and strength [3]. The motor nerve cells also decrease with age which is responsible for sending signals from the brain to the muscles to initiate movement [3, 5]. Sarcopenia negatively affects the quality of life for the elderly, since it causes motor dysfunction, higher risks of falls and fractures, and disability to independent life [6]. It is important to identify and prevent sarcopenia in susceptible populations.
Epidemiological studies have found that T2DM is an independent risk factor for sarcopenia. A meta-analysis revealed that the risk of sarcopenia in diabetes individuals was $109\%$ higher than in patients without diabetes [7]. Insulin resistance (IR) in skeletal muscle is reported to mediate the link between sarcopenia and T2DM [8]. pre-diabetes is characterized by impaired fasting glucose or impaired glucose tolerance [9]. There are few studies to clarify the association between pre-diabetes and sarcopenia. Therefore, the purpose of this study is to explore [1] whether pre-diabetes is an independent risk factor for sarcopenia and [2] the relationship between HbA1c, serum glucose, and muscle mass.
## Data sources
The NHANES (National Health and Nutrition Examination Survey) is a population-based cross-sectional survey designed to collect information on the health and nutrition status of adults and children in the United States. Most of the data in NHANES is freely accessible to researchers worldwide. This investigation pooled data from 1999 to 2006 and 2011 to 2018.
## Study population
A total of 80,630 individuals were enrolled in NHANES from 1999 to 2006 and 2011 to 2018. Participants ($$n = 37$$,702) with age <20 years were excluded. Other participants were excluded due to lack of sociodemographic status ($$n = 98$$), questionnaire ($$n = 7451$$), physical examination [10,683] and laboratory data ($$n = 1$$,903). Individuals with diabetes onset age <30 years ($$n = 311$$) was also excluded to minimize the confounding from type 1 diabetes mellitus. Finally, a total of 22,482 subjects were included for data analysis. See details in Figure 1. The survey protocol was approved by the Institutional Review Board of the Centers for Disease Control and Prevention (CDC). Written consent informs were obtained from all participants.
**FIGURE 1:** *Flow chart of participants included in the study.*
## Outcomes
The primary outcomes were sarcopenia and appendicular skeletal muscle mass (ASM) adjusted by body mass index (BMI) (ASMBMI). ASM was calculated by the sum of muscle mass in arms and legs measured by dual-energy X-ray absorptiometry (DXA) of the whole body by well-trained technicians. Sarcopenia was defined as ASMBMI <0.512 for females and <0.789 for males. The cut-off values were based on the Foundation for the National Institutes of Health (FNIH) sarcopenia project criteria [10]. Participants with weigh over 450 pounds or height over 192.5 cm were not measured by DXA.
## Exposure
Pre-diabetes, diabetes status, HbA1c, and serum glucose were Exposures. T2DM was defined as a self-report of diabetes (ever told by a doctor that had diabetes) or HbA1c = $6.5\%$ or serum glucose = 7.0mmol/L. Pre-diabetes was defined as HbA1c [5.7, 6.5)%, or serum glucose [5.6, 7.0) mmol/L in the non-DM population. Others were defined as having normal glucose [11]. Serum glucose and HbA1c were obtained for the laboratory part in the NHANES.
## Covariates
Covariates include five parts, which are sociodemographic variables, questionnaire data, examination data, dietary data, and laboratory data. Sociodemographic variables include age, gender, race, and education level. Hypertension (HTN), diabetes, alcohol use, cigarette use, vigorous activity status, and medicine use to lower blood glucose and cholesterol were included in the questionnaire part. Total energy intake was obtained from dietary data (24 h dietary recall interviews). Laboratory data-related variables include total cholesterol (TC), triglyceride (TG), alanine aminotransferase (ALT), serum uric acid (SUA), and Serum creatinine (SCr). Examination data-related variables include body mass index, blood pressure [systolic blood pressure (SBP) and diastolic pressure (DBP)], and total fat percentage (TFP) which was assessed by DXA of the whole body. HTN comes from three aspects: self-reported to be HTN or SBP = 140 mmHg or DBP = 90 mmHg. All data can be found in the NHANES project.1
## Statistical analyses
All analysis was performed by the R project2 and Empower Stats.3 NHANES sample weights were used as recommended by the National Center for Health Statistics (NCHS). p-Values < 0.05 were considered to be statistical significance. The baseline characteristics were shown as survey-weighted mean ($95\%$ CI) for continuous variables and survey-weighted percentage ($95\%$ CI) for categorical variables, respectively. The difference of ASM/BMI and prevalence of sarcopenia in different age groups with different diabetes status was calculated by analysis of variance. The association between diabetes status, HbA1c, or serum glucose and sarcopenia, or ASMBMI were evaluated by generalized linear models to estimate theirs βs and their $95\%$ CIs. The models were adjusted by the covariates such as age, race, gender, education level, BMI, HTN, anti-glycemic medicine and cholesterol, TG, TC, ALT, SUA, alcohol and cigarette use, TFP, and energy intake. Smooth fitting curve conducted by the generalized additive model was used to explore the non-linear relationship between HbA1c and ASMBMI in non-T2DM male population, and the covariates above except for gender were also adjusted.
## Results
The weighted characteristics of the population included in the study was shown in Table 1. The prevalence of sarcopenia was $11.67\%$ (2,$\frac{623}{22}$,482), however, the weighted percentage prevalence was 8.15 (7.61,8.71)% in the whole population. The prevalence of sarcopenia in normal, pre-DM, and T2DM groups were 5.41 (4.91, 5.95)%, 11.80 (10.65, 13.05)%, and 22.37 (20.26, 24.63)%, respectively ($p \leq 0.001$). The differences still existed in both sexes. See details in Table 2. ASMBMI in normal, pre-DM, and T2DM groups were 0.82 (0.82,0.83), 0.79 (0.78,0.80), and 0.74 (0.73,0.75) ($p \leq 0.001$). The differences also existed in both sexes. See details in Table 3. In different grades for ages (20–40, 40–50, 50–60, 60–70, >70 years), the prevalence of sarcopenia in the pre-DM and T2DM groups was higher than in the normal group in both sexes. In different grades for ages (20-40, 40-50, 50-60,60-70, > 70 years), ASMBMI in pre-DM and T2DM groups was lower than T2DM group in both sexes. See details in Figure 2.
## The association between sarcopenia, ASMBMI, and diabetes status
After fully adjusting for multiple covariates, the pre-diabetes [OR ($95\%$CI) = 1.230 (1.057, 1.431), $$p \leq 0.008$$] and T2DM [OR ($95\%$CI) = 2.106 (1.625, 2.729), $p \leq 0.001$] are independent risk factor for sarcopenia in the whole population. In both sexes, pre-diabetes and T2DM are risk factors for sarcopenia. Similarly, both pre-diabetes and T2DM were associated with lower ASMBMI than the normal group [β ($95\%$CI) = −0.007 (−0.010, −0.004), $p \leq 0.001$ for pre-diabetes, and β ($95\%$CI) = −0.021 (−0.027, −0.016), $p \leq 0.001$for TDM]. In males, pre-diabetes and T2DM were associated with lower ASMBMI. In females, T2DM, not pre-diabetes, was associated with lower ASMBMI. See details in Table 4.
**TABLE 4**
| Exposure | Exposure.1 | OR (95%CI) p-value | β (95%CI) p-value |
| --- | --- | --- | --- |
| | | Sarcopenia as outcome | ASMBMI as outcome |
| | Normal | Reference | Reference |
| Total | Pre-diabetes | 1.230 (1.057, 1.431)0.008 | −0.007 (−0.010, −0.004) < 0.001 |
| Total | T2DM | 2.106 (1.625, 2.729) < 0.001 | −0.021 (−0.027, −0.016) < 0.001 |
| Male | Pre-diabetes | 1.215 (1.014,1.455)0.038 | −0.009 (−0.014, −0.005) < 0.001 |
| Male | T2DM | 2.268 (1.546,3.327) < 0.001 | −0.028 (−0.038, −0.019) < 0.001 |
| Female | Pre-diabetes | 1.253 (1.013,1.551)0.040 | −0.003 (−0.007,0.001)0.117 |
| Female | T2DM | 1.950 (1.309,2.904)0.001 | −0.008 (−0.015, −0.001)0.021 |
## Association between HbA1, serum glucose and ASMBMI
After fully adjusting for multiple covariates, ASMBMI was negatively correlated with HbA1c [β ($95\%$CI) = −0.004 (−0.006, −0.002), $p \leq 0.001$] in the whole population. The correlation existed only in males [β ($95\%$CI) = −0.007 (−0.009, −0.004), $p \leq 0.001$]. After fully adjusting for multiple covariates, ASMBMI was negatively correlated with HbA1c in the non-T2DM population. After stratified by gender, the correlation only existed in males. After fully adjusting for multiple covariates, serum glucose was negatively correlated with ASMBMI [β ($95\%$CI) = −0.001 (−0.002, −0.001), $$p \leq 0.003$$]. The correlation only existed in males. The correlation disappeared in T2DM and non-DM. see details in Table 5.
**TABLE 5**
| Exposure | β (95%CI) p-value | β (95%CI) p-value.1 | β (95%CI) p-value.2 |
| --- | --- | --- | --- |
| | Total | T2DM | Non-DM |
| HbA1c (%) | HbA1c (%) | HbA1c (%) | HbA1c (%) |
| Total | −0.004 (−0.006, −0.002) < 0.001 | 0.000 (−0.002,0.002)0.950 | −0.009 (−0.013, −0.005) < 0.001 |
| Male | −0.007 (−0.009, −0.004) < 0.001 | 0.000 (−0.003,0.004)0.872 | −0.015 (−0.021, −0.009) < 0.001 |
| Female | 0.000 (−0.002,0.002)0.985 | 0.000 (−0.002,0.003)0.796 | −0.001 (−0.006, 0.004)0.717 |
| Serum glucose (mmol/L) | Serum glucose (mmol/L) | Serum glucose (mmol/L) | Serum glucose (mmol/L) |
| Total | −0.001 (−0.002, −0.001)0.003 | −0.000 (−0.001,0.001)0.513 | −0.001 (−0.003,0.002)0.683 |
| Male | −0.002 (−0.003, −0.000)0.010 | −0.000 (−0.002,0.002)0.828 | 0.000 (−0.003,0.004)0.862 |
| Female | −0.001 (−0.002,0.000)0.219 | −0.001 (−0.002,0.001)0.286 | −0.001 (−0.004,0.003)0.670 |
Smooth fitting curve conducted by the generalized additive model was used to explore the non-linear relationship between HbA1c and ASMBMI in non-T2DM male population. There is an inversely U-shaped relationship between HbA1c and ASMBMI (Figure 3). Two piecewise models found an inflection point at $5.2\%$ in HbA1c. Before the inflection point, the HbA1c seems positively correlated with ASMBMI but without statistical significance ($$p \leq 0.590$$). After the inflection point, HbA1c was negatively correlated with ASMBMI ($p \leq 0.001$). The log-likelihood ratio between a standard model and two piecewise models was of statistical significance (p-value < 0.001). See details in Table 6.
**FIGURE 3:** *Smooth fitting curve reflecting the relationship between HbA1c and ASMBMI in male non-T2DM population. Adjusted for age, race, BMI, HTN, alcohol use, cigarette use, vigorous activity status, education level, TC, TG, SUA, SCr, ALT, TBP, taking medicine for lowing cholesterol, and total energy intake. HbA1c below $5.2\%$ may indicate malnutrition, which is associated with less muscular mass. HbA1c over $5.2\%$ may indicate over energy storage and abnormal glycemia metabolism. ASM, appendicular skeletal muscle mass. ASMBMI = ASM/BMI.* TABLE_PLACEHOLDER:TABLE 6
## Discussion
This study firstly revealed that pre-diabetes was associated with loss of appendicular skeletal muscle (ASM) mass, and it increases risk of sarcopenia in US adults. It suggests that decreasing ASM occurs in the early stage of abnormal glucose metabolism and more attention should be paid to identify and prevent sarcopenia. HbA1c was the independent risk factor for sarcopenia in the male non-DM population and ASM tended to decrease when the HbA1c exceed $5.2\%$.
The prevalence of sarcopenia in T2DM can be as high as $30\%$ [12]. The strong relationship between sarcopenia and glycometabolism can be explained by several mechanisms. First, insulin plays an anabolic role in skeletal muscle. The impairment in insulin action may intervene protein synthesis and decrease muscle mass and strength [13]. Second, chronic hyperglycemia promotes the accumulation of advanced glycosylation end products (AGEs) in skeletal muscle which is related to the reduction of grip strength, leg extension strength, and walking speed [14]. Third, increasing inflammatory cytokines may contribute to the reduction of muscle mass and strength [15]. Forth, complications of T2DM are also related to sarcopenia. Chronic renal insufficiency is closely related to sarcopenia. Diabetic retinopathy may lead to decreased exercise, and then muscle mass decline. Diabetic peripheral neuropathy also can impair muscle strength [12]. Effects of pre-diabetes on muscles may share the same mechanisms with diabetes such as insulin resistance, the increase of inflammatory cytokines, and accumulation of AGEs [16, 17].
Some studies have explored the relationship between pre-diabetes and grip strength or muscle mass. A study conducted by Shan Hu etc., found greater grip strength was associated with lower incidence of pre-diabetes in Chinese adults [18]. A prospective cohort study showed that a higher level of handgrip strength promise a lower risk of pre-diabetes [HR $95\%$ CI = 0.38 (0.21–0.71)] among adults in 2 years follow-up [19]. Besides, Srikanthan found that every $10\%$ increase in skeletal muscle index (SMI) was associated with $12\%$ reduction in pre-or overt diabetes [20]. Few studies had been conducted to investigate the correlation between pre-diabetes and sarcopenia. A recent study conducted in Japan revealed increasing sarcopenia rate in pre-diabetic older men, but not women [21]. Focusing on the whole US population, our study found that pre-diabetes was an independent risk factor for sarcopenia.
The results of association between HbA1c and loss of ASM or sarcopenia was not consistent. It was reported that a higher HbA1c level (> $8\%$) was associated with lower skeletal muscular index (SMI) (OR = 5.35, $p \leq 0.001$) compared with lower HbA1c (<$6\%$) [22]. However, another study found that male patients with sarcopenia had lower HbA1c than those without sarcopenia (6.5 vs. $7.1\%$) [23]. Our investigation focused on the glucose metabolism and ASM in the non-T2DM male population. The serum glucose showed statistical significance with loss of ASM only in the whole population [β ($95\%$CI) = −0.001 (−0.002, −0.001), $$p \leq 0.003$$]. However, the association did not exist in both sexes and T2DM or not. HbA1c was negatively correlated with ASMBMI [β ($95\%$CI) = −0.015 (−0.021, −0.009), $p \leq 0.001$] in males. Furthermore, a non-linear relationship between HbA1c and ASMBMI was found that when HbA1c exceeds $5.2\%$, the skeletal muscle mass tends to decline. It is presumed that HbA1c below $5.2\%$ may indicate a state of malnutrition, which may cause loss of skeletal muscles. It was reported that HbA1c levels were significantly decreased in young adult malnourished patients without disease [24]. Low concentrations of HbA1c was a significant predictors of poor nutritional status (defined by ≥1 micronutrient deficiency in blood of vitamins B6, B12, and C; selenium; or zinc) [25]. Although previous studies have shown that people with lower HbA1c levels have higher prevalence of malnutrition, weight loss and other complications [26], our research couldn’t reach a definite conclusion since there is no statistically significant difference for the relationship between ASM/BMI and HbA1c when HbA1c <$5.2\%$. On the other hand, HbA1c over $5.2\%$ indicates a state of insulin resistance and poorly controlled diabetes, which may cause skeletal muscle decrease by the mechanism mentioned above.
Our study found that the effects of pre-diabetes on ASMBMI seemed more remarkable in males than in female [β ($95\%$CI) = −0.009 (−0.014, −0.005) in males vs. −0.003 (−0.007, 0.001) in females]. Similarly, the effect of HbA1c on ASMBMI was detected only in males. Such a gender differences were also seen in other investigations. A cohort study, the English Longitudinal Study of Ageing, shown that diabetes was a risk factor for sarcopenia only in males, not in females [OR$95\%$CI = 2.43 (1.50, 3.95) in males vs. 1.49 (0.83, 2.68) in females] [27]. A meta-analysis shown a OR of sarcopenia in T2DM men was 1.72 ($95\%$CI: 1.1–2.69) and was 1.46 ($95\%$CI: 0.94–2.25) in women [28]. An Asian study conducted in Japan reported the sex different for pre-diabetes to sarcopenia existed [OR$95\%$CI: 2.081 (1.031–4.199) for men vs. 1.036 (0.611–1.757) for women]. The gender difference might be explained by [1] the muscle mass decline with age was more remarkable in males than in females [29] and [2] ASM mass is greatly influenced by other factors such as testosterone level, which decreased in men with T2DM and obesity.
Our research has some limitations. First, a cross-sectional designed study cannot identify the causal relationship between sarcopenia and diabetes status. Less active life style and decreasing ASM may lead to decreasing insulin sensitivity and diabetes, which could also explain our results. Second, sarcopenia was defined from ASMBMI in this study. Grip strength and walking speed, reflecting the quality of ASM, were not taken into consideration due to data limitation.
## Conclusion
Both pre-diabetes and T2DM are associated with higher risk of loss of ASM and sarcopenia. In non-T2DM male population, increasing HbA1c is associated with the decrease ASMBMI and increase risk of sarcopenia. The inverted U-shaped relationship between HbA1c and ASMBMI reminds us malnutrition or too much energy store may intend to loss of appendicular skeletal muscles.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Review Board of the National Center for Health Statistics approved all NHANES protocols. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
SL contributed to the data collection, statistical analysis, and writing and revising of the manuscript. JM and WZ supervised the study and contributed to the polishing and reviewing of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: National and sub-national burden and trend of type 1 diabetes in 31 provinces
of Iran, 1990–2019
authors:
- Fatemeh Bandarian
- Yeganeh Sharifnejad Tehrani
- Maryam Peimani
- Nazli Namazi
- Sahar Saeedi Moghaddam
- Shahnaz Esmaeili
- Mohammad-Mahdi Rashidi
- Ensieh Nasli Esfahani
- Masoud Masinaei
- Negar Rezaei
- Nazila Rezaei
- Farshad Farzadfar
- Bagher Larijani
journal: Scientific Reports
year: 2023
pmcid: PMC10014831
doi: 10.1038/s41598-023-31096-8
license: CC BY 4.0
---
# National and sub-national burden and trend of type 1 diabetes in 31 provinces of Iran, 1990–2019
## Abstract
The aim of the study was to report the burden of type one diabetes mellitus (T1DM) by sex, age, year, and province in Iran over the past 30 years, according to data provided by the global burden of disease (GBD) study. Incidence, prevalence, death, disability-adjusted life-years (DALYs), years of life lost, and years lived with disability due to T1DM by age groups and sex was reported for 31 provinces of Iran from 1990 to 2019 with their $95\%$ uncertainty intervals (UI). In 2019, national age-standardized incidence (11.0 ($95\%$ UI: 8.9–13.5)), prevalence (388.9 (306.1–482.1)), death (0.7 (0.6–0.8)), and DALYs (51.7 (40.9–65.1)) rates per 100,000 wre higher than 1990 except for death. Also, the mortality to incidence ratio reduced in all provinces over time particularly after 2014 as well. GBD data analysis showed that age-standardized incidence and prevalence rates of T1DM have increased, the death rate reduced, and DALYs remained unchanged during the past 30 years in Iran and its 31 provinces. death rate reduced and DALYs remained unchanged during the past 30 years in Iran and its 31 provinces.
## Introduction
Currently, diabetes mellitus is the 5th leading cause of death in Iran, as previously predicted by World Health Organization (WHO)1, but it has reached this point sooner than expected. According to WHO projection, diabetes will be the 5th leading cause of death worldwide by 20301.
Type 1 diabetes mellitus (T1DM), known as juvenile diabetes or insulin-dependent diabetes, is the second most common type of diabetes and makes up around 5–$10\%$ of diabetes cases2. Pathogenesis of T1DM is different from type 2 diabetes. T1DM is an organ-specific autoimmune disease that causes T cell-mediated destruction of insulin-producing β -cells of islets of Langerhans, which results in almost complete ablation of β-cell secretory function that leads to complete or partial insulin deficiency3,4.
Risk factors of T1DM are less known. However, positive family history of T1DM, genetics, race/ethnicity, and environmental factors such as geography, nutrition, and some viral infections and vaccinations have been linked to the development of T1DM2,5.
According to the latest report, the proportion of T1DM among all types of diabetes in Iran was approximately $11.4\%$ in 20166. The incidence rate of T1DM increased 3–$4\%$ annually in European countries from 1989 to 2013, indicating a doubling incidence rate over 20 years7.
Similarly, an increasing annual rate of $1.8\%$ in the United States (2002–2012)8 and $2.8\%$ worldwide (1990–1999) for T1DM has been reported as well9. The highest estimated number of T1DM patients (0–19 y) in 2019 reported by the International Diabetes Federation (IDF) was in Europe, with 296.5 in 1000 s2. South and East Asia and the Middle East and North Africa (MENA) regions were in the 3rd and 4th rank of T1DM numbers in the world, respectively2. The number of T1DM (0–19 y) in Iran reported by IDF in 2021 was 8.2 in 1000 s10.
As the onset of T1DM is usually in childhood and early adulthood (younger than type 2 diabetes) and patients need regular insulin injections for glycemic control for the long term which are expensive with an invasive application, it reduces the quality of life and imposes a significant burden to the society and health system11.
The objective of this study was to present new results for the burden of T1DM (including incidence, prevalence, death and DALYs) in 31 provinces of Iran over the period of 1990 to 2019.
## Materials and methods
*The* general methods of the global burden of disease (GBD), including estimation of incidence, prevalence, and death rates of non-communicable diseases (NCDs), such as T1DM, and their protocols and updates, were published previously elsewhere12–16. The detailed methodology of GBD 2019 for estimating the burden of disease and risk factors and their changes from GBD 2017 have been described elsewhere15,16. Briefly, to estimate the prevalence of T1DM, obesity prevalence per province was used as a covariate in the DisMod-MR, a Bayesian meta-regression tool. Years lived with disability (YLDs) were calculated as the product of prevalence by age, sex, year and province times the diabetes-specific disability weight. To estimate the death due to T1DM, healthcare access and quality index, education years per capita, age-standardised fertility rate, latitude, age-standardised underweight (weight-forage) summary exposure variable, percentage of births occurring in women > 35 years old, percentage of births occurring in women > 40 years old, Socio-demographic Index (SDI), age-standardised stunting (height-for-age) summary exposure variable, and mean birth weight were considered as the covariates. Years of life lost (YLLs) were calculated from age-sex-province-specific estimates of death due to T1DM by life expectancy at each age. Disability-adjusted life-years (DALYs) were calculated as the summation of YLDs and YLLs.
In GBD 2019, death due to T1DM was calculated using mapping of the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes, including E10–E10.1, E10.3–E10.9, and P70.215. In addition, E10–E10.11 and E10.3–E10.9 were used to map new cases of T1DM17. Decomposition analysis was applied to extract and identify the most effective factors in trends and changes in incidence rates due to T1DM in different provinces from 1990 to 201918. Also, SDI was calculated for all 31 provinces. SDI is a measure that indicates the range of development of countries or other geographical areas. SDI is a composite average of the rankings of the gross domestic product per capita, average educational attainment among individuals aged older than 15 years, and fertility rates among females under the age of 25 years and is expressed on a scale of 0–119,20. The SDI of each province was provided in Supplementary Table 1. All estimates were reported as point estimations with their $95\%$ uncertainty intervals (UI).
In addition, the mortality to incidence ratio (MIR), which is an index of quality of care and indicator of survival, was calculated and compared in 31 provinces as well as the whole country21. GBD 2019 data analysis was performed by Stata version 13 and R version 3.5.022,23.
## The level and trend of the burden of T1DM in Iran from 1990 to 2019
All age numbers and age-standardized rates of incidence and prevalence of T1DM increased from 1990 to 2019 in the country in males, females, and both sexes. While the number of deaths had a similar increasing trend, the death rates decreased from 1990 to 2019 (Fig. 1). The age-standardized incidence rate of T1DM for both sexes in Iran increased from 5.8 ($95\%$ UI: 4.7–7.2) in 1990 to 11.0 (8.9–13.5) per 100,000 in 2019 (Table 1, Supplementary Table 2, and Fig. 1). The prevalence rate of T1DM in both sexes increased from 199.6 (157.2–250.0) in 1990 to 388.9 (306.1–482.1) per 100,000 in 2019 (Table 1, Supplementary Table 2, and Fig. 1). During the same period, the death rate of T1DM in both sexes decreased from 1.1 (0.8–1.3) to 0.7 (0.6–0.8) per 100,000 (Table 1, Supplementary Table 2, and Fig. 1).Figure 1Time trend of T1DM burden by all ages number and age-standardized rate by sex in the country from 1990 to 2019.Table 1All ages number and age-standardized rate (per 100,000), Iran. MeasureMetricYear% Change (1990–2019)19902019BothFemaleMaleBothFemaleMaleBothFemaleMaleIncidenceAll ages number4020 (3151–5047)1917 (1496–2409)2103 (1658–2641)8780 (6990–10,897)4205 (3363–5206)4575 (3639–5669)118.4 (98.9–142.8)119.4 (99.6–144.5)117.5 (98.5–141.9)Age-standardized rate (per 100,000)5.8 (4.7–7.2)5.7 (4.6–7)5.9 (4.8–7.3)11 (8.9–13.5)10.8 (8.7–13.3)11.2 (9–13.7)89.3 (84.8–93.8)90.6 (85.5–95.9)87.9 (82.9–93.2)PrevalenceAll ages number90,635 (70,205–114,686)43,082 (33,297–54,832)47,552 (36,960–59,912)344,242 (270,789–427,806)166,040 (130,459–207,901)178,202 (139,793–221,505)279.8 (260.5–300.7)285.4 (264.2–308.9)274.7 (253.6–294.9)Age-standardized rate (per 100,000)199.6 (157.2–250)194.5 (151.8–244.3)204.3 (161.2–254.3)388.9 (306.1–482.1)380.4 (297.9–473.8)397.1 (312–489.2)94.9 (89.5–99.9)95.5 (89.7–101.2)94.4 (88.7–100.5)DeathsAll ages number351 (275–402)173 (129–208)178 (139–216)547 (441–602)256 (192–300)292 (230–333)55.9 (29.4–96.6)48 (15.4–102.1)63.5 (25–106)Age-standardized rate (per 100,000)1.1 (0.8–1.3)1.2 (0.8–1.4)1.1 (0.8–1.4)0.7 (0.6–0.8)0.7 (0.6–0.9)0.8 (0.6–0.9)− 35.6 (− 46.7–− 13.2)− 37.9 (− 52–− 7.9)− 32.3 (− 49.3–− 7.7)DALYsAll ages number21,730 (18,358–25,532)10,753 (8780–12,785)10,977 (9195–13,292)44,086 (34,612–56,048)20,699 (15,941–26,479)23,387 (18,341–29,594)102.9 (66.5–137.3)92.5 (56.2–132.1)113.1 (72.5–148.3)Age-standardized rate (per 100,000)49.4 (40.1–58.1)49.3 (39–59.4)49.2 (39.9–59.5)51.7 (40.9–65.1)49.7 (39–63.3)53.7 (42.3–67.5)4.7 (− 11.2–23.3)0.7 (− 17.4–24.6)9 (− 9.7–28.7)YLLsAll ages number15,673 (13,325–18,055)7851 (6286–9317)7822 (6527–9609)19,375 (15,395–21,137)8608 (5871–9828)10,766 (8672–12,058)23.6 (− 5.1–49.5)9.6 (− 23–39.4)37.6 (2–66.7)Age-standardized rate (per 100,000)34.7 (27.3–39.6)34.9 (25.9–42)34.4 (26.9–41.6)23.5 (18.9–25.6)21.8 (15.5–24.8)25.1 (20–28.2)− 32.4 (− 44.9–− 14.8)− 37.6 (− 53.3–− 15.4)− 26.9 (− 43.4 to − 8.1)YLDsAll ages number6057 (3977–8944)2902 (1905–4278)3155 (2071–4649)24,711 (16,183–36,137)12,091 (7822–17,663)12,620 (8199–18,732)308 (283.3–331)316.7 (288.9–345.6)300 (275.4–325)Age-standardized rate (per 100,000)14.7 (9.5–21.5)14.4 (9.5–21.1)14.9 (9.7–21.9)28.2 (18.6–41.4)27.9 (18.3–40.7)28.6 (18.7–42.2)92.5 (86.4–98.9)93.3 (85.6–101.7)92.1 (84.3–100.5)Data in parentheses are $95\%$ UIs.
The trend of age-standardized DALYs rate was relatively descending while its absolute number was ascending (Fig. 1, Table 1, and Supplementary Table 2).
T1DM YLDs in the country in both sexes increased from 14.7 (9.5–21.5) in 1990 to 28.2 (18.6–41.4) per 100,000 in 2019, with three steps upward climb in the ranking (Table 1, Supplementary Table 2, and Supplementary Fig. 1A).
## The sub-national burden of T1DM from 1990 to 2019
The trend of T1DM prevalence in all 31 provinces ascended during 1990–2019. The highest age-standardized prevalence rate of T1DM in both sexes from 1990 to 2019 was in Tehran province (212.7 [167.3–264.6] in 1990 and 414.4 [328.8–515.8] in 2019) constantly, and the lowest rate in 1990 and 2019 was in Fars (179.8 [138.7–228.4] in 1990) and Sistan and Baluchistan (349.2 [274.2–433.5] in 2019), respectively (Supplementary Fig. 1B). The map of Iran by the measures of incidence, prevalence, deaths, and DALYs of T1DM in 1990 and 2019 in 31 provinces is depicted in Fig. 2.Figure 2The map of Iran by the measures of incidence, prevalence, deaths, and DALYs of T1DM in 1990 and 2019 in 31 provinces.
The trend of age-standardized death rate descended in the country from 1990 to 2019. The highest death rate due to T1DM in both sexes from 1990 to 2019 was in Khuzestan province, with a fixed first rank and descending number (1.6 [0.8–2.2] in 1990 and 1.2 [0.6–1.5] in 2019) and the lowest was in Tehran province again with a fixed rank and descending number (0.8 [0.4–1] in 1990 and 0.5 [0.2–0.6] in 2019) (Supplementary Fig. 1C).
Regarding the T1DM age-standardized DALYs rate from 1990 to 2019, among 31 provinces, the highest rate of DALYs in T1DM in both sexes from 1990 to 2019 was in Khuzestan province, with the fixed first rank (64.8 [42.2–82.2] in 1990 and 66.2 [45.5–83] in 2019). Conversely, the lowest rates were in Zanjan (36.7 [29.3–50.7] in 1990 and 41.9 [31.6–55] in 2019) and Chaharmahal Bakhtiari (39.9 [32–50.1] in 1990 and 44.4 [34.3–58.3] in 2019) provinces with fixed last ranks (Supplementary Fig. 1D).
Among 31 provinces, the greatest T1DM age-standardized rate for YLDs in both sexes was in Alborz from 1990 to 2019, while the least YLDs rates were in Fars [1990] and Sistan and Baluchistan [2019] (Supplementary Fig. 1A).
## The burden of T1DM by socio-demographic index (SDI) from 1990 to 2019
The trends of age-standardized incidence and prevalence rates of T1DM were ascending in all SDI groups. In low, low-middle, and middle SDI quintiles, incidence and prevalence rates were slightly below, while high-middle and high SDI quintiles were slightly above the average of the country (Fig. 3). The SDI values among 31 provinces were reported in Supplementary Table 1.Figure 3Time trend of age-standardized T1DM burden by SDI quintiles in the country from 1990 to 2019.
The trend of the age-standardized death per in 100,000 of T1DM was descending in all SDI groups particularly since 2002 that reduced rapidly. In low and high SDI death rate was slightly below average and in low-middle, middle and high-middle SDI slightly above the average of the country. The decrease in death rate in high SDI increased sharply since 1999 and dropped below the national average while the death rate in low SDI increased and reached to national average since 2014 (Fig. 3).
The trend of age-standardized DALYs rate was slightly descending till 2000 and after that increased with slight slope until 2004 that experienced a small fall till 2011 and then increased with a steep slope till 2019 with a small fall in 2016. However, it had no steady trend and had frequent fluctuations in average and in all SDI groups (Fig. 3).
## Percent changes
Percent changes of T1DM age-standardized incidence and prevalence rates in all 31 provinces and national level had the same pattern (zone a: age-standardized rates increased more rapidly after vs before 2005). Assessing the percent changes of age-standardized death rates across provinces revealed that in five provinces rates decreased more slowly after than before 2005 (Sistan and Baluchistan, Mazandaran, Hamadan, Ardebil, and Kermanshah) but in other 26 provinces and national level, rates decreased more rapidly after than before 2005 (zone d). The national and provincial percent changes in the age-standardized DALYs rate had four different patterns but most provinces and national level were included in zone f (rates decreased before 2005 but increased after 2005) (Fig. 4, Supplementary Table 3).Figure 4National and sub-national percent change in the age-standardized burden of T1DM in 1990–2005 compared to 2005–2019 (a), rates increased more rapidly after vs before 2005. ( b) Rates increased more slowly after vs before 2005. ( c) rates increased before 2005 but decreased after 2005. ( d) rates decreased more rapidly after than before 2005. ( e) rates decreased more slowly after than before 2005. ( f) rates decreased before 2005 but increased after 2005.
## Age distribution of T1DM
The highest incidence rate for T1DM was in the 5–9 years age group in males and females. Also, the highest prevalence, death, and DALYs rates were in 75–79, above 80, and 75–79 years, respectively, in males and females (Fig. 5).Figure 5Burden of T1DM in the country by sex and age groups in 1990 and 2019.
## Factors affecting T1DM burden from 1990 to 2019
By decomposition analysis, factors affecting trends and change in the new cases of T1DM were extracted in different provinces in Iran in males and females. The overall percent change in the incidence of T1DM in Iran was $118.4\%$. About $44\%$ of this rise in the number of T1DM cases was attributed to population growth, $101.9\%$ to incidence rate change, and − $27.5\%$ to age structure change (Table 2). Decomposition analysis of T1DM new cases at national and sub-national levels by sex has been shown in Supplementary Table 4.Table 2Decomposition and the percent changes in T1D incidence number and its causes in 31 provinces in both genders. LocationNew casesExpected new cases in $2019\%$ 1990–2019 new cases change cause% 1990–2019 new cases overall change (%)19902019Population growthPopulation growth + AgingPopulation growth (%)Age structure change (%)Incidence rate change (%)Iran (Islamic Republic of)402087805789468244− 27.5101.9118.4Subnational Alborz10931421417095.4− 40131.8187.2 Ardebil81131896810.9− 26.177.462.2 Bushehr50136867371.4− 27126.9171.3 Chahar Mahaal and Bakhtiari51105705635.3− 2796.3104.6 East Azarbayejan23541027822018.2− 24.78174.5 Fars23547231924435.9− 32.197.1100.9 Gilan16226318113911.6− 25.576.762.7 Golestan9520713711143.9− 26.5100.5117.9 Hamadan114176118933.4− 22.673.253.9 Hormozgan64210132113105− 29.6151.8227.2 Ilam3265423232− 30.5103.2104.7 Isfahan26854636829037.1− 29.195.5103.5 Kerman13436923920078.3− 29.1125.4174.6 Kermanshah11919913810716.3− 26.376.866.8 Khorasan-e-Razavi33472447238941.4− 24.7100.4117.1 Khuzestan22752134528352.2− 27.3105129.9 Kohgiluyeh and Boyer-Ahmad3479524251.9− 29.6109.6132 Kurdistan851641148933.8− 29.688.492.6 Lorestan1081871239714.3− 24.383.373.3 Markazi841481007819.2− 26.683.576.1 Mazandaran18236123918631.5− 29.496.298.3 North Khorasan4394595037.5− 21.1100.7117.1 Qazvin67136937339.9− 3195.5104.4 Qom52145967884.1− 34.5129.4179 Semnan3480534454.9− 26.6106.9135.2 Sistan and Baluchistan103339208191101.9− 16.9143.7228.8 South Khorasan4692574924.2− 17.792.498.9 Tehran601152198580863.8− 29.4118.5152.9 West Azarbayejan15934223319046.6− 26.995.4115.2 Yazd49133826969.2− 26.9131.4173.6 Zanjan62110755821.6− 27.48377.1
## Quality of T1DM care
According to GBD data, the MIR number reduced in all provinces in both males and females from 0.087 in 1990 to 0.062 in 2019, which indicates the improving quality of T1DM care and survival during the study period (Fig. 6). Another analysis showed that MIR has reduced in all SDI groups during the past 30 years as well. However, MIR in low and high SDI provinces was below the country’s average curve. The MIR curve remained constant and relatively plateau until 2002, and after that, it decreased with a moderate slope until 2012, and then it dropped again with a steeper slope (Fig. 7).Figure 6Time trend of age-standardized MIR due to T1DM in the country from 1990 to 2019.Figure 7Time trend of all ages MIR due to T1DM by SDI quintiles in the country from 1990 to 2019.
## Discussion
This study showed the status and trend of the T1DM burden in 31 provinces of Iran during the past 30 years (1990–2019). The age-standardized rates and all ages numbers of incidence and prevalence of T1DM in most provinces, as well as the whole country, increased dramatically in recent ten years. The increasing trend of incidence and prevalence of diabetes were also reported in other studies24,25. However, during the same period, death rates were reduced in all provinces except for one. Age-standardized DALYs rate reduced in most provinces and increased in some provinces and had no similar trend. According to the IDF, the rates of new cases of T1DM in 0–14 years old children in Iran were similar without changes in 2010 and 2019 (0.7)2, and the estimated rate of T1DM in 0–19 years old children was 8.2 per 100,000 in 202110. While according to the GBD’s results, the rate of new cases of T1DM was 5.8 [4.7–7.2] in 1990 and 11 [8.9–13.5] in 2019.
GBD data showed that the incidence rate of T1DM has approximately doubled from 1990 to 2019 in Iran and in most of its provinces, while during the same time death rate reduced by one-third. In a previous study, the incidence rate of type 1 and 2 diabetes was increased by $102\%$24. However, when T1DM was analyzed separately, although the number of incident cases increased by about one-third, the age-standardized incidence rate was constant with no changes24. Despite such changes in our analysis, DALYs increased during the study period, especially after 2011 and 2016.
Gale’s study indicated that from the 1960s–1990s incidence of T1DM has increased by 2–$4\%$ each year26, which is similar to 1990–2019 in the current study that the incidence has doubled during the past 30 years.
The cause of such increases in the incidence and prevalence may be due to the improved health care coverage that leads to better registration and recording of T1DM cases.
Several maternal and perinatal risk factors, including increased maternal age27,28, tea drinking during pregnancy29, maternal pre-eclampsia28–30 and infections29, higher birth weight30,31, delivery by Caesarean section31,32, premature rupture of membranes31, maternal urinary tract infection during pregnancy31, gestational diabetes30, preterm birth28 and neonatal infectious28, have been identified for TIDM. Although, there is no consistency among different studies about these risk factors and some other studies did not confirm them31,33. Among the mentioned risk factors, a small percentage of the increase in the incidence of childhood T1DM in the past decades may be attributed to the increase in maternal age, as was shown in Ayati et al. study30. According to the latest study in our country, gestational diabetes, pre-eclampsia, and birth weight of more than 4 kg, but not maternal age, were identified as risk factors for developing T1DM in the offspring in the future30. According to the decomposition analysis (Table 2), $44\%$ of new cases increment during the study period (1990–2019) was due to the population growth.
In addition, stress which is one of the trigger factors of T1DM onset has increased in the past decades, which may cause increased incidence and prevalence of T1DM. Also, the increase in other environmental risk factors such as new viruses, more prevalent cow's milk and formula consumption, and processed foods with higher amounts of advanced glycation end-products and nitrites or N-nitroso compounds in processed meat products, may have contributed to such increased incidence and prevalence of T1DM34–37.
Regarding the age distribution of T1DM, as it was predicted, the greatest incidence rate was in ages 5–9 years. The highest prevalence of T1DM was observed in ages between 75 and 79 years due to improved diabetes care, availability of insulin, and increased survival of T1DM patients, that cause most patients to reach older ages.
In the current study, age-standardized incidence rates of T1DM in high and high-middle SDI were higher than in the low and low-middle SDI groups, which is similar to the worldwide population in Liu et al. study24. In our country, the increasing incidence of T1DM after 2000 continued with a steeper slope, and it was similar in all SDI groups. But in Liu's study, age-standardized incidence rates increased across all four SDI regions, with the greatest increase in the high SDI region and was stable in the low SDI regions24. This rapid growing incidence of T1DM may be attributed to changing diagnostic criteria for T1DM in 199738 that reduced FBS threshold criteria from 140 to 126 mg/dl and shifting from the oral glucose tolerance test to fasting plasma glucose or may be due to improving health care and T1DM patients detection in the country.
It seems that in deprived areas (low SDI), by increasing incidence and prevalence of T1DM, due to limited access to medications and treatment, the number of YLLs and consequently DALYs has increased, which is contradictory to the reduced death rates. It may be explained that by death reduction, YLDs increase and affect DALYs more than reduced YLLs. In other provinces, although incidence and prevalence of T1DM have increased, due to adequate access to treatment and health care facilities, death and consequently DALYs has reduced and DALYs has affected more by reduced YLLs than increased YLDs. In Qom province with the fastest death rate reduction, also the highest speed of DALYs reduction was observed.
Higher YLLs than YLDs in 1990 were due to the higher rate of T1DM deaths, and the higher YLDs than YLLs in 2019 were due to the reduction of the T1DM deaths rate. The T1DM death rate reduction was the result of the improvement of T1DM care by the introduction and increased availability of insulin pens and its application in the treatment of T1DM during the last five years. However, YLLs decreased, and YLDs increased in all provinces except for three provinces. The cause of higher YLLs in Sistan and Baluchestan, Khuzestan, and Golestan than YLDs was the higher death rates in these provinces (first, second, and fourth death rates in 2019). Sistan and Baluchistan was categorized as a low SDI province, but Khuzestan and Golestan had higher SDIs in 2019.
For the age groups under 20, both in 1990 and 2019, the YLLs were higher than the YLDs, but in 1990 due to the higher death rate, YLLs were higher than YLDs, and in 2019 due to the improvement of T1DM treatment, YLDs increased although yet it was lower than the YLLs.
For ages above 20, YLLs were more than YLDs in 1990, but in 2019 YLDs were higher than YLLs except for a few provinces, including Sistan and Baluchistan, Khuzestan, and Golestan.
As the onset of T1DM is in childhood and early adulthood, YLLs are more because of death in childhood results in a higher loss of life years. During the study period although YLLs reduced due to the reduction of T1DM death rate DALYs increased. This indicates that DALYs have moved in the same direction as YLDs and have been affected more by YLDs instead of YLLs.
Considering population growth and aging, the DALYs count was increasing during the study period, but age-standardized DALYs were almost steady during the past 30 years, and only a few small fluctuations were observed in the trend of DALYs that may be attributed to political changes in the country and changes in health care policy and strategies.
In this study, MIR reduced from 2002 to 2019, which may be due to either improvement in survival (decreasing mortality) or improvements in the diagnosis of T1DM (increasing in the reported incidence), or a combination of both. Death rates and MIRs in the low SDI group increased after 2014 and reached the country average, but at the same time, death rates and MIRs in the low-middle SDI and high-middle SDI group fell below the country average after 2014. However, after 2017 the curve for low-middle and high-middle SDI groups returned to above the country average (Fig. 7). After 2014 the distance between all SDI curves reduced, and the curves came close together. In 2019, the least MIR, which was near zero, was observed in the high-SDI group, and the highest MIR (between 0.007 and 0.08) was in the middle SDI group as well. Such MIR reduction in the high SDI group of T1DM patients may be due to improving diabetes care, accessibility, and increased insurance coverage and availability of drugs such as insulin pens in the country. However, at the same time, MIRs increased in low SDI groups unexpectedly (while it was expected to reduce, like high SDI group), which can be explained with improved diabetes care coverage and accessibility (mainly due to establishment and increased number of health houses throughout the country) that caused improved case registry and records especially in low-income and remote areas.
After 2014, DALYs increased or remained unchanged in most SDI groups but only reduced in low-middle and high-middle SDI groups because the death rate in these groups reduced after 2014, significantly which caused YLLs and DALYs reduction. Also, during the same time distance between the trend of number and age-standardized rate of T1DM death and DALYs reduced significantly and reached each other.
In all provinces, age structure changes and population aging had a noticeable effect on the incidence of T1DM. Also, the contribution of population growth in the change of T1DM incidence rate was lower than the incidence rate change in all provinces according to decomposition analysis. Change in incidence rate may be indicative of improved health care coverage in the country that caused improved case findings. Age structure change may be due to the migration or the aging of the population.
This study has limitations. The overall quality of burden estimates were according to the accuracy of data sources used in the modeling. In addition, a revision on the T1DM risk factors’ selection is highly recommended as in the current format, only high fasting plasms glucose and non-optimal temperature were included, while the list can be improved.
## Conclusion
In conclusion, the analysis of GBD data in Iran with 31 provinces showed that the age-standardized incidence and prevalence rate of T1DM increased from 1990 to 2019, while the death rate decreased and the DALYs rate was steady during the same period.
MIR has reduced all over the country in all SDI groups since 2002, especially from 2014 to 2019. This means that the quality of T1DM care has improved in the country since 2014, significantly.
## Supplementary Information
Supplementary Information 1.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9. The online version contains supplementary material available at 10.1038/s41598-023-31096-8.
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26. Gale EA. **The rise of childhood type 1 diabetes in the 20th century**. *Diabetes* (2002.0) **51** 3353-3361. DOI: 10.2337/diabetes.51.12.3353
27. Cardwell CR. **Maternal age at birth and childhood type 1 diabetes: A pooled analysis of 30 observational studies**. *Diabetes* (2010.0) **59** 486-494. DOI: 10.2337/db09-1166
28. Svensson J, Carstensen B, Mortensen HB, Borch-Johnsen K. **Early childhood risk factors associated with type 1 diabetes- is gender important?**. *Eur. J. Epidemiol.* (2005.0) **20** 429-434. DOI: 10.1007/s10654-005-0878-1
29. Majeed A, Hassan M. **Risk factors for type 1 diabetes mellitus among children and adolescents in Basrah**. *Oman. Med. J.* (2011.0) **26** 189-195. DOI: 10.5001/omj.2011.46
30. Ayati M, Mosayebi Z, Movahedian AH. **Association between perinatal risk factors and development of type 1 diabetes in children**. *J. Compr. Ped.* (2020.0) **11** e82902. DOI: 10.5812/compreped.82902
31. Waernbaum I, Dahlquist G, Lind T. **Perinatal risk factors for type 1 diabetes revisited: A population based register study**. *Diabetologia* (2019.0) **62** 1173-1184. DOI: 10.1007/s00125-019-4874-5
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---
title: A mutation in the ZNF687 gene that is responsible for the severe form of Paget’s
disease of bone causes severely altered bone remodeling and promotes hepatocellular
carcinoma onset in a knock-in mouse model
authors:
- Sharon Russo
- Federica Scotto di Carlo
- Antonio Maurizi
- Giorgio Fortunato
- Anna Teti
- Danilo Licastro
- Carmine Settembre
- Tommaso Mello
- Fernando Gianfrancesco
journal: Bone Research
year: 2023
pmcid: PMC10014847
doi: 10.1038/s41413-023-00250-3
license: CC BY 4.0
---
# A mutation in the ZNF687 gene that is responsible for the severe form of Paget’s disease of bone causes severely altered bone remodeling and promotes hepatocellular carcinoma onset in a knock-in mouse model
## Abstract
Paget’s disease (PDB) is a late-onset bone remodeling disorder with a broad spectrum of symptoms and complications. One of the most aggressive forms is caused by the P937R mutation in the ZNF687 gene. Although the genetic involvement of ZNF687 in PDB has been extensively studied, the molecular mechanisms underlying this association remain unclear. Here, we describe the first Zfp687 knock-in mouse model and demonstrate that the mutation recapitulates the PDB phenotype, resulting in severely altered bone remodeling. Through microcomputed tomography analysis, we observed that 8-month-old mutant mice showed a mainly osteolytic phase, with a significant decrease in the trabecular bone volume affecting the femurs and the vertebrae. Conversely, osteoblast activity was deregulated, producing disorganized bone. Notably, this phenotype became pervasive in 16-month-old mice, where osteoblast function overtook bone resorption, as highlighted by the presence of woven bone in histological analyses, consistent with the PDB phenotype. Furthermore, we detected osteophytes and intervertebral disc degeneration, outlining for the first time the link between osteoarthritis and PDB in a PDB mouse model. RNA sequencing of wild-type and Zfp687 knockout RAW264.7 cells identified a set of genes involved in osteoclastogenesis potentially regulated by Zfp687, e.g., Tspan7, Cpe, Vegfc, and Ggt1, confirming its role in this process. Strikingly, in this mouse model, the mutation was also associated with a high penetrance of hepatocellular carcinomas. Thus, this study established an essential role of Zfp687 in the regulation of bone remodeling, offering the potential to therapeutically treat PDB, and underlines the oncogenic potential of ZNF687.
## Introduction
Paget’s disease of bone (PDB) is a late-onset skeletal disorder characterized by impaired bone remodeling activity due to high bone degradation activity by osteoclasts followed by disorganized bone deposition by osteoblasts.1,2 PDB affects one (monostotic) or more (polyostotic) skeletal sites, and although any bone can be affected, there is a predilection for the skull, spine, pelvis, femur, and tibia. Bone pain, deformity, and pathological fractures, typically in the lytic phase of the disease, are the most common symptoms.2 A common complication of PDB is osteoarthritis (OA).3 Indeed, patients with PDB are more likely than age-matched controls to need total hip and knee arthroplasty as a result of secondary OA.4 A peculiar feature of OA is the formation of osteophytes, which are osteocartilaginous outgrowths that typically form at the joint margins in the region where the synovium attaches to the edge of the articular cartilage and merges with the periosteum.5 Osteophytes are established through the growth of an initial cartilage template that is at least partially replaced with bone-containing marrow cavities.6 In PDB, the osteophyte occurrence is less clear. The original description of Sir James Paget in 1876 included the postmortem analysis of a pathologic specimen of the femur of case 5, which demonstrated features of PDB at the proximal and distal femur, with femoral head remodeling and osteophyte formation.7 However, whether the formation of osteophytes is a prominent feature of PDB remains to be determined.
The most relevant PDB complication is the neoplastic degeneration of affected bones in osteosarcoma (OS/PDB) or giant cell tumor (GCT/PDB).8 Although rare, their occurrence, commonly observed in polyostotic PDB, is associated with more severe manifestations of the disease and reduced life expectancy. Osteosarcoma is the most severe malignant transformation of PDB and shows an extremely poor prognosis that has not improved over the years, showing a 5-year survival rate almost nil, especially after metastasis to the lungs.9 In contrast, GCT rarely metastasizes but is locally aggressive.10,11 In the last decade, it has been proven that the distinct forms of the disease have different genetic bases.12–15 The common form of PDB is due to mutations in the SQSTM1 gene, encoding the p62 protein, which is involved in important cellular mechanisms, such as autophagy or the regulation of the NFκB signaling pathway.12,13 Mutations in SQSTM1 lead to an impaired and continuously activated NFκB pathway, resulting in more activated and multinucleated osteoclasts.16 Two Sqstm1 knock-in mouse models have been generated, harboring the most common mutation (P392L), where mutant mice developed osteolytic/osteosclerotic lesions predominantly affecting the long bones but not involving the spine.17–19 Interestingly, the most common antiresorptive treatment with bisphosphonates prevented the formation of pagetic-like lesions in mutant animals.19 PDB associated with giant cell tumor is instead negative for SQSTM1 mutations and caused by a founder mutation (P937R) in the ZNF687 gene. ZNF687-mutated patients present with a more severe clinical phenotype in terms of age of onset and number of affected sites.14 Importantly, unlike SQSTM1 mutation carriers, ZNF687-mutated patients show an inadequate response to bisphosphonates and need massive doses of antiresorptive drugs to cure the disease.20 Moreover, pagetic patients harboring the ZNF687 mutation were found to develop OA degeneration in $42.8\%$ of cases as a PDB complication.10,14 However, the role of ZNF687 in bone metabolism needs to be further explored. Although ZNF687 seems to be under the transcriptional regulation of NFκB,14 the pathway in which it is involved appears to be different than SQSTM1. To elucidate the molecular and pathological role of ZNF687 in PDB, in this study, we generated a knock-in mouse model carrying the P937R mutation in the homologous *Zfp687* gene and performed skeletal characterization.
## ZNF687 is upregulated during human and murine osteoclastogenesis
Previously, we profiled the expression of ZNF687 during the osteoclast differentiation of peripheral blood mononuclear cells (PBMCs) from healthy donors and noted a progressive upregulation of its expression.14 Here, we first confirmed this upregulation at the protein level during the physiological differentiation process in humans and mice, confirming the key role of ZNF687 (Fig. 1a, b). Interestingly, ZNF687-mutated osteoclasts showed a greater expression of ZNF687 itself than those of healthy individuals, supporting the gain-of-function status of the P937R mutation (Fig. 1c, left). Additionally, the expression of genes whose upregulation is crucial for successful osteoclastic differentiation (e.g., TRAP, MMP9, CTSK) was increased in mutation-bearing osteoclasts (Fig. 1c, right). These data agree with a previous study demonstrating that patient-derived monocytes formed larger and more active osteoclasts in response to pro-osteoclastic stimuli in vitro.20 We therefore underline the relevance of ZNF687 in osteoclast biology, highlighting its positive modulatory effect on the osteoclastogenic process. Fig. 1ZNF687 has a key role in osteoclastogenesis. a Western blot detection (left) and densitometric quantification (right) of ZNF687 protein levels in differentiated osteoclasts derived from a healthy donor upon sRANKL stimulation of PBMCs at days 6, 12, and 21. Western blots were normalized with β-actin. Data are presented as the mean ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (**$P \leq 0.01$). b Western blot detection (left) and densitometric quantification (right) of Zfp687 protein levels in differentiated osteoclasts derived from RAW264.7 cells subjected to sRANKL stimulation for 5 days. Western blots were normalized with α-tubulin. Data are presented as the mean ± s.d. Statistical significance was assessed by unpaired t test (**$P \leq 0.01$, two-tailed). c Bar graphs showing gene expression analysis of ZNF687 and the osteoclastogenic markers TRAP, MMP9, and CTSK during osteoclast differentiation of healthy and P937R-mutated PBMCs at days 7, 14, and 21. Data are presented as the mean ± s.d. Statistical significance was assessed by two-way ANOVA with Sidak’s multiple comparison test (**$P \leq 0.01$; ***$P \leq 0.001$). d Representative TRAP-stained sections of femoral growth plates of 3-month-old wild-type, Zfp687P937R/+, and Zfp687P937R/P937R mice showing osteoclastic activity in purple; nuclei were counterstained with hematoxylin. e Box plots showing the osteoclast surface to bone surface ratio (Oc. S/BS; top) and osteoclast number to bone surface ratio (Oc. N/BS; bottom) in Zfp687P937R/+ ($$n = 5$$) and Zfp687P937R/P937R mice ($$n = 5$$) compared with wild-type mice ($$n = 6$$) at 3 months of age. Data are presented as the median ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (*$P \leq 0.05$)
## ZNF687 mutation alters bone cell differentiation processes
To deepen the understanding of the effect of the ZNF687 mutation on bone metabolism, we generated a knock-in mouse model harboring the c.2810C>G (P937R) mutation detected in GCT/PDB patients.14,20 The mutation has been introduced through site-directed mutagenesis in the targeting vector containing arms of the homologous Zfp687 mouse gene, exploiting the $80.4\%$ nucleotide homology between the human and mouse genes, even higher ($84\%$) at exon 6, where the mutation is located (Fig. S1a, b). Both heterozygous and homozygous mice (referred to herein as Zfp687P937R/+ and Zfp687P937R/P937R, respectively) were viable and fertile, and the Mendelian distribution of genotypes in the litters was respected. Three-dimensional microcomputed tomography (µCT) evaluation of the trabecular bone composition of femurs and L4 lumbar vertebrae, as well as the cortical bone of the femoral midshaft of 3-month-old mice, was analyzed, revealing neither skeletal abnormalities nor bone volume alterations in mutant animals (Fig. S2, S3). Although there are no macroscopic differences at this stage, we observed different bone cellular activities. First, femur sections were analyzed for tartrate-resistant acid phosphatase (TRAP) expression, revealing increased osteoclast-dependent activity in mutant bones (Fig. 1d). Accordingly, we found significantly higher levels of both osteoclast surface (Oc. S/BS) and number (Oc. N/BS) per bone surface in histological sections of Zfp687P937R/P937R mice, and a similar trend was observed in Zfp687P937R/+ mice (Fig. 1e). Interestingly, we also noted an increased osteoblast number (Ob. N/BS) per bone surface within histological sections of mutant mice, especially homozygous mice (Fig. 2a, b). Taken together, these results underline the positive effect of the P937R mutation on the number and activity of bone cells even before overt phenotypic manifestation. On the one hand, the effect of the ZNF687 mutation on osteoclasts was previously explored;14,20 on the other hand, nothing is known about osteoblast differentiation. To fill this gap, we isolated bone marrow-derived mesenchymal stem cells (BM-MSCs) from 8-week-old wild-type and homozygous mutant mice, which were then subjected to osteoblast differentiation. We first demonstrated a strong upregulation of Zfp687 during the physiological differentiation process, allowing us to hypothesize a role for this transcription factor in regulating osteoblast differentiation (Fig. 2c). In fact, Zfp687P937R/P937R osteoblasts manifested a remarkably higher capability of mineralization and bone nodule formation in the presence of osteogenic factors (ascorbic acid and β-glycerophosphate) than wild-type osteoblasts (Fig. 2d). Notably, mutant cells became fully differentiated osteoblasts as early as 8 days after osteogenic induction, while wild-type cells displayed an expected slower differentiation process (Fig. 2d, e). We therefore conclude that the P937R mutation accelerates osteoblast formation and function, consistent with what was expected in bone remodeling alterations leading to PDB.Fig. 2Zfp687P937R differentiated osteoblasts display an increased mineralization potential. a Representative H&E-stained images of proximal tibial sections analyzed for osteoblast (arrowheads) quantification in wild-type, Zfp687P937R/+, and Zfp687P937R/P937R mice at 3 months of age. Scale bars 50 µm. b Box plots showing histomorphometric quantification of the osteoblast number per bone surface ratio (Ob. N/BS) in wild-type ($$n = 5$$), Zfp687P937R/+ ($$n = 4$$), and Zfp687P937R/P937R ($$n = 7$$) mice at 3 months of age. Data are presented as the median ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (*$P \leq 0.05$; **$P \leq 0.01$). c Bar graph showing Zfp687 expression analysis in wild-type BM-MSCs either untreated (d0) or stimulated for 8 days toward osteoblastogenesis (d8). Data are presented as the mean ± s.d. Statistical significance was assessed by an unpaired t test (**$P \leq 0.01$, two-tailed). d Representative Alizarin Red Staining (ARS) images of wild-type and Zfp687P937R/P937R BM-MSCs after 8 days of osteogenic induction. e Bar graph showing ARS quantification of wild-type ($$n = 4$$) and Zfp687P937R/P937R ($$n = 4$$) osteoblast differentiation after 8 days of osteogenic induction. Data are presented as the mean ± s.d. Statistical significance was assessed by an unpaired t test (****$P \leq 0.000$ 1, two-tailed) During the histological analyses conducted on 3-month-old mice, we surprisingly observed an increase in marrow adipocytes in mutant femurs. A mutual correlation between bone marrow adipose tissue (BMAT) and bone loss exists;21–24 however, no research has been conducted to analyze this correlation in PDB. Therefore, we decided to quantify BMAT in the Zfp687 mouse model. The distal tibiae of wild-type and mutant mice at 3 months of age were stained with hematoxylin-eosin and subjected to measurement of constitutive bone marrow adipose tissue (cBMAT). Our analysis highlighted that cBMAT was increased by ~1.5-fold in both Zfp687P937R/+ and Zfp687P937R/P937R mice ($$P \leq 0.009$$ 9 and $$P \leq 0.007$$ 9) compared to wild-type mice (Fig. 3a, b). Both adipocyte number and area increased in mutant sections (Fig. 3a), indicating that mutant bone marrow contains more and larger fat cells. To confirm this observation, we subjected BM-MSCs to adipocyte differentiation in vitro, and as expected, mutant adipocytes appeared larger than wild-type cells and contained a large amount of lipid droplets, as shown by ORO staining (Fig. 3c–e).Fig. 3Zfp687 mutant mice display increased cBMAT composition at 3 months of age. a Box plots showing the quantification of the tibial cBMAT volume expressed as the percentage of BMAT to total bone marrow ratio (BMAT/BM; left), the adipocyte area (middle), and the adipocyte number (right) of wild-type ($$n = 7$$), Zfp687P937R/+ ($$n = 6$$), and Zfp687P937R/P937R ($$n = 8$$) mice at 3 months of age. Data are presented as the median ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (*$P \leq 0.05$; **$P \leq 0.01$). b Representative H&E-stained images of distal tibial sections analyzed for adipocyte measurements of the indicated genotypes. c, d Oil Red O (ORO) staining of wild-type, Zfp687P937R/+, and Zfp687P937R/P937R BM-MSCs upon adipogenic induction and differentiation (plate view in c) and 20X magnification in (d). Scale bars, 100 µm. e Bar graphs show the intensity of ORO staining (absorbance at 490 nm) of wild-type ($$n = 4$$), Zfp687P937R/+ ($$n = 5$$), and Zfp687P937R/P937R ($$n = 6$$) adipocytes. Data are presented as the mean ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (**$P \leq 0.01$) Taken together, these data reveal that, in addition to the osteoclast differentiation program, stromal cell commitment is also altered by the mutation in Zfp687.
## Adult Zfp687 mutant mice show trabecular bone loss and initial altered bone deposition
To further elucidate the early phase of Paget’s disease, we subjected mice to skeletal phenotyping at 8 months of age, which corresponds to approximately 30 years in humans, considering that the initial PDB diagnosis in ZNF687-mutated patients is generally at approximately 45 years of age.14 *Parametric analysis* revealed significant bone mass reduction affecting the hind limbs and spine of mutant mice. In particular, the ratio of bone volume (BV) to total volume (TV; BV/TV) of femoral trabecular bone was decreased by $31\%$ in Zfp687P937R/+ and $35\%$ in Zfp687P937R/P937R mice compared to wild-type mice ($$P \leq 0.007$$ and $$P \leq 0.003$$, respectively) (Fig. 4a, b). The bone trabecular mass reduction was mainly due to a decrease in trabecular number (Tb. N) and, consequently, an increase in trabecular separation (Tb. Sp) (Fig. S4a). Additionally, trabecular bone mass in the spine was reduced by $25\%$ ($$P \leq 0.008$$) and $27\%$ ($$P \leq 0.005$$) in the L4 vertebrae of Zfp687P937R/+ and Zfp687P937R/P937R mice, respectively, with sparser and thinner trabeculae than wild-type littermates (Figs. 4c, d, S4b). Although significant cortical thickening (Fig. 5a), bone expansion, or deformity was not found at the midshaft of femurs of mutant animals, we sporadically observed the presence of lytic lesions affecting the cortical bone of long bones, in which TRAP-positive osteoclasts appeared giant-sized and multinucleated compared to the controls (Fig. 5b). A closer look at the osteoblast activity of 8-month-old mutant mice revealed that dysregulated bone deposition also occurred. In fact, histomorphometric analysis of femur sections through von Kossa/van Gieson staining showed an increased osteoid volume at both the trabecular and cortical levels in Zfp687P937R/P937R mice that was compatible with an enhanced deposition of non-mineralized bone matrix (Fig. 5c, d). To examine whether this increased osteoid deposition was due to a generalized mineralization defect, we repeated von Kossa/van Gieson staining in younger mice (3 months old). We did not detect any increase in osteoid thickness in the mutant samples compared with the WT samples (data not shown), indicating that altered mineralization only occurred in adult mice. Collectively, these data indicate that the P937R mutation leads to bone mass reduction and altered matrix deposition in 8-month-old mice. Fig. 4Zfp687 mutant mice show remarkable trabecular bone loss at the appendicular and axial skeleton at 8 months of age. a Box plots showing the percentage of BV/TV by µCT in the trabecular bone of the femoral distal epiphysis of 8-month-old wild-type ($$n = 9$$), Zfp687P937R/+ ($$n = 9$$), and Zfp687P937R/P937R ($$n = 8$$) mice. The region between the two gray lines represents the region of interest selected for the trabecular analysis. Data are presented as the median ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (**$P \leq 0.01$). b Representative µCT 3D reconstruction showing trabecular bone of femurs from wild-type, Zfp687P937R/+, and Zfp687P937R/P937R mice. Scale bars 1 mm. c Box plots showing the percentage of BV/TV by µCT in the trabecular bone of the L4 vertebra of 8-month-old wild-type ($$n = 9$$), Zfp687P937R/+ ($$n = 9$$), and Zfp687P937R/P937R ($$n = 8$$) mice. Data are presented as the median ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (**$P \leq 0.01$). d Representative µCT 3D reconstruction of L4 vertebrae from wild-type, Zfp687P937R/+, and Zfp687P937R/P937R mice. Scale bars 1 mmFig. 5Severe impairment of bone remodeling in Zfp687 mutant mice at 8 months of age. a Box plots showing quantitative measurements of cortical thickness of the femoral midshaft by µCT of 8-month-old wild-type ($$n = 9$$), Zfp687P937R/+ ($$n = 9$$), and Zfp687P937R/P937R ($$n = 8$$) mice. Data are presented as the median ± s.d. Statistical analysis was assessed by one-way ANOVA with Dunnett’s multiple comparison test. b TRAP staining of a femur section of a Zfp687 mutant mouse depicting a representative cortical osteolytic lesion. The figure is the result of a mosaic of 4 images of adjacent regions taken at 10X magnification. Scale bar, 100 µm. c Representative von Kossa and van Gieson images of trabecular (upper) and cortical (lower) femur sections of wild-type, Zfp687P937R/+, and Zfp687P937R/P937R mice. Mineralized bone (black) and osteoid (pink) are visualized. Arrowheads indicate increased osteoid deposition. d Box plots showing quantification of osteoid volume over bone volume percentage (OV/BV) from von Kossa-stained sections counterstained with van Gieson in trabecular (left) and cortical (right) bone of wild type ($$n = 5$$), Zfp687P937R/+ ($$n = 5$$), and Zfp687P937R/P937R ($$n = 5$$) mice. Data are shown as the median ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (*$P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$)
## Zfp687 mutation causes a severe PDB-like phenotype in aged mice
Next, we performed skeletal phenotyping of 16-month-old mice, the equivalent of 55-year-old humans, a state of full-blown pathology.14,20,25 Remarkably, $87\%$ of aged Zfp687P937R/+ and Zfp687P937R/P937R mice developed polyostotic osteolytic-like lesions, affecting the lumbar spine and the calvarial bones, sites usually affected in pagetic patients (Fig. 6a–c). Three-dimensional reconstruction from µCT analyses also revealed the formation of large protruding osteophytes at the medial and lateral knee joint margins in 8 out of 16 mutant animals (Fig. 6d). These ectopic outgrowths, although less frequent, were also identified at the spine (Fig. 6e). Altogether, osteophytes made the movement of affected mice slow and difficult (Supplementary Movie S1). In fact, bidimensional µCT reconstruction highlighted enlargement of the distal epiphysis of femurs and structural changes in the subchondral trabecular bone microstructure affected by osteophyte formation (Fig. 6f, arrowhead). The occurrence of osteophytes together with vertebral fusion (shown in Fig. 6a, b) is compatible with an ongoing osteoarthritic process.26Fig. 6Aged Zfp687P937R mutant mice develop pagetic lesions and osteophytes. a Representative µCT reconstructed 3D images of the spine (lumbar vertebrae) of wild-type and Zfp687P937R/+ mice, showing osteolytic cortical lesions and vertebral fusion in mutant mice at 16 months. b Representative H&E-stained sections showing the intervertebral disk degeneration and cartilage degradation of the joint space between lumbar vertebrae of a Zfp687P937R/+ mutant animal compared to a wild type animal (left). c Representative µCT 3D images of osteolytic cortical lesions in calvarial bone of the Zfp687P937R/+ mutant (right) compared to the wild type (left). Scale bar 1 mm. d, e Representative µCT 3D images of osteophyte formation at the knee joint (in d) and lumbar vertebrae (in e) of wild-type and Zfp687P937R mutant mice. Scale bar 1 mm. f Representative µCT cross-sections showing microarchitectural changes in subchondral bone (arrowhead) corresponding to osteophyte formation in the femur of the Zfp687P937R/+ mutant compared to the wild type. Scale bar 1 mm. g Representative µCT cross-sections showing osteosclerotic lesions and the ivory region (arrowhead) in the L4 vertebra of a Zfp687P937R/+ mutant compared to the wild type. Scale bar 1 mm. h Representative µCT cross-sections showing chaotic structure and trabecularization (arrowhead) of the femoral cortical bone in a Zfp687P937R/+ mutant compared to wild type. Scale bar 1 mm. i Histological sections of pagetic lesions in Zfp687P937R/+ femurs, showing woven bone through H&E staining (left) and polarized-light microscopy (right) Consistent with a pagetic phenotype, µCT scanning of 16-month-old mice also revealed osteosclerotic lesions in the lumbar vertebrae (Fig. 6g) and distal epiphyses of femurs (Fig. 6h) of mutant mice. We detected enlarged bones with ivory regions and trabecularization of cortical bone (Fig. 6h, arrowhead). Histological analysis of these lesions showed an increase in bone resorption and formation with accumulation of woven bone, as detected by polarized light microscopy (Fig. 6i). The frequency and type of skeletal defects detected in aged mice are reported in Table 1. Thus, altogether, these results illustrate that the P937R mutation is necessary and sufficient to fully develop a severe form of PDB-like. Table 1Phenotypic analysis of skeletal alterations in 16-month-old miceosteolysis in appendicular skeletonosteolysis in axial skeletonosteosclerosis in appendicular skeletonosteosclerosis in axial skeletonosteophyte at knee jointosteophyte at spineat least 2 sites affectedat least 3 sites affectedWild type$0\%$$11\%$$0\%$$0\%$$0\%$$0\%$$0\%$$0\%$Zfp687P937R/+$22\%$$70\%$$11\%$$20\%$$67\%$$30\%$$80\%$$45\%$Zfp687P937R/P937R$40\%$$60\%$$0\%$$0\%$$0\%$$0\%$$60\%$$20\%$
## Zfp687 is an essential driver of osteoclast differentiation
To obtain mechanistic insights into the role of the *Zfp687* gene in bone metabolism, we induced CRISPR/Cas9-mediated Zfp687 knockout in the murine RAW264.7 macrophage cell line. We selected three different heterozygous clones (Zfp687+/−). TRAP staining performed after 5 days of sRANKL stimulation revealed that osteoclast formation and differentiation were severely impaired in Zfp687+/− cells (Fig. 7a). Indeed, the number of mature osteoclasts, identified as TRAP-positive cells with more than 3 nuclei, dramatically decreased by an average of $73\%$ in all KO clones analyzed compared to the wild-type counterparts (Fig. 7b). Moreover, we observed that Zfp687+/− osteoclasts showed a strongly reduced surface area (average 3 800 µm2), while wild-type cells were typically larger (average 11 000 µm2) (Fig. 7c). This result indicates that a single copy of the Zfp687 transcription factor is not sufficient to drive proper osteoclastogenesis upon sRANKL stimulation. Fig. 7The pivotal role of ZNF687 in proper osteoclast differentiation. a Representative images of TRAP-stained osteoclasts from wild-type and Zfp687+/− RAW264.7 cells after 5 days of sRANKL stimulation. b Bar graph showing the mean number of TRAP+ osteoclasts (more than 3 nuclei/cell) in wild-type ($$n = 2$$) and Zfp687+/− ($$n = 6$$) clones. Data are presented as the mean ± s.d. Statistical significance was assessed by one-way ANOVA with Dunnett’s multiple comparison test (****$P \leq 0.000$ 1). c Bar graph showing the mean area of TRAP+ osteoclasts in wild-type and Zfp687+/− clones. d Volcano plot showing the distribution of differentially expressed genes (DEGs) between wild-type RAW264.7 and Zfp687+/− clones. In light blue, DEGs shared by wild type and Zfp687-KO osteoclastogenesis; in purple, DEGs in wild type but unchanged in Zfp687-KO osteoclastogenesis (log2foldchange ≥ 1 and ≤ −1); in red, selected DEGs in wild type but unchanged in Zfp687-KO osteoclastogenesis (log2foldchange ≥ 2 and ≤ −2). e Venn diagram of DEGs in wild-type and Zfp687-KO osteoclastogenesis. f Bar graphs showing the relative expression of genes selected for RNA-seq validation. Undiff: undifferentiated; OCs: osteoclasts. Data are presented as the mean ± s.d. Statistical significance was assessed by one-way ANOVA with a multiple comparison test (*$P \leq 0.05$; ****$P \leq 0.000$ 1) To identify Zfp687 target genes that may influence the correct process of osteoclast differentiation, we performed RNA sequencing on RNA extracted from wild-type RAW264.7 cells and three Zfp687+/− clones before and after RANKL osteoclastogenic induction for 5 days. Under physiological conditions, i.e., in wild-type extracts, a total of 1 322 genes were differentially expressed during the process of osteoclast differentiation: 1 055 upregulated and 267 downregulated genes (Fig. 7d). CLEAR (coordinated lysosomal expression and regulation) signaling, which regulates lysosomal biogenesis and function, was the top upregulated pathway ($$P \leq 5.24$$E-10).27 Conversely, transcriptomic profiling of the osteoclast differentiation process in Zfp687+/− cells highlighted that 381 genes previously detected as upregulated in the wild-type context remained unchanged (Fig. 7e, purple). Similarly, 47 genes previously found to be downregulated during control osteoclastogenesis were unchanged in the three mutant processes (Fig. 7d). These data suggest that these genes, whose expression was unperturbed by RANKL stimulation in Zfp687+/− cells, might be under the transcriptional control of Zfp687. We observed that the crucial genes for osteoclastogenesis, including Ctsk, Acp5 and Mmp9, were significantly upregulated in both control and mutated processes, confirming the phenotypic evidence that mutant osteoclastogenesis was severely impaired but not completely abolished. Therefore, we focused on the genes that remained unchanged in Zfp687+/− cells after stimulation. By using a more stringent cut-off parameter, we restricted our analysis to 92 upregulated and 5 downregulated genes (Fig. 7e, red). From this list, we selected the top differentially expressed genes and those with a clear involvement in osteoclast differentiation, obtaining a high-ranking list of 16 genes (15 up- and 1 downregulated) (Table 2). Among them, Tspan7, Cpe, Vegfc, and Ggt1 were previously found to have a role in osteoclastic differentiation.28–34 We confirmed through real-time PCR that their expression only increased in physiological osteoclastogenesis and remained unchanged in stimulated Zfp687+/− cells (Fig. 7f). In conclusion, we demonstrated that the Zfp687 transcription factor is a crucial regulator of osteoclast differentiation by regulating several important genes whose study could allow us to discover further molecular mechanisms at the base of the PDB.Table 2Information of 16 differential expressed osteoclastic genes associated with the loss of *Zfp687* geneGeneDescriptionGene IDlog2FoldChange in wild type OCsP-valuelog2FoldChange in Zfp687+/− OCsFunctionTSPAN710,20tetraspanin 7ENSMUSG000000582545,8043714532,04E-060,056380196regulates actin ring formation necessary for the bone resorbing activity of osteoclastsMSLNmesothelinENSMUSG000000630114,9138528816,01E-050,192468283DOK7docking protein 7ENSMUSG000000447164,5735574793,37E-050,114239406SLC4A3solute carrier family 4, member 3ENSMUSG000000065764,2801050791,33E-040AEBP1AE binding protein 1ENSMUSG000000204734,1813087451,73E-040,284734917CPE21carboxypeptidase EENSMUSG000000378524,1418463731,49E-040,483595016prohormone-processing enzyme upregulated during osteoclast differentiation induced by RANKLVEGFC22,23vascular endothelial growth factor CENSMUSG000000315204,0278540232,38E-050,148573253regulating osteoclast activity through an autocrine mechanismGGT124,25gamma-glutamyltransferase 1ENSMUSG000000063454,0154032441,55E-050,432081647induces the osteoclast formation independently of its enzymatic activity, by acting as a local cytokineNrgnneurograninENSMUSG000000533103,8456593244,21E-040,263986934AQP9aquaporin 9ENSMUSG000000322043,2571566971,25E-030,336258443CA6carbonic anhydrase 6ENSMUSG000000289723,0600209141,54E-030,194646503CTHcystathionine gamma-lyaseENSMUSG000000281792,6005143723,43E-040,330195106ANO7anoctamin 7ENSMUSG000000341072,5203906079,59E-080,416112132C22orf23chromosome 22 open reading frame 23ENSMUSG000000330292,1575037499,05E-050,473352822NUPR1nuclear protein 1, transcriptional regulatorENSMUSG000000307172,009656364,95E-050,17320238PDCD1programmed cell death 1ENSMUSG00000026285−2,4269260918,54E-05−0,409674667
## Zfp687P937R drives hepatocellular carcinoma in PBD model mice
Since PDB patients with the ZNF687 mutation are prone to developing giant cell tumor degeneration, we looked for bone tumors in Zfp687 mutant mice. Up to 24 months of age, this model did not develop any bone tumors. However, mutant mice still seem to be predisposed to tumorigenesis because 2 out of 6 heterozygous and 5 out of 11 homozygous mutant mice at 20 months of age developed multiple macroscopic hepatic nodules. We detected an average of 9 nodules/mouse ($95\%$ CI: 2–17), with a size of 46.41 ± 36.30 mm2 ($95\%$ CI: 36.7–56.1) (Fig. 8a). Histological characterization revealed that these nodules were fully developed hepatocellular carcinomas with occasional nodule-in nodule appearance (Fig. 8b), frequently hemorrhagic with large vascular lacunae, and commonly of trabecular or compact histotypes (Fig. 8c, d). Interestingly, the tissue architecture often appears severely deranged, with peliosis-like dilated sinusoids and loss of cell cohesiveness (a feature of more aggressive tumors in the Edmondson-Stainer score), even in the presence of low nuclear atypia and a conserved nuclear/cytoplasmic ratio (suggestive of well to moderately differentiated tumors in the WHO score) (Table 3).Fig. 8Histological characterization of hepatic nodules. a Gross appearance of a mutant Zfp687 liver. b Nodule-in-nodule HCC, (c) Trabecular HCC, (d) Macrotrabecular HCC with peliosis-like sinusoids. Scale bar, 100 µmTable 3HCC histological classification according to WHO and Edmondson-Steiner grade systemsWHO scoringWellModeratePoorZfp687P937R/+1 ($20\%$)4 ($80\%$)0 ($0\%$)Zfp687P937R/P937R7 ($25\%$)14 ($50\%$)7 ($25\%$)Nodule n. (%)8 ($24\%$)18 ($55\%$)7 ($21\%$)ES scoringG1G2G3G4Zfp687P937R/+0 ($0\%$)0 ($0\%$)5 ($100\%$)0 ($0\%$)Zfp687P937R/P937R0 ($0\%$)12 ($43\%$)9 ($32\%$)7 ($25\%$)Nodule n. (%)0 ($0\%$)12 ($36\%$)14 ($42\%$)7 ($21\%$)
## Discussion
Paget’s disease of bone (PDB) is a focal bone remodeling disorder in which osteoclasts appear giant-sized with increased bone destruction activity, and osteoblasts follow this process through their disorganized bone deposition activity.2 *As a* result, the remodeled bone becomes weaker, deformed and more likely to fracture. Previously, we described that the founder P937R mutation in the ZNF687 gene causes a severe form of PDB complicated by giant cell tumor degeneration (GCT/PDB).14,20,35 To determine the effect of the P937R mutation on bone metabolism and PDB pathogenesis, we generated the Zfp687 knock-in mouse model. Mice harboring the mutation, in heterozygosity and homozygosity, develop an impressive skeletal phenotype that worsens as they age, mirroring the late onset of PDB in humans. Specifically, µCT analyses performed on adult and aged mutant mice displayed bone remodeling alterations starting from 8 months of age, affecting both the axial and appendicular skeleton. This phenotype was highly pervasive at 16 months of age, in agreement with the age of appearance of full-blown disease in human patients. Of note, unlike PDB patients, adult knock-in mice show generalized trabecular bone loss, reminiscent of an osteoporotic phenotype. Nonetheless, mutant mice exhibit focal osteosclerotic lesions, indicating that focal bone remodeling alterations occur on a background of global bone reduction. Furthermore, adult mutant mice displayed increased osteoid thickness, which was not observed in younger mice and hence could likely be due to an enhanced rate of deposition of bone matrix, which is not yet mineralized, in response to high bone resorption. The ability of matrix mineralization detected ex vivo fully excluded a mineralization defect due to the P937R mutation.
Intriguingly, no P937R homozygous patient has ever been found in our cohort of PDB individuals, leading to the hypothesis that homozygosity for the mutation could be lethal. Nonetheless, homozygous mutant mice are viable and do not show a more aggressive phenotype than heterozygous animals, indicating that the human mutation is so rare that the chance for an individual to inherit two mutated alleles is highly unlikely. This observation also indicates that the inheritance of a single mutated allele is necessary and sufficient to drive the disease.
As PDB is a genetically heterogeneous disease, two other PDB mouse models have been described thus far, harboring the most common Sqstm1 mutation (P394L) found in human patients.17–19 These mutant mice displayed increased osteoclastic resorption,17 with typical nuclear inclusion bodies in osteoclasts as well as osteolytic lesions and woven bone.18,19 Bone alterations were exclusively detected at the appendicular skeleton, not earlier than 18 months of age. This phenomenon is presumably due to the pleiotropic role of p62, the multifunctional protein encoded by Sqstm1, which is involved in multiple cellular functions, such as clearance of misfolded protein, autophagy, cell survival, and regulation of the Keap1–Nrf2 and NFκB pathways.36 Another well-established Paget’s disease mouse model is generated by the transgenic expression of the measles virus nucleocapsid (MVNP) in the osteoclast lineage.37 MVNP mice developed PDB-like lesions that required MVNP-dependent induction of high IL-6 expression levels in osteoclasts, which in turn resulted in greater expression of IGF-1 and the coupling factor EphrinB2 as well as EphB4 on osteoblasts.38,39 Interestingly, this peculiar expression profile was not found in our mouse model, suggesting that various molecular pathways could underlie the occurrence of PDB-like lesions. The Zfp687 mouse model developed a severe bone phenotype, fully replicating the occurrence of osteolytic, mixed osteolytic/osteosclerotic, and osteosclerotic phases of Paget’s disease. Furthermore, here, we report that Zfp687 mutants display bone remodeling alterations at the spine, enabling researchers to model and study the disease. This finding is also in agreement with a more severe human PDB when caused by ZNF687 mutations. In these mice, the disease is so severe that additional complications occur. We indeed found osteophytes at the knee and vertebral joints and vertebral fusion in $50\%$ of aged mutant mice as a consequence of degeneration in osteoarthritis (OA). In fact, OA is a quite common complication of PDB, which becomes even more frequent in ZNF687-mutated patients (>$40\%$ of cases).3,14 In our mouse model, we also highlighted an unexpected increase in BMAT in both heterozygous and homozygous knock-in mice. However, increased marrow fat is not a prominent feature in ordinary human PDB. Therefore, we cannot exclude that this trait could be a peculiarity of ZNF687-dependent PDB. Such fat replacement of the bone was also observed in patients with familial expansile osteolysis, a focal bone remodeling disorder with a second peak of onset in elderly individuals, suggesting that BMAT alterations can be observed in bone dysplasia.40 In contrast, two different studies independently reported that BMAT-derived RANKL induces osteoclastogenesis and bone remodeling, indicating that excessive RANKL generated by bone marrow adipocytes is an underlying cause of skeletal disorders.41,42 Therefore, we cannot exclude a positive effect of the mutation on BMAT that further enhances osteoclast differentiation and therefore results in a more aggressive phenotype. Our data also indicated that BM-MSCs harboring the P937R mutation are capable of differentiating toward both osteoblast and adipocyte cell lineages with a higher efficiency than control cells. To explain this apparently counterintuitive phenomenon, which counteracts the mutually exclusive differentiation program described for fate-decision in one of the two different lineages,43 we could speculate that bone marrow of mutated mice might be enriched either in progenitors with both osteo- and adipo-differentiation properties or in MSCs with an increased proliferative ability. Thus, additional studies are needed to better understand the involvement of Zfp687 during BM-MSC fate commitment and differentiation.
In this study, we also reported a set of genes involved in osteoclastogenesis under the control of Zfp687. Among them, Tspan7, Cpe, Vegfc, and Ggt1 are described as having a role in regulating the bone-resorbing function of osteoclasts.28–34 Of course, additional functional studies are necessary to determine the specific effect of these genes on PDB pathogenesis or to identify additional molecular mechanisms. Finally, an open question related to this model remains the development of the tumor. PDB patients with the ZNF687 mutation, if untreated, undergo giant cell tumor degeneration. Up to 24 months of age, this model did not develop any bone tumors. Nonetheless, the evidence that mutants of Zfp687 did not drive GCT tumorigenesis within 24 months of age is not surprising: a single genetic alteration is frequently not sufficient to lead to a malignant phenotype in mice or otherwise with a lower penetrance than typically seen in humans.44 This phenomenon could be related to the notion that cancer is a multistep process, and more genetic and environmental events may be necessary for its development. However, mutant mice still seem to be predisposed to tumorigenesis because mutant mice at 20 months of age exhibited fully developed hepatocellular carcinomas (HCC). This result was consistent with the role for ZNF687 as an oncogene found in HCC.45 It remains to be investigated whether the development of HCC is due to biochemical alterations similar to those underlying the bone phenotype.
In conclusion, our Zfp687 mouse model provides a new tool to study and treat Paget’s disease of bone and its related complications, and functional analyses derived from RNA-seq data will enable a deep understanding of the transcriptional network regulated by Zfp687.
## Generation of the Zfp687P937R knock-in mouse model
*To* generate the PDB mouse model carrying the P937R mutation in the *Zfp687* gene, we adopted the homologous recombination strategy (Fig. S1a). Human and mouse gene homology was verified through the mVista bioinformatics tool (https://genome.lbl.gov/vista/index.shtml). A BAC library was used as a template to amplify the Zfp687 locus for homologous recombination. PCR fragments were cloned into the pGND vector as long and short homology arms. The P937R mutation (c.2810C>G) was introduced in the long homology arm by PCR-mediated mutagenesis (QuikChange Lightning, Agilent) using the following primers: sense 5′-GTTGGTCGGGGTCGCTCAGGGGAGC-3′; antisense 5′-GCTCCCCTGAGCGACCCCGACCAAC-3′. Targeting of the construct was performed in the E14Tg2a embryonal stem (ES) cell line, and targeted ES cell clones were identified by Southern blot analysis using a 3′ probe and an internal Neo probe on SphI-cut genomic DNA and a 5′ probe on BglII-cut genomic DNA. Sanger sequencing confirmed the presence of the c.2810C>G mutation. One Zfp687-mutated ES clone was injected into C57BL6 blastocysts to establish a mutant mouse colony. The flox-flanked neomycin resistance gene was removed by crossing the mice with a Cre transgenic mouse. These mice were generated on a B6D2F1/J background and maintained on a mixed genetic background of C57BL/6J and DBA/2J. Genotyping was performed with allele-specific primers: forward 5′-GACAGCCCTCTAAACCTCAAGACC-3′; reverse 5′-AGCAGGAGCATTAGTGTTGGATTC-3′, leading to the amplification of two different sized products depending on the presence or absence of the loxP site. Animals were handled in accordance with authorization no. 125-2021-PR released by the Italian Ministry of Health; all mice were housed in a pathogen-free barrier environment.
## Microcomputed tomography (μCT) analysis
Male mice were sacrificed by CO2 inhalation at the indicated age. The skin was removed, and femurs, tibiae, spine, and skull were cleaned from adherent and soft tissue, fixed in $4\%$ paraformaldehyde, PFA (Sigma‒Aldrich, #158127) for 24 h at 4 °C, and then stored in $70\%$ ethanol. μCT analyses were conducted using the SCANCO Medical-μCT40 (Scanco Medical AG, Bassersdorf, Switzerland). For bone morphometry, femoral trabecular and cortical bone were scanned using the following parameters: $E = 70$ kV; $I = 114$ μA; integration time of 600 ms; 3 μm isotropic voxel size. Femurs were scanned 1 mm from the distal board of the growth plate. Cortical thickness was measured at the midshaft region of the femur diaphysis. For femur trabecular bone and for midshaft cortical bone, 209 and 36 slices were analyzed, respectively. Vertebral trabecular bone was analyzed by selecting and scanning the whole L4 vertebra, using the last rib-bearing thoracic vertebra as a reference, with the following parameters: $E = 55$ kV; $I = 145$ μA; integration time of 300 ms; and 12 μm isotropic voxel size. The trabecular bone of the vertebral body was evaluated immediately below the superior growth plate. A lower threshold of 270 was used for the evaluation of all scans. For trabecular bone of the femur and L4 vertebra, the structural parameters bone volume/total volume (BV/TV), trabecular thickness (Tb. Th), trabecular number (Tb. N), and trabecular separation (Tb. Sp) were considered. For reconstruction of solid 3D images, selected bone samples were scanned at high resolution. Femur, lumbar spine, and skull samples were scanned using the following parameters: $E = 70$ kV; $I = 114$ μA; integration time of 300 ms; 6 μm isotropic voxel size. The reconstructed solid 3D images were applied to visualize bone morphology and microarchitecture.
## Histological analysis
For histology, long bones (femurs and tibiae) and the lumbar region of the spine were decalcified in $14\%$ ethylenediaminetetraacetic acid (EDTA, Sigma–Aldrich, #27285) for 14 days, replacing the solution every 3 days. Then, bone samples were dehydrated with an ethanol series ($70\%$, $80\%$, $90\%$, $100\%$ Et-OH), treated with xylene, and paraffin-embedded. Bone slices of 3 and 5 μm were obtained by manual microtome. Bone sections were subjected to a wax-removal procedure by xylene treatment and rehydration through a graded series of alcohol ($100\%$, $90\%$, $80\%$, $70\%$ Et-OH) and tap water. Bone sections were stained with hematoxylin and eosin (H&E) for general tissue morphology and with tartrate-resistant acid phosphatase (TRAP) (Sigma‒Aldrich, #387A) to detect osteoclast activity, according to standard protocols. After staining, bone sections were dehydrated, mounted with mounting medium (Bio-Optica, #05-BMHM), covered with a coverslip, and analyzed by transmission light microscopy using a Nikon Motorized Eclipse Ni-U Microscope and Nikon Manual Optical Microscope. Osteoclast surface and number were measured by TRAPHisto open-source software.46 For osteoblast quantification (Ob. N/BS), paraffin-embedded tibial samples were stained with H&E. Osteoblasts were identified as cuboidal cells lining the trabecular bone surface in the proximal metaphyseal region of tibiae. Osteoblasts were manually counted from 5 fields, 2 slices per animal (20X magnification), and the mean was calculated for each animal ($$n = 5$$ wild type; $$n = 4$$ Zfp687P937R/+; $$n = 7$$ Zfp687P937R/P937R). The bone surface was determined by ImageJ software. For BMAT quantification and adipocyte measurements, H&E staining was performed on tibiae. Adipocyte cells were identified by their thin cytoplasmic layer that lines the lipid droplet and forms the ghost-like remnant of the adipocytes. Adipocyte ghost cells were manually counted from at least 3 sections spaced 100 µm apart per animal (4X magnification) ($$n = 7$$ wild type; $$n = 6$$ Zfp687P937R/+; $$n = 8$$ Zfp687P937R/P937R). All adipocytes in each section were counted, and 160 adipocytes/section were measured for area evaluation. All measurements were determined using ImageJ software.
Liver tissues were collected and fixed in $4\%$ PFA for 24 h at 4 °C and then stored in $70\%$ ethanol. Then, samples were dehydrated with an ethanol series ($70\%$, $80\%$, $90\%$, $100\%$ Et-OH), treated with xylene, and paraffin-embedded. Hepatic sections of 5 μm were obtained by manual microtome and were subjected to wax-removal procedure by xylene treatment and rehydration through a graded series of alcohol ($100\%$, $90\%$, $80\%$, $70\%$ Et-OH) and tap water. Liver histology was assessed on H&E-stained sections. Tumor nodules were characterized using the WHO (“Classification of Tumours of the Digestive System”47 and Edmondson and Steiner48 grading systems.
## Von Kossa/van Gieson staining and osteoid quantification
For osteoid and matrix mineralization evaluation, 7 μm sections from methyl methacrylate (MMA)-embedded femurs of 3- and 8-month-old mice were stained with von Kossa and counterstained with van Gieson, according to standard procedures. Briefly, aqueous silver nitrate solution was added to the slides, which were then incubated with soda-formol solution for 5 min. Then, sodium thiosulfate was added and incubated for 5 min to remove unreacted silver. Von Kossa-stained samples were rinsed in tap water and counterstained with van Gieson solution for 30 min. Slices were mounted and covered with a coverslip and analyzed by transmission light microscopy. Osteoid quantification was performed using the threshold color function of ImageJ software.
## Generation of Zfp687 knockout RAW264.7 cell clones using CRISPR‒Cas9 technology
Zfp687 knockout RAW264.7 cell clones were obtained by CRISPR‒Cas9 technology. The small guide RNA (sgRNA) was designed using the tool at http://crispor.tefor.net/, targeting exon 2 at the fourth ATG, predicted to be surrounded by a *Kozak consensus* sequence (sense 5′-CACCGCCTCAAGGGGCCTTGAAAC-3′). The sgRNA was cloned into the pSpCas9(BB)-2A-GFP plasmid (Addgene, #48138). Then, the genetic transformation of RAW264.7 cells was obtained by nucleofection using the Amaxa Cell Line Nucleofector Kit V (Lonza) for RAW264.7 and following the protocol for Amaxa Nucleofector. After 24 h, single GFP-positive cells were sorted in 96-well plates with Becton Dickinson FACSAria III system. We obtained three heterozygous Zfp687 knockout clones; homozygous knockout clones were never detected. Clones harboring distinct heterozygous frameshift mutations (Zfp687+/−) were confirmed by Sanger sequencing.
## Cell culture
Primary murine bone marrow-derived mesenchymal stromal cells (BM-MSCs) were obtained from femurs and tibiae of 8-week-old mice, adapted from the method described in.49 Briefly, mice were euthanized through CO2, and immediately after sacrifice, femurs and tibiae were carefully cleaned of all connective tissues; both the distal and proximal ends of bones were cut, and the marrow was centrifuged out. ACK (ammonium-chloride-potassium) lysing buffer was used to eliminate red blood cells. Total bone marrow cells were cultured in complete expansion medium (MesenCult Expansion Kit (Mouse) #05513, Stem Cell Technologies) at 37 °C and $5\%$ CO2. BM-MSCs were expanded for 7 days. For osteogenic differentiation, cells were detached using $0.25\%$ trypsin-EDTA, plated in 24-well plates and cultured in complete expansion medium until they reached $80\%$–$90\%$ confluency. Then, for osteogenic differentiation, the medium was replaced with complete MesenCult Osteogenic Medium (MesenCult Osteogenic Stimulatory Kit (Mouse) #05504, Stem Cell Technologies), and the cells were cultured at 37 °C and $5\%$ CO2. The medium was changed every 3 days for 8 days. Differentiated osteoblasts were fixed in $70\%$ ethanol and stained with Alizarin Red Solution (Sigma‒Aldrich #A5533). Destaining was conducted to quantitatively determine mineralization by adding acetic acid. Absorbance was measured in the microplate reader PerkinElmer luminometer (Victor X3) at 405 nm.
For adipogenic differentiation, the medium was replaced with MesenCult Adipogenic Differentiation Medium (Mouse) #05507 (Stem Cell Technologies) for 6 days. Adipogenic differentiation was assessed by Oil Red O (ORO) staining (Sigma‒Aldrich #O1392) following the manufacturer’s instructions. For quantification, ORO was extracted by adding isopropanol, and absorbance was read in the microplate reader PerkinElmer luminometer (Victor X3) at 490 nm.
RAW264.7 cells were cultured in DMEM High Glucose GlutaMAX (Gibco) with $10\%$ FBS, $1\%$ penicillin/streptomycin, and $1\%$ L-glutamine at 37 °C and $5\%$ CO2. For osteoclast differentiation, 5 × 103 cells were plated in 24-well plates, and the medium was switched to Minimum Essential Medium α (MEM- α) GlutaMAX (Gibco) with $10\%$ FBS, $1\%$ penicillin/streptomycin, and $1\%$ L-glutamine. The next day, the medium was changed and supplemented with 100 ng·mL−1 sRANKL (Peprotech) for osteoclastogenic induction. The medium was changed every 48 h until the end of the differentiation (5 days upon stimulation). Differentiated osteoclasts were fixed in $4\%$ PFA and stained with tartrate-resistant acid phosphatase (TRAP) (Sigma‒Aldrich).
## Protein extraction and Western blotting
Total protein extraction from murine cell lines (RAW264.7 cells and osteoclast-derived cells) was performed in RIPA buffer (50 mmol·L−1 Tris–HCl pH 7.5; 150 mmol·L−1 NaCl; 1 mmol·L−1 DTT; 50 mmol·L−1 sodium fluoride; $0.5\%$ sodium deoxycholate; $0.1\%$ SDS; $1\%$ NP-40; 0.1 mmol·L−1 phenylmethanesulfonylfluoride; 0.1 mmol·L−1 sodium vanadate) with 1X proteinase inhibitor cocktail (Applied Biological Materials #G135). Protein quantification was obtained by the Bradford method (Bio-Rad #5000006). Protein samples were boiled at 95 °C for 5′ and then separated by SDS‒PAGE electrophoresis using $8\%$–$16\%$ Tris-glycine gels (Invitrogen #XP08160). Samples were transferred to a nitrocellulose membrane (Invitrogen #IB23002) and blocked with $4\%$ w/v nonfat dry milk dissolved in TBS-T (1X TBS, $0.05\%$ Tween-20) for 1 h at RT. Primary antibodies used for the Western blot experiments were rabbit anti-ZNF687 (1:3 000, Novus NBP2-41175), mouse anti-β-actin (1:10 000, Santa Cruz #47778), and mouse anti-α-tubulin (1:15 000, Sigma‒Aldrich #T6074). Membranes were incubated with secondary antibodies conjugated with HRP for 1 h at RT. The bands were visualized using enhanced chemiluminescence detection reagents (Advansta #K-12043-D10) and autoradiographic films (Aurogene #AU1101). Equal loading was confirmed by using antibodies against β-actin and α-tubulin. The intensity of the Western blot signals was determined by densitometric analysis using ImageJ software and normalized to the density value of the loading control. Protein extracts of peripheral blood mononuclear cells (PBMCs) and differentiated osteoclasts from a healthy donor and P937R-mutated patient were previously collected20 and already present in our laboratory.
## RNA isolation and qRT‒PCR analysis
Total RNA extraction (from BM-MSCs, mouse osteoblasts, RAW264.7 cells, and differentiated osteoclasts) was obtained using TRI Reagent (Sigma‒Aldrich #T9424) following the manufacturer’s instructions. One microgram of total RNA was reverse transcribed into cDNA using the RevertAID RT kit (Thermo Fisher #K1622). qRT‒PCR was performed using SYBR Select Master Mix for CFX (Applied Biosystems) and specific primers (listed in Table 4) on a CFX Opus RT PCR System instrument. The transcript levels were normalized to the levels of Gapdh within each sample, and the ΔΔCT method was used. The reaction was conducted in triplicate. cDNA of PBMCs and differentiated osteoclasts derived from healthy donors and P937R-mutated patients was already present in our laboratory. Table 4Sequence of primers used for qRT-PCRGeneSequence 5′-3′CTSKF: GTCAAAAATCAGGGTCAGTGR: GAAGGCATTGGTCATGTAGCMMP9F: CCCGGACCAAGGATACAGTTR: TTCAGGGCGAGGACCATAGATRAPF: TTCTCTGACCGCTCCCTTCGR: AGGCTGCTGGCTGAGGAAGTZNF687F: AGGCCAAGCTGATCTACAAGR: GATGGGTGTTCTTGAGATGTCpeF: CGAGCTGAAGGACTGGTTTGR: GGGGCAAGCTTTGAATTTTGGGgt1F: CTCAGAGATTGGACGGGATAR: GTGCTGTTGTAGATGGTGAAGTspan7F: CTGTCTCAAAACCCTCCTCAR: GAGGCCAAAAACCACGATGVegfcF: GATGTGGGGAAGGAGTTTGGR: TGATTGTGACTGGTTTGGGGZfp687F: AAGGAACATGGTAAGTCAGTR: GACCTGAACATGCTTCTCCA
## Library preparation and RNA sequencing
The libraries were generated using depleted RNA obtained from 1 μg of total RNA by a TruSeq Sample Preparation RNA Kit (Illumina, Inc., San Diego, CA, USA) according to the manufacturer’s protocol without further modifications. All libraries were sequenced on the Illumina HiSeq 1000, generating 100 bp paired-end reads. Illumina BCL2FASTQ v2.20 software was used for demultiplexing and production of FASTQ sequence files. FASTQ raw sequence files were subsequently quality checked with FASTQC software (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Subsequently, sequences with low quality scores or including adaptor dimers or mitochondrial or ribosomal sequences were discarded from the analysis. The resulting set of selected reads was aligned onto the complete mouse genome using Spliced Transcripts Alignment to a Reference algorithm STAR version 2.7.3 using GRCm39 Genome Assembly and GRCm39.105.gtf as gene definition.50 The resulting mapped reads were used as input for the feature Counts function of Rsubread packages and used as gene counts for differential expression analysis using the Deseq2 package.51 We used the shrinkage estimator from the apeglm package for visualization and ranking.52 Differentially expressed genes (DEGs) were selected based on an adjusted P value < 0.05 and by setting Log2FoldChange ≥ 1 for upregulated and ≤ −1 for downregulated genes. The cutoff was further increased to Log2FoldChange ≥ 2 for upregulated and ≤ −2 for downregulated genes for a more stringent filter. Selected DEGs were used as input to perform pathway enrichment analysis by the IPA system (Ingenuity® Systems http://www.ingenuity.com). IPA annotation was used as a starting point for focusing on osteoclast differentiation.
## Quantification and statistical analyses
All data are presented as the mean or median ± standard deviation (s.d.). The sample size for each experiment and the replicate number of experiments are included in the figure legends. Statistical significance was defined as $P \leq 0.05.$ Statistical analyses were performed using ordinary one-way ANOVA or Student’s t test (GraphPad Prism; version 9.3.1).
## Supplementary information
Figure S1 Figure S2 Figure S3 Figure S4 Video S1 Supplementary figure and video legends The online version contains supplementary material available at 10.1038/s41413-023-00250-3.
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|
---
title: 'Effect of combined therapies including nutrition and physical exercise in
advanced cancer patients: A pooled analysis'
authors:
- Lena J. Storck
- Alexandra Uster
- Lucia Gafner
- Maya Ruehlin
- Sabine Gaeumann
- David Gisi
- Martina Schmocker
- Peter J. Meffert
- Reinhard Imoberdorf
- Miklos Pless
- Peter E. Ballmer
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10014851
doi: 10.3389/fnut.2023.1063279
license: CC BY 4.0
---
# Effect of combined therapies including nutrition and physical exercise in advanced cancer patients: A pooled analysis
## Abstract
### Background and aims
Although many cancer patients suffer from malnutrition or cancer cachexia, there is no standard of care so far due to limited intervention trials. Pooled data from two combined trials were analyzed regarding nutritional status and survival time.
### Materials and methods
Data from two trials with advanced cancer patients were included. In both trials, patients in the intervention group received at least three times nutritional counseling and supervised training sessions. Patients in the control group continued being treated according to usual care. Nutritional status was measured using BMI, body composition and handgrip strength. Survival time was analyzed using the Cox proportional hazard model with the period between the beginning of the trial and death as underlying time scale.
### Results
68 men ($61.8\%$) and 42 women ($38.2\%$) were randomized either to the intervention ($$n = 56$$) or the control ($$n = 54$$) group. The inter-group difference for changes in BMI and body composition was not statistically significant after 3 months. Handgrip strength improved significantly from 34.4 ± 10.2 kg to 36.3 ± 9.9 kg at 3 months in the intervention compared to 33.9 ± 9.2 kg to 34.9 ± 9.1 kg in the control group ($$p \leq 0.006$$). The analysis of survival time showed no inter-group difference for all patients. A detailed analysis for different diagnoses showed that in patients with lung cancer, the covariates “CRP value,” “days from first diagnosis to randomization” as well as “gender” were significantly associated with survival time. Patients with higher CRP value had a shorter survival time and female patients had a shorter survival time than male patients in our analysis. In addition, patients with pancreatic cancer randomized to the control group had a $20\%$ shorter survival time than those in the intervention group ($$p \leq 0.048$$).
### Conclusion
The pooled analysis showed a significant improvement of handgrip strength in advanced cancer patients through the implementation of a combined therapy. Handgrip strength is of prognostic significance in hospitalized patients due to its association with mortality and morbidity. However, no improvements in further tests were detected. There is great need for further investigations examining the effect of nutritional and exercise therapy on survival time with focus on different cancer diagnoses.
## 1. Introduction
Approximately half of all tumor patients experience involuntarily weight loss during or even before their disease and suffer from malnutrition or cancer cachexia, especially patients with gastrointestinal cancer (1–3). Cancer cachexia is defined as “a multifactorial syndrome characterized by ongoing loss of skeletal muscle mass (with or without loss of fat mass) that cannot be fully reversed by conventional nutritional support and leads to progressive functional impairment” [4]. Therefore, the consequences of malnutrition and cachexia are a substantial impact on quality of life (QoL), impaired functional status, reduced therapy tolerance, and an increased number of unplanned hospital admissions [5, 6].
Although many patients are affected by malnutrition or cachexia, there is no therapy and no standard of care so far [7]. Since the pathophysiology of cachexia is complex, therapeutic approaches are intensely studied with a research focus on combined or multimodal therapy [8, 9]. In recent years, research activity on this topic has increased significantly, which is also evident in numerous systematic reviews. For example, Prado et al. [ 10] conducted a review about the effect of nutrition interventions on muscle status in cancer patients. They summarized that “given the positive findings and theoretical benefits of combining nutrition with other treatments, it is likely that such interventions would be beneficial for individuals with cancer at risk for losing muscle” [10].
In 2014, Grande et al. published a Cochrane Analysis on “exercise for cancer cachexia in adults” with the conclusion that there were no studies to make a qualified statement on effectiveness, acceptability, and safety of multimodal interventions [11]. Continued research activity allowed Grande et al. to publish an update of their Cochrane Analysis, including four new trials. But due to bias in most domains, i.e., selection bias or blinding, they were still uncertain to make a statement, referring to another update in the future [12]. Further reviews regarding exercise in patients with cancer include those by Allan et al. [ 13], focusing on exercise and energy regulation in cancer cachexia and Avancini et al. [ 14], investigating physical activity in patients with lung cancer [13, 14]. Both emphasized positive effects of physical activity on, for example, fatigue, QoL, pulmonary function, muscle mass, strength and psychological status. However, Allan et al. pointed out that exercise could increase the gap between energy need and energy intake in patients with cancer cachexia, emphasizing the importance of supporting those patients with nutritional counseling and individual exercise advice [13].
Several reviews about multimodal interventions in advanced cancer patients pointed out that there are positive effects on single components like endurance or depression scores as well as lean mass. The reviews concluded that further high-quality studies are needed in order to give clear recommendations [15, 16].
In recent years, we conducted several combined intervention studies in advanced cancer patients and were not able to achieve the calculated sample size in some of them [17, 18]. The reasons for this problem were manifold. For example, many patients could not participate in our trials because they did not meet the inclusion and exclusion criteria. For other candidates, the intervention was too strenuous or not feasible in addition to their cancer disease and treatment. Other researchers made the same experience. A two-arm, open-label, randomized multicenter controlled phase II trial conducted by Pascoe et al. [ 19] was terminated early due to slow recruitment rates. In this study for patients with advanced lung cancer, all patients received structured nutritional, exercise and symptom control advice. Patients in the intervention group additionally received a nutritional supplement to improve the management of cancer cachexia. The calculated sample size was $$n = 96$$ and only $$n = 38$$ patients could be recruited in five centers within 1 year. In the intervention group, 9 of 19 patients withdrew from the trial or died of tumor progression [19]. In another clinical trial investigating the effect of nutrition and electromyostimulation on gait parameters and physical function in advanced cancer patients, data from only $$n = 26$$ patients out of $$n = 58$$ in the intervention group could be analyzed. The main reasons for drop-out were a fast deterioration in clinical status, lack of time, death, therapy side effects, surgery or mental stress [20].
For the study at hand we pooled data from two clinical studies to obtain a larger sample size and thus more robust results [17, 18]. Using similar methodologies, we had investigated in both trials the effect of a combined therapy including nutritional counseling and physical exercise on nutritional status, QoL, and clinical course in advanced cancer patients.
## 2. Patients and materials and methods
This study used a pooled database of advanced cancer patients prospectively enrolled in two clinical trials. The two trials were designed to investigate the effect of a combined therapy including nutritional counseling and physical exercise on physical performance, nutritional status, body composition, fatigue and QoL. Both studies have been previously published [17, 18]. The study protocols were approved by the Cantonal Ethics Committee Zurich (Switzerland) and registered at http://clinicaltrials.gov (NCT01540968 and NCT0285362). Written informed consent was obtained from all patients before study inclusion.
## 2.1. Procedures
Eligibility criteria for the two trials included in this pooled analysis were as follows: patients with metastatic or locally advanced lung or gastrointestinal cancer, an Eastern Cooperative Oncology Group (ECOG) performance status (PS) of ≤ 2 and a life expectancy greater than 6 months as judged by the responsible physician. Patients were considered ineligible if they (i) were on artificial nutrition, (ii) had symptomatic brain metastases or bone metastases or (iii) had had an ileus within the last month.
In the second of the two studies, patients with the following tumor sites were also eligible for inclusion: breast, ovarian, prostate, renal cell, and urothelial. In addition, palliative breast and prostate patients had to be receiving chemotherapy. In the same trial, patients were ineligible if they (iv) had a milk protein allergy and (v) consumed supplements with branched-chain amino acids.
In both trials, the primary investigator enrolled patients and conducted the baseline assessment after written informed consent. After that, patients were randomized using block sizes of six respectively eight. Patients were assigned to either the intervention or the control group at a 1:1 ratio. Patients in the intervention group participated in a nutrition and physical exercise program, while patients in the control group were treated according to the cancer center’s standard medical therapy, following good clinical practice. All parameters were evaluated first at baseline, then 3 months later at the end of the intervention and again 3 months post intervention.
## 2.2.1. Physical exercise
In both trials, the patients in the intervention group conducted two training sessions per week in the hospital’s training facilities. Patients exercised in groups of two to six patients under the supervision of an experienced physiotherapist. One training unit of 90 min included a cardio-pulmonary endurance training either on bicycle-ergometers or on treadmills, and a strength training circuit covering different stations to train all larger muscle groups. The endurance intensity corresponded to a Borg scale-value of four to six (on a scale from zero to ten). When patients were receiving chemotherapy the same day, the intensity was set to a maximum of three on the Borg scale. For the strength part, the training goal was three sets of 10 to 15 repetitions. The strength training workload was adjusted at each session according to the individual patients’ fitness, and participants were instructed to increase resistance as soon as they were able to complete more than 15 repetitions. The second training session at the hospital consisted of a gym training of 60 min with focus on strength, endurance, balance and coordination. The training intensity corresponded to a Borg scale-value of four to five. In the second study, an additional third training session was conducted at home. According to their specific goals, patients could either choose to do an additional strength session with strength bands or an endurance training with walking or cycling for 30 min.
## 2.2.2. Nutritional counseling
The nutritional intervention by a registered dietitian comprised an extensive initial nutritional assessment followed by individual nutritional measures, i.e., enrichment of foods or energy- and protein-rich snacks. The patients’ nutritional situations were reassessed after 6 weeks and 3 months after the baseline-assessment. Further visits could be arranged as required throughout this period, depending on the clinical and nutritional course. The main objective of the nutritional intervention was for patients to meet protein requirements set at 1.2 g of protein per kg of actual body weight. The energy requirement was calculated according to the Harris-Benedict formula, taking into account factors for disease severity and activity [21]. In case of a BMI > 28 kg/m2, the energy requirement was calculated using the adjusted body weight. In both trials, nutritional supplements were given to the patients in the intervention group: protein-dense oral nutritional supplements in the first and a leucine-rich whey protein supplement in the second study.
## 2.3.1. Nutritional status
Patients were weighed without shoes and in light clothing. Body composition was assessed using bioelectrical impedance analysis (Body Composition Monitor, Fresenius Medical Care, Switzerland respectively BIA, Akern STA, Florence, Italy). In addition, the nutritional risk screening 2002 (NRS-2002) [22] was conducted. Handgrip strength was measured in the dominant hand using a hydraulic dynamometer (Jamar, Smith and Nephew, Memphis, TN, USA). The test was performed with patients in sitting position holding the elbow flexed at 90° and the forearm and wrist in neutral position. The test was repeated three times with a 1-min rest period between each repetition. The best result of the three measurements was recorded in kilograms (kg) [23, 24].
## 2.3.2. Dietary intake
After each study assessment, patients were asked to keep a non-consecutive 3-day food diary, including one weekend day, and to record the amount of all ingested foods, beverages, food fortifications, and supplements. The diary was explained with the help of a detailed manual. Volumes and portion sizes were estimated using a photo catalog containing several pictures of serving sizes, which was also handed out to the patients. Portion size was classified into three categories: small, medium, or large. All dietary records were analyzed by the same person, using the software “PRODI 6.2 basis” in the first respectively “6.7 swiss” in the second study (Nutri-Science GmbH, Hausach, Germany).
## 2.3.3. QoL
Quality of life (QoL) was determined with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire version 3.0 (EORTC QLQ-C30). The EORTC QLQ-C30 is a 30-item cancer-specific questionnaire including six function scales (physical, emotional, cognitive, social, role and global health status), three symptom scales (fatigue, pain, nausea/vomiting), and six single items assessing the symptoms and financial impact of the disease. Results of the EORTC QLQ-C30 were translated into scores corresponding to a scale of 0 to 100. Higher scores on the function scales indicate better functioning, whereas higher scores on the symptom scales denote impaired functioning [25, 26].
## 2.3.4. Clinical data
C-reactive protein (CRP), adverse and serious adverse events and unplanned hospital admissions were evaluated based on computerized patient hospital records.
## 2.4. Statistical analysis
Statistical analyses were performed using the programming language R version 3.6.0 (R Foundation, Vienna, Austria). We used Student’s t-test to compare changes in values within the 3 and 6 month period, respectively. If variable distribution were not approximately of Gaussian distribution, we applied the Mann-Whitney U test. To be able to use all datasets as sensitivity analyses, and since the missing values were assumed not to be completely at random, we used 20-fold multiple imputation by chained equations to estimate the missing values [27] implemented in the package “mice.” The number of imputations was chosen as the maximum percentage of missing variables according to recommendations of White et al. [ 28]. For the imputation, 99 relevant variables were used. T-tests for imputed data were done using the packages “MKmisc” and “mitools.” Since numbers within the intervention and control group were small, we also applied regression models to adjust for covariables. Mortality was analyzed using the Cox proportional hazard model with the period between beginning of the trial and death as underlying time-scale.
## 3. Results
In total, 110 patients were included in the pooled analysis (58 from the first and 52 from the second study). The baseline characteristics are shown in Table 1. 68 men ($61.8\%$) and 42 women ($38.2\%$) were randomized either in the intervention ($$n = 56$$) or control ($$n = 54$$) group. The mean age was 63.0 ± 10.2 years, and the average body mass index (BMI) was 25.3 kg/m2. Patients with lung cancer constituted the largest group with ($$n = 42$$, $38.2\%$), followed by patients with colorectal ($$n = 25$$, $22.7\%$) and pancreatic cancer ($$n = 20$$, $18.2\%$). At study inclusion, the groups were well-balanced with regard to demographics, medical characteristics, nutritional status and physical function. Groups were different, though, regarding the days that had passed from first tumor diagnosis to trial start: 419.7 ± 535.5 days for intervention and 772.0 ± 1056.7 days for control patients.
**TABLE 1**
| Unnamed: 0 | Total (n = 110) | Intervention (n = 56) | Control (n = 54) |
| --- | --- | --- | --- |
| Age (years) | 63.0 (± 10.2) | 63.0 (± 11.1) | 63.0 (± 9.2) |
| BMI (kg/m2) | 25.3 (± 4.8) | 25.0 (± 4.6) | 25.8 (± 4.9) |
| Days since diagnosis | 592.6 (± 847.9) | 419.7 (± 535.5) | 772.0 (± 1056.7) |
| Gender | Gender | Gender | Gender |
| Male | 68 (61.8%) | 36 (64.3%) | 32 (59.3%) |
| Female | 42 (38.2%) | 20 (35.7%) | 22 (40.7%) |
| Site of primary tumor | Site of primary tumor | Site of primary tumor | Site of primary tumor |
| Lung | 42 (38.2%) | 23 (41.1%) | 19 (35.2%) |
| Colorectal | 25 (22.7%) | 14 (25.0%) | 11 (20.4%) |
| Pancreatic | 20 (18.2%) | 9 (16.1%) | 11 (20.4%) |
| Others | 23 (20.9%) | 10 (17.9%) | 13 (24.1%) |
| Laboratory parameters | Laboratory parameters | Laboratory parameters | Laboratory parameters |
| CRP (mg/l) | 12.0 (± 23.0) | 12.8 (± 23.3) | 10.9 (± 23.3) |
| Albumin (g/l) | 40.1 (± 4.1) | 40.5 (± 4.4) | 39.6 (± 3.8) |
| Performance status (WHO) | Performance status (WHO) | Performance status (WHO) | Performance status (WHO) |
| 0 | 11 (10.0%) | 7 (12.5%) | 4 (7.4%) |
| 1 | 75 (68.2%) | 38 (67.9%) | 37 (68.5%) |
| 2 | 21 (19.1%) | 11 (19.6%) | 10 (18.5%) |
| Unavailable | 3 (2.7%) | | |
The inter-group difference for changes in BMI, body compartments, NRS, dietary intake, global health status and all symptoms of the EORTC were not statistically significant after 3 and 6 months (Table 2). The inter-group difference for changes in phase angle after 6 months was significant after t-test ($$p \leq 0.025$$), but not anymore after adjustment for covariates (Table 2).
**TABLE 2**
| Unnamed: 0 | Baseline | Baseline.1 | Δ 3 months | Δ 3 months.1 | Δ 3 months.2 | Δ 3 months.3 | Δ 6 months | Δ 6 months.1 | Δ 6 months.2 | Δ 6 months.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Intervention | Control | Intervention | Control | p | 95% CI | Intervention | Control | p | 95% CI |
| BMI (kg/m2) | 24.8 ± 4.1 | 25.9 ± 5.4 | 0.55 | 0.28 | 0.355 | −0.83, 0.30 | 0.64 | 0.60 | 0.908 | −0.74, 0.66 |
| NRS | 2.0 ± 1.0 | 1.9 ± 1.1 | −0.28 | 0.09 | 0.092 | −0.06, 0.81 | −0.34 | −0.14 | 0.241 | −0.14, 0.54 |
| Phase angle (°) | 5.1 ± 1.0 | 5.3 ± 0.8 | 0.06 | −0.12 | 0.199 | −0.46, 0.10 | 0.12 | −0.20 | 0.025 | −0.61, −0.04 |
| Lean tissue mass (kg) | 54.1 ± 21.2 | 56.2 ± 19.9 | −0.10 | 0.18 | 0.740 | −1.39, 1.95 | −0.75 | 0.26 | 0.356 | −1.16, 3.19 |
| Hand strength (kg) | 34.4 ± 10.2 | 33.9 ± 9.2 | 1.79 | −0.53 | 0.006 | −3.93, −0.69 | 1.25 | 0.55 | 0.422 | −2.44, 1.03 |
| Energy intake (kcal) | 2213.8 ± 689.4 | 2098.6 ± 738.5 | 30.14 | −142.73 | 0.251 | −470.25, 124.53 | −145.41 | −229.41 | 0.560 | −370.96, 202.96 |
| Energy intake (%) | 97.6 ± 30.9 | 90.7 ± 33.7 | −0.27 | −6.89 | 0.318 | −19.71, 6.48 | −6.93 | −8.32 | 0.818 | −13.41, 10.64 |
| Protein intake (g) | 87.8 ± 25.9 | 79.6 ± 29.9 | 6.07 | −3.65 | 0.082 | −20.69, 1.25 | −4.68 | −11.24 | 0.311 | −19.43, 6.31 |
| Protein intake (%) | 110.7 ± 41.3 | 95.6 ± 47.2 | −10.82 | −11.37 | 0.958 | −20.92, 19.83 | −23.20 | −34.58 | 0.360 | −35.99, 13.24 |
| Carbohydrate intake (g) | 241.5 ± 87.9 | 233.0 ± 91.0 | 3.56 | −0.20 | 0.845 | −41.81, 34.29 | −15.12 | −12.72 | 0.895 | −33.83, 38.63 |
| Fat intake (g) | 90.8 ± 36.5 | 85.9 ± 34.6 | −2.02 | −14.45 | 0.124 | −28.33, 3.48 | −7.66 | −13.24 | 0.484 | −21.45, 10.29 |
| Global health status | 65.8 ± 21.0 | 58.2 ± 19.8 | 2.16 | 5.00 | 0.463 | −4.81, 10.50 | 5.03 | 4.52 | 0.913 | −9.91, 8.88 |
Importantly, handgrip strength improved significantly from 34.4 ± 10.2 kg at baseline to 36.3 ± 9.9 kg at 3 months in the intervention group compared to 33.9 ± 9.2 kg at baseline to 34.9 ± 9.1 kg at 3 months in the control group ($$p \leq 0.006$$), both after t-test as well as after adjustment for covariates (Tables 2, 3).
**TABLE 3**
| Unnamed: 0 | β | p |
| --- | --- | --- |
| Handgrip strength baseline | −0.221 | 0.002 |
| Intervention group | 2.702 | 0.002 |
| Female | −3.672 | 0.005 |
| Age (years) | −0.101 | 0.025 |
| CRP (mg/l) | −0.775 | 0.046 |
Patients in the intervention group joined a mean of 16.3 ± 6.3 of 24 training sessions at the hospital ($67.9\%$). The mean number of individual nutritional counseling sessions was 3.5 ± 1.1 ($116.7\%$). No serious adverse events relating to the nutrition and physical exercise program occurred. There was neither a significant inter-group difference in the average of unplanned hospital admissions nor in the survival time (Table 4).
**TABLE 4**
| Unnamed: 0 | Unnamed: 1 | Parameter estimate | Risk ratio | p-value |
| --- | --- | --- | --- | --- |
| Lung cancer patients (n = 40, events n = 33) | Intervention group | 0.281 | 1.325 | 0.501 |
| Lung cancer patients (n = 40, events n = 33) | Female | −1.000 | 0.368 | 0.016 |
| Lung cancer patients (n = 40, events n = 33) | Age (years) | 0.005 | 1.005 | 0.813 |
| Lung cancer patients (n = 40, events n = 33) | Days since diagnosis* | −0.506 | 0.603 | 0.002 |
| Lung cancer patients (n = 40, events n = 33) | CRP (mg/l)* | 0.567 | 1.764 | 0.002 |
| Colorectal cancer patients (n = 21, events n = 16) | Intervention group | -0.316 | 0.729 | 0.667 |
| Colorectal cancer patients (n = 21, events n = 16) | Female | -0.037 | 0.964 | 0.959 |
| Colorectal cancer patients (n = 21, events n = 16) | Age (years) | 0.042 | 1.043 | 0.149 |
| Colorectal cancer patients (n = 21, events n = 16) | Days since diagnosis* | -0.553 | 0.593 | 0.175 |
| Colorectal cancer patients (n = 21, events n = 16) | CRP (mg/l)* | 0.255 | 1.29 | 0.407 |
| Pancreatic cancer patients (n = 18, events n = 18) | Intervention group | −1.191 | 0.304 | 0.048 |
| Pancreatic cancer patients (n = 18, events n = 18) | Female | 1.047 | 2.848 | 0.196 |
| Pancreatic cancer patients (n = 18, events n = 18) | Age (years) | −0.089 | 0.915 | 0.027 |
| Pancreatic cancer patients (n = 18, events n = 18) | Days since diagnosis* | 0.395 | 1.485 | 0.303 |
| Pancreatic cancer patients (n = 18, events n = 18) | CRP (mg/l)* | 0.844 | 2.326 | 0.026 |
The covariates “CRP” and “days from first diagnosis to randomization” were significantly associated with survival time. Patients with higher CRP value had a shorter survival time. A detailed analysis of survival time for the three main diagnoses (lung, colorectal, and pancreatic cancer) showed that in patients with lung cancer, the covariates “CRP value,” “days from first diagnosis to randomization,” and “gender” were significantly associated with survival time. Female patients had a shorter survival time than male patients in our analysis. The analysis for patients with colorectal cancer showed no significant associations at all. Patients with pancreatic cancer randomized to the control group had a $20\%$ shorter survival time than patients in the intervention group ($$p \leq 0.048$$), though.
## 4. Discussion
Data from two randomized intervention trials with advanced cancer patients were included in a pooled analysis regarding nutritional status and survival time. Handgrip strength, as an indicator for muscle strength and associated with short- and long-term mortality and morbidity [24, 29, 30], was the only parameter that showed significant improvement through the implementation of a combined therapy. No significant changes were detected in any of the other parameters, such as BMI, NRS, lean body mass, phase angle, energy, and protein intake, as well as QoL, though. In addition, we observed associations between survival time and several parameters, such as CRP. In our analysis, patients with pancreatic cancer randomized to the intervention group had a $20\%$ longer survival time.
Our results for nutritional status and QoL concur with the results of other trials investigating combined or multimodal therapies in advanced cancer patients (7, 31–33). In line with our results, an improvement in handgrip strength was observed [31] but further effects on muscle mass [7, 32] or QoL could not be detected [34]. In contrast to our results, Henke et al. [ 33] described a clear improvement in physical function [33], and both Schink et al. [ 34] and Stuecher et al. [ 35] observed a significantly higher muscle mass [34, 35]. Our results emphasize that muscle strength can be affected by a combined therapy including physical exercise due to muscular adaptation, which can lead to a greater increase in muscle strength than in muscle mass [36].
The reasons why multimodal interventions seldom effect significant changes could be multifaceted. Dhillon et al. [ 32] described the possibility of contamination or selection bias, when patients who were highly motivated to participate in an exercise program started to exercise more, even though they were randomized to the control group. This effect may have a high impact on the results by minimizing inter-group differences [32].
A second reason could be the heterogeneity of our study population. To achieve our sample size, we had to include patients with different diagnoses. Albeit focusing on patients with cancer types that are commonly associated with malnutrition (such as lung or pancreatic cancer), the state of malnutrition or cancer cachexia was no inclusion criteria. Jain et al. [ 37] investigated “the impact of baseline nutritional and exercise status on toxicity and outcomes in phase I and II oncology clinical trials” and found that patients with baseline malnutrition had poor outcomes. Hence, to strengthen trial results, the baseline nutritional and exercise status should be taken into consideration [37].
A third reason could be a particular imbalance between the study arms in both our trials: for patients in the control group, a substantially longer period had passed between diagnosis and study randomization than for those in the intervention group. Regarding this variable, the randomization inexplicably did not ensure a balanced distribution. On the one hand, it could be speculated that patients who have suffered from their tumor disease for a longer time could be in a worse general condition. On the other hand, these patients could have achieved a more stable general condition. Ultimately, the effect of this imbalance remains unclear.
Fourth, advanced cancer patients are dealing with a dynamic disease situation. Thus, potential positive effects of the intervention on QoL or other aspects might be overridden by the negative impact of disease progression [38].
Fifth, caloric intake and coverage of energy and protein requirements presented a small positive trend for the intervention, but no statistical significance. The large scatter in the data could be one reason for the failed significance. Since the intervention patients showed good adherence to the training and nutritional counselling sessions and adequately implemented the nutritional recommendations, we can rather exclude bad adherence to the study program as a principal reason for the wide scattering of the data. Ester et al. [ 39] conducted a feasibility trial of a “multimodal exercise, nutrition and palliative care intervention in advanced lung cancer patients.” While they could not find a significant change in energy and protein intake, either, they observed a $75\%$ class attendance, which is in line with our results [39].
In the follow-up analysis after 6 months, no parameter changed significantly between the two groups in comparison to the baseline level. The results of the intervention group seem to converge with the control group, although they have not yet reached the same level. A statement on possible long-term effects cannot be made with our study results.
Even though our nutrition and exercise program showed no significant positive effect on unplanned hospital admissions, adverse events and survival time in our pooled analysis, no negative inter-group impact could be observed, either. This is an important finding with regard to the safety of combined or multimodal programs and in line with several other trials. Combined trials including nutritional and physical therapy seem to be safe and feasible for advanced cancer patients [7, 15, 19, 40].
The patients’ survival time was analyzed depending on the three main diagnoses lung, pancreatic and colorectal cancer in this pooled analysis. We observed a significant association between survival time and the combined intervention in patients with pancreatic cancer. To date, survival has only been analyzed in few studies, and in particular, the impact of a combined program on different tumor diagnoses has not yet been conclusively investigated. Bargetzi et al. [ 41] conducted “a secondary analysis of a prospective randomized trial, comparing the effect of protocol-guided individualized nutritional support to standard hospital food on the mortality of hospitalized cancer patients.” They found significant improvements in mortality and other outcomes in the intervention group in the short-term. However, interaction tests did not show any significant differences in mortality across the cancer type subgroups [41]. In the future, more studies should be conducted with a research focus on survival, as it is undeniably an important outcome.
Three intervention studies are currently ongoing in which multimodal therapy options in cancer patients are investigated: First, the “Multimodal–Exercise, Nutrition and Antiinflammatory medication for Cachexie trial (MENAC)” [7], second, the “Nutrition and Exercise in elderly patients with advanced non-small cell lung or pancreatic cancer study (NEXTAC TWO)” [42] and third, the “Multimodal intervention care on cachexia in patients with advanced cancer (MIRACLE)” [43]. We are eagerly awaiting the results of these studies to further discuss our own results, especially because disability free survival is the primary endpoint in the NEXTAC TWO trial [42, 44].
Our pooled analysis has some limitations. First, only two trials could be included, and in both studies, the calculated sample size could not be reached. Notably, the problem of not achieving the sample size and the reasons why patients decline study participation – especially in trials with advanced cancer patients – should get addressed in future studies. Bland et al. ’s qualitative study [2022] focused on how people with advanced cancer and cachexia perceive exercise and identified barriers that keep them from exercising, such as, for example, fatigue. They concluded that cancer patients should get offered a combination of home-based and supervised options for exercise: “Combining unsupervised home-based with supervised exercise, which may include incorporating telehealth, may help balance patient exercise preferences that we identified in the current study” [45].
Second, the nutrition and exercise interventions in the two studies were not identical. In the second study, patients were instructed to perform an additional, third exercise session at home, and a leucine-rich supplement was used as part of the nutritional intervention.
The third and main limitation is the imbalance between the two groups. For patients in the control group, a longer period had passed between diagnosis and study randomization than for those in the intervention group, and the influence of this imbalance remains unclear.
In conclusion, the pooled analysis showed a significant improvement in handgrip strength in advanced cancer patients that had participated in a combined therapy. An impaired handgrip strength is an indicator of increased complications during hospital stays and decreased physical status [24]. Hence, handgrip strength is associated with mortality and morbidity and is consequently of prognostic significance in hospitalized patients. However, no improvements in further tests were detected. There is great need for further investigations examining the effects of nutritional and exercise therapy, especially on survival time with focus on different cancer diagnoses.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Kantonale Ethikkommission Zürich, Stampfenbachstrasse 121, 8090 Zürich. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
PB was the principal investigator in both studies. AU and LS were in charge of the study and data collection. LS and LG were in charge of writing the manuscript. PM conducted statistical analysis. All authors contributed to the analysis of the data, writing of the manuscript, and read and approved the final manuscript.
## Conflict of interest
PM was employed by company Corvus, Statistical Analysis Consulting, Altkalen, Germany. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: A novel ex-vivo isolated rabbit heart preparation to explore the cardiac effects
of cervical and cardiac vagus nerve stimulation
authors:
- Bettina Kronsteiner
- Max Haberbusch
- Philipp Aigner
- Anne-Margarethe Kramer
- Patrick M. Pilz
- Bruno K. Podesser
- Attila Kiss
- Francesco Moscato
journal: Scientific Reports
year: 2023
pmcid: PMC10014867
doi: 10.1038/s41598-023-31135-4
license: CC BY 4.0
---
# A novel ex-vivo isolated rabbit heart preparation to explore the cardiac effects of cervical and cardiac vagus nerve stimulation
## Abstract
The cardiac responses to vagus nerve stimulation (VNS) are still not fully understood, partly due to uncontrollable confounders in the in-vivo experimental condition. Therefore, an ex-vivo Langendorff-perfused rabbit heart with intact vagal innervation is proposed to study VNS in absence of cofounding anesthetic or autonomic influences. The feasibility to evoke chronotropic responses through electrical stimulation ex-vivo was studied in innervated isolated rabbit hearts ($$n = 6$$). *The* general nerve excitability was assessed through the ability to evoke a heart rate (HR) reduction of at least 5 bpm (physiological threshold). The excitability was quantified as the charge needed for a 10-bpm HR reduction. The results were compared to a series of in-vivo experiments rabbits ($$n = 5$$). In the ex-vivo isolated heart, the baseline HR was about 20 bpm lower than in-vivo (158 ± 11 bpm vs 181 ± 19 bpm). Overall, the nerve remained excitable for about 5 h ex-vivo. The charges required to reduce HR by 5 bpm were 9 ± 6 µC and 549 ± 370 µC, ex-vivo and in-vivo, respectively. The charges needed for a 10-bpm HR reduction, normalized to the physiological threshold were 1.78 ± 0.8 and 1.22 ± 0.1, in-vivo and ex-vivo, respectively. Overall, the viability of this ex-vivo model to study the acute cardiac effects of VNS was demonstrated.
## Introduction
There is growing evidence showing that various pathological conditions are associated with and affected by autonomic imbalances that manifest as dominance of the sympathetic over the parasympathetic activity1–4. Therefore, vagus nerve stimulation (VNS) has been considered to be a promising therapeutic approach to treat diverse pathological conditions, such as epilepsy5–8, depression9, and cardiac diseases10–14 by restoring the vagal tone to a physiological level.
In cardiac medicine, VNS has proven to promote cardioprotective and anti-fibrillatory effects, thus providing a promising therapeutic approach for non-pharmacological treatment of various cardiac pathological conditions, such as ventricular arrhythmias15, atrial fibrillation13 and heart failure16,17. However, the fact that VNS is mostly applied to the cervical level due to surgical ease of access and the possibility to target various organ-related fibers, it is often accompanied by difficultly controllable systemic off-target effects18–20.
Although numerous studies have explored diverse effects of VNS on HR and hemodynamic function, in-vivo11,21–24 and in-situ10,25–27, including anti-antiarrhythmic15 and cardioprotective effects28, or alleviation of hypertension29, the outcomes are diverse, and the impact of VNS on the cardiac activity is still not fully understood. One main hindrance to better understand the cardiac effects of VNS is the presence of autonomic reflexes in-vivo, anesthetic and analgesic effects, and inter-individual variations.
Addressing this problem, the purpose of this study was to establish a novel model of vagally innervated and fully isolated rabbit heart in order to study the cardiac effects of ex-vivo VNS under well-controllable und reproducible experimental conditions. The results were then compared to a series of in-vivo experiments.
## Ethical approval
For all experiments, female rabbits (New Zealand White, $$n = 5$$ in-vivo and $$n = 6$$ ex-vivo, 2.5–3.3 kg body weight, age of 3–4 months) were used. All experiments were approved by the Institutional Animal Care and Use Committee of the city of Vienna (BMBWF 2020-0.016.858-GZ 2020-0.016.858) and conducted following relevant guidelines and regulations. Experiments were conducted and reported in accordance with the ARRIVE guidelines. All surgical procedures were carried out under deep anesthesia and analgesia in ventilated animals.
## Anesthesia
Animals were premedicated using intramuscular injections of ketamine (Ketasol®, Richter Pharma, 50 mg/ml, 0.6 ml/kg bodyweight) and dexmedetomidine (Dexdomitor®, Zoetis, 0.5 mg/ml, 0.2 ml/kg bodyweight). Maintenance of anesthesia was achieved using sevoflurane (Sevorane®, AbbVie AG, Baar, Switzerland) dissolved in 4 l/min of $100\%$ oxygen through an endotracheal tube (inner diameter 2.5 cm). Fentanyl (Fentanyl Hameln, 50 μg/ml, 0.2 ml/kg/h, Hameln pharmaceuticals GmbH, Hameln, Germany) was administered intravenously for analgesia. In addition, fluids and electrolytes were provided by crystalloid solution (Elo-Mel isoton, Fresenius Kabi, Graz, Austria) and lactate-buffered Ringer’s solution, respectively, to maintain physiological blood pH and electrolyte levels. Blood gas was regularly measured and kept within physiological ranges (pO2: 95–100 mmHg, pCO2: 35–45 mmHg, pH 7.35–7.45) by adjustment of the ventilation frequency (25-26/min) and of the tidal volume (10 ml/kg body weight).
## Vagus nerve dissection in-vivo
For in-vivo, a surgical window of three to five cm was opened at the cervical level (Fig.1). The carotid sheath, containing the vagus nerve, the carotid artery, the internal jugular vein and the sympathetic trunk was opened. The cervical vagus nerve was dissected and separated from the aortic depressor nerve and the sympathetic trunk and was then cleaned from surrounding tissues in order to avoid any tissues between the cuff electrode and the nerve. Figure 1Surgical window showing the dissection of the right vagus nerve (VN) at the cervical level for instrumentation with cuff electrodes in a rabbit. The carotid artery runs parallelly to the cervical VN. Cr cranial, cd caudal.
After finalization of in-vivo VNS experiments, which are described in the following section, animals were euthanized using pentobarbital (Release®, WDT, 300 mg/ml, 1.6 ml/kg bodyweight).
## In-vivo setup and instrumentation
In-vivo VNS stimulation was performed at the mid-cervical level on the intact cervical VN, approximately 4–5 cm cranial to the branching point of the superior cardiac branch. The nerves were instrumented using a bipolar cuff-electrode of 0.75 mm diameter with contact spacing of 3 mm that was wrapped around the right cervical vagus nerve at the mid cervical level as shown in Fig. 2a,b. The leads of the electrode were connected to a linear isolated stimulator (STMISOLA, BioPac Systems). A standard 3-lead electrocardiogram (ECG) was acquired with the needle electrodes (MyoStim® Bipolar Bifurcate) placed on the limbs. The ECG signal was pre-amplified, low-pass filtered at 1000 Hz and high-pass filtered at 1 Hz using a differential amplifier (Warner Electronics DP-304A). All data were digitized and recorded using a dSPACE MicroLabBox system and a custom-developed software. Figure 2(a) Schematic of a cuff electrode wrapped around the cervical VN at mid-cervical level, approximately 4–5 cm cranial to the cardiac branching point of the superior cardiac branch. ( b) Surgical window of a rabbit instrumented with the cuff electrode placed at the mid-cervical level. Cr cranial, cd caudal, CB cardiac branch, VN vagus nerve.
## Vagus nerve dissection ex-vivo
Vagus nerve dissection for ex-vivo experiments was performed as described previously30,31. Briefly, a median skin incision was made from the mandibles to the sternum and further extended to the sternal xiphoid. The right cervical muscles were identified, and the carotid sheath, containing the internal jugular vein, common carotid artery, the VN and sympathetic trunk (ST) was opened followed by blunt dissection of the VN from the nodose ganglion (NG) caudally to the thoracic aperture. The cardiac branching point, where the superior cardiac branch separated from the VN trunk, could be identified approximately between 0.5 and 2 cm cranial to the epicardial fat pad, approximately at the level of the thoracic aperture as shown in Fig. 3.Figure 3Surgical dissection of the vagus nerve (VN) for ex-vivo experiments including superior cardiac branch (CB). Here, the VN is shown from its caudal half of the cervical level to caudal to the superior CB. The heart is exposed after opening of the pericardium. Cr cranial, cd caudal, PA pulmonary artery, RA right atrium, RV right ventricle.
## Isolation of the innervated isolated heart
The VN was completely dissected from the cervical level to the superior cardiac branch as described above. Next, the pectoral muscles were identified and cut to expose the subclavian vessels and the VN diving down under the muscles. Next, the thorax was opened by median sternotomy, the pericardium was opened and the right VN including the superior cardiac branch were further traced towards caudal until it dived into the epicardial fat pad as shown above in Fig. 3. Prior to excision of the heart, a bolus of heparin (1000 I.E./kg) was administered and the right VN was cut just above the nodose ganglion, followed by carefully separating the heart caudal from the thoracic arteries and cranial at level of the aortic arch. Care was taken to prevent the VN and the cardiac branch from mechanically rupture during surgery and heart excision. The heart was rapidly excised and immediately placed into ice-cold Krebs–Henseleit buffer.
## Ex-vivo setup and instrumentation
Finally, the aorta was quickly cannulated, and the heart was mounted to the isolated heart system. Perfusion of the innervated isolated heart was performed under constant pressure (80 mmHg) in Langendorff mode with erythrocyte-enriched Krebs-Henseleit buffer in order to improve oxygenation of the cardiomyocytes. The nerve was kept moist throughout the experiment using isotonic sodium chloride ($0.9\%$) to maintain vitality and excitability. Details on the setup of the isolated heart system and the chemical composition of the erythrocyte- enriched Krebs–Henseleit buffer are described in32,33.
The right VN was instrumented ex-vivo using two concentric needle electrodes connected to a linear isolated stimulator (STMISOLA, BioPac Systems), same as used for in-vivo experiments, with the cathode placed approximately 2 to 5 mm cranial to the cardiac vagal branch (CB) and the anode placed 2–5 mm further cranial to the anode as shown in Fig.4a,b. The ex-vivo experiments were principally performed in preparation for heart rate control strategies using a trans-fascicular intraneural microelectrode (TIME) array. Therefore, intraneural stimulation for the cervical and cardiac vagus nerve was performed using a bipolar array of needle electrodes to mimic the stimulation using TIME electrodes. Figure 4(a) Schematic showing the placement of needle electrodes. ( b) Isolated innervated rabbit heart mounted to the working heart system. A pair of needle electrodes is placed close to the superior cardiac branch (cathode) and cranial to the cervical VN (anode). ECG leads were placed into the right atrium for heart rate detection. cr cranial, cd caudal, CB cardiac branch, ECG electrocardiogram, VN vagus nerve.
In order to acquire ECG, two wire electrodes (MyoStim® Bipolar Bifurcate) were inserted into the right atrium (Fig. 4b). Same as for in-vivo, the ECG signals were pre-amplified with a gain of 1000 using a differential amplifier low-pass filtered at 1000 Hz, high-pass filtered at 1 Hz (Warner Electronics DP-304A). Same as in-vivo, a dSPACE MicroLabBox system and a custom-developed software were used for digitalization and recording of data.
## Assessing the chronotropic effects of VNS in-vivo and ex-vivo
In both setups, VNS was performed in synchronization with the cardiac cycle, where bursts of cathodic-anodic charge-balance rectangular stimulation pulses were applied in each cardiac cycle with respect to the R-peak of the ECG (Fig. 5a). Cardiac-synchronized stimulation may be defined by five main stimulation parameters: current amplitude (C), pulse width (PW), frequency (F), number of pulses (NP), and delay (D). The charge was calculated from the area under the stimulation signal curve (Fig. 5b).Figure 5Schematic of the cardiac-synchronized stimulation. ( a) Electrocardiogram (ECG) trace along with a stimulation burst with respect to the R-peak highlighted by the dashed lines in the ECG. ( b) Detailed view of biphasic pulses applied per stimulation burst. C current amplitude, PW pulse width, NP number of pulses, F frequency, D delay between R-peak and stimulation onset.
The right VN was stimulated for 30 s, followed by 30 s stimulation pause. *The* general excitability of the VN was assessed through the ability to reduce the HR by 5 bpm. The sensitivity to stimulate was assessed as the charge needed to reduce the HR by 10 bpm.
In-vivo, VNS was performed in open-loop, testing different value combinations for the five main stimulation parameters in the ranges specified in Table 1.Table 1VNS stimulation parameters applied in-vivo and ex-vivo. In-vivo VNSEx-vivo VNSPulse width (µs)50–200500Frequency (Hz)10–4030Number of pulses1–68Delay (ms)0–3000 In the ex-vivo isolated heart, closed-loop stimulation was applied where the current amplitude was adjusted to reduce the error between the measured instantaneous HR and the set HR (reduction by 10 bpm from baseline) while all other stimulation parameters remained constant (Table 1).
The initial selection of the stimulation parameter ranges was determined based on a previous work by Ojeda et al., where they presented a novel closed-loop method that allowed an optimized adaption of the stimulation parameters to VNS applications21. Selection of parameter ranges was refined from pilot in-vivo experiments that were performed in anesthetized rabbits in our institution. Basically, the upper limits for current, pulse width, etc. were increased from the initial values in order to evoke greater cardiac effects. Further details on the stimulation strategies can be found in Haberbusch et al.31,34.
## Data processing and analysis
All data were processed using MATLAB R2022a (Mathworks, Natick, Massachusetts, United States) and GraphPad prism software (GraphPad Software, San Diego, CA, United States).
## Comparison of baseline heart rates between in-vivo and ex-vivo condition
To ensure comparability of the HR in the ex-vivo isolated heart and in-vivo condition, the baseline HR prior to VNS was calculated for both preparations and is presented as mean and standard deviation. The mean baseline HR of the in-vivo group was calculated from recordings of HR over 5 min after surgical dissection and instrumentation of the VN when HR was settled at stable values. Similar to that, for the ex-vivo group, the HR was calculated as the mean HR recorded during surgical dissection of the VN and as 5 min mean after the heart was reperfused ex-vivo.
The baseline HR recorded in ex-vivo conditions, just prior to heart excision (in-situ) was determined for comparisons of the baseline HR in-vivo versus ex-vivo (in-situ) (Fig. 6) in order to ensure comparability of the two experimental setups. Figure 6Comparison of the baseline heart rate in-vivo versus ex-vivo before and after heart explantation. Data are represented as mean and standard deviation (SD). The “*” symbol indicates statistical significance. Bpm: beats per minute. In-vivo: heart rate measurements after instrumentation in-vivo, in-situ: heart rate measurements prior to heart explantation in the ex-vivo animals, ex-vivo: after heart explantation in Langendorff mode, HR heart rate.
## Comparison of stimulation charges
In order to distinguish stimulation responses from physiological HR fluctuations, the physiological threshold charge was determined as the charge needed to reduce HR by 5 bpm. To compare the 10-bpm charges between ex-vivo and in-vivo condition, they were normalized with respect to the physiological threshold charge as listed in Table 2.Table 2Mean baseline heart rate and charges required to reach a reduction of heart rate by 5 bpm (physiological threshold charge) and 10 bpm (10-bpm-charge), respectively. ParameterIn-vivoEx-vivoBaseline HR (bpm)181 ± 19158 ± 11Physiological threshold charge (µC)549 ± 3709 ± 610 bpm charge (µC)918 ± 546 10 + 7Normalized 10-bpm-charge (xPT)1.78 ± 0.81.22 ± 0.1Overshoot$\frac{3}{52}$/6Stimulation typeOpen loopClosed loop10-bpm-charges were normalized to enable a comparison between both stimulation strategies in-vivo versus ex-vivo. Overshoot responses describe cases of short heart rate increases by 5 bpm after heart rate has returned to baseline after stimulation was turned off. xPT: physiological threshold charge. Data are given as mean and standard deviation.
For ex-vivo data, physiological threshold charges and charges needed for reducing the HR by 10 bpm were calculated from the last stimulation burst preceding the time point where the HR reached 5 bpm and 10 bpm reduction, respectively.
For the analysis of the in-vivo data, HR responses had to be pooled, because the stimulation parameters (Table 1) were not fixed but the parameters C, PW, NP, F and D as described in “Assessing the chronotropic effects of VNS in-vivo and ex-vivo”. were combined differently. For this purpose, the HR responses were binned in blocks of 5 bpm and the mean charge, and the threshold charge were calculated identically to the ex-vivo experiments.
## Assessing waveform features of HR decrease after VNS ex-vivo and in-vivo
Curves representing the time-course of HR changes before, during, and after VNS were analyzed to observe peculiar dynamic features following stimulation, which were described as overshoots in this study.
## Statistical analysis
Data analysis was performed using MATLAB R2022a (Mathworks, Natick, Massachusetts, United States) and GraphPad prism software (GraphPad Software, San Diego, CA, United States). The data were analyzed using descriptive statistics. Normal distribution of data was assessed by the Shapiro–Wilk test. An unpaired t-test was used to compare mean baseline HR and normalized charges between in-vivo and ex-vivo condition. An F-test was applied to compare variances of normalized 10-bpm charges between groups. Data is presented as mean ± standard deviation (SD). Results were deemed statistically significant for $p \leq 0.05.$
## Comparison of baseline heart rates between in-vivo and ex-vivo conditions
Baseline values obtained after preparation of the nerves and the heart [in-vivo: 181 ± 19 bpm, ex-vivo (in-situ) 182 ± 10 bpm] were similar in both preparations. After explantation, the baseline HR in the ex-vivo heart declined from 182 ± 10 bpm to 158 ± 11bpm. Differences in baseline HR between both setups are displayed in Fig. 6.
The difference between in-vivo and ex-vivo (in-situ) was not statistically significant (unpaired t-test, $$p \leq 0.21$$, Fig. 6), whereas the difference between mean baseline values in-vivo and ex-vivo post-explantation groups was statistically significant (unpaired t-test, $$p \leq 0.03$$, Fig. 6).
## Stimulation charges needed for different levels of chronotropic responses
The charges needed to reduce the HR by 5 bpm were 549 ± 370 µC in-vivo and 9 ± 6 µC ex-vivo. To reach a 10-bpm HR reduction, the respective charges were normalized to the physiological threshold charge, was 1.78 ± 0.8 in-vivo and 1.22 ± 0.1 ex-vivo (Table 2).
Next, the differences between mean normalized 10-bpm charges in-vivo and ex-vivo (Fig. 7) were compared, which was 0.6 ± 0.3 xPT, being not statistically significant (two-tailed t-test, $$p \leq 0.14$$) In contrast, the variance of normalized 10-bpm charges were significantly different in-vivo and ex-vivo condition (F-test, $$p \leq 0.0005$$), suggesting that greater variations of in-vivo than ex-vivo in order to achieve a 10-bpm HR reduction. Figure 7Comparison of the normalized charges required to reduce heart rate by 10 bpm ex-vivo versus in-vivo. Data represent the mean values from five experiments in-vivo ex-vivo, and six experiments ex-vivo. Data are presented as mean and standard deviation (SD).
## Overshoots observed after VNS ex-vivo and in-vivo
Overshoots were observed in several experiments in-vivo and even ex-vivo. In both setups, an overshoot was detectable as a short rise of HR about 5–10 bpm above baseline, after VNS was turned off, followed by a return to baseline. Out of all experiments, three overshoots were observed in $\frac{3}{5}$ experiments in-vivo and two overshoots were observed in $\frac{2}{6}$ experiments ex-vivo (Table 2) as also exemplarily shown in Fig. 8. Here, the HR curves before, during and after VNS for the in-vivo (Fig. 8a,b) and ex-vivo conditions (Fig. 8c,d) are presented. Figure 8a,c represent the HR curves without overshoot in-vivo and ex-vivo, respectively (“post stimulation”). Figure 8b,d represent the HR curves with overshoot in-vivo and ex-vivo, respectively. In Fig. 8a an exemplary HR response to open-loop VNS in the in-vivo experiment shows a HR reduction by approximately 19.1 bpm from 178.6 bpm baseline HR to 159.5 bpm after stimulation. There was no overshoot present. Figure 8b shows the HR change in response to open-loop VNS with overshoot. The HR was reduced by 21.3 bpm, which is similar to the stimulation response presented in Fig. 8a. However, interestingly we can see an overshoot of the HR after stimulation was turned off, represented by a pronounced increase of the HR to 9.2 bpm above baseline before it eventually settled to the baseline level again. Figure 8Representative ECG recordings for in-vivo (a,b) and ex-vivo VNS (c,d), each represented without and with overshoot. Curves a and c represent the ECG curves without overshoot in-vivo (a) and ex-vivo (c), respectively (“post stimulation”). Curves (b,d) represent the ECG curves with overshoot in-vivo (b) and ex-vivo (d), respectively after stimulation was turned off. The onset of stimulation is demonstrated by the decrease of heart rate in this ECG, whereas the stop of the stimulation is highlighted increasing heart rate. Overshoots are defined as an immediate increase of heart rate by at least 5 bpm above baseline after stimulation was turned off, followed by a return of heart rate back to the actual baseline.
Figure 8c,d represents an exemplary HR response for closed-loop VNS in the ex-vivo isolated heart. Here, the HR declined (Fig. 8c) from 162.5 to 142.8 bpm by 19.7 bpm without overshoot. Figure 8d shows the HR change in response to closed-loop VNS with overshoot. The HR was reduced by 18.5 bpm from 168.8 to 150.3 bpm, which is quite similar to the stimulation response presented in Fig. 8c. In contrast, this HR curve (Fig. 8d) shows an overshoot of the HR after stimulation was turned off, as presented by a conspicuous increase of the HR to 172.4 bpm before it returns to the baseline level again.
## Discussion
Vagus nerve stimulation (VNS) has proven to be an organ-protective and potential alternative therapeutic approach to treat various pathological conditions by restoration of the autonomic balance, such as atrial fibrillation13, ventricular arrhytmias15, persistent tachycardia35, and heart failure2,36,37. However, previous studies have also shown that in-vivo VNS is still mainly performed at the cervical level of the VN, which has shown to often provoke unwanted off-target effects, and consequently limits the therapeutic efficacy of those applications30,38,39. In cardiac medicine, in-vivo VNS has shown to have strong impact on cardiac activity, such as by reduction of HR and atrioventricular conduction40. The decrease of HR after VNS is also accompanied by activation of antagonistic hemodynamic feedback loops and inter-individual variations, which further complicates the investigations of specific effects of VNS strategies in-vivo14,41,42. As an alternative attempt to account for this problem, the research group of Ng., Brack et al. have previously presented a model of in-situ innervated isolated rabbit heart, in which the heart was separated from the circulation but was still left in-situ. Both autonomic nerves were dissected and stimulated in order to investigate the direct stimulation effects on cardiac activity and atrioventricular conduction26,43–45. However, both approaches of VNS, in-vivo and in-situ, could not yet provide a model to study target-specific effects of VNS, such as more selective stimulation of the cardiac VN in a fully ex-vivo isolated innervated heart model.
In this study, a novel experimental setup of a fully ex-vivo isolated innervated rabbit heart with vagal innervation is presented that allows for investigations of acute cardiac effects of VNS. Our ex-vivo model delivered reproducible results in $$n = 6$$ isolated innervated rabbit hearts, therefore potentially providing an experimental setup for VNS without interventions of physiological reflexes and inter-individual variations.
This present study, to the best of our knowledge, is the first approach that investigated the feasibility and viability of stimulating a fully ex-vivo innervated isolated heart by comparing applied stimulation charges in this ex-vivo model to well-studied models of in-vivo VNS21,22,46,47. The in-vivo experiments were performed using a cuff electrode as this is a commonly used non-invasive approach for cervical VNS. The ex-vivo experiments, in contrast, were performed in preparation for heart rate control strategies using a trans-fascicular intraneural microelectrode (TIME) array. Therefore, ex-vivo stimulation was performed as an intraneuronal stimulation approach using a bipolar array of needle electrodes mimicking the stimulation with two TIME contacts, which serves the purpose to reduce off-target stimulation and increase the cardiac selectivity. For the ex-vivo stimulation of the VN, the stimulation site was moved closer to the heart and stimulated the VN just proximal to the superior cardiac branch using needle electrodes, which is also described in Berthoud et al.48 and Haberbusch et al.49. The fact that this intraneural approach is more invasive compared to the approach of cervical VNS using cuff electrodes, however, limits this kind of study to acute investigations. Nevertheless, this approach of intraneural stimulation provides a potential improvement of acute experiments for selective cardiac VNS with recruitment of an increased number of cardiac and less off-target fibers30,50–52.
The evaluation of the viability and comparability of this ex-vivo approach was performed by comparing this ex-vivo model to an already well-established in-vivo experimental setup. Firstly, the baseline HR obtained were investigated in-vivo and ex-vivo in order to ensure that comparable physiological conditions were given before VNS was performed. Therefore, the baseline HR was compared between the in-vivo group versus the ex-vivo group before explantation (ex-vivo “in-situ”, Fig. 6) and after heart explantation (“ex-vivo”, Fig. 6). It turned out that the mean baseline HR in-vivo and ex-vivo (in-situ) was almost the same in all animals. In contrast, a significant decrease of HR was observed ex-vivo after heart explantation versus in-vivo (from 182 ± 10 bpm to 158 ± 11 bpm ($$p \leq 0.014$$)). Comparing the findings of this study to literature, it turned out that our recorded baseline HR ex-vivo were similar to those reported by Brack et al., who measured baseline HR in Langendorff-perfused rabbit hearts about 146 ± 210 and 153 ± 4 bpm25. A possible reason for the abrupt decrease of baseline HR ex-vivo are most likely caused by multiple factors. One possible explanation is that, from a physiological perspective, the transfer of the heart and the de-afferentiated nerve to ex-vivo conditions cause a sudden shift in the cardiac metabolism and in the electrophysiology of the VN. The activity of sinoatrial node cells, for instance, slows down when changes of temperature occur in their environment, which is the case when the heart was briefly cooled down after explantation in order reduce the cardiac metabolism and the speed of degradation53. Additionally, during the short time frame between heart explantation and reperfusion of the isolated innervated heart, one cannot exclude the occurrence of reperfusion injuries and short periods of ischemic conditions.
Secondly, the charges that were required to reduce HR by 5 bpm (physiological threshold charge) and 10 bpm were compared in-vivo versus ex-vivo. Data has shown that the absolute charges applied for VNS greatly varied between both setups. In-vivo, a tendence towards higher absolute charges was observed to elicit a chronotropic response compared to ex-vivo and the inter-individual differences were also greater in-vivo compared to ex-vivo. Therefore, in order to reach a better comparability of the data by reducing the differences between both data sets, the 10-bpm charges were normalized to the physiological threshold charge. The normalized data have revealed a greater inter-individual variation of charges in-vivo versus ex-vivo but still delivered satisfying reproducibility and controllability of the stimulation results. In contrast, the data of absolute charges demonstrated the greater inter-individual differences of applied charges in-vivo versus ex-vivo, which possibly emphasizes the impact of the type of electrodes used for stimulation on the one hand and individual reactions in-vivo to anesthesia, circulating catecholamines, and the presence of autonomic feedback loops on the other hand17,54,55.
Lastly, the number of observations of HR overshoot responses after termination of VNS was counted in the data sets of in-vivo and ex-vivo experiments. There are various reasons for the occurrence of such events, all being related to autonomic cardiac control. However, since we observed these events not only in-vivo but also ex-vivo, we compared the occurrence of these events in both experimental conditions. Overshoot events were observed in $\frac{3}{5}$ in-vivo and $\frac{2}{6}$ ex-vivo experiments (Fig. 8). Interestingly, overshoots were also found in the ex-vivo recordings, even though the heart was isolated from the neuronal control centers and the circulating catecholamines in the hemodynamic system. Previous studies have stated that the autonomic control of the heart is regulated at three stages, namely by the central, intrathoracic, and intracardiac level and that all three centers communicate via afferent and efferent directions with each other56–58. The intracardiac regulation is composed of ganglionated sympathetic and parasympathetic plexuses, which are embedded in the epicardial fat pad forming an interconnected neuronal network that integrates neural signals between intracardiac ganglia and higher cardiac regulation centers59,60. A former study by Hanna et al. reported about a similar observation, which was referred to as “post-vagal tachycardia”61. This observation was explained by the fact, firstly, that cholinergic and catecholaminergic neurons are anatomically clustered in ganglia and interconnected near the sinoatrial node. Secondly, both groups of cells are involved in this biphasic response to initial reduction and subsequent increase of HR during stimulation of the intracardiac nerve cells. Hence, we hypothesize that the occurrence of overshoots in our ex-vivo study might also be explained by the intracardiac circuits and crosstalk between intracardial sympathetic and parasympathetic ganglia as the HR decreases.
One aim of this study, besides selective cardiac VNS ex-vivo, was the surgical development of this model as well as its establishment by means of a proof-of-concept, which was achieved with a HR reduction of at least 5 to 10 bpm. In particular, the cofounding factors described in this study (surgery, invasiveness of the intraneural electrodes, stimulation protocols) are important to be taken into consideration. Therefore, a series of pilot experiments was necessary in which both, open-loop and closed-loop stimulation approaches, were investigated. From a series of 25 rabbit hearts, six hearts could be used for a complete closed-loop VNS protocol. Out of the remaining 19 hearts, we could provoke stimulation responses in 16 hearts, of which 8 hearts were used to establish the setup and instrumentation. This work completes a previous work by our group, demonstrating the approach of closed-loop VNS in this isolated-heart setup31.
In sum, this study highlights the challenges and advantages of an ex-vivo experimental setup and provides a novel approach towards advanced experimental setups for cardiac neuromodulation under defined experimental conditions. It further helps to gain a deeper understanding of specific questions with respect to effects of VNS on electrophysiology and cardiac physiology31,41.
## Limitations of the study
The main limitation of this study is that we used two different types of electrodes in the ex-vivo and in-vivo setup. While this compromises a direct comparability of the results, normalizing the 10-bpm charges to the physiological threshold charge still allows the two settings to be compared. The use of two different types of electrodes and the presence in-vivo of physiological factors absent in the ex-vivo setup are most likely causing the differences in stimulation thresholds in the two setups. The quantification of the relative importance of these two effects were not performed systematically apart from the proposed normalization strategy. Also, an evaluation of VNS stimulation in the ex-vivo and in-vivo setups in terms of ECG derived parameters (e.g. heart rate variability, arrhythmia) would be also advisable, but could not be performed in the current setting. Therefore, follow-up research and experimentation efforts are ongoing to the use of intraneural electrodes also in-vivo for direct comparability and also to extend the ex-vivo system to the so-called working mode (closed-loop circulation) in which an extensive full characterization in terms of chronotropic, dromotropic and inotropic responses to VNS can be performed.
Furthermore, we expect to improve this model to systematically provoke greater ranges in HR reduction that are comparable to current therapeutic applications of VNS.
## Conclusion
To conclude, in this study we presented a novel ex-vivo innervated isolated heart model that allows for investigation of direct cardiac effects of VNS in absence of uncontrollable cofounding factors, as present under in-vivo conditions. We could stimulate the VN from the cervical level to the superior cardiac branch and consistently achieved physiological heart responses for several hours. Lastly, we showed that the normalized charges for HR reduction were similar in this ex-vivo preparation compared to the in-vivo setup. Overall, this study provides promising results that pave the way for further investigations in this field.
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|
---
title: Self-reported ongoing adherence to diet is associated with lower depression,
fatigue, and disability, in people with multiple sclerosis
authors:
- Maggie Yu
- George Jelinek
- Steve Simpson-Yap
- Sandra Neate
- Nupur Nag
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10014880
doi: 10.3389/fnut.2023.979380
license: CC BY 4.0
---
# Self-reported ongoing adherence to diet is associated with lower depression, fatigue, and disability, in people with multiple sclerosis
## Abstract
### Introduction
Increasingly, dietary improvements have been shown to have positive associations with health outcomes in people with multiple sclerosis (pwMS). However, adhering to a MS-specific or high-quality diet may be a challenge. We therefore assessed the level of diet-adherence necessary to improve health outcomes of depression, fatigue, and disability.
### Methods
Data from an international population of pwMS followed over 7.5 years ($$n = 671$$) were analyzed. Self-reported diet quality via diet habits questionnaire (DHQ), and adherence to six MS-diets [Ashton Embry Best Bet, McDougall, Overcoming MS (OMS), Paleolithic (Paleo), Swank, and Wahls] were queried at two timepoints. Four levels of diet adherence were assessed: non-adherence at either timepoint; ceased at second timepoint; commenced at second timepoint; and ongoing at both timepoints. Associations between adherence to OMS and high-quality diet (DHQ score > median) with depression, fatigue, and disability, were assessed by log-binomial regression models adjusted for confounders.
### Results
Forty-two percent of pwMS reported ongoing-adherence to a MS-diet at both timepoints, OMS ($33\%$), Swank ($4\%$), Wahls ($1.5\%$), other (<$1\%$). Of these, only OMS-diet adherence was analyzed for associations due to data availability. Ongoing-adherence to the OMS-diet or a high-quality diet, was associated with lower depression compared to non-adherence [OMS: Risk ratios (RR) = 0.80, $$p \leq 0.021$$; DHQ: RR = 0.78, $$p \leq 0.009$$] and ceased-adherence (OMS: RR = 0.70, $$p \leq 0.008$$; DHQ: RR = 0.70, $$p \leq 0.010$$), respectively. Ongoing-adherence to OMS-diet was associated with lower fatigue (RR = 0.71, $$p \leq 0.031$$) and lower severe disability (RR = 0.43, $$p \leq 0.033$$) compared to ceased-adherence.
### Conclusion
Results suggest potential benefits of adherence to the OMS- or a high-quality diet on MS health outcomes, with ongoing-adherence likely best. Diet modification and maintenance may serve as a point of intervention to manage MS symptoms, especially depression, in pwMS.
## 1. Introduction
Dietary modification is increasingly emerging as a safe and feasible approach to manage symptoms and improve health and wellbeing of people with multiple sclerosis (pwMS) [1]. The earliest reports suggesting a role for diet in MS were from epidemiological studies showing that populations living in coastal parts of Norway with more seafood intake had lower frequencies of MS compared to inland populations with diets high in saturated animal fats [2, 3]. Subsequent studies based on these observations, showed pwMS adhering to a diet of < 20 g saturated fat per day, had less disability and less mortality over multiple decades’ follow-up [4]. These studies lead to the development of the Swank diet in 1950s, which recommends minimized intake of saturated fats and processed food [5].
In addition to the Swank-diet, MS-diets proposed include Overcoming MS (OMS), Ashton Embry Best Bet, McDougall, Paleolithic (Paleo), and Wahls [6, 7]. These diets are similar in recommend intake of fruit and vegetables and limited intake of processed foods; and differ in elimination of certain foods. Some studies have shown adherence to MS-diet is associated with improved health outcomes in pwMS [8, 9]. For example, the OMS diet, a plant-based whole food (food that has been processed or refined as little as possible), low-saturated fat diet with seafood [10], has been cross-sectionally associated with lower fatigue, depression, and disability [11]. Adherence to the OMS diet, as part of a multimodal lifestyle program, was prospectively associated with improved quality of life (QoL), and reduced fatigue and depression among 274 pwMS [12]. Adherence to the low-fat, plant-based McDougall resulted in reduced body mass index (BMI) and improved fatigue in a 12-month clinical trial of 61 pwMS [13]. Adherence to the Paleo diet, which limits foods that became common when farming emerged, was associated with reduced fatigue and improved QoL in a 3-month clinical trial among 17 pwMS [14, 15]. The Wahls diet, a modified Paleo diet to increase intake of nutrients key to neuronal health and limit lectins, has also been shown to reduce fatigue and improve QoL in a clinical trial of 77 pwMS [16]. The Ashton Embry Best Bet diet excludes dairy, gluten, legumes, and refined sugars [17]; however, associations between this diet with MS health outcomes have not yet been reported. In addition to MS-specific diet programs, many pwMS adhere to the Mediterranean and other anti-inflammatory diets [6, 9] and reported better MS outcomes such as lower fatigue and disability in observational [18, 19] and clinical trial studies [20, 21].
High-quality diets, which emphasize an overall pattern of intake that is high in fruits, vegetables, whole grains, and fish, and low in refined sugars, processed meat, and saturated fat, have been associated with better health outcomes in pwMS [1]. Diet quality has been assessed using different measurements such as food diaries, food frequency questionnaire [22], dietary screener questionnaire (DSQ) [23], and the diet habits questionnaire (DHQ). Cross-sectionally, studies have reported associations between high-quality diet with lower depression, pain, fatigue, disability and higher QoL [11, 23, 24]. Prospectively, high-quality diet was associated with lower disability at 2.5-year follow-up [25] and lower depression at 10-years [26]. MS-diet adherence has been positively associated with diet quality [7]. Higher DSQ scores were seen in pwMS who followed Swank and Wahls diets [1]. PwMS who adhered to the OMS-diet were 3.5 times more likely to have a high-quality diet indicated by DHQ scores above the median [11].
While the MS diets adherence and high-quality diet have demonstrated potential benefits on health outcomes, robust evidence supporting long-term dietary modification is limited due to insufficient sample sizes and short durations of dietary adherence [15, 27]. Moreover, ongoing adherence to diet may be challenging for some due to individual barriers of health, finance, lack of support, and personal motivation [28, 29]. Furthering our previous study showing that adherence to OMS- and high-quality diets are cross-sectionally associated with lower fatigue, depression, and disability [11], we assess whether level of adherence impacts health outcomes. We compare non-adherence to partial- and ongoing-adherence, to gain insights into potential health benefits, and whether these are sustained upon cessation of diet, or whether they are beneficial with later commencement. These findings may guide pwMS and health professionals on prioritization of dietary behaviors for MS management and secondary prevention.
## 2.1. Study design and participants
Data were analyzed from the health outcomes and lifestyle in a sample of pwMS (HOLISM) longitudinal observational study, the methodology of which has been previously described [30]. Briefly, pwMS were recruited via social media platforms for pwMS from October to December 2012. Consenting participants aged ≥ 18 years, and with a self-reported clinician-diagnosis of MS were eligible ($$n = 2$$,466). Participants completed an online survey capturing sociodemographic, clinical, lifestyle behaviors, and health data. Participants were then surveyed at 2.5-year intervals thereafter. Adherence to MS-diet was queried at 5- [2017] and 7.5-year [2019] timepoints; thus, analyses were restricted to pwMS who completed both surveys ($$n = 671$$, $27\%$). Ethical approval was granted by the University of Melbourne Human Research Ethics Committee (ID # 1545102).
## 2.2. Demographics and clinical characteristics
Age was calculated from reported dates of birth and survey completion. Sex, country of residence, highest level of education (no formal education, primary school, secondary school, vocational school, bachelor’s degree, and postgraduate degree), employment (employed in paid work, unemployed and seeking paid employment, unemployed and not seeking paid employment, stay at home parent or carer, student, retired due to age, retired due to medical reasons or disability), and perceived relative socioeconomic status (SES) [31] were queried and re-categorized (Table 1).
**TABLE 1**
| Unnamed: 0 | Excluded participants (0-year) | Study participants (0-year) | Study participants (at 7.5-year) |
| --- | --- | --- | --- |
| | N = 1,795 | N = 671 | N = 671 |
| | Mean (SD) | Mean (SD) | Mean (SD) |
| Age, years (mean, SD) | 45.7 (10.6) | 45.7 (10.2) | 53.3 (10.2) |
| MS duration, years (mean, SD) | 8.15 (7.5) | 7.88 (6.9) | 15.5 (6.9) |
| | n (%) | n (%) | n (%) |
| Sex | Sex | Sex | Sex |
| Male | 288 (16.9%) | 129 (19.2%) | 129 (19.2%) |
| Female | 1,415 (83.1%) | 542 (80.8%) | 542 (80.8%) |
| Country of residence | Country of residence | Country of residence | Country of residence |
| Aus/NZ | 523 (29.1%) | 314 (46.8%) | 391 (41.2%) |
| UK | 300 (16.7%) | 119 (17.7%)** | 180 (19.0%) |
| US/Canada | 758 (42.2%) | 157 (23.4%)*** | 253 (26.7%) |
| Other | 214 (11.9%) | 81 (12.1%)** | 124 (13.1%) |
| University degree | University degree | University degree | University degree |
| No | 791 (44.4%) | 208 (31.1%) | 178 (26.8%) |
| Yes | 992 (55.6%) | 461 (68.9%)*** | 486 (73.2%) |
| Employment | Employment | Employment | Employment |
| Paid employment | 916 (51.4%) | 438 (65.3%) | 361 (54.6%) |
| Unemployed | 349 (19.6%) | 104 (15.5%)*** | 80 (12.1%) |
| Retired | 518 (29.1%) | 129 (19.2%)*** | 220 (33.3%) |
| BMI | BMI | BMI | BMI |
| Under/healthy | 966 (54.6%) | 435 (64.8%) | 398 (59.3%) |
| Overweight | 412 (23.3%) | 144 (21.5%)* | 155 (23.1%) |
| Obese | 393 (22.2%) | 92 (13.7%)*** | 118 (17.6%) |
| MS type | MS type | MS type | MS type |
| Non-progressive | 1,105 (63.0%) | 486 (73.0%) | 486 (73.0%) |
| Progressive | 393 (22.4%) | 105 (15.8%)*** | 105 (15.8%) |
| Unsure/other | 255 (14.6%) | 75 (11.3%)** | 75 (11.3%) |
| Disability (P-MSSS) | Disability (P-MSSS) | Disability (P-MSSS) | Disability (P-MSSS) |
| Normal/mild | 894 (54.8%) | 444 (68.0%) | 445 (67.1%) |
| Moderate | 436 (26.7%) | 141 (21.6%)*** | 153 (23.1%) |
| Severe | 302 (18.5%) | 68 (10.4%)*** | 65 (9.8%) |
| Fatigue (FSS > 5) | Fatigue (FSS > 5) | Fatigue (FSS > 5) | Fatigue (FSS > 5) |
| No | 749 (49.4%) | 382 (61.3%) | 381 (61.1%) |
| Yes | 766 (50.6%) | 241 (38.7%)*** | 243 (38.9%) |
| Depression (PHQ-2 > 2)a | Depression (PHQ-2 > 2)a | Depression (PHQ-2 > 2)a | Depression (PHQ-2 > 2)a |
| No | 1,213 (77.2%) | 586 (90.0%) | 553 (87.0%) |
| Yes | 359 (22.8%) | 66 (10.1%)*** | 83 (13.1%) |
| Comorbiditiesb | Comorbiditiesb | Comorbiditiesb | Comorbiditiesb |
| 0 | 964 (53.7%) | 411 (61.3%) | 482 (71.8%) |
| ≥ 1 | 831 (46.3%) | 260 (38.8%)** | 189 (28.2%) |
MS phenotype was re-categorized into non-progressive (benign/RRMS), progressive (SPMS/PPMS/PRMS) and unsure/other; MS duration was calculated from year of diagnosis and survey completion date. Participants’ report of ongoing symptoms due to relapse in the preceding 30 days was dichotomised to No/Yes. BMI was calculated by weight/height2 and classified as per World Health Organization guidelines [32]; underweight and normal weight were consolidated due to small sample size in the former group. Comorbidity number was assessed by self-administered comorbidity questionnaire (SCQ) [33] and dichotomised to 0 and ≥ 1. Participant use of prescription medication for depression and fatigue was also queried (No/Yes). Participants were also queried at each timepoint whether they were experiencing ongoing symptoms due to recent relapse in the preceding 30 days.
## 2.3. MS-diet adherence
Diet adherence was queried by No/Yes response to “Do you currently follow a particular MS diet?,” with a Yes response allowing multiple selection from options of Ashton Embry Best Bet, McDougall, OMS, Paleo, Swank, and Wahls. As multi-diet selection was possible, and follow-up was 2.5 years, it is possible that an individual may be represented in more than one diet group.
Duration of adherence to each MS-diet was queried by response to “How long have you been following this diet?” with options from < 12 months and 1–20 years (1-year intervals). Stringency of adherence was queried by response to “How rigorously have you followed this diet?” assessed on a 5-point Likert scale, where 1 = not rigorously at all and 5 = very rigorously. For analysis, adherence to a MS-diet was defined as ≥ 12-month duration and stringency of ≥ $\frac{3}{5}$, consistent with our prior study [11].
## 2.4. High-quality diet adherence
Diet quality was assessed using a modified form [34] of the DHQ [35], querying intake of fruit/vegetables, takeaway, fat, fiber, food choices, and food preparation. A summary score was calculated with a possible score range of 20–100. Higher DHQ scores indicate higher quality of diet. Total DHQ score was dichotomised at the median to differentiate high- and low-quality diet [11].
MS-diet and high-quality diet adherence was defined based on adherence at each timepoint (0 = No; 1 = Yes): non- (0–0), commenced- (0–1), ceased- (1–0), and ongoing-adherence (1–1).
## 2.5. Health outcomes
Depression was measured via the patient health questionnaire short version (PHQ-2) at baseline (0-year), which contains two items inquiring about the frequency of depressed mood over the past 2 weeks on a 4-point Likert scale (0 = not at all to 3 = nearly every day). The PHQ-2 score ranges from 0 to 6 and with scores > 2 indicate major depressive disorder [36]. At 5- and 7.5-year timepoints, depression was measured via the PHQ-9. The PHQ-9 includes PHQ-2 and additional 7 items on depression; total scores range from 0 to 27 and score > 4 represent depressive symptoms [37].
Fatigue was measured by the 9-item fatigue severity scale (FSS), where a mean score > 5 was defined as clinically significant fatigue [38].
Disability status was measured by the patient determined disease steps [PDDS; [39]], from which the disease duration-adjusted patient-determined MS severity score (P-MSSS) was derived and categorized as normal/mild (0–3), moderate (4–5) and severe (6–8) disability [40].
## 2.6. Statistical analysis
All analyses were conducted in Stata version 16.0 (StataCorp, College Park, USA). Differences in cohort characteristics between the analysis sample and those lost to follow-up (LTFU) were assessed by t-test and log-binomial regression for continuous and binary/categorical variables, respectively. Statistical significance was set at $p \leq 0.05.$
Only adherence to OMS-diet was analyzed individually, as other MS diets had too few participants adhering. Participants followed OMS-diet was tested against not following OMS-diet. Associations between OMS-diet and high-quality diet (DHQ scores > median) adherence at 5- to 7.5-year with depression and fatigue were assessed by log-binomial regression models. Categorical disability was compared between normal/mild vs. moderate, normal/mild vs. severe and moderate vs. severe, using log-binomial regression models. Risk ratios (RR) and $95\%$ confidence intervals (CI) were generated. All models were adjusted for ongoing symptoms from recent relapse at 5- and 7.5-years, and clinical outcomes at 5-years. Models were further adjusted for age, sex, perceived SES, education, employment, MS duration, disability (for fatigue and depression), fatigue (for disability), number of comorbidities, and use of antidepressant and anti-fatigue medication (for fatigue and depression).
## 3.1. Participant characteristics
Of 2,466 baseline HOLISM participants, 671 ($27\%$) completed 5- and 7.5-year surveys and were included in the study (Table 1). Compared to participants LTFU ($73\%$), the included participants were more likely to be residents of Australia or New Zealand, university educated, employed, of non-progressive MS type, having normal/mild disability, and less likely to be overweight or obese, or to have fatigue, depression, or ≥ 1 comorbidity.
Study participants in the analysis sample at 7.5-year timepoint were predominantly female, of mean age 53 years, $41\%$ living in Australia or New Zealand, $73\%$ with a university degree, and $55\%$ undertaking paid employment. A majority were of non-progressive MS type, and mean MS duration was 16 years. Most participants were of underweight/healthy BMI, $67\%$ of participants had normal/mild disability, $58\%$ reported fatigue, $51\%$ reported depression, and $28\%$ reported ≥ 1 comorbidity.
## 3.2. Adherence to MS-diet and high-quality diet
Overall, $54\%$ of pwMS reported adherence to a MS-diet at 5-years; comprising $44\%$ OMS, $7\%$ Swank, and $7\%$ other diets (Table 2). Ongoing-adherence was higher than ceased or commenced in most MS diet programs. The highest rate of ongoing-adherence was for OMS-diet, with $76\%$ ($\frac{221}{292}$) adhering at 5- and 7.5-years and $11\%$ ($\frac{71}{292}$) ceasing. Of the $56\%$ ($\frac{379}{671}$) of pwMS who did not follow OMS-diet at 5-years, $5\%$ ($\frac{34}{379}$) commenced-adherence at 7.5-years.
**TABLE 2**
| Adherence at 5-year | No (0) | No (0).1 | No (0).2 | Yes (1) | Yes (1).1 | Yes (1).2 |
| --- | --- | --- | --- | --- | --- | --- |
| Adherence at 7.5-year | Non (0–0) | Commenced (0–1) | Total | Ceased (1–0) | Persistent (1–1) | Total |
| MS-dieta | 269 (40%) | 41 (6%) | 310 (46%) | 81 (12%) | 280 (42%) | 361 (54%) |
| OMS | 345 (51%) | 34 (5%) | 379 (56%) | 71 (11%) | 221 (33%) | 292 (44%) |
| Swank | 613 (91%) | 13 (2%) | 626 (93%) | 20 (3%) | 25 (4%) | 45 (7%) |
| Wahls | 648 (97%) | 5 (1%) | 653 (97%) | 8 (1%) | 10 (1%) | 18 (3%) |
| Paleo | 652 (97%) | 7 (1%) | 659 (98%) | 8 (1%) | 4 (1%) | 12 (2%) |
| Ashton Embry Best Bet | 665 (99%) | 1 (0%) | 666 (99%) | 1 (0%) | 4 (1%) | 5 (1%) |
| McDougall | 664 (99%) | 4 (1%) | 668 (99%) | 2 (1%) | 1 (0%) | 3 (1%) |
| High-quality dietb | 267 (43%) | 47 (8%) | 314 (51%) | 47 (8%) | 255 (41%) | 302 (49%) |
Ongoing-adherence to Swank-diet was lower: $56\%$ ($\frac{25}{45}$) adhered at both timepoints, $44\%$ ceased. Of $93\%$ ($\frac{626}{671}$) pwMS who did not adhere to Swank-diet at 5-year, only $2\%$ commenced-adherence at 7.5-years. Very few pwMS adhered to other MS diets, ranging from $3\%$ (Wahls diet) to below $1\%$ (McDougall diet) at 5-years.
Ongoing-adherence to high-quality diet was $41\%$ (Table 2); $8\%$ of pwMS increased (commenced) or decreased (ceased) diet quality from 5- to 7.5-years.
## 3.3. Associations between OMS-diet adherence and health outcomes
Ongoing-adherence to OMS-diet was associated with lower relative risk of depressive symptoms, fatigue and disability compared to non- and/or ceased-adherence to the diet (Table 3 and Figure 1A). Ongoing-adherence to OMS-diet had $20\%$ (RR = 0.80, $p \leq 0.05$) lower relative risk of depressive symptoms than non-adherence and $30\%$ (RR = 0.70, $p \leq 0.01$) lower relative risk of depressive symptoms than ceased-adherence. Ongoing-adherence to OMS-diet also had $29\%$ (RR = 0.71, $p \leq 0.05$) lower relative risk of fatigue compared to ceased-adherence (Figure 1A). For disability, ongoing-adherence to OMS-diet had $57\%$ (RR = 0.43, $p \leq 0.05$) lower relative risk of severe rather than moderate disability compared to ceased-adherence (Figure 1A). Significant differences were not observed between non-adherence and ceased/commenced-adherence, or between commenced- and ongoing-adherence.
## 3.4. Associations between high-quality diet and health outcomes
Adherence to high-quality diet was associated with depressive symptoms, but not fatigue and disability (Table 3 and Figure 1B). Compared to non-adherence, ongoing-adherence to high-quality diet was associated with $22\%$ (RR = 0.78, $p \leq 0.05$) lower relative risk of depressive symptoms (Table 3). Ongoing-adherence was also associated with $30\%$ (RR = 0.70, $p \leq 0.05$) lower relative risk of depressive symptoms than ceased-, but not commenced-, adherence (Figure 1B).
## 4. Discussion
While diet has been associated with positive health outcomes in pwMS, the role of ongoing diet-adherence is under-explored. We compared non-adherence to the OMS- and high-quality diet to partial and ongoing-adherence at two timepoints over 2.5-year period on depressive symptoms, fatigue, and disability. Compared to non- or ceased-, ongoing-adherence was associated with optimal health outcomes in all analyses. Ongoing-adherence to the OMS- or a high-quality diet was associated with lower depressive symptoms than non- and ceased-adherence; and ongoing-adherence to the OMS-diet was also associated with lower fatigue and severe disability than ceased-adherence. No difference in health outcomes was observed between commenced and other adherence levels.
The study population was $81\%$ female, $73\%$ reporting non-progressive MS type, and the majority with mild disability as well as university educated, as reported in other MS cohorts [23, 24]. The analysis population comprised $27\%$ of baseline participants, with characteristics of less severe disability, higher education, more Australian and New Zealand residents, employed, and were less likely to have progressive MS type, have one or more comorbidities, or to report fatigue or depression, compared to excluded participants. *The* generalisability of these results may thus be limited. A range of demographics and clinical confounders were included in statistical models to adjust for these and other potential biases.
Fifty percent of the analysis reported having adhered to a MS-diet for at least 12-month at the 5-year timepoint: $44\%$ to OMS, $7\%$ to Swank, $1.5\%$ to Wahls, and < $1.5\%$ to other MS-diet. The proportions are similar to prior studies, such as a USA longitudinal study of 6,990 pwMS that reported although $45\%$ of participants modified their diet after their MS diagnosis, only $2\%$ followed a MS-diet specifically Swank or Wahls [1]. A survey of 337 pwMS reported $42\%$ adhering a MS-diet in Germany [41] and $11\%$ of 428 pwMS in South Australia followed the Swank-diet [42]. The proportion of pwMS adhering to the OMS-diet in the current study is markedly higher than the 6–$20\%$ reported in previous studies [24, 43], possibly reflective of recruitment primarily through sites promoting healthy lifestyle behaviors [30], as well as participants’ awareness of and engagement with the multimodal OMS lifestyle program [44].
In the current study, in addition to diet-adherence, we assessed ongoing-adherence at 5- and 7.5-year timepoints, which was high for all diets: $75\%$ for OMS, $56\%$ for Swank, and $61\%$ for other diets, suggestive of commitment to dietary modification by pwMS. Previous studies have shown 75–$90\%$ pwMS adhered to a Mediterranean diet at 6-month follow-up [20, 45] and $50\%$ of pwMS followed Swank-diet rigorously for 34 years [4]. While diet commitment for an extended period can be challenging, our data show that it is achievable by pwMS.
Ongoing-adherence to OMS-diet was associated with 20–$30\%$ lower risk of depressive symptoms than both non- and ceased-adherence. These results corresponding with our cross-sectional study showing $27\%$ lower depression associated with adherence to OMS-diet at 5-years [11]. The OMS-diet recommends a low saturated-fat, plant-based whole food diet plus seafood; this diet has been shown to be a high-quality diet [23, 24], and adherence to OMS-diet has been found to be associated with 10-point higher DHQ scores [7]. While the mechanisms linking MS-diet to depression are uncertain, a role of the gut-brain axis has been suggested [27]. Inflammation is known to play a key role in MS progression [46] and the anti-inflammatory and neuroprotective effects of fruit/vegetables and of low saturated fat diet have been documented [9, 47, 48]. Our findings may be in part explained by the anti-inflammatory effects of a low saturated fat diet such as the OMS-diet. The benefits of OMS-diet on depressive symptoms were not observed in pwMS who commenced of the diet at 7.5-year, suggesting that early and ongoing-adherence may be required for reduced depressive symptoms.
Ongoing-adherence to OMS-diet was associated with improved fatigue or disability compared to ceased-, but not compared to non- or commenced-adherence. These results suggest diet is unlikely the only factor that contributes to better outcomes, a multimodal lifestyle approach may be best. Compared to ceased- ongoing-adherence was associated with $29\%$ lower fatigue and $26\%$ lower severe disability, suggesting benefits are not sustained if OMS-diet adherence ceases. Prior studies have also shown high-quality diet associated with 30 and $44\%$ lower fatigue and disability, respectively [11]. Diet modification may affect fatigue via modulation of inflammation or oxidative stress [9, 13, 14]; therefore, anti-inflammatory diets could be a potential intervention for pwMS. However, ongoing-adherence did not show better outcomes in fatigue and disability than non- and commenced-adherence, results should therefore be interpreted with caution. While there are potential benefits of ongoing-adherence to OMS-diet on fatigue and disability, possibility for reverse causality in which people ceased-adherence due to those symptoms exists, warrants further study. MS is a chronic disease requires long-term management. Fatigue and disability have been reported by pwMS as common barriers for lifestyle modification [28]. Current results show a robust beneficial impact of ongoing-adherence with depressive symptoms may suggest that pwMS are more able to adhere dietary modification despite those symptoms, while fatigue and disability are stronger barriers for sustained engagement. However, conclusions could not be fully drawn with the current data and future longitudinal assessments are required to ascertain the associations. Regardless of potential reverse causality, support to improve diet-adherence, especially for pwMS experiencing fatigue and disability symptoms, are important.
Ongoing-adherence to high-quality diet at both timepoints over a 2.5-year period, compared to non- and ceased-adherence, had $22\%$ and $33\%$ lower depressive symptoms, respectively. This aligns with reported observations of pwMS who maintained a high-quality diet over 11-years had fewer symptoms of depression compared to those whose diet quality was consistently low or worsened over time [49]. Current results are also concordant with studies showing high-quality diet cross-sectionally associated with lower depression [23, 24]. Together the results support ongoing-adherence to a high-quality diet may improve depression in pwMS. No difference was observed between high-quality diet adherence and fatigue or disability, partially contradictory to our prior prospective findings from 0 to 2.5-years that showed 36–$41\%$ lower risk of disability progression but no association with fatigue [24]. The disparity may be due to less generalizable population in the current study, showing healthy participant bias including < $10\%$ prevalence of disability, lower than the reported $14\%$ in pwMS [25], and more likely to be adhering to multimodal healthy lifestyle behaviors [25]. Both ongoing-adherence to OMS- and high-quality diet showed benefits on depressive symptoms, suggesting that sustained diet that is high in fruits, vegetables, whole grains, and fish, and low in saturated fat, refined sugar and processed meat should be encouraged. However, only ongoing-adherence to OMS-diet showed reduced fatigue and disability than ceased-adherence. This suggests important elements of the OMS-diet for MS management such as low saturated fat and omega-3 supplementation. Alternatively, it may be that pwMS with fatigue and disability are more likely to cease a restrictive MS-diet. Future longitudinal studies are needed to determine the associations.
No differences in associations with health outcomes were observed between non- and commenced-adherence to either OMS- or high-quality diet, nor between commenced- and ceased- or ongoing-adherence. These may suggest that early dietary modification is needed to observe the benefits, however, it is possible that no association was found due to small size in the commenced-adherence group, the subjective measure of diet adherence in our study, as well as our study population adhering to other healthy lifestyle behaviors that are also associated with improved health outcomes [50, 51]. Future research assessing individual and additive impacts of lifestyle behaviors, as well as adherence and duration information may provide further insight.
A limitation of our study was $73\%$ attrition of baseline HOLISM participants; while characteristics of the analysis population were comparable with other MS cohorts, they may not be representative of the broader population of pwMS in the real world, and thus our results need to be interpreted with caution. Healthy participant bias is acknowledged; and compared to participants LTFU, pwMS returning at 5-year timepoint, had adopted healthy behaviors and engaged with information on a multimodal lifestyle program for pwMS [44]. Therefore, associations between OMS-diet and health outcomes may be due to adoption of multiple healthy lifestyle behaviors. Future studies may consider assessing associations of individual lifestyle behaviors independently and potential additive effects. There is the potential for participants to select more than one diet, and non-specific querying of foods and drinks that were adhered to, as well as self-assessment of degree of adherence based on Likert scale, limits data accuracy. Additionally, whether diet was adhered to for the entire 2.5-year interval was not assessed. It is possible that pwMS may have altered their diet type and/or stringency of adherence in between the two timepoints. Future studies may consider assessing diet and level of adherence using validated tools that allow substantiation of self-defined labels of adherence. Few pwMS commenced-adherence from 5- to 7.5-year timepoints, which may account for insignificant findings in this group. Finally, the number of pwMS adhering to MS-diets other than OMS was few, therefore associations for other MS-diets on health outcomes should be investigated in larger populations.
Nonetheless, the study is the first to our knowledge to assess associations between partial- and ongoing-adherence on health outcomes over a 2.5-year period. The strengths of our study include that the analysis comprised 671 participants from 33 countries and therefore having exposure to different diet guidelines for MS management. The survey includes a comprehensive collection of demographic and clinical characteristics, enabling appropriate adjustment for selection bias and relevant confounders. Additionally, by restricting adherence to being ≥12 months and ≥$\frac{3}{5}$ rigour, we were able to ensure true adherence rather than brief for diet modification.
## 5. Conclusion
Adherence to a MS- or high-quality diet over 2.5-year was high, suggesting ongoing-adherence to diet is acceptable and achievable by pwMS. Ongoing-adherence to the OMS-diet may help improve MS outcomes, especially depressive symptoms; however, further assessments are required to confirm the causality. Partial-adherence was not associated with better outcomes than non-adherence. These findings suggest that potential benefits of diet require ongoing efforts, therefore care management should consider methods to support pwMS to maintain high-quality diet. Ongoing-adherence to MS-diet may be more challenging for pwMS with fatigue and disability. Healthcare providers should consider strategies and tools that are tailored to the individual’s needs. Future studies assessing ongoing-adherence to other MS-diets would be worthwhile.
## Data availability statement
The raw data may not be shared due to conditions approved by our institutional ethics committee. Access to de-identified aggregate group data may be requested through SN or NN, [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by the University of Melbourne Human Research Ethics Committee (ID# 1545102). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
NN: conceptualization, visualization, supervision, and project administration. SS-Y and MY: data curation. NN and MY: methodology and writing—original draft preparation. MY: formal analysis. GJ and SN: resources. NN, MY, SS-Y, GJ, and SN: writing—review and editing. GJ: funding acquisition. All authors approved the final version of the manuscript.
## Conflict of interest
GJ is the data custodian for HOLISM study, and the author of “Overcoming Multiple Sclerosis” and co-editor of “Overcoming Multiple Sclerosis Handbook. Roadmap to Good Health”. SN is a co-editor of “Overcoming Multiple Sclerosis Handbook. Roadmap to Good Health”. GJ and SN receive royalties from aforementioned authored publications, have previously received remuneration from facilitation of Overcoming MS residential workshops. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Bidirectional Mendelian randomization study of insulin-related traits and risk
of ovarian cancer
authors:
- Xinghao Wang
- Jing Sun
- Jia Li
- Linkun Cai
- Qian Chen
- Yiling Wang
- Zhenghan Yang
- Wenjuan Liu
- Han Lv
- Zhenchang Wang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10014907
doi: 10.3389/fendo.2023.1131767
license: CC BY 4.0
---
# Bidirectional Mendelian randomization study of insulin-related traits and risk of ovarian cancer
## Abstract
### Background
It is well known that the occurrence and development of ovarian cancer are closely related to the patient’s weight and various endocrine factors in the body.
### Aim
Mendelian randomization (MR) was used to analyze the bidirectional relationship between insulin related characteristics and ovarian cancer.
### Methods
The data on insulin related characteristics are from up to 5567 diabetes free patients from 10 studies, mainly including fasting insulin level, insulin secretion rate, peak insulin response, etc. For ovarian cancer, UK *Biobank data* just updated in 2021 was selected, of which the relevant gene data was from 199741 Europeans. Mendelian randomization method was selected, with inverse variance weighting (IVW) as the main estimation, while MR Pleiotropy, MR Egger, weighted median and other methods were used to detect the heterogeneity of data and whether there was multi validity affecting conclusions.
### Results
Among all insulin related indicators (fasting insulin level, insulin secretion rate, peak insulin response), the insulin secretion rate was selected to have a causal relationship with the occurrence of ovarian cancer (IVW, $P \leq 0.05$), that is, the risk of ovarian cancer increased with the decrease of insulin secretion rate. At the same time, we tested the heterogeneity and polymorphism of this indicator, and the results were non-existent, which ensured the accuracy of the analysis results. Reverse causal analysis showed that there was no causal effect between the two ($P \leq 0.05$).
### Conclusion
The impairment of the insulin secretion rate has a causal effect on the risk of ovarian cancer, which was confirmed by Mendel randomization. This suggests that the human glucose metabolism cycle represented by insulin secretion plays an important role in the pathogenesis of ovarian cancer, which provides a new idea for preventing the release of ovarian cancer.
## Introduction
Ovarian cancer is a kind of cancer that occurs in female ovarian tissue. There are many pathological subtypes, among which high-grade serous ovarian cancer [1] is the most common. In developed countries, ovarian cancer is the main cause of death among all gynecological cancers [2]. Due to the lack of specific signs and symptoms at the early stages of the disease, ovarian cancer is usually found in late stage, with extensive peritoneal (Phase III) or extraperitoneal (Phase IV) spread. Tumor reduction surgery and platinum and taxane drug chemotherapy could make $75\%$ of patients achieve clinical remission. At present, the 5-year survival rate of ovarian cancer patients is roughly less than $30\%$ (3–5). There are many risk factors for ovarian cancer, including age, reproductive history, changeable lifestyle factors, family history and gene mutation [6].
Insulin [7] is the main regulator of glucose, lipid and protein metabolism. When oral glucose load or mixed meal is ingested, plasma glucose concentration increases, and islets of β Cells are stimulated to secrete insulin. Insulin can inhibit the production of endogenous glucose (the main target organ is the liver), stimulate the uptake and storage of glucose by muscle, liver and fat cells, and inhibit the decomposition of fat, leading to a decrease in plasma free fatty acids concentration [8], which helps to inhibit the production of glucose in the liver and increase the uptake of glucose in the muscle, and can relax muscle vessels, which helps to enhance muscle glucose disposal.
The incidence rate of various cancers is higher in patients with insulin secretion disorder (especially in patients with type 2 diabetes). Many studies and observations in the field of overseas studies have confirmed this view. It is reported that among patients with type 2 diabetes, the relative risk of endometrial cancer, liver cancer and pancreatic cancer is more than 2 times, while the relative risk of bladder cancer, breast cancer and colorectal cancer is as high as 1.5 times (9–12). In addition to the increase in incidence rate, the overall mortality rate of diabetes patients when diagnosed with cancer is higher [13] than that of the non-disease group. Systematic reviews [14, 15] could suggest that overweight people have a higher risk of ovarian cancer, and the risk of ovarian cancer increases with obesity. The increase and abnormality of obesity or body mass index often could lead to the disorder of endocrine system in the human body, such as insulin resistance, estrogen level change and other characteristics, which are factors that cannot be ignored in the role of obesity factors in weight related cancer.
Therefore, it is important to understand the hormone specific relationship between metabolism and cancer [16]. In this paper, bidirectional mendelian randomization analysis was used to confirm the causal relationship between insulin related characteristics and ovarian cancer risk.
## GWAS statistics of insulin-related traits
This study included six insulin related indicators from three studies, including Fasting blood insulin, Fasting blood insulin adjusted for BMI, Insulin secret rate, Peak insulin response, Acute insulin response and Insulin disposition index. The specific description of relevant data can be shown in Table 1.
**Table 1**
| Traits | Population | Sample size | Year | Number of SNPs |
| --- | --- | --- | --- | --- |
| Fasting blood insulin | European | 51750 | 2012 | 2598774 |
| Fasting blood insulin adjusted for BMI | European | 30825 | 2015 | 103869 |
| Insulin secretion rate | European | 527 | 2017 | 6919421 |
| Peak insulin response | European | 2337 | 2017 | 9694532 |
| Acute insulin response | European | 2087 | 2017 | 9663724 |
| Insulin disposition index | Hispanic or Latin American | 2345 | 2017 | 9652444 |
## Fasting blood insulin
The GWAS data [17] came from Genome wide association studies for fast glucose (FG) and fast insulin (FI), which analyzed the exon array data of 33231 non-diabetes patients of European origin. The data and SNP of fasting insulin came from this study.
## Fasting blood insulin adjusted for BMI
The data of this indicator [18] came from a study of “Genome wide method considering body mass index determines genetic variation affecting fasting blood glucose characteristics and insulin resistance”, which includes 96496 non diabetes patients. The fasting insulin data here were adjusted by body mass index.
## Insulin secretion rate
This study explored the genome-wide association study based on IVGTT’s first phase insulin secretion measurement, which refined the potential physiology of type 2 diabetes variation. Insulin secretion rate (ISR) is the estimated insulin secretion rate (ISR) [19] based on the measured serum C-peptide concentration at 0, 2, 4, 6, 8 and 0, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16 and 19 (FAMILY) minutes. ISEC software [20] is used to calculate the secretion rate according to predefined C-peptide kinetic parameters, including each person’s weight, height, age Gender and clinical status (glucose tolerance and obesity status) were determined in a population-based study [21]. The ISR provides an estimate of the rate of insulin secretion before hepatic insulin clearance.
## Peak insulin response
Peak insulin response was measured as peak insulin minus baseline insulin. Determine the peak insulin time point of each study according to the time point when the average insulin value of all individuals is the highest.
## Acute insulin response
The acute insulin response (AIR) was measured as the incremental area under the insulin curve in the first 10 minutes. Or if the 10 minutes measurement is not available, the minimum insulin value at 0, 2, 4, 6 and 8 minutes during the IVGTT using the trapezoidal equation during the first 8 minutes. Incremental insulin was calculated by subtracting fasting insulin levels.
## Insulin disposition index
Insulin disposition index was calculated as the product of AIR, and insulin sensitivity index was calculated by the MINMOD [22], which took into account the level of background insulin resistance.
## GWAS statistics of ovarian cancer
Through IEU Open GWAS (MR Base) [23] public database(https://gwas.mrcieu.ac.uk/)to retrieve and obtain data on ovarian cancer. The data of ovarian cancer patients are from UK Biobank. According to the data, the latest update is 2021, which includes 9822229 SNPs from 199741 Europeans. The classification of data is binary data, that is, whether ovarian cancer has occurred. The website shows that 1218 patients were included, while 198523 patients were included in the control group.
## Mendelian randomization statistical analysis
Two-sample bidirectional MR was used to test the causal relationship between insulin-related traits and tumors. In order to determine whether insulin-related traits could be a risk factor for various tumors, we first selected closely related SNPs from ovarian cancers’ GWAS results. In this process, insulin-related traits acted as exposure and ovarian cancer occurs as a result. In order to verify whether ovarian tumors cause insulin-related traits, SNPs related to various tumors are selected as the instrumental variable in the reverse MR analysis process, with ovarian tumors as the exposure, and insulin-related traits as the result.
Three different MR methods, including inverse variance weighted random effects (IVW), MR Egger and weighted median, were used to evaluate heterogeneity and test multiple effects. SNPs and outliers of insulin related traits identified by MR-PRESSO were removed. In the face of Mendelian randomization, IVW was used as the main analysis method, which was widely accepted. The threshold for screening exposure variables was 10^-6. MR Egger [24] was often a test that allows all genetic variations to have pleiotropic effects, but requires pleiotropic effects to be independent of the exposure association between variations. For Mendelian randomization pleiotropy test, MR Egger intercept test and retention analysis were used to further evaluate the level pleiotropy. Cochran’s Q test was implemented in each MR analysis to detect data heterogeneity between exposure and outcome, which was an important indicator affecting the reliability of final results. In the final visualization part, the funnel chart was used to evaluate the possible directional pleiotropy, similar to the evaluation of publication bias in meta-analysis, and also to observe the data distribution. The forest map is used to show the results of each SNP and the final MR, which was a convenient and intuitive method for visualizing the results.
All bidirectional mendelian randomization statistical analysis and data visualization used “TwoSampleMR” (https://github.com/MRCIEU/TwoSampleMR) in R software version 4.1.1. RStudio (https://posit.co/products/open-source/rstudio/) was used as a platform tool for opening and analysis, which was an integrated development environment for R and Python. It included a console, syntax highlighting editor that supports direct code execution, and tools for drawing, history, debugging, and workspace management. Bilateral P value less than 0.05 was considered statistically significant.
## Results
Six insulin related indicators, including Fasting blood insulin, Fasting blood insulin adjusted for BMI, Insulin secret rate, Peak insulin response, Acute insulin response and Insulin disposition index, went Two-sample bidirectional MR with ovarian cancer. We conducted a total of 12 statistical tests in 6 groups.
As shown in Table 2, we summarize all positive MR results into this table. The insulin secretion rate was statistically significant (IVW, $p \leq 0.05$).
**Table 2**
| Traits | IVW-derived P value | OR (95% Confidence intervals) | Cochran’s Q-derived P value | MR-Egger intercept-derived P value |
| --- | --- | --- | --- | --- |
| Fasting blood insulin | 0.62030831 | 0.9976788 (0.9885484, 1.0068936) | 0.2304561 | 0.02442566 |
| Fasting blood insulin adjusted for BMI | 0.4649689 | 0.9965644 (0.9874069, 1.005807) | 0.4126454 | 0.9639076 |
| Insulin secretion rate | 0.0179683 | 0.9991305 (0.9984108, 0.9998507) | 0.8227378 | 0.4466693 |
| Peak insulin response | 0.6999933 | 1.000156 (0.9993622, 1.000951) | 0.5413675 | 0.9516094 |
| Acute insulin response | 0.4477386 | 0.9996450 (0.9987289, 1.000562) | 0.8945821 | 0.6796435 |
| Insulin disposition index | 0.4849515 | 0.9996270 (0.9985809, 1.000674) | 0.11898331 | 0.6133764 |
On the contrary, the other five insulin related indicators (Fasting blood insulin, Fasting blood insulin adjusted for BMI, Peak insulin response, Acute insulin response and Insulin disposition index) did not show any correlation with the risk of ovarian cancer (Supplementary Figures S1 - S5).
When taking insulin secretion rate as the exposure factor, we found that impaired insulin secretion was associated with an increased risk of ovarian cancer (OR 0.9991305 (0.9984108, 0.9998507), $$p \leq 0.017968$$, Figure 1), which was confirmed in the positive MR analysis (Figure 2). There were 9 SNPs related to the above results (rs10830963, rs10983538, rs11135317, rs138478706, rs1779638, rs58858201, rs7756992, rs9425530, rs9479886), and the details were shown in S-Table 1 of Supplementary Materials. For the pleiotropy test of MR analysis, no obvious pleiotropy was found ($p \leq 0.05$, Table 2). The retention analysis of the above results shows that all SNPs are generally stable (Figure 3), and the funnel plot did not show significant heterogeneity (Supplementary Figure S6).
**Figure 1:** *Mendelian randomization results of the association of the insulin secretion rate on ovarian cancer (Forward).* **Figure 2:** *The pleiotropy test of MR analysis (Forward).* **Figure 3:** *The retention analysis of the SNPs (Forward).*
However, we still got statistically significant results (IVW, $p \leq 0.05$) when we performed reverse MR analysis on the insulin secret rate. That was to say, with ovarian cancer as the exposure factor and the insulin secret rate as the outcome variable, we still got the causal relationship of the above two parties (OR 3.092427e-13 (3.816945e-23, 2.505434e-03), $$p \leq 0.013$$, Figure 4), which was confirmed in the inversive MR analysis (Figure 5). The above results indicate that ovarian cancer had a causal relationship with human insulin secret rate. In order to test the reliability of the above results, we conducted Cochran’s Q test ($$p \leq 0.4528141$$) and pleiotropy test ($$p \leq 0.6568936$$). However, these test results indicate that the above results do not have the pleiotropy and heterogeneity of imaging conclusions. There were 10 SNPs related to the above results (rs114858887, rs1358253, rs1687403, rs2143612, rs28678815, rs35486093, rs4443540, rs76264086, rs78231145, rs79693379), and the details were shown in S-Table 2 of Supplementary Materials. The retention analysis of the above results showed that all SNPs were generally stable (Figure 6), and the funnel plot did not show significant heterogeneity (Supplementary Figure S7).
**Figure 4:** *Mendelian randomization results of the association of ovarian cancer on the insulin secretion rate (Reverse).* **Figure 5:** *The pleiotropy test of MR analysis (Reverse).* **Figure 6:** *The retention analysis of the SNPs (Reverse).*
## Discussion
This study is a Bidirectional Mendelian Randomization Study, which used MR to analyze the two-way causal relationship between insulin related traits and the risk of ovarian cancer. We found that the insulin secretion rate has a two-way causal relationship with ovarian cancer, which is rarely reported.
Insulin is an important hormone in mammalian homeostasis regulation, which regulates metabolism together with glucagon antagonism. The insulin secretion rate provides an estimate of the insulin secretion rate before hepatic insulin clearance [25]. The main physiological stimulation of insulin secretion is the increase of circulating glucose concentration in the postprandial state. Impaired insulin secretion is often associated with high body mass index, and a large number of statistics have proved the association between overweight and ovarian cancer [15, 26]. As mentioned in the introduction of this article, impaired insulin status is associated with the risk or survival of many cancers.
By analyzing the MR results in this paper, we could easily find that impaired insulin secretion was associated with an increased incidence of ovarian cancer. We will analyze the influence of insulin secretion on ovarian cancer from the following aspects. First, from the perspective of insulin and tumor cell energetics, compared with healthy cells, ovarian cancer tumor cells have a huge energy demand to support the abnormal proliferation and metastasis of tumors. Compared with normal human cells, tumor cells tend to change their metabolic mode, such as the transformation of primary glucose utilization pathway from oxidative phosphorylation to glycolysis, namely Warburg effect [27]. Insulin also controls systemic and intracellular metabolism through substrate (glucose) distribution [28]. However, tumors have changes in PI3K mTOR signaling pathway, and mTOR also changes the availability of glucose in tumor cells by regulating glucose uptake and glycogen decomposition [29]. At the same time, anti-tumor drugs targeting systemic glucose homeostasis and tumor growth regulation have also entered the clinical trial stage [30]. Second, impaired insulin secretion is associated with glucagon. Hyperinsulinemia is associated with the increased risk of breast cancer [31], endometrial cancer [32], ovarian cancer [33] and prostate cancer [34], and is closely related to the increased mortality of pancreatic cancer and breast cancer [35, 36], and some studies indicate that glucagon increases the overall mortality of cancer [37]. However, it should be noted that many studies have pointed out that the postmenopausal serum insulin level is not related or is very weak to ovarian cancer after adjustment and correction [33, 38], which is consistent with the negative results of this study. However, we found that the significant results are insulin secretion rather than simple serum levels. At the same time, there is no denying that insulin and glucagon, which are hormones, are closely related to lipid peroxidation and metabolism [39], fibroblast growth factor receptor-1 [40], and inflammatory cytokines [41], and these factors undoubtedly play a key role in the progress of cancer. Third, the repeated mention of obesity or overweight is undoubtedly related to impaired insulin secretion. Ovarian cancer cells use fat cells as a source of energy for growth and migration [42]. At the same time, as metabolic disorders, their internal metabolism affects each other. Because of changes in lifestyle factors, the prevalence of metabolic disorders is increasing year by year worldwide, just as obesity, type II diabetes and metabolic syndrome are all associated with ovarian cancer (43–46). A recent meta-analysis [47] showed that the risk of diabetes and OC was weak but still related, and many studies had many bias or confounding factors. It has also been pointed out that despite normal BMI, people with unhealthy metabolism or central obesity have a higher risk of cancer [48]. Fourth, insulin is also related to immunity. Insulin [49] is related to regulating different immune phenotypes and responses, and the expression of insulin receptors on T cells, B cells and macrophages proves this view [50].
At the same time, another study showed that the existence of ovarian cancer was related to insulin secretion. The mechanism involved in this is very complex, because the metabolism of tumor variant fish is very complex. We speculate that the anaerobic glycolysis of tumors occupies the main form of metabolism, and pentose phosphate shunt pathway and its nucleotide products [51] play a certain role in the regulation of insulin secretion. Among them, glucose-6-phosphate dehydrogenase (G6PDH) [52] can explain the impairment of insulin secretion by islet cells through the impairment of NADPH production, and the 6-phosphogluconate dehydrogenase (6PGDH) [53] negative impact is attributed to the accumulation of intermediate metabolites of this pathway, leading to the activation of extracellular regulated kinase (ERK). Currently, it is known that ERK [53] can promote insulin transcription in response to acute signals, but its continuous activation may lead to β Cell dysfunction and apoptosis.
These studies have many defects and deficiencies, as follows: [1] Avoiding the pleiotropy of SNPs selected as instrumental variables is an important principle to ensure the accuracy of MR analysis. Usually, MR Egger intercept and MR-PRESSO methods are used to detect horizontal pleiotropy to reduce bias, but the method is not absolute for detection of pleiotropy. The MR analysis results of this study did not find heterogeneity and level pleiotropy, which proved the robustness of the results, but still could not completely rule out the interference of potential pleiotropy. This limitation is due to the existing analysis methods, and there are also some works [54] exploring other multiple validity testing methods. [ 2] The Insulin related Trains included in this study may lack some indicators. When selecting indicators, we selected open and common indicators. For the selection of databases, we also selected databases based on the same species, recent time and large number of people. However, this may not fully represent the function and release of insulin. We try to avoid these limitations, but it is undeniable that they may still exist. [ 3] *There is* also the race problem in the database. In order to control the same race, we try to select European samples, which will undoubtedly affect the conclusion to be extended to other colored people. [ 4] The sample size of some indicators may not be enough to avoid bias, which is also caused by database restrictions.
## Conclusion
Through the Bidirectional Mendelian Randomization analysis, we obtained the two-way causal relationship between the insulin secret rate and ovarian cancer, that is, the reduction of the insulin secret rate is related to the risk of ovarian cancer, and the occurrence of ovarian cancer also has an impact on the insulin secret rate. When this research needs large sample data research in the real world, we hope to have research to further verify this conclusion.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors.
## Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
XW wrote the main manuscript. JL and LC developed the model, WL and JS collected and analyzed the data. ZY reviewed and revised the manuscript. ZW and HL designed the study. Other personnel participate in discussion and article revision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1131767/full#supplementary-material
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|
---
title: Bifidobacterium-derived short-chain fatty acids and indole compounds attenuate
nonalcoholic fatty liver disease by modulating gut-liver axis
authors:
- Sang Jun Yoon
- Jeong Seok Yu
- Byeong Hyun Min
- Haripriya Gupta
- Sung-Min Won
- Hee Jin Park
- Sang Hak Han
- Byung-Yong Kim
- Kyung Hwan Kim
- Byoung Kook Kim
- Hyun Chae Joung
- Tae-Sik Park
- Young Lim Ham
- Do Yup Lee
- Ki Tae Suk
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10014915
doi: 10.3389/fmicb.2023.1129904
license: CC BY 4.0
---
# Bifidobacterium-derived short-chain fatty acids and indole compounds attenuate nonalcoholic fatty liver disease by modulating gut-liver axis
## Abstract
Emerging evidences about gut-microbial modulation have been accumulated in the treatment of nonalcoholic fatty liver disease (NAFLD). We evaluated the effect of Bifidobacterium breve and *Bifidobacterium longum* on the NAFLD pathology and explore the molecular mechanisms based on multi-omics approaches. Human stool analysis [healthy subjects ($$n = 25$$) and NAFLD patients ($$n = 32$$)] was performed to select NAFLD-associated microbiota. Six-week-old male C57BL/6 J mice were fed a normal chow diet (NC), Western diet (WD), and WD with B. breve (BB) or B. longum (BL; 109 CFU/g) for 8 weeks. Liver/body weight ratio, histopathology, serum/tool analysis, 16S rRNA-sequencing, and metabolites were examined and compared. The BB and BL groups showed improved liver histology and function based on liver/body ratios (WD 7.07 ± 0.75, BB 5.27 ± 0.47, and BL 4.86 ± 0.57) and NAFLD activity scores (WD 5.00 ± 0.10, BB 1.89 ± 1.45, and BL 1.90 ± 0.99; $p \leq 0.05$). Strain treatment showed ameliorative effects on gut barrier function. Metagenomic analysis showed treatment-specific changes in taxonomic composition. The community was mainly characterized by the significantly higher composition of the *Bacteroidetes phylum* among the NC and probiotic-feeding groups. Similarly, the gut metabolome was modulated by probiotics treatment. In particular, short-chain fatty acids and tryptophan metabolites were reverted to normal levels by probiotics, whereas bile acids were partially normalized to those of the NC group. The analysis of gene expression related to lipid and glucose metabolism as well as the immune response indicated the coordinative regulation of β-oxidation, lipogenesis, and systemic inflammation by probiotic treatment. BB and BL attenuate NAFLD by improving microbiome-associated factors of the gut-liver axis.
## Introduction
Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease and one of the major public health problems (Younossi, 2019). The pathology is mostly prevalent not only in obese and diabetic patients but also in nondiabetic and lean individuals (Younossi et al., 2012). The progression of NAFLD is triggered by an increased synthesis and reduced utilization of lipids, which results in excessive deposition of triglyceride-rich lipid droplets in the liver, commonly termed hepatic steatosis (Cohen et al., 2011). Metabolic dysregulation leads to cellular stresses, such as oxidative stress, and consequently induces hepatic inflammation that can progress into nonalcoholic steatohepatitis (NASH; Kleiner and Makhlouf, 2016) and further into severe forms of liver diseases, including cirrhosis and hepatocellular carcinoma (Debes et al., 2020).
Dietary patterns high in fat are the most important risk factors for the development and progression of NAFLD (Yang et al., 2020). Therefore, the diet-induced animal model for NAFLD is being widely applied. A western diet (WD), which is high in saturated fat, has been repeatedly demonstrated to be efficient in designing preclinical models for NAFLD due to its relative simplicity and ability to trigger pathological outcomes similar to those in humans (Yang et al., 2020). While the underlying mechanisms are largely unknown, recent studies have reported that high-fat diet-related liver injuries are accompanied by higher expression of lipogenesis genes, reduced expression of β-oxidation genes, elevated production of pro-inflammatory cytokines and reactive oxygen species, and alteration of the gut microbiome (Leung et al., 2016).
The impact of the WD on the complex interactions between gut microbes and the host has become an interesting area of medical research. There is a general understanding that the WD greatly influences the composition and function of gut microbiota. WD increase Firmicutes/Bacteroidetes ratio and this change is driven by increases in Erysipelotrichales, Bacilli, and Clostridiales (Firmicutes; Malesza et al., 2021). However, little is known about the pathological mechanisms and roles of WD-induced gut dysbiosis during the progression of NAFLD. The basic idea lies in the roles of microbial antigens, production of microbe-derived metabolites, and intestinal permeability along with translocation to the portal vein. As the liver obtains most of its blood flow from the intestine, it is highly exposed to these microbial products as well as the intact microbes themselves. Therefore, the gut microbiome-liver axis can be used as a target for therapeutic interruptions of NAFLD.
Probiotics are believed to delay the progression of NAFLD with therapeutic endpoints, such as modulation of gut microbiota, intestinal permeability, and inflammatory pathways. This is a lucrative choice considering its simple availability, cost convenience, and absence of severe side effects. Different Bifidobacterium species have been proven effective treatments for hepatic steatosis and inflammation, acute liver injury and cirrhosis (Yan et al., 2020). Bifidobacterium breve (BB) and B. longum (BL) are some of the commonly used probiotic species. They are generally dominant in infants and were first isolated from the feces of breast-fed newborns (Milani et al., 2017). Both species have been reported to possess an array of enzymes that enable them to adapt and compete in an environment with changing nutritional conditions, such as the gut. A strain from BB has been recently reported to suppress body weight gain and fat deposition in a dose-dependent manner accompanied by a reduced level of serum total cholesterol (Minami et al., 2018). Similarly, BL has also been shown to attenuate liver fat accumulation, lower serum total cholesterol, and induce growth of Bacteroides in rats fed a high-fat diet (Yin et al., 2010). However, the mechanisms by which Bifidobacterium strains exert their attenuating effects on NAFLD, especially regarding modulation of the gut microbiota, are poorly understood.
This study aimed to elucidate the WD-gut microbiome-liver axis along with an evaluation of probiotic interruptions. This was conducted by establishing a mouse NAFLD model using a WD challenge, investigating the effects on gut microbiota, examining the underlying mechanisms by which alteration of gut microbiota is involved in progression of NAFLD, and evaluating the effects of the probiotics BB and BL with a focus on gut modulation and reduction of lipogenesis and inflammatory responses.
## Distinct gut-microbial compositions in NAFLD patients
Altogether, a total of 57 human subjects (healthy controls, $$n = 25$$ and NAFLD patients, $$n = 32$$) were included in this study. Differences in gut-microbial compositions and functional biomarkers were compared between the individuals with and without NAFLD. The microbiota compositions and relative abundances of functional markers were significantly altered in the NAFLD group compared to those of healthy control subjects.
At the phylum level, Bacteroidetes ($56\%$) were predominant in the healthy control subjects, followed by Firmicutes ($31\%$), while Firmicutes ($59\%$) were dominant, followed by Bacteroidetes ($35\%$) in the NAFLD patients (Figure 1A). Similarly, the composition at the order level showed noticeable changes in NAFLD, with Bacteroidales being predominant in healthy controls and Clostridiales and Bacteroidales having similar abundances in the case of NAFLD (Figure 1B).
**Figure 1:** *Comparisons of relative abundances of fecal microbiota and functional biomarkers between healthy control and NAFLD patients. (A) Structural comparisons of microbial compositions at phylum level. (B) Structural comparisons of microbial compositions at order level. (C) Taxonomic relative abundances of individual lactic acid bacteria. (D) Taxonomic relative abundances of individual phyla. (E) Taxonomic relative abundances and ratio of Firmicutes and Bacteroidetes. (F) Heat map showing taxonomic relative abundances of individual species. (G) Alpha diversity based on CHAO and Shannon indexes and phylogenetic diversity. (H) PCA plot representing beta diversity. (I) Functional biomarker analysis. NAFLD, Nonalcoholic fatty liver disease; PCA, principal component analysis. Independent t-test: *p < 0.05 and **p < 0.01.*
At the genus level, six lactic acid bacteria were separately compared between healthy controls and NAFLD patients. Bifidobacterium and Lactobacillus showed significant variation between healthy controls and NAFLD patients, while no significant difference was observed for Lactococcus, Leuconostoc, Pediococcus, and *Weissella* genera (Figure 1C). Additionally, we separately compared the relative abundances of 11 more individual genera and family, namely, Akkermansia, Bacteroides, Blautia, Christensenellaceae, Clostridium, Enterobacteriaceae, Faecalibacterium, Prevotella, Proteobacteria, Ruminococcaceae, and Lachnospiraceae, among which only Bacteroides and Enterobacteriaceae showed significant reduction in NAFLD patients (Figure 1D). In Firmicutes-to-Bacteroidetes ratio (F/B ratio) result, Firmicutes level, Bacteroidetes level, and F/B ratio shows the significant difference between the healthy control group and NAFLD patients’ group (Figure 1E). However, no significant differences were observed in alpha diversities based on CHAO and Shannon indices and phylogenetic diversity between the two groups (Figure 1G). Clear differences were observed in the heat map profiles of the relative abundances of most genera, and noticeable discriminations were visible between the healthy and NAFLD groups during principal component analysis (PCA; Figures 1F,H). No significant differences between the two groups were observed during biomarker analysis of 10 pathways (Figure 1I).
## Supplementation with Bifidobacterium strains ameliorates the progression of NAFLD
The animal experimental design is described in Figure 2A. Mice were fed a normal chow (NC) diet, WD ($42\%$ fat), or WD supplemented with probiotic strains, BB and BL for 9 weeks. Increased rates of hepatic lipogenesis and resultant steatosis were determined in the WD group by histological evaluations of the accumulation of triglycerides in hepatocytes (Adams and Angulo, 2005). As shown in the gross liver images and H&E staining results, the livers of the WD group animals showed a noticeable increase in size along with a strikingly whitish appearance caused by the accumulation of lipid droplets, indicating drastic liver steatosis, while the livers of mice fed a normal diet remained dark and of normal size (Figure 2B). Similarly, histological assessments using H&E staining showed severe vacuolations of liver tissue in the WD group characterized by both microvascular and macrovascular steatosis because of lipid deposition in hepatocytes.
**Figure 2:** *Bifidobacterium breve and Bifidobacterium longum supplement on western diet-induced NAFLD in mice. (A) Experiment design depicting the animal model used. (B) Top: Representative liver specimens of gross examinations; Bottom: representative H&E-stained liver sections. (C) Liver weight to body weight ratio. (D) NAS. Left: Bar graph representation; Right: Individual gradings showing steatosis, inflammation, and ballooning score. H&E-stained liver sections were assessed blindly by an experienced liver pathologist for steatosis, hepatocyte ballooning and lobular inflammation. (E) Serum levels of liver function test enzymes and cholesterol. NAFLD, nonalcoholic fatty liver disease; NC, normal chow diet group; WD, western diet group; BB, WD + B. breve group; BL, WD + B. longum group; NAS, NAFLD activity score; AST, aspartate aminotransferase; ALT, alanine aminotransferase; TBIL, total bilirubin; CHOL, total cholesterol. # obtained statistics by comparing ND and WD. * statistics were obtained by comparing WD with the experimental group. One-way analysis of variance (ANOVA): ##p < 0.01, ###p < 0.001, ####p < 0.0001, *p < 0.05, **p < 0.01, and ****p < 0.0001.*
The average body weight of the WD group was significantly higher ($p \leq 0.05$) than that of the NC group, which corresponded with the substantial increase in liver weight and liver-to-body weight (L/B) ratio ($p \leq 0.05$) compared to the NC group (Figure 2C). Quantitative evaluations of steatosis stage and necroinflammation activity were estimated from H&E staining of liver sections based on standard histological scoring methods. The steatosis score, hepatitis score, and NAFLD activity score (NAS) were significantly higher ($p \leq 0.05$) in the WD group than in the NC group (Figure 2D). We further analyzed serum levels of aspartate transaminase (AST), alanine transaminase (ALT), total bilirubin (TBIL), and total cholesterol (CHOL) to evaluate liver function. Significant increases in AST, ALT, and CHOL levels were observed in the WD group compared to those in the NC group (Figure 2E). Probiotic supplementation with B. breve and B. longum significantly ameliorated the progression of hepatic steatosis. Both strains resulted in a significant reduction in the L/B ratio and a close to complete remission of steatosis. Similarly, both probiotic strains resulted in substantial reductions in AST and TBIL levels compared to the WD group. Both probiotic strains were able to reduce the gain of fat mass and hepatic lipid accumulation, which also showed a positive correlation with liver enzyme analysis.
## Gut-microbial community varies by different treatments
The compositions at the phylum level differed significantly among the four groups (NC, WD, BB, and BL; Figure 3A). Firmicutes ($63\%$) was significantly enriched in the WD and BL ($67\%$) groups compared to the NC ($50\%$) and BB ($55\%$) groups, while Bacteroidetes ($45\%$) composition was more abundant in the NC group than in compared to all the other groups. The WD group also featured a higher composition of Proteobacteria, Verrucomicrobia, Deferribacteres, and Actinobacteria than the NC group. The BB and BL groups presented transitional patterns of the phyla composition between the NC and WD groups.
**Figure 3:** *Comparisons of relative abundances of fecal microbiota and functional biomarkers between normal diet, western diet model, and probiotic supplementation in mice NAFLD model. (A) Structural comparisons of microbial compositions at phylum level. (B) Alpha diversity based on CHAO and Shannon indexes. (C) Ratio of taxonomic relative abundances Firmicutes and Bacteroidetes. (D) Structural comparisons of microbial compositions at genus level. (E)
Bifidobacterium breve species level competition. (F)
Bifidobacterium longum species level competition. (G)
Bifidobacterium genus level competition. (H) PCA plot representing beta diversity. (I) Heat map showing taxonomic relative abundances of individual species. (J) Functional biomarkers. NAFLD, nonalcoholic fatty liver disease; NC, normal chow diet group; WD, western diet group; BB, WD + B. breve group; BL, WD + B. longum group. # obtained statistics by comparing ND and WD. * statistics were obtained by comparing WD with the experimental group. One-way analysis of variance (ANOVA): ##p < 0.01, ###p < 0.001, and *p < 0.05.*
The F/B ratios of the probiotic-feeding groups were marginally lower than that of the WD group, where the Firmicutes composition was similar, but the Bacteroidetes composition was more enriched. The profiles of 16S rRNA gene amplicon sequencing were comparably analyzed to investigate characteristic changes in the gut-microbial composition. In the alpha diversity analysis based on CHAO and Shannon indices, the WD, BB, and BL groups had reduced microbial richness relative to the NC group, but no significant difference was observed between the WD group and probiotic-supplemented groups (Figure 3B). During a separate comparison of the F/B ratio between groups, the ratio in the WD group (4.9) was substantially higher than that in the NC group (1.1; Figure 3C). A remarkable reduction in the F/B ratio was obtained in the probiotic supplementation groups. The bacterial composition at the genus level also showed a significant difference between the control group and WD, with slightly distinct profiles observed between the WD and treatment groups (Figure 3D).
The most pathologically relevant observation was a noticeable increase in *Helicobacter in* the WD group. Helicobacter is a member of the phylum Proteobacteria, and an increase in the relative abundances of members of this genus has been reported to alter immune homeostasis in mice (Ray et al., 2015). The relative abundance of this genus increased from $2\%$ in the NC group to $8.3\%$ in the WD group, and a slight reduction was seen in both treatment groups, with BL showing a more significant reduction. Similar patterns were observed for the genus Pseudoflavonifractor, where its relative abundance markedly increased in the WD and probiotic groups. While a remarkable increase in Bacteroides was observed in the WD group, supplementation with both strains showed no effect. Separate analysis of the relative abundance of the individual probiotic strains used also showed results that confirmed the validity of the effect of the supplementation, where each strain was predominant in the respective groups (Figures 3E–G).
The beta-diversity analysis using PCoA based on the Bray–Curtis dissimilarity matrix demonstrated clear discrimination of the NC group from the other groups (Figure 3H). Heat map analysis of 28 identified genera showed clear discrimination for NC vs. WD and both treatment groups. Probiotic treatments were able to result in slight modulations for a few bacterial members (Figure 3I). Biomarker analysis was performed for the same pathways as in the human samples. Similar to the human fecal analysis, there was no significant difference between the four mouse groups (Figure 3J).
## The gut metabolome is substantially normalized by strain treatment
Targeted and untargeted metabolomics was applied to acquire the comprehensive profiles of the cecal metabolites. The metabolic profiles were collected from the NC, WD, BB, and BL groups. The metabolic signals were structurally identified, which resulted in 290 unique primary and secondary metabolites. The identified metabolites were categorized by chemical ontology analysis as follows: organic acids ($26\%$), lipids ($19\%$), organic oxygen compounds ($16\%$), and organoheterocyclic compounds ($14\%$) at the superclass level (Figure 4A; Supplementary Figure 2A).
**Figure 4:** *Characteristic alteration of gut metabolomic profiles by Bifidobacterium supplementation. (A) Overview of the metabolic features. The network is constructed based on chemical structural similarity (Tanimoto score) and KEGG reaction pair (substrate-product relation), which results in distinctive metabolic modules indicated by box. Red and blue colors present significantly higher or lower abundant in NC, BB, and BL groups, respectively, compared to WD (Student’s t-test, p < 0.05). Node sizes are determined by the ratios. (B) Pie charts present the number of metabolites that were significantly different in other groups, respectively, compared to WD (Student’s t-test, p < 0.05). Red and blue colors present significantly higher or lower abundance in other groups, respectively, compared to WD (p < 0.05). Volcano plot for identification of metabolites with significant differences in the NC, BB, and BL, respectively, compared to WD group. The X-axis presents the fold change in the log10 scale, and the Y-axis indicates the statistical significance (value of p) in the log10 scale based on Student’s t-test. NC, normal chow diet group; WD, western diet group; BB, WD + Bifidobacterium breve group; BL, WD + Bifidobacterium longum group.*
The metabolic profiles of the four groups were characterized based on principal component analysis (PCA). Similar to the microbial taxonomic profiles, a clear discrimination was determined between NC and the other groups (Supplementary Figure 2B). To provide an overview of the characteristic metabolic classes according to the different treatments, chemical enrichment analysis was conducted, which provided comprehensive classification with statistical criteria based on chemical similarity and ontology mapping (Lee B. M. et al., 2020). The map consisted of 15 major clusters as follows: hexoses, pyridines, fatty acids, sugar alcohols, pyrimidinones, azoles, disaccharides, sugar acids, hydroxybutyrates, dicarboxylic acids, indoles, butyrate, pyrimidine nucleosides, amino acids, and cholestenes. The enrichment analysis demonstrated chemical class-wise quantitative features of the NC, BB, and BL groups compared to the WD group (Supplementary Figures 1, 2C). Compared to the WD groups, the NC and probiotic-feeding groups showed increases in the metabolic modules of amino acids, indoles, and butyrates. A decrease in the taurine-conjugation class was a common feature for the NC, BB, and BL groups. The NC group showed specific changes in the modules of hexose, sugar alcohol, disaccharides, pyrimidine nucleosides, basic amino acids, cholestenes, and azoles, whereas an alteration in the module of unsaturated fatty acids was specific to the BB group.
We further verified the metabolites that were significantly different in the NC, BB, and BL groups compared to those in the WD group. Among the 290 metabolites, 147 metabolites were significantly different in the NC group compared to those in the WD group. Approximately $38\%$ of metabolites were significantly higher in the NC group than in the WD group, and 5-hydroxyindole-3-acetic acid, thiamine, and 4-methyl-5-thiazole ethanol showed the largest differences. In contrast, taurochenodeoxycholic acid and taurocholic acid presented the highest upregulation in the WD group (Figure 4B). The BB and BL groups showed significantly higher levels in 25 and $18\%$ of metabolites, respectively, while $11\%$ and $4\%$ of metabolites were at substantially lower abundance, respectively, compared to those in the WD group (Figure 4B). Note that SCFAs (butyric acid and acetic acid), indole compounds (indone-3-propionic acid and methyl indole-3-phosphate), and bile acids (taurodeoxycholic acid and taurocholic acid) were associated with the level of the NC group with the highest fold-changes compared to that of the WD group. Pairwise metabolomic comparison between NC group and probiotic-feeding groups (BB and BL) provided in Supplementary Table 1.
## Gut microbiota-derived metabolites are partially normalized by Bifidobacterium supplementation
Next, we investigated common metabolic signatures assuming that the metabolites similarly regulated between the NC and probiotic-feeding groups may play key roles in preventing the progression of NAFLD. A total of 44 metabolites showed significant changes that were common among the NC and probiotic-feeding groups (BB and BL) compared to the WD group ($p \leq 0.05$; Figure 5A). Most of the common metabolites were more enriched in the NC, BB, and BL groups than in the WD group (37 out of 44 common metabolites). The heat map analysis indicated the partial normalization of the common metabolome by the probiotic treatment (Figure 5B). The PCA plot with the score scatterplots of common 33 metabolites of cecal contents showed different discrimination among the NC, WD, and two probiotic-feeding groups (Figure 5C).
**Figure 5:** *Gut microbiota-derived common metabolic signatures. (A) Venn diagram of common and unique metabolites among the NC, BB, and BL groups as compared to the WD group. Statistical significance is determined based on Student’s t-test (p < 0.05). (B) Heat map showing common metabolites that are classified into 7 superclasses. (C) core scatter plot of the common metabolites by PCA. (D) Relative abundance of cecal SCFAs. (E) Relative abundance of cecal tryptophan metabolites. (F) Relative abundance of cecal bile acids. Statistical significance is determined based on Mann–Whitney U-test. NC, normal chow diet group; WD, western diet group; BB, WD + Bifidobacterium breve group; BL, WD + Bifidobacterium longum group; PCA, principal component analysis; SCFA, short-chain fatty acid. # obtained statistics by comparing ND and WD. * statistics were obtained by comparing WD with the experimental group. One-way analysis of variance (ANOVA): #p < 0.05, ##p < 0.01, *p < 0.05, and **p < 0.01.*
Among the common metabolites, short-chain fatty acids (SCFAs), tryptophan metabolites, and bile acids are gut microflora-derived compounds that are directly related to various types of pathology. Accordingly, we analyzed the profiles and the statistical significance across all groups based on the Mann–Whitney U-test with adjustment for multigroup comparisons ($p \leq 0.05$). Indeed, all SCFAs were at significantly higher levels in the NC, BB, and BL groups than in the WD group (Figure 5D). Most tryptophan metabolites showed similar patterns to SCFAs, where the abundances were substantially higher in the NC, BB, and BL groups than in the WD group. The metabolites included indole-3-propionic acid, indole-3-acrlyic acid, 5-hydroxyindole-3-acetic acid, methyl indole-3-acetic acid, and kynurenic acid (Figure 5E). In contrast, marginal differences were determined in bile acids among the four groups. Glycocholic acid, taurocholic acid, and taurochenodeoxycholic acid showed significant differences in the NC group compared to those in the WD group (Figure 5F). Bile acids were found in a similar pattern but showed unsubstantial differences in the BB and BL groups.
## Bifidobacterium modulates gene expression associated with lipid metabolism, inflammation, glucose metabolism, immune cell infiltration, and gut barrier function
To evaluate the effect of the probiotics B. breve and B. longum on important NAFLD progression both in vivo and in vitro, analyses of common biomarkers for hepatic lipid metabolism, inflammation, and gut barrier function were conducted. Western blotting analysis was performed to determine the relative occludin expression in mouse intestine tissue. The results showed that the western diet reduced the expression of occludin in the intestine (Figure 6A). Supplementation with B. breve and B. longum resulted in a significant increase in its expression, indicating a modulating effect of the strains on gut barrier function. This evidence was strengthened by an increased Trans-epithelial electrical resistance (TEER) measurement during incubation of the probiotic strains on the Caco-2 cell monolayer, which correlated with the western blotting results (Figure 6B). During the determination of hepatic mRNA levels of the selected markers, the western diet significantly upregulated genes related to lipid metabolism (Figure 6C) and glucose metabolism (Figure 6D).
**Figure 6:** *Effects of Bifidobacterium breve and Bifidobacterium longum supplementation on hepatic lipid metabolism and inflammation, and gut-liver axis markers. (A) Western blot analyses of the tight junction protein occludin and GADPH in mice intestine. Left: Representative blots shown with densitometry, right: Quantified results. (B) TEER measurements on Caco-2 cells monolayer. Left: Change in TEER, right: Quantified comparison between control and treatment groups. (C) mRNA levels of lipid metabolism genes. (D) mRNA levels of glucose metabolism genes. (E) mRNA levels of pro-inflammatory cytokines. (F) mRNA levels of immune cell recruitment chemokines. NC, normal chow diet group; WD, western diet group; BB, WD + B. breve group; BL, WD + B. longum group; TEER, trans-epithelial electrical resistance; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; PPAR, peroxisome proliferator-activated receptor; SREBP, sterol regulatory element-binding protein; CD, cluster of differentiation; ACC1, acetyl-CoA carboxylase; FAS, fatty acid synthase; DGAT, diglyceride acyltransferase; ChREBP, carbohydrate response element binding protein; TNF, tumor necrosis factor; IL, interleukin; CCL, C-C motif chemokine ligand; CXCL, C-X-C motif chemokine ligand. # obtained statistics by comparing ND and WD. * statistics were obtained by comparing WD with the experimental group. One-way analysis of variance (ANOVA): #p < 0.05, ##p < 0.01, ###p < 0.001, *p < 0.05, **p < 0.01, and ***p < 0.001.*
The progression of NAFLD is tightly linked to lipid and glucose metabolism (Gastaldelli et al., 2009). Sterol receptor element-binding protein-1c (SREBP-1c) induces lipogenesis in the liver, while peroxisome proliferator-activated receptor-alpha (PPARα) mediates fatty acid (FA) β-oxidation (Goto et al., 2013). The SREBP-1c/PPARα ratio has been reported as a good marker for hepatic steatosis (Pettinelli et al., 2009). Accordingly, the mRNA levels of these genes in the liver were determined based on qPCR. The WD group showed significantly higher ($p \leq 0.001$) mRNA levels of SREBP-1c, while the PPARα level was slightly lower than that in the NC group. Consequently, the ratio of SREBP-1c/PPARα was significantly higher ($p \leq 0.01$) in the WD group than in the NC group. The BB group was characterized by significant ($p \leq 0.05$) upregulation of PPARα, whereas the BL group showed significant ($p \leq 0.001$) downregulation of SREBP-1c, which resulted in reduced ratios compared to that of the WD. Similarly, cluster of differentiation (CD) 36 was significantly upregulated in the WD, and both probiotic strains were able to reverse it. No significant difference between the NC and WD groups was observed in the case of acetyl-coenzyme A carboxylase 1 (ACC-1) and fatty acid synthase (FAS), which are also genes that play important roles in lipid metabolism. The mRNA levels of four glucose metabolism markers known to play important roles during NAFLD progression were also analyzed. Similar patterns to those of lipid metabolism genes were observed for the triglyceride synthesis and glucose metabolism genes acyl coenzyme A (CoA): diacylglycerol acyltransferase 1 (DGAT1), DGAT2, and carbohydrate response element-binding proteins chREBP-α and chREBP-β which are key participants in insulin responses (Figure 6D).
Additionally, the mRNA levels of inflammation markers and chemokines associated with immune cell infiltration were investigated. Cytokines and chemokines intervene in essential biological processes, such as inflammation and immunity, which are also associated with many pathologies, including NAFLD (Braunersreuther et al., 2012). In contrast to metabolic markers, the mRNA levels of the pro-inflammatory cytokines tumor necrosis factor (TNF)-α, interleukin (IL)-6 and IL-1β were not significantly different between the NC and WD groups (Figure 6E). The expression levels of IL-6 and IL-1β were found to be significantly lower in the BB and BL groups than in the WD group. In addition, the BL group showed significant downregulation of TNF-α. Since we could not observe significant differences in the mRNA levels of the above pro-inflammatory cytokines in vivo, we examined their effects in vitro using lipopolysaccharides (LPS) and indole metabolites as negative and positive controls, respectively. First, we incubated RAW 264.7 and HepG2 cells with bacterial cultures or LPS (positive control). Both strains significantly reduced the expression of TNF-α in both cell lines (Figures 7A,B). Next, we prepared a nanoparticle of cell free supernatant (CFS) of the probiotic strains and used 3-indole propionic acid (IPA) and indole-3-acetic acid (IAA) at different concentrations (100, 250, 500 μM) with or without LPS. As we expected, treatment with IPA and IAA significantly reduced TNF-α mRNA levels at all concentrations (Figure 7C). In a similar manner, treatment with B. breve and B. longum CFS-based nanoparticles downregulated TNF-α expression in a comparable manner (Figure 7D).
**Figure 7:** *Comparison of anti-inflammatory effects of probiotic culture, indole metabolites, CFS nanoparticles, and fecal microbial transplantation (A) CFS on RAW 264.7 cells. (B) CFS on HepG2 cells. (C) IPA & IAA on RAW 264.7 cells, (D) CFS on RAW 264.7 cells. (E) Experiment design depicting the animal model used. (F) Representative liver specimens of gross examinations and H&E-stained liver sections. (G) NAS, and liver enzyme, and inflammatory cytokines. CFS, Cell free supernatant; BB, Bifidobacterium breve CFS; BL, Bifidobacterium longum CFS; TNF, Tumor necrosis factor; LPS, Lipopolysaccharides; IPA, 3-Indole propionic acid; IAA, Indole-3-acetic acid. # obtained statistics by comparing Cont. and LPS group. *statistics were obtained by comparing LPS group with the experimental group. One-way analysis of variance (ANOVA): #p < 0.05, ###p < 0.001, *p < 0.05, **p < 0.01, and ***p < 0.001.*
In the case of hepatic expression of chemokines, remarkable increases in mRNA levels of C–C motif chemokine ligand 2 (CCL2), CCL3, C-X-C motif chemokine ligand 9 (CXCL9) and CXCL16 were found in the WD group compared to those in the NC group (Figure 6F). These chemokines are known to induce cytotoxic T-cell recruitment to the liver (Oo et al., 2010). The chemokine CCL2 instigates inflammation in fat-accumulated tissues by facilitating the migration of inflammatory cells from the circulating blood (Arner et al., 2012). All four chemokines were significantly reduced in the probiotic supplement groups. In summary, probiotic treatments resulted in modulation of western diet-induced disturbances in hepatic metabolism, inflammation, and immune cell recruitment.
In the fecal microbial transplantation with BB and BL, NAS scores, liver enzyme, and cytokines were improved in BB and BL group (Figures 7E–G).
## Discussion
NAFLD comprises a broad array of liver pathogenesis ranging from simple steatosis to more severe complications (e.g., liver cirrhosis and hepatocellular carcinoma). A saturated fat-enriched western diet causes the development of NAFLD via lipid metabolic pathways in the liver (Cameron-Smith et al., 2003). One of the underlying mechanisms is a disrupting effect on gut microbiota. Alteration of gut microbiota is consequently involved in liver pathogenesis by disrupting gut barrier function and stimulating fat accumulation, inflammatory responses, and oxidative burden (Backhed et al., 2007). As gut microbiome research is still in its infancy, the literature provides inadequate data on the gut microbiota-liver axis. This limits progress in understanding the pathophysiological mechanisms and establishing targets for therapeutic strategies.
Accordingly, we investigated the effects of a western diet and evaluated the protective effects of two probiotic Bifidobacterium strains on the NAFLD progression. Indeed, the Western diet resulted in alteration in the gut microbiota and hepatic steatosis, thereby activating the pathophysiological mechanisms leading to NAFLD. The Bifidobacterium induced significant attenuation in regulating the gut microbiota, downregulating hepatic steatosis and inflammatory biomarkers, and improving liver function. Similarly, the intestinal metabolism group was treated by probiotic modulation. Gene expression related to lipid and glucose metabolism and immune responses suggests coordinated regulation of β-oxidation, lipid production and body inflammation by probiotic treatment.
Western diet challenge for 9 weeks resulted in an overall increase in body weight, liver weight, and liver size and consequently induced severe steatosis. The Western diet is mainly characterized by dietary intake of foods with higher saturated fat contents (Tilg and Moschen, 2015), and this study applied a mouse diet with $42\%$ fat content. Our results showed that mice fed this Western diet exhibited higher steatosis scores, hepatitis scores and NASs, which indicated the development of pronounced NAFLD. Steatosis during a high-fat diet is caused by the availability of abundant saturated fat, which is responsible for intrahepatic triglyceride accumulation (Ress and Kaser, 2016). Several studies have reported that mice fed a Western diet develop NAFLD through weight gain and fat accumulation manifested by vacuolation of hepatocytes, accumulation of perilipin proteins, inflammation, and oxidative stress in the liver (Yang et al., 2020). The progression of NAFLD in this study was clearly revealed by the accumulation of microvesicular lipid droplets in the liver tissue, as shown by gross specimens of liver and H&E staining of liver tissue. Note the severe vacuolation of hepatocytes, which resulted in a strikingly whitish appearance of the liver of mice in the Western diet group. Treatment with the probiotic strains improved the above pathological indicators through a significant reduction in hepatic steatosis compared with the WD. A recent study reported that B. lactis V9 attenuates NAFLD induced by a high-fat diet by mitigating hepatic steatosis (Yan et al., 2020). Our results showed that rapid development of a full-fledged chronic NAFLD pathology can be easily achieved in mice using a Western diet model. This is of paramount importance for the advancement of fundamental and preclinical therapeutic targeting studies on NAFLD, a disease that affects millions of people worldwide with an ever increasing trend (Wegermann et al., 2020).
To investigate whether the Western diet induces changes in gut microbiota, metagenomics analysis was conducted on fecal samples. At the phylum level, we noticed that the Western diet triggered a reduction in the relative abundance of Bacteroidetes, while it promoted an increase in Proteobacteria and Firmicutes. Among the most noticeable changes at the genus level was an increase in the relative abundance of Helicobacter, which is a member of Proteobacteria. While the mechanisms are still poorly understood, our results are in agreement with a previous study that reported the same trends in mice fed a high-fat diet (Hildebrandt et al., 2009). Previous studies have also confirmed that dysbiosis is linked to a high-fat diet and it plays important roles in the pathogenesis of NAFLD (Schnabl and Brenner, 2014). Dysbiosis due to a high-fat diet is suggested to be attributed to the creation of nutrient stress in the gut. For example, the lower proportion of carbohydrates in a high-fat diet is believed to cause a decrease in metabolism genes due to nutrient deficiency. Such conditions may enhance the overgrowth of certain bacterial taxa better suited for adapting to the environment while inhibiting others with selective pressure. It has been reported that Bacteroidetes are known to have large numbers of genes that encode carbohydrate-active enzymes, making them better suited to carbohydrate metabolism, while members of Proteobacteria are enhanced by a high-fat diet in the gut (Flint et al., 2012). Therefore, it is suggested that the high-fat content in the Western diet promoted overgrowth of Proteobacteria while inhibiting Bacteroidetes. Further analysis of bacterial and host metabolic enzyme patterns in the gut is required to determine such mechanisms.
Other important players in the gut microbiota and liver axis are microbe-derived metabolites. Some metabolites are synthesized by the microbes, and others are products of their enzymatic processes. We conducted metabolomic analysis of fecal microbe-derived metabolites, including SCFA, bile acids, and indole metabolites. Distinct metabolite profiles were observed among the different mouse groups. The reduction in SCFA levels in the WD group may indicate a decrease in the number and activity of bacteria capable of producing these metabolites. A previous study reported a decrease in fecal SCFA, such as acetate, propionate, and butyrate, in NAFLD patients with significant fibrosis, while no significant difference was observed for the moderate NAFLD stage (Rau et al., 2018). This trend was remarkably reversed in the probiotic Bifidobacterium-fed groups. A recent study demonstrated a decrease in Bifidobacterium and Lactobacillus in NAFLD patients (Niccolai et al., 2019). Therefore, the ameliorating effects of probiotic Bifidobacterium observed in this study can be attributed to their SCFA-producing ability. SCFAs are well known to inhibit hepatic cholesterol and lipogenesis while activating hepatic lipid oxidation (den Besten et al., 2013). Some indole derivatives, including methyl indole-3-acetic acid, indole-3-propioic acid, indole-3-acetic acid, 5-hydroxyindole-3-acetic acid, and indole-2-carboxylic acid, showed noticeable reduction in the WD group. This shows that the dysbiosis induced by the Western diet resulted in altered tryptophan metabolism. This is in agreement with a recent study that demonstrated a reduction in intestinal indole derivatives during dysbiosis of alcoholic liver disease in humans as well as experimental rodent models (Hendrikx and Schnabl, 2019). Indole derivatives alleviate hepatic steatosis and inflammation mainly by enhancing intestinal tight junctions and regulating intestinal immune homeostasis. For instance, some indole derivatives serve as ligands for the aryl hydrocarbon receptor, which is expressed by immune cells in the lamina propria and involved in pathogen defense through IL-22 expression (Ma et al., 2020). It also appears that the Western diet induced elevation of conjugated bile acids in this study. Bile acids undergo extensive microbe-mediated metabolism in the gut and are well known to greatly influence hepatic lipid accumulation. Probiotic supplementation remarkably reduced conjugated bile acid levels in the gut. Deconjugation is catalyzed by bacterial enzymes, primarily bile salt hydrolases, which are widespread in gut microorganisms, including Bifidobacterium and Lactobacillus (Rani et al., 2017).
To better understand the pathophysiological mechanisms of Western diet-related NAFLD at the molecular level, the mRNA levels of SREBP-1c, PPARα, and CCL2 genes in liver tissue were analyzed by qPCR. In the WD group, a marked elevation in hepatic mRNA of SREBP-1c was observed, while that of PPARα was lower than that in the control group, resulting in a higher SREBP-1c/PPARα ratio. The progression of NAFLD is mainly regulated by the expression of genes related to lipid metabolism. SREBP-1c plays a key role in the induction of lipogenesis in the liver, while PPARα favors fatty acid (FA) β-oxidation (Goto et al., 2013). The SREBP-1c/PPARα ratio has also been reported to be a good marker for determining the rate of hepatic steatosis (Pettinelli et al., 2009). The higher SREBP-1c/PPARα in the WD mice group was accompanied by an upregulation of CCL2, an inflammation initiator in fat-accumulated tissues (Arner et al., 2012). These results indicate that steatosis has led to pathologically considerable inflammation in the liver. In fact, a significant increase in the expression of IL-1β was observed, which suggests that it was induced by CCL2. CCL2 has been previously reported to induce significant secretion of several inflammatory cytokines, including IL-6 and IL-1β (Semple et al., 2010).
Randomized clinical trial studies have demonstrated that administration of probiotics attenuates NAFLD by alleviating hepatic steatosis and reducing hepatic inflammation (Ahn et al., 2019). Recent preclinical studies have reported amelioration of NAFLD by probiotic Lactobacillus and Bifidobacterium through modulation of gut microbiota-dependent pathways (Lee N. Y. et al., 2020). The results of the two Bifidobacterium strains used in this study are consistent with previous animal studies. Bifidobacterium breve and B. longum displayed efficient improvement of NAFLD by reducing liver weight, modulating gut microbiota, alleviating hepatic steatosis, and lowering inflammatory signaling molecules in the liver. According to the metagenomics data, the most noticeable gut microbes among the WD and probiotic treatment groups were Bacteroidetes and Firmicutes. These bacteria have been reported to be essential participants in host energy metabolism. Firmicutes are rich in genes involved in lipid digestion and nutrient movements, while Bacteroidetes have a lower capability to release extra energy from fat. Probiotic treatment with Bifidobacterium strains significantly increased the relative abundance of Bacteroidetes. This resulted in reduced mRNA levels of SREBP-1c (lipogenesis inducer) and CCL2 (inflammation initiator), while an upregulation of PPARα (inducer of β-oxidation) was observed compared with that in the WD group. Therefore, the attenuating effects of these strains on the overall NAFLD pathogenesis are mainly associated with their modulatory effect on the gut microbiota, resulting in reduced release of extra energy from fat, less triglyceride accumulation, and an inflammatory response. Of note, WD resulted in a significant increase in Proteobacteria, especially the Helicobacter genus. The relative abundances of *Helicobacter tended* to show a slight reduction in the probiotic treatment groups. Members of this genus are known to induce the development of acute and chronic inflammation in the intestine (Blosse et al., 2018). Considering that the gut microbiota is a potential driver of liver inflammation (Chassaing et al., 2014), it can be concluded that Bifidobacterium suppresses the inflammatory response. Compared with the WD group, significant reductions in the mRNA levels of the pro-inflammatory cytokines TNF-α, IL-6 and IL-1β were observed in the probiotic treatment groups.
In addition to the above pathological indicators of NAFLD, liver function was evaluated by measuring serum levels of AST, ALT, TBIL, and total CHOL. Serum levels of AST, ALT, TBIL, and total CHOL were markedly reduced in the probiotic B. breve and B. longum treatment groups compared to those in the WD group. All liver function test results showed a positive correlation with the biomarkers of NAFLD, indicating that liver injury can be prevented by alleviating the progression of NAFLD.
In summary, our results show that WD induced significant changes in microbial composition and resulted in development of hepatic steatosis as well as activation of inflammatory pathways. Treatment with B. breve and B. longum attenuated NAFLD by modulating the gut microbiota, downregulating hepatic steatosis and inflammation, and improving liver function. We suggest that these strains have the potential to be applied in the treatment of NAFLD patients.
## Probiotic strains
Two Bifidobacterium species namely B. breve CKDB002 and B. longum CKDB004 were used as probiotic strains in this study. These strains were originally isolated from feces of newborns and were obtained from Chong Kun Dang bioCorp (Gyeonggi-do, Korea) as processed lyophilized powder preparations.
## Patients
A total of 32 patients with NAFLD and 25 healthy subjects from Hallym University hospital (Admitted in from $\frac{2017}{03}$ to $\frac{2021}{03}$) were randomly recruited for the fecal microbial composition analysis (ClinicalTrials.gov NCT04339725). Patients with elevated liver enzyme [aspartate aminotransferase (AST) or alanine aminotransferase (ALT) ≥ 50 IU/L] were included in the hepatitis group. Enrolled patients for NAFLD who did not drink excessive alcohol and other liver diseases were excluded. Patients with viral hepatitis, autoimmune hepatitis, pancreatitis, hemochromatosis, Wilson’s disease, drug-induced liver injury, and other cancers were excluded. The eligibility criteria were based on age (40–60), NAFLD stage (hepatic steatosis-hepatitis), and body mass index (healthy subjects BMI ≦ 23 and NAFLD patients BMI > 23).
Baseline studies included family history, diet pattern, alcohol history, abdominal ultrasound, and computed tomography scan, X-ray, electrocardiography, complete blood count, electrolytes, liver function test, and viral markers. This project followed the ethics at 1975 Helsinki Declaration, as reflected by a prior approval by the institutional review board for human research in hospitals [2016-134]. Informed consent was obtained from all participants.
## Animal experiments
Six weeks of age specific-pathogen free male C57BL/6 J mice were purchased from DooYeol Biotech (Seoul, Korea). Animals were housed at 22°C under controlled conditions with a 12-h: 12-h light/dark cycle and relative humidity of 55 ± $10\%$. During the 1-week adaptation period, mice had free access to normal chow diet and sterile water. After 1 week of acclimatization, mice were randomly divided into four different diet groups as follows. Normal chow diet group; $18\%$ protein rodent diet (2018S TD, Envigo), WD group; rodent diet with $42\%$ fat, $42.7\%$ carbohydrate, $15\%$ protein (TD88137, DooYeol Biotech), Probiotic administration groups B. breve CKDB002 and B. longum CKDB004; Provided with distilled water containing probiotic strains at 109 CFU/g. After 9 weeks of treatment, animals were sacrificed after inhalation of anesthesia isoflurane. Body and liver weights were recorded. Whole blood samples were centrifuged at 19,000 ×g to collect serum. Liver, stool, and intestine samples were excised and immediately stored at −80°C.
The animals received humane care and all procedures were performed in accordance with National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. All procedures were approved by the Institutional Animal Care and Use Committee of the College of Medicine, Hallym University (Hallym 2019-30).
## Chemicals and reagents
Normal rodent diet (2018S TD, Envigo) and WD (TD88137, DooYeol Biotech) were purchased from commercial suppliers, respectively. Lipopolysaccharide (LPS), 3-Indolepropionic acid (IPA), and Indole-3-acetic acid (IAA) were purchased from Sigma-Aldrich (St. Louis, MO, United States). HPLC grade methanol, acetonitrile, and deionized water were purchased from J.T. Baker Co. (Phillipsburg, NJ, United States). All the other reagents were of analytical grade.
## Histopathological examinations
Specimens were fixed with $10\%$ formalin for 24 h, embedded in paraffin and tissue sections were cut for hematoxylin and eosin (H&E) staining analysis. The images of H&E-stained section were taken using a fluorescence microscope. Fatty liver was classified as according to NASH clinical research network scoring system for NAFLD from grades 0 to 3 (0: <$5\%$, 1: $5\%$~$33\%$, 2: $34\%$~$66\%$, 3: >$66\%$ of steatosis). Inflammation was classified from grades 0 to 3 (0: none, 1: 1~2 foci per ×20 field, 2: 2~4 foci per ×20 field, and 3: >4 foci per ×20 field). All biopsy specimens were analyzed by a pathologist (S. H. H.). The NAFLD activity score (NAS), an objective index for classifying the grade of fatty liver, is suggested by Kleiner which is sum of the scores of diabetes, bovine inflammation, and balloon dilatation (Kleiner et al., 2005). According to the guidelines, NAS can help us recognize a histological scoring system addressing the full spectrum of NAFLD (Brunt et al., 1999). For statistical analyses, the patients were grouped into the three different NAS groups (group 1 = NAS 0–2: probable no NASH; group 2 = NAS 3–4: borderline; group 3 = NAS 5–8: probable NASH).
## Quantitative real time-polymerase chain reaction
Liver tissue samples stored at −80°C were homogenized in 1 mL TRIzol reagent (Invitrogen, Gaithersburg, MD, United States) and the total mRNA was isolated in accordance with the manufacturer’s instructions. Synthesis of cDNA was performed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, United States) with random primers. The house-keeping gene GAPDH was used as an internal control to analyze the mRNA levels of TNF-α, IL-1β and IL-6. cDNA was amplified for quantitative real time PCR with One Step real-time PCR system (Applied Biosystems, Forster City, CA, United States) using PowerUp SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA, United States) and primer pairs (GenoTech, Daejeon, Korea). PCR primers were designed based on cDNA sequences from GenBank and were BLAST searched for specificity. Primers used in this study were as follows: GAPDH, forward 5′-AAATGGGGTGAGGCCGGT-3′ and reverse 5′-ATTGCTGACAATCTTGAGTGA-3′; TNF-α, forward 5′-CTGTAGCCCACGTCGTAGC-3′ and reverse 5′-TTGAGATCCATGCCGTTG-3′; IL-1β, forward 5′-TGTAATGAAAGACGGCACACC-3′ and reverse 5′-TCTTCTTTGGGTATTGCTTGG-3′; IL-6, forward 5′-CCACTTCACAAGTCGGAGGCTTA-3′ and reverse 5′-CCAGTTTGGTAGCATCCATCATTTC-3′. In qRT-PCR, the quantity of cDNA was calculated using the ΔΔCt method.
## Western blotting analysis
Western blot analysis was conducted as described previously (Nonoguchi et al., 1995). Total protein was isolated from the mouse intestine. Equal amounts of total protein were separated on a $12\%$ SDS-polyacrylamide gels (SDS-PAGE) and transferred on to a nitrocellulose membrane. Membrane was blocked overnight in Tris-buffered saline (TBS) containing $0.05\%$ Tween (TBST) and $5\%$ dry powdered milk and then washed three times for 5 min each with TBST and incubated for 2 h at room temperature in primary antibody (rabbit anti-occludin, Sigma). After three washes with TBST, the membranes were incubated for 1 h with horseradish peroxidase-conjugated secondary antibody. Following two washes with TBST and one wash with TBS Blots were developed using the Enhanced Chemiluminescence (ECL) Western blotting detection reagents (Amersham-Pharmacia Biotech) and utilizing image capturing software (Amersham-Imager 680, version. 2.0.).
## In vitro assays
RAW 264.7, widely used as murine macrophage cell lines and HepG2 cells obtained from the Korean Cell Line Bank (KCLB) were used for the in vitro experiments. Cells were grown in Dulbecco’s Modified Eagle’s Medium (DMEM, Gibco BRL). For the stimulation and treatment assays, cells were plated at 3 × 105 cells/well on 12 well plate with DMEM media. After 24 h of incubation, bacterial suspension, LPS, and CFS-based nanoparticles were added. After 12 h of incubation, cells were harvested for qRT-PCR analysis. For the indole metabolites treatment experiments, cells were pre-treated with IPA and IAA for 6 h followed by addition of LPS. Subsequently, cells were harvested for total RNA isolation after 12 h of incubation.
## Trans-epithelial electrical resistance measurements
Caco-2 cells were seeded onto Transwell-Clear inserts (12-well clusters, 6.5-mm inserts with polyester membrane, pore diameter 0.4 μm, Corning NY) at a density of 105 cells/insert. Each insert was placed on top of a well in a 24-well plate with 1 ml in the bottom and 200 μL media in the top as described previously (Anderson et al., 2010). Caco-2 cells were grown for 5 days until confluence in Minimum Essential Medium Eagle (MEM) with $20\%$ fetal bovine serum (FBS) without antibiotic-antimycotic (Gibco, Carlsbad, CA, United States) at 37°C in a humidified $5\%$ atmosphere. TEER measurements were performed using a Millicell Electrical resistance system (Millipore, Billerica, MA, United States). When monolayer of cells reached the confluence, Caco-2 cells were co-incubated with 200 μL of bacterial culture grown to OD600 0.3 (7 × 107 CFU/mL) in MEM media. Consequently, the TEER was measured after 8 h of incubation.
## Serum analysis
AST, ALT, TBIL, and CHOL were determined using a commercial biochemical analyzer of blood (KoneLab 20, Thermo Fisher Scientific, Waltham, Finland).
## Statistical analysis
Continuous variables were expressed as means and standard deviations. One-way ANOVA and independent sample t-test were performed during the liver and body weight, L/B ratio, liver function test, and histopathological analyses. All statistical analyses were done using IBM SPSS statistics program (IBM software, Armonk, NY, United States). Any values lying below $p \leq 0.05$ were considered statistically significant. Results were represented as mean ± standard deviation.
## Bioinformatics
Statistical analyses were conducted on all continuous variables acquired from GC-MS and LC-MS. All datasets were normalized using the “MS total useful signal” (Li et al., 2017). Significant differences between two groups were determined by Mann–Whitney U-test and Student’s t-test. A Kruskal-Wallis test with Dunn’s post hoc was conducted to evaluate significant differences among four groups using package Dunn’s Test in the software R (Dinno and Dinno, 2017). The p-value was corrected by Benjamini-hochberg’s adjustment (false discovery rate) and pathway over-representation analysis were performed based on the statistical modules implemented in MetaboAnalyst 4.0 (based on the hypergeometric test and relative-betweenness centrality; Chong et al., 2018). Treemap and Pie chart were created through Microsoft Excel (Microsoft, Seattle, WA, United States) using compound classification by Human Metabolome Database (Wishart et al., 2018). The metabolic network map was constructed based on structural similarity (Tanimoto score) and biochemical liaison (KEGG reaction pair information), and visualized by a prefuse force-directed layout using Cytoscape version 3.7.2 (Shannon et al., 2003). SIMCA 15 (Umetrics AB, Umea, Sweden) was applied for multivariate statistics including principal component analysis. Heat map, Column scatter graph, Violin plot, and Volcano plot were generated using GraphPad prism software ver. 7 (GraphPad Software Inc., San Diego, CA, USA). Co-inertia analysis was performed in the M2IA server1 (Ni et al., 2020). Interomic correlation matrix between individual metabolite and microbial composition (at genus level) was constructed based on Spearman’s rank analysis (package stats in the software R).
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
## Ethics statement
This project followed the ethics at 1975 Helsinki Declaration, as reflected by a prior approval by the institutional review board for human research in hospitals [2016-134]. Informed consent was obtained from all participants. The patients/participants provided their written informed consent to participate in this study. All procedures were approved by the Institutional Animal Care and Use Committee of the College of Medicine, Hallym University (Hallym 2019-30).
## Author contributions
SY, JY, DL, and KS designed the study and interpreted the work. SY and JY wrote the manuscript. BM, HG, S-MW, HP, SH, B-YK, KK, BK, HJ, and T-SP performed experiments. SH provided tissue specimens. All authors contributed to the article and approved the submitted version.
## Funding
This research was supported by Hallym University Research Fund, Korea National Research Foundation (2020R1A6A1A03043026 and 2021M3A9I4021433), Bio Industrial Technology Development Program [20018494] funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea), the Promotion of Innovative Businesses for Regulation-Free Special Zones funded by the Ministry of SMEs and Startups (MSS, Korea; P0020622), and The Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through High Value-added Food Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA; 321036-05-1-HD020).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1129904/full#supplementary-material
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|
---
title: Biochemical and antioxidant activity of wild edible fruits of the eastern Himalaya,
India
authors:
- Heiplanmi Rymbai
- Veerendra Kumar Verma
- Hammylliende Talang
- S. Ruth Assumi
- M. Bilashini Devi
- Vanlalruati
- Rumki Heloise CH. Sangma
- Kamni Paia Biam
- L. Joymati Chanu
- Badapmain Makdoh
- A. Ratankumar Singh
- Joiedevivreson Mawleiñ
- Samarendra Hazarika
- Vinay Kumar Mishra
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10014916
doi: 10.3389/fnut.2023.1039965
license: CC BY 4.0
---
# Biochemical and antioxidant activity of wild edible fruits of the eastern Himalaya, India
## Abstract
The eastern Himalayas, one of the important hotspots of global biodiversity, have a rich diversity of wild edible fruit trees. The fruits of these tree species have been consumed by the tribal people since time immemorial. However, there is limited information available on the biochemical and antioxidant properties of the fruits. Therefore, the present investigation was undertaken to study the physico-chemical and antioxidant properties of the nine most important wild fruit trees. Among the species, *Pyrus pashia* had the maximum fruit weight (37.83 g), while the highest juice ($43.72\%$) and pulp content ($84.67\%$) were noted in *Haematocarpus validus* and Myrica esculenta, respectively. Maximum total soluble solids ($18.27\%$), total sugar ($11.27\%$), moisture content ($88.39\%$), ascorbic acid content (63.82 mg/100 g), total carotenoids (18.47 mg/100 g), and total monomeric anthocyanin (354.04 mg/100 g) were recorded in H. validus. Docynia indica had the highest total phenolic content (19.37 mg GAE/g), while H. validus recorded the highest total flavonoids and flavanol content. The antioxidant activities of the different fruits ranged from 0.17 to 0.67 IC50 for DPPH activity and 3.59–13.82 mg AAE/g for FRAP. These fruits had attractive pigmentation of both pulp and juice and were a good potential source for the extraction of natural edible color in the food industry. The fruits also possess high market prices; *Prunus nepalensis* fetched $ 34.10–$ 141.5 per tree. Therefore, these fruits are rich sources of antioxidants, pigments and have a high market value for livelihood and nutritional security.
## 1. Introduction
The eastern Himalayan region of India has a diverse agro-climate, ranging from tropical to alpine, and receives very high rainfall. The region is an important part of the Indo-Myanmar biodiversity hotspot of the world [1]. The diverse agro-climatic conditions of this region offer immense scope for the evolution and development of different wild edible species. Of the 800 wild edible tree species found in India, about 300 are consumed by the hill populace of this region alone. Therefore, the region is considered a reservoir of several crop species, including wild relatives, grown naturally in the forests and also in the backyards of the local tribes. The economy of this region is basically rural based; agriculture and allied sectors play a predominant role. The fruits collected from the forest as well as from their own land are consumed locally and also sold in local markets at a premium price. Recently, several trainings and demonstrations have been conducted by various government agencies to impart knowledge and skills to the local people, resulting in improved resource utilization and entrepreneurial skills. Several value-added products, such as wine, vinegar, jam, jelly squash, RTS, pickles, etc., of these wild fruits are prepared and marketed by self-help groups (SHGs) and entrepreneurs. However, the availability of value-added products on the market is still lacking due to poor commercial production.
These fruit plants are found in tropical, subtropical, and temperate regions of the Indian subcontinent, East Asian nations, South East Asian nations, and European nations, which suggests that they are adaptable to a wider range of environments (Table 1). These fruits have been important constituents of diet and health care and have contributed significantly to the livelihood security of the local people over centuries [2]. Locally, these crops are also used to extract natural pigments [3]; and ethnobotanical uses of fruits in the treatment of cancer [4]; fever, cough, and jaundice, gastrointestinal, respiratory, and cardiovascular [5]; anti-obesity [6]; cognitive boosting properties, liver health, and reducing fatty liver buildup [7]; leaves of *Pyrus pashia* were used as fodder [8] and the Monpa community of Tawang, Arunachal Pradesh, India, used extracts of P. pashia as butter tea beverages [9]. This could be due to the nutraceutical properties of these fruits. It has been proven that ingestion of natural antioxidants from fruit sources, such as polyphenols, have significant anticarcinogenic, antipyretic, anticoagulant, anti-inflammatory, and hypoglycemic properties [10]. This might be attributed to the powerful antioxidant capacity of polyphenols and their additives, and their synergistic effects with associated bioactive constituents. Such constituents provide protection to the cellular system against oxidative impairment, which consequently reduces the oxidative stress in the human body [11]. In addition, fruit-based natural antioxidants are also fascinating because of their safety and wide applications in the cosmetic, pharmaceutical, and food industries as alternative sources to synthetic antioxidants [12]. In spite of these potential applications, research on these crops is at an infant stage, although morphological characterization of some fruit crops, such as Prunus nepalensis, Elaeagnus latifolia, Pyrus pashia, and other wild edible fruits has been described (13–15). However, their nutraceutical properties have not yet been scientifically assessed. Hence, the present study was carried out to determine the biochemical and antioxidant properties of popular wild edible fruit trees grown in the eastern Himalayas of India. The information generated could lead to a better understanding of the potential functional food sources and an increased consumption of these fruits. This, in turn, could have a significant impact on the most vulnerable tribal population’s long-term economic, nutritional, and health system in the near future.
**TABLE 1**
| Crops | Habitat and distribution | Uses |
| --- | --- | --- |
| Prunus nepalensis Ser. (Family: Rosaceae and Vernacular name: Sohiong) | ● Habitat: Subtropical and temperate Himalayan regions at an altitude of 800–3,000 m amsl. ● Native: Eastern Himalayas including the Khasi and Jaintia Hills of Meghalaya, India. ● Domestication status: Growing wild and semi cultivated. ● Distribution: It is found in Meghalaya, and other parts of Himalayas India (2). The species also found in Bhutan, Nepal, Myanmar, and China (16). | ● Edible portion: Epicarp and mesocarp. ● Dessert purpose: Fresh fruits. ● Processing: The processed products prepared from this fruit are ready to serve drink (RTS), squash, candy, powder, wine, tooty fruity (Sohiong + chow chow). The products developed retained the natural purple color of the fruits for longer period (up to 1 year). ● Others: Fruits are used as astringent, leaf as diuretic agent against edema (17). |
| Elaeagnus latifolia L. (Family: Elaeagnaceae and Vernacular name: Sohshang) | ● Habitat: Thrives well in open forest and swamps of the foothills track of Eastern Himalayas up to elevations of 2,600 m amsl. ● Native: The lower hill tracks of Himalayas considering its wider genetic variability. ● Domestication: The shrub mostly found in back yard as semi-wild and semi cultivated (13). The genus is also reported to be cultivated in warmer parts of southern Europe, North America and Vietnam (18). ● Distribution: Subtropical and temperate Himalaya including Myanmar and China. | ● Edible portion: Epicarp and mesocarp of fleshy drupe fruits. ● Dessert purpose: Fresh fruits. ● Processing: pulp used for preparation of pickle, jam, jelly and leather. Refreshing drink prepared from fruit juice possess attractive reddish or pinkish color. ● Others: Fruits are astringent (19) and found to reverse the growth of cancers (4). Pulp also used for dye extraction and seed as source of oil. The plant also possesses ornamental values. |
| Elaeagnus pyriformis Hook. f. (Family: Elaeagnaceae and Vernacular name: Sohkhlur) | ● Habitat: Thrives well in open forest up to elevations of 2,600 m amsl. ● Native: Foot hill tracks of the Eastern Himalayas India. ● Domestication: The shrub mostly found in forest areas. ● Distribution: Subtropical and temperate Himalaya including Myanmar and China. | ● Edible portion: Epicarp and mesocarp of fleshy drupe fruits. ● Dessert purpose: Fresh fruits. ● Processing: Processed products such as pickle, jam and jelly were prepared from pulp. Fruit beverage develop an attractive attractive reddish or pinkish color. ● Others: Fruits are capable of reducing cancer and reversing the growth of cancers (4). |
| Myrica esculenta Buch. -Ham. ex D. Don. (Family: Myricaceae and Vernacular name: Sohphie bah) | ● Habitat: It flourish well in mixed forests of Pinus sp., Quercus leucotrichophora and marginal lands of nitrogen depleted soils up to an altitude of 2,000 m amsl in the sub-tropical Himalayas (20). ● Native: North east and northern India, southern Bhutan and Nepal. ● Domestication: Semi-cultivated. ● Distribution: In the Indian subcontinents, it is confined in the sub-tropical Himalayas ranging from Punjab eastward to Assam. It was also found in the temperate and sub-tropical regions of China of both hemispheres except Australia (21). | ● Edible portion: Epicarp and mesocarp of drupaceous fruits. ● Dessert purpose: Fruits are edible as fresh at all stages of its growth. ● Processing: Fruits are used for making refreshing drink and pickle. The extracted juice emits a very attractive sparkling red color. ● Others: Fruit juice are used for treatments of jaundice in Khasi Hills (22), fever in Khasi Hills, Vietnam, South China (23), Ulcer, and Anthelmintic in Himachal (24, 25), Bronchitis, dysentery in Nepal (26). The bark is used as aromatic, tonic for rheumatism, astringent, carminative, asthma, odontalgia, diarrhea, lung infection, fever, cough, bronchitis, dysentery, antiseptic indigenous medicine (27, 28). Tannin extract from the barks are used as a yellow dyeing agent (29). |
| Myrica nagi Thunb. (Family: Elaeagnaceae and Vernacular name: Sohphienam) | ● Habitat: The tree is evergreen in the sub-temperate of mid-hill and hill tracks of the Himalayas up to 2,100 m amsl. ● Native: Eastern Himalayas, India. ● Domestication: Found wild in the forest. ● Distribution: It is found in the mid-Himalayas of India including the Khasi Hills. It is also found in Bangladesh, Singapore, Malayan islands, China and Japan (30). | ● Edible portion: Epicarp and mesocarp of drupaceous fruits. ● Dessert purpose: Fruits are edible as fresh at all stages of its growth. ● Processing: Pulp are used for preparation of refreshing drink and pickle. The juice possesses a very attractive sparkling pink color. ● Others: Bark powdered is used against dysentery (31). |
| Baccaurea sapida (Roxb.) Müll.Arg. (Family: Phyllanthaceae and Vernacular name: Sohramdieng/ Sohmyndong) | ● Habitat: Grow favorably in moist tropical up to an altitude of 900 m asml. ● Native: South East Asian region. ● Domestication: Growing wild and semi-cultivated in the sub-Himalayan tract of eastern India. It is cultivated in China, Myanmar, Thailand, Vietnam, and Malaysia (18). ● Distribution: It is found from Bihar to Arunachal Pradesh and in the lower hills and valleys (of Meghalaya, Assam, Nagaland, Manipur, Mizoram, and Tripura), Orissa and Andaman and Nicobar Islands. Globally, its distribution is from Indo-Malaysia to the West Pacific. | ● Edible portion: Arils is very delicious at ripening stage. ● Dessert purpose: Fresh fruits at ripened stage. ● Processing: Products prepared are squash, RTS, wine, jam and jelly due to its rich sources of pectin (14.1%). Fruit rinds are also used for making pickle. ● Others: Fruits and leaf produced dye of chocolate color which can be used as natural colorants in processed products. Seed was used to extract annatto dye (4.8–6.0%) for coloring of silk, cotton and other textile materials for orange-red color (3). Fruit juice are used for treatment against arthritis, abscesses, injuries, and constipation (32). |
| Pyrus pashia Buch.-Ham. ex D. Don. (Family: Rosaceae and Vernacular name: Sohjhur) | ● Habitat: It thrives well in moist soil up to an elevation 2,700 m amsl. It is tolerant to drought and atmospheric pollutants. ● Native: Southern Asia. ● Domestication: It is cultivated in Khasi and Jaintia Hills as back yard and border tree crops. ● Distribution: It is distributed from East Afghanistan, North Pakistan through Himalaya to Vietnam. | ● Edible portion: Thalamus or receptacle of pome fruits. ● Dessert purpose: Fruit are eaten fresh, and preferred for its sweetness and grittiness. ● Processing: ● Others: Fruit juice is astringent and diuretic (33) used against constipation (34), dysentery (35), leishmaniasis (36), eye problems (37), digestive disorder, sore throat, irritability, abdominal pain, anemia, curing eye disorder (38) and curing of gastrointestinal, respiratory and cardiovascular related problems (5). Decoctions of dried fruits with other plant parts improves in spleen and stomach functions (39). Leaf extract as a tonic for hair loss, treatment of digestion related ailments and possess antimicrobial activity (40). Staining of crushed leaves in palm, feet and nails improve cosmetic appearance (41). The barks are used to treat digestive disorders (42), sore throat, fever, peptic ulcer, gastric ulcer and typhoid fever (43). Leaves are used as fodder for goats and sheep (8) and it extracts as butter tea beverages by the Monpa community of Tawang, Arunachal Pradesh, India (9). The fruit is added to cattle fodder to enhance milk production (44). Seedlings can be used as rootstock for pear and quince grafts (28). |
| Docynia indica (Wall.) Decne. (Family: Rosaceae and Vernacular name: Sohphoh/shoptet) | ● Habitat: Grows well in moist soil, open places, and upland temperate to subtropical at an elevations of 700–3,000 m amsl. ● Native: Eastern Himalayas of India through Nepal to South Central China. ● Domestication status: Growing in the back yard and also naturally in forest areas of Khasi and Jaintia Hills of Meghalaya, India. In Northern Vietnam, the species is domesticated in large area (3,000 ha) with a production of 6,500 tons of fruit (45). ● Distribution: Southern foothill tracks of the Himalayan range from Pakistan through India, Nepal, Bhutan, and Bangladesh to the mountains of northern Myanmar, Thailand, Laos, Vietnam, and southern China. | ● Edible portion: Mesocarp of matured fruits. ● Dessert purpose: The fully ripe fruit is eating as fresh, while, the half ripe fruits are consumed as fresh with salt. ● Processing: The fruits soaked in brined solution or boiled with syrup and sundried. Pulp is used as raw materials for production of juice, tea, vinegar, wine, pickles and jelly (13, 45). ● Others: Fruits are used as natural remedy in treatment of infectious diseases, obesity (6), digestive problems and possess hypoglycemic and hypolipidemic properties (46). Fruit extract possesses antioxidant and antibacterial properties which have industrial potential as food preservative (47). The species as rootstock imparting semi-dwarf in apple. |
| Haematocarpus validus (Miers) Bakh. f. ex Forman (Family: Menispermaceae and Vernacular name: Sohsnam) | ● Habitat: An evergreen vine and prefers the tropical to subtropical conditions with a large number of herbaceous undergrowth on a hilly landscape up to altitude 1,250 m amsl. ● Native: Eastern Himalayas India to West Java. ● Domestication status: Growing in the forest naturally. ● Distribution: The geographical distribution of this species is limited and it has been categorized as critically endangered in Meghalaya (48). In India, it is found in Meghalaya, Tripura, Assam, Arunachal Pradesh, Sikkim, West Bengal, and Andaman and Nicobar Islands. It is also found in Bangladesh, Indonesia, Pakistan, and South east Asian. | ● Edible portion: Mesocarp of ripened fruits. ● Dessert purpose: Ripened fruits. ● Processing: Immature fruits are used for value-added products such as squash, pickles, and chutney. ● Others: Fruits and seeds were consumed for treatments against anemia, roots for curing itching and tender shoots for treatment of jaundice (49), while leaf decoction was used against body ache (50). The fruit is a rich source of choline for cognitive boosting properties, liver health and reduce fatty liver build up (51). Fruits are rich source of purple to bright reddish pigmentation used for dyeing local handicrafts and may have potential application as natural colorant in food industries. |
## 2.1. Materials and experimental site
The fruits of wild edible plant species such as *Baccaurea sapida* (Roxb.) Müll. Arg., *Docynia indica* (Wall.) Decne., *Elaeagnus latifolia* L., E. pyriformis Hook. f., *Haematocarpus validus* (Miers) Bakh. f. ex Forman., *Myrica esculenta* Buch. -Ham. ex D. Don., Myrica nagi Thunb., *Prunus nepalensis* Ser., and *Pyrus pashia* Buch.-Ham. ex D. Don. grown in the forests and/or backyards, were collected for the study (Figure 1). The collection was made from various locations in the region, particularly the Khasi Hills, Jaintia Hills, Ri Bhoi, and Garo Hills, distributed between 20.1–26.5°N latitude and 85.49–92.52°E longitude with altitude ranging from 100 to 2,000 m amsl (Figure 2). The collected fruits were analyzed for different biochemical and functional attributes at the ICAR Research Complex for the North Eastern Hill Region, Umiam, Meghalaya, India, during 2019–2020.
**FIGURE 1:** *Fruits of wild edible plants grown in the eastern Himalayas, India.* **FIGURE 2:** *Collection sites of wild edible fruits grown in the eastern Himalayas, India (Generated by subjected the global positioning system (GPS) data to quantum geographic information system (QGIS) version 3.20.1).*
## 2.2. Quantitative analysis
Twenty-five ripe fruits of each species were used for carrying out all the physical and biochemical analyses. Fruit samples were harvested at an appropriate maturity. The harvested fruits were washed with distilled water, wiped with tissue paper, and kept at room temperature for 10 min to remove the adhering water before analysis. The parameters, viz., fruit, and seed weights, were determined using an electronic balance (Adair Dutt-1620C). Fruit length and diameter were measured using a digital caliper (Code 1108-150). The pulp recovery percentage was estimated using the following formula:
## 2.3. Determination of biochemical attributes
Biochemical parameters such as total soluble solids (TSS) were determined using a hand-held refractometer (HI 96801) and titratable acidity, ascorbic acid, reducing sugars, and total sugars were analyzed according to Rangana [52]. The moisture content of the fruits was determined gravimetrically as per the method of Akter et al. [ 53] and Raaf et al. [ 54]. The fresh fruit samples were weight before and after drying in a hot air oven (thermostatically controlled, Model–IC7). About 20 g finely shredded fresh sample was placed in a clean and dried crucible with a cover, and accurately weighed on an electronic weighing balance (Model–AUX220). The samples were dried in the oven at 105°C for 24 h or until a constant weight was achieved for two consecutive weights. Following drying, the crucible was cooled in a desiccator. The moisture content (MC) was calculated as below and expressed as a percentage.
Where W1–fresh weight W2–dried weight
## 2.4. Measurement of total carotenoids
Total carotenoids were determined as per the method of Chen et al. [ 55]. The extraction of carotenoids was carried out according to the method developed by Chen et al. [ 55]. Pulp (10 g) was placed in a vessel, protected from light, and mixed with 50 mL of extraction solvent (hexane/acetone/ethanol: 70:15:15, v/v/v). The mixture was stirred for 1 h using an orbital shaker. About 5 mL of a $40\%$ KOH in methanolic solution were added, and the solution was saponified at 25°C in the dark for 2 h. Subsequently, 30 mL of hexane were added, the mixture was shaken vigorously, and the upper layer was collected. The lower layer was extracted twice, and the supernatant was also collected and filtered through sodium sulfate powder to remove traces of water. The supernatant obtained was pooled and stored at −80°C under a nitrogen atmosphere ($99.9\%$ purity) in the dark until analysis. The total carotenoid content of the extracts was measured using a UV-Visible spectrophotometer at 450 nm. A calibration curve (0–50 ppm) was prepared using β-carotene as the standard and the results were expressed as mg β-carotene/100 g sample.
## 2.5.1. Preparation of fruit extract
The pulp (5 g) of each fruit was grinded, and 50 mL of aqueous methanol was added at ambient temperature. The mixture was incubated for 1 h at room temperature with continuous magnetic stirring at 200 rpm and centrifuged at 1,000 g for 20 min. The supernatant was collected and stored at −20°C until analysis. The aliquot was used for assessments of total phenolic content, total monomeric anthocyanins, total flavonoids, total flavonol, DPPH free radical scavenging capacity, and FRAP reducing power.
## 2.5.2. Determination of total phenolic content
The crude extracts were estimated for total phenolic content using the Folin–Ciocalteu procedure as per the method of Singleton and Rossi [56]. About 1 mL of the extract was transferred to 2 mL of Folin–Ciocalteu reagent (1:10 v/v distilled water). After 10 min, 1.6 mL ($7.5\%$) of sodium carbonate was added. The mixture was vortexed for 15 s before being left to stand for 30 min at room temperature to develop its color. The absorption was measured at 743 nm in a UV-visible spectrophotometer (Model: UV 3200). The concentration of polyphenols in samples was derived from a standard curve of Gallic acid, and the total phenolic content was expressed as Gallic acid equivalents (GAE) in mg/g of pulp.
## 2.5.3. Determination of total monomeric anthocyanin content
Total monomeric anthocyanin was determined as per the procedure of Giusti and Wrolstad [57]; Lako et al. [ 58]. About 0.4 mL of the extract solution was taken, and 3.6 mL of the corresponding buffer; pH 1.0 buffer (potassium chloride 0.025 M) and pH 4.5 buffer (sodium acetate, 0.4 M) was added. The absorbance of each solution was taken against a blank in a cuvette with a 1 cm path length at 510 nm and 700 nm using a UV-Visible spectrophotometer. Total monomeric anthocyanin pigment concentration was expressed as cyanidin-3-glucoside equivalents (mg cyd-3-gluE/100 g) as follows: Where A = (A510nm – A700nm) pH 1.0 – (A510nm – A700nm) pH 4.5; MW (molecular weight) = 449.2 g/mol for cyanidin-3-glucoside (cyd-3-glu); DF = dilution factor established in D;
l = pathlength in cm; ε = 26,900 molar extinction coefficients for cyd-3-glu; and 1,000 = factor for conversion from g to mg.
## 2.5.4. Measurement of total flavonoids
The total flavonoid content of extracts was estimated using Aluminum chloride (AlCl3) colourimetric assay as previously described by Zhishen et al. [ 56]. About 0.3 mL of $5\%$ NaNO2 was added to 1 mL extract. After 5 min, 0.3 mL of $10\%$ AlCl3.6H2O was added, and incubated for 5 min. About 2 mL NaOH (1M) was added, and the final volume of the solution was adjusted to 5 mL with distilled water. After 15 min of incubation, the mixture turned to pink and the absorbance was measured at 510 nm (UV-visible spectrophotometer, Model: UV 3200). Total flavonoid content was presented as mg quercetin equivalent per gram (mg QE/g).
## 2.5.5. Determination of total flavonols
Total flavonols in the fruit sample extracts were determined according to the method of Miliauskas et al. [ 59]. 2 mL of $2\%$ AlCl3 and 6 mL ($5.0\%$) sodium acetate solutions were added to 2.0 mL of extract. The mixture was incubated at 25°C for 2.5 h and absorption at 440 nm (UV-visible spectrophotometer, Model-UV 3200) was read. Total flavonol content was expressed as quercetin equivalent (mg QE/g).
## 2.5.6. Measurement of DPPH free radical scavenging activity
The free radical scavenging activity of the fruit extracts was estimated with the DPPH (1, 1-diphenyl-2- picrylhydrazyl) method [60]. Ascorbic acid was used as a reference standard. 100 μL of aliquot was transferred to test tubes, to which 3.9 mL of freshly prepared DPPH solution (25 mg/L in methanol) were added. The mixtures were then thoroughly mixed and allowed to stand for 30 min. The absorbance was measured at 517 nm (UV-visible spectrophotometer, Model: UV 3200). The percent scavenging activity of DPPH was calculated using the following formula: Where, *Ac is* the absorbance of the control reaction and *At is* the absorbance of the sample of the extracts. The antioxidant activity of the extract was expressed as IC50 (the concentration of fruit sample required to decrease the absorption at 517 nm by $50\%$). The IC50 value was expressed as the concentration in milligram of extract per mL that inhibited the formation of DPPH radicals by $50\%$.
## 2.5.7. Measurement of FRAP reducing power
The reducing power of the extracts was assessed as per the method of Oyaizu [61]. About 100 μL of fruit extracts were mixed with phosphate buffer (2.5 mL, 0.2 M, pH 6.6) and $1\%$ potassium ferricyanide (2.5 mL). This mixture was incubated at 50°C for 20 min, to which 2.5 mL aliquots of trichloroacetic acid ($10\%$) was added. The content was centrifuged at 3,000 rpm for 10 min. The upper layer of the solution (2.5 mL) was extracted and mixed with 2.5 mL of distilled water and 0.5 mL of freshly prepared ferric chloride solution ($0.1\%$). Then the measurement of absorbance was recorded at 700 nm (UV-visible spectrophotometer, Model: UV 3200) and the reducing power was expressed in terms of ascorbic acid equivalent (AAE) in milligram per gram of extract (mg AAE/g).
## 2.6. Color, season of availability and market price of fruits
Color measurements of ripened fruits of different fruit tree species were carried out using a Color Hunter meter (HunterLab Color Quest XE). The instrument was calibrated using the black and white tiles. The value was expressed as L* values indicated lightness (black, L* = 0 and white, L* = 100), a* values indicated redness-greenness (red, a* = 100 and green, a* = −100), b* values indicated yellowness-blueness (yellow, b* = 100 and blue, b* = −100). The observation was replicated thrice for each sample. Observations were taken at the base, middle, and apex of fruits at an equidistant space under the aperture of the color meter. Through image analysis, an Android application (Color Grab version 3.9.2) was used to determine the color of fruit juice. A local market survey in Shillong city and 10 weekly markets in Khasi and Jaintia Hills were conducted. Informants (60 no.) were randomly selected among the local vendors and farmers for data collection on period of fruit availability in the markets and the market price of fruits. The selection of key informants was done with the help of village workers and elders as per the ethnoecological methods of Martin [62]. The yield of fruits per tree was determined by counting the number of fruits per tree at harvest and multiplying it by its fruit weight, expressed in kg per tree.
## 2.7. Statistical analysis
The replicated (three of each parameter) data were analyzed using statistical package for the social sciences (SPSS) (Version 14.0) software, and the data were presented as mean ± SE using one-way ANOVA ($p \leq 0.05$) of Tukey’s HSD (honestly significant difference) test. The possible relationship between antioxidant compounds and antioxidant activity was analyzed through Pearson’s correlation coefficient. Using quantum geographic information system (QGIS) version 3.20.1, a map of the collection sites was created subjecting the global positioning system (GPS) data.
## 3.1. Physico-chemical characteristics
The biochemical traits of fruits contribute to the consumer’s perception of quality traits, including those associated with taste, mouth feel, and appearance. The results revealed a significant variation among the fruit morphological and biochemical characteristics of different wild edible fruit species ($p \leq 0.05$, Table 2). The maximum fruit length ranged from 4.38 cm in H. validus to 1.52 cm in M. nagi; fruit diameter (1.23 cm in M. nagi to 4.39 cm in P. pashia); fruit circumference (12.38 cm in P. pashia to 3.64 cm in M. nagi); fruit weight (7.32 g in E. pyriformis to 37.83 g in P. pashia); fruit volume (39.89 cm3 in P. pashia to 7.32 cm3 in E. pyriformis); juice content ($21.22\%$ in M. nagi to $43.72\%$ in H. validus) and pulp content ($56.69\%$ in B. sapida to $84.67\%$ in M. esculenta). The significant differences in fruit physical characteristics indicated greater variability among fruit crops. The maximum fruit weight was observed in P. pashia, followed by D. indica, H. validus, and E. latifolia; juice content was recorded in H. validus, followed by M. esculenta, B. sapida, and E. pyriformis; and pulp content (>$70\%$) in M. esculenta, followed by M. nagi, P. pashia, P. nepalensis, D. indica, and H. validus. Similarly, there was a significant difference ($p \leq 0.05$) among fruits for biochemical attributes as given in Table 3. The moisture content was the highest in H. validus (88.39 ± $1.85\%$) and the lowest in P. pashia (73.75 ± $1.88\%$). The determination of moisture content in food is considered to be one of the most important assays since moisture greatly influences the physical properties and stability of the food [63]. The total soluble solids (TSS) was the maximum in H. validus (18.27 ± $1.49\%$) and the minimum in M. esculenta (5.83 ± $0.30\%$). The titratable acidity was the highest in M. esculenta (3.32 ± $0.06\%$), followed by E. latifolia (2.68 ± $0.04\%$) and the lowest in P. pashia (0.31 ± $0.03\%$). Total sugar ranged from 3.26 ± $0.05\%$ in E. latifolia to 11.27 ± $1.26\%$ in H. validus. Reducing sugar content ranged from 1.32 ± $0.03\%$ to 7.38 ± $0.54\%$, the minimum was recorded in E. latifolia and the maximum in H. validus. Our results indicated that the fruits of H. validus, P. nepalensis, B. sapida, E. latifolia, M. esculenta, and D. indica contained higher levels of TSS and acidity. TSS and acidity are the two important factors for determining the quality traits in a fruit, which also influence the taste, sweetness, and also act as an indicator of the maturity of the fruit and its suitability for processing. This was indicated by a strong relationship between TSS and total sugar (0.711**), ascorbic acid (0.838**), total monomeric anthocyanin (0.732**), total carotenoids (0.407*), total flavonoids (0.479**), and total flavonol (0.532**). Similar observations have been reported by Canan et al. [ 64]. Hence, the fruits rich in TSS and acidity were found suitable for fresh consumption as well as processing and value addition [65], and can be promoted for different value-added products such as ready to serve (RTS), wine, etc. as a cottage industry.
## 3.2.1. Ascorbic acid content
Ascorbic acid is regarded as the most important antioxidant vitamin. However, it cannot be synthesized by humans due to the lack of gulonolactone oxidase enzyme, and a deficiency of dietary ascorbate results in clinical syndrome and scurvy [66]. Hence, supplementing the diet with ascorbic acid-rich foods is very vital. In this study, the ascorbic acid content of wild edible fruits had significant variations (Figure 3A). The highest ascorbic acid content was recorded in H. validus (63.82 mg/100 g pulp) and the lowest in P. pashia (9.62 mg/100 g pulp). These results agree with the reports of Contreras-Calderón et al. [ 67] on the variability of vitamin C content in several wild edible fruits. Furthermore, the finding demonstrated that these wild edible fruits have a higher vitamin-C content than commercially available major fruits: *Citrus sinensis* (10.13 ± 0.10 mg/100 g), *Ananas comosus* (6.40 ± 0.18 mg/100 g), *Malus domestica* (7.94 ± 0.13 mg/100 g), and *Prunus persica* (5.92 ± 0.12 mg/100 g). However, they had a lesser content than the richest known sources of vitamin C, such as *Psidium guajava* (198.05–221.47 mg/100 g), *Phyllanthus emblica* (375.68 mg/100 g), and *Emblica officinalis* (756.32 mg/100 g) (68–70). Our results showed that the ascorbic acid content of E. latifolia was lower than that reported from Sikkim by Dasila and Singh [71]. This variation may be attributed to different analytical methods, as reported by Dias et al. [ 72]. The E. pyriformis reported in this study had a lower ascorbic acid content than that reported from Manipur (20.10 mg/100 g) by Khomdram et al. [ 70]. The reason for variations may be due to the unique genetic make-up among genotypes and environmental factors [73], and differences in soil physico-chemical attributes such as pH, nutrients, and agro-ecology [74]. A variation in the pH of the soil is known to determine the availability of nutrients to the roots and their uptake, which could be influenced by soil geology and climatic factors [75]. A significant positive correlation of ascorbic acid with total soluble solids (0.838**) and total sugar (0.784**) was observed (Table 4). A high positive correlation of ascorbic acids with sugars was due to the recurring and elaborate interactions between organic acids and sugars [76], which may be associated with the synthesis of ascorbic acid from glucose [77]. Ascorbic acid also showed a significant negative correlation with total antioxidant activity (−0.397*) which was represented by the IC50 of DPPH and analyzed by Pearson’s correlation coefficient (r). It is well-established that lower IC50 values indicate high antioxidant activity [78]. Therefore, an increase in the ascorbic acid content will enhance the antioxidant activity of these fruits, as shown by the lower IC50 of DPPH value. Our findings suggested that ascorbic acid may be one of the factors contributing to antioxidant properties, as evidenced by their positive relationship in a variety of other food sources [79]. Hence, the daily consumption of these fruit crops will enrich the diet and act as an additional or alternative source of ascorbic acid.
**FIGURE 3:** *Functional attributes of wild edible fruits grown in the eastern Himalayas, India. (A) Ascorbic acid content (AA, mg/100 g fw); (B) total carotenoids content (TCC, mg/100 g fw); (C) total monomeric anthocyanins content (TAC, mg/100 g fw); (D) total flavonoids content (TFC, mg QE/g); (E) total flavonol content (TFLC, mg QE/g); (F) total phenol content (TPC, mg GAE/g); (G) DPPH antioxidants capacity (DDPH, IC50 value mg/mL); (H) FRAP antioxidants capacity (FRAP, mg AAE/g) content in wild edible fruits. IC50 ascorbic acid (0.012 ± 0.002). Mean value of three replications (each replication consisted 10 fruits) with ± S.E followed by different letters on each bar indicate significant difference from each other according to Tukey’s test ($p \leq 0.05$).* TABLE_PLACEHOLDER:TABLE 4
## 3.2.2. Total carotenoid content
Carotenoids as antioxidant compounds are known to be present in several fruit crops, and the dietary intake of carotenoid-rich foods has been reported to retard cancer, cardiovascular disease, and several other ailments in humans [80]. Results showed that the total carotenoids content of different wild edible fruit species varied significantly ($p \leq 0.05$; Figure 3B). H. validus recorded the highest total carotenoids (18.47 mg/100 g pulp), followed by D. indica, and the lowest total carotenoids (4.52 mg/100 g pulp) were noted in E. pyriformis. The fruits of H. validus, D. indica, and P. nepalensis contain higher total carotenoids than mangoes [4,926.76–14,942.46 μg/100 g fw, [81]] and cashews [0.4 mg/100 g fw, [82]]. The variations in genetic make-up among the species may be the cause of the variations in total carotenoids. Dias et al. [ 72] have also reported the great influences of varieties, maturity, cultural management, environment, postharvest care, storage conditions, and analytical methods on the formation of secondary metabolites, including the total carotenoid content in fruit crops. The high carotenoid content of these fruits is an important indicator of their quality and high nutritional value [83]. In addition, our study also found a strong negative correlation between total carotenoid content and DPPH (−0.818**). The presence of high level of total carotenoids in the fruits of H. validus, P. nepalensis, D. indica, B. sapida, E. latifolia, and M. esculenta indicates their powerful ability to scavenge oxygen free radicals and active oxygen. The previous study [84] revealed that carotenoid scavenging ability would increase due to an increase in the lipophilicity of carotenoid. Lycopene was effective in reducing Fe (III) to Fe (II), given the fact that lycopene contains 11 conjugated double bonds [84]. Although lycopene content was not analyzed in our study, Dasila and Singh [71] found that it was 2.5 times higher in E. latifolia (2.06 ± 0.38 mg/100 g) than β-carotene (0.83 ± 0.02 mg/100 g). Our results also indicated that the a* value (redness) of the peel (31.5 ± 3.90a) and juice (23.4 ± 1.9a) of E. latifolia were the highest among these wild edible fruits (Table 5). It is well established that the red color of certain fruits and vegetables, such as tomato, pink grapefruit, red grapes, watermelon, and red guava, is due to the presence of lycopene [85]. Therefore, lycopene may be one of the major pigments responsible for the red color in the fruits of E. latifolia.
**TABLE 5**
| Fruits | Peel pigmentation | Peel pigmentation.1 | Peel pigmentation.2 | Juice pigmentation | Juice pigmentation.1 | Juice pigmentation.2 | Season of availability | Yield per tree (kg/tree) | Market price per kg ($) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | L* value | a* value | b* value | L* value | a* value | b* value | | | |
| Baccaurea sapida | 71.8 ± 10.42a | −6.500.46d | 30.9 ± 4.78a | 35.7 ± 4.75d | 3.7 ± 0.32d | 2.5 ± 1.36f | June to July | 45–138 | 0.85 |
| Docynia indica | 54.5 ± 6.80b | −6.7 ± 3.77d | 30.0 ± 2.55a | 50.3 ± 5.15bc | 2.4 ± 1.29d | 15.1 ± 0.71cd | November to December | 27–125 | 0.57 |
| Elaeagnus latifolia | 22.5 ± 2.93cd | 31.5 ± 3.90a | 12.9 ± 1.66bc | 39.8 ± 2.08cd | 23.4 ± 1.9a | 17.6 ± 0.71bc | March to May | 15–117 | 0.5 |
| Elaegnus pyriformis | 54.5 ± 2.92b | 1.8.0 ± 2.03cd | 17.7 ± 3.27b | 70.9 ± 8.15a | 11.1 ± 2.8c | 20.4 ± 0.40b | April to May | 7–26 | 0.21 |
| Haematocarpus validus | 16.2 ± 4.33de | 6.3 ± 2.12c | −0.1 ± 1.22d | 14.5 ± 1.56e | 19.3 ± 3.04b | 6.5 ± 1.25e | June to August | 35–83 | 1.14 |
| Myrica esculenta | 54.2 ± 6.36b | 15.7 ± 4.29b | 29.6 ± 2.05a | 67.5 ± 2.61a | 1.7 ± 1.47e | 17.6 ± 1.29bc | June to July | 17–116 | 0.78 |
| Myrica nagi | 25.6 ± 1.57cd | 28.5 ± 1.51a | 9.3 ± 0.67c | 41.5 ± 3.75cd | 24.6 ± 2.95a | 13.4 ± 1.21d | June to July | 12–53 | 0.99 |
| Prunus nepalensis | 15.8 ± 1.30e | 2.6 ± 0.44cd | −0.7 ± 0.71d | 8.2 ± 1.21e | 14.1 ± 2.15bc | 4.3 ± 1.90ef | August to October | 20–125 | 1.7 |
| Pyrus pashia | 33.2 ± 1.33c | 20.1 ± 1.91b | 18.1 ± 0.95b | 61.1 ± 2.21bc | 0.7 ± 0.29d | 31.2 ± 0.55a | August to October | 40–132 | 0.64 |
## 3.2.3. Total monomeric anthocyanin content
Anthocyanins are water-soluble and vacuolar pigments found in most species in the plant kingdom. Its accumulation mostly occurs on flowers and fruits, which impart an attractiveness to the fruit, hence; it is considered a color indicator and a natural colorant [86]. It also plays a role in preventing, ameliorating, and scrubbing oxidative stress, thus retarding several diseases and physiological malfunctions [87]. A significant variation ($p \leq 0.05$) in total monomeric anthocyanin content was observed among wild edible fruits (Figure 3C). The highest total monomeric anthocyanin content was recorded in H. Validus (354.04 mg/100 g), followed by P. nepalensis (341.70 mg/100 g) and the lowest was found in E. pyriformis (6.02 mg/100 g). These wild fruits contain more total monomeric anthocyanin than commercial fruit cultivars such as sweet cherry cv. Black Gold [44.19 ± 1.38 mg/100 g fw, [88]], red currants (12.14 ± 0.87 mg/100 g fw), black currant (287.78 ± 0.08 mg/100 g fw) [89], purple tomato [20.73 ± 2.86 mg/100 g fw, [90]], and guava [0.40–0.69 mg/100 g, [82]]. The varied total anthocyanin levels between species indicate genetic variations in the synthesis of these bioactive substances. Horbowicz et al. [ 86] have also reported the considerable variation in anthocyanin content of the fruits among different species or cultivars within the same species. This difference in anthocyanin content among these fruits might be due to the effects of genetics, agro-ecological conditions such as pH, light, temperature, and horticultural practices [91]. In our study, it was also observed that the fruits with higher anthocyanin content, such as H. Validus (−0.7 ± 0.71) and P. nepalensis (−0.1 ± 1.22) had the lowest b* value (Table 5). Similarly, the lowest L* values were recorded in the darkest colored fruits (H. Validus, 16.2 ± 4.33 and P. nepalensis, 15.8 ± 1.30). In the previous study by Muzolf-Panek and Waskiewicz [92], it was revealed that the effect of variety was predominant in fruit peel color and that the darkest table grapes had the lowest L* values, indicating blue-black and violet-black peel in varieties of table grapes. According to Ponder et al. [ 93], anthocyanins are responsible for the specific dark blue color of fruit berries, and the darker the fruit, the more anthocyanins it contains. Therefore, the dark purple and blue fruit color of H. validus and P. nepalensis might be due to their high anthocyanin content. Furthermore, a significant inverse relationship (−0.645**) between total monomeric anthocyanin content and DPPH demonstrated their high antioxidant properties. Similarly, Katiresh et al. [ 94] found that anthocyanins in Sesbania sesban had high antioxidant activity and were effective at scavenging free radical DPPH. Structurally, monomeric anthocyanin possesses loose structures that are easier to undergo oxidation and thus will exhibit better antioxidant activity compared to non-monomeric anthocyanin [95]. This is also in agreement with Castaneda-Ovando et al. [ 96], who claimed that the molecule that donates a free electron (ionization potential) or hydrogen atoms (bond dissociation energy) to the reactive free radicals is often the best antioxidant, and increasing the stability of the anthocyanin will reduce its antioxidant stability. As a result, consuming fruits with high concentrations of these compounds may provide protection to the body against various illnesses [97].
## 3.2.4. Total flavonoids and flavonols
Flavonoids and flavonols are naturally occurring phenolic compounds found in fruits, vegetables, and/or medicinal plants. They have significant biological effects and exhibit promising antioxidant activity due to their ability to effectively scavenge reactive oxygen species. Dietary flavonoids are recognized for their antioxidant potential, antiproliferative effects, and protective effects on lipids and vital cells against oxidative damage. These properties also play a significant role in the prevention of cardiovascular disease, inflammation, and antiproliferative or anticancer activities [98]. A significant variation was also recorded in total flavonoids and flavonol content among different wild fruit species ($p \leq 0.05$, Figures 3D, E). Total flavonoid content values ranged from 2.77 ± 0.06 mg QE/g (P. pashia) to 5.46 ± 0.04 mg QE/g (H. validus). Similarly, total flavonol content also varied significantly among the studied fruits, being the maximum in H. validus (3.12 ± 0.05 mg QE/g) and the minimum in E. pyriformis (0.74 ± 0.03 mg QE/g). These fruits contained a higher level of flavonoids than most of the plants reported by Fouad et al. [ 99] and also higher concentrations of flavonols than Prunus mahaleb [1.24 ± 0.06 g/kg, [100]]. This variation in total flavonoids and flavonol content among different fruit species could be due to various intrinsic and extrinsic factors, such as genetic and environmental factors. Our results revealed a strong negative correlation of total flavonoids content (−0.794**) and total flavonol content (−0.427**) with DPPH content, which indicates that flavonoids and flavonols play an important role in the antioxidant activity of these fruits. Our study is in line with that of Chandra et al. [ 101], who reported that $32\%$ of the antioxidant activity in crops was contributed by flavonoids, which constitute a major group of antioxidant compounds and act as primary antioxidants [102]. The redox properties of total flavonoids were due to the unique positions of OH ortho (C-3′ and C-4′) and oxo functional groups (C-4) in flavonoids [103]. Therefore, fruit trees such as H. validus, M. esculenta, B. Sapida, M. nagi, E. latifolia, D. indica, and P. nepalensis are rich in flavonoids and flavonol content, suggesting their consumption can help people meet their nutritional needs and protect them from developing a variety of degenerative diseases.
## 3.2.5. Total phenolic content
Plant-derived phenolic compounds are a diverse group of secondary metabolites that interact with reactive oxygen species to prevent oxidative damage, thereby aiding plant defense mechanisms and protecting humans from a variety of degenerative diseases [104]. Our results indicated the presence of significant variations ($p \leq 0.05$) in total phenolic content among fruits in the following descending order: D. indica (19.37 ± 0.07 mg GAE/g) followed by H. validus, P. nepalensis, M. esculenta, M. nagi, E. latifolia, B. sapida, P. pashia, and E. Pyriformis (7.32 ± 0.11 mg GAE/g) (Figure 3F). Interestingly, fruits like D. indica, H. validus, P. nepalensis, M. esculenta, M. nagi, and E. latifolia had higher total phenolic content than the commercial crops of the region, such as pineapple (47.9 mg GAE/100 g), banana (7.2 ± 0.5–18.9 ± 1.4 mg GAE/g dw), and papaya (57.6 mg GAE/100 g) [105, 106]. These fruits are comparable with the known richest sources of total phenolic content, such as Aonla (944.85–4,969.50 mg/100 g pulp), which are grown locally [107]. According to Robards et al. [ 108], phenolic compounds exhibit heterogeneity in distribution and concentration across and within plant species. Furthermore, the higher phenol accumulation in the fruits under our study might depend on several factors, viz., agroclimatic conditions, organ, plant developmental stage, and their interaction with the genotype [10]. The presence of different concentrations of sugars, carotenoids, or ascorbic acid, as well as extraction methods, may influence the amount of phenolics [109]. Czyczyło-Mysza et al. [ 110] also suggested the importance of both additive and epistatic gene effects on total phenolic content in species, which affect other adaptation traits of the species. Total phenolic content had a positive correlation with total sugar (0.551**), ascorbic acid, DPPH (−0.794**), total monomeric anthocyanin, total carotenoids, total flavonoids, and total flavonol. According to Fitriansyah et al. [ 111], if the r value is −0.61 ≤ r ≤ −0.9723, it showed a high negative correlation, which indicates that TPC had a strong negative correlation with DPPH. It is well established that phenolic compounds are important plant constituents with redox properties responsible for antioxidant activity. The higher the TPC, the greater is the total antioxidant activity of these fruits as demonstrated by low IC50 of DPPH. Our study exhibits that TPC was one of the major contributory compounds for antioxidant activity, which was also confirmed by Nariya et al. [ 112] for their scavenging ability due to their unique hydroxyl groups. This indicates that these wild food resources are highly nutritious and rich sources of bioactive compounds, and their consumption will further help improve nutrition.
## 3.2.6. DPPH free radical scavenging activity
The free radical chain reaction is widely accepted as the most important mechanism of lipid peroxidation. Radical scavengers terminate the peroxidation chain reaction by directly counteracting and quenching peroxide radicals. The capacity of polyphenols to transport labile H atoms to radicals is a probable mechanism of antioxidant protection, which can be assessed universally and rapidly using DPPH. Furthermore, DPPH is the most common and cost-effective way to determine the free radical scavenging capacity of natural products, which are major factors in biological damage caused by oxidative stress [113]. Our results revealed a significant variation ($p \leq 0.05$) in DPPH free radical scavenging activity among the studied fruits (Figure 3G), and it ranges from (0.17 ± 0.01 IC50 mg/mL) in M. nagi to 0.67 ± 0.02 IC50 mg/mL in E. pyriformis. The present results showed lesser values than those recorded in commercial fruits such as grapes (0.79 ± 0.34 IC50 mg/mL), pineapple (0.83 ± 0.24 IC50 mg/mL), and guava (1.71 ± 0.61 IC50 mg/mL) [79]. The antioxidant capacity of fruits and vegetables was influenced by factors such as genetic makeup, maturity, and other environmental factors such as sunlight exposure, soil, and the gene-environment interaction [10]. According to Matuszewska et al. [ 78], the lower IC50 values of DPPH indicate a high level of antioxidant activity, which means that these fruits, viz., M. nagi, D. indica, H. validus, and P. nepalensis with a low IC50 value can scavenge the DPPH radicals to form a stable reduced DPPH molecule. The high accumulation of total sugar, ascorbic acid, total monomeric anthocyanin, total carotenoids, total phenolics, total flavonoids, and total flavonol content increases the antioxidant activity, as demonstrated by the lower IC50 of DPPH value in our result, which agreed with the finding of Sundaramoorthy and Packiam [114]. Therefore, the high antioxidant activity of these fruits might be due to a strong negative correlation of different compounds with IC50 DPPH. Previous studies have also found that the antioxidant activity in plant tissue was mainly due to the unusual redox properties of not just one particular compound but also of different bioactive compounds including TPC, tannin, anthocyanin, TFC, phenols, alkaloids, and pro-anthocyanins [115, 116]. The antioxidant effect is due to the ability of compounds in the plant extract to transfer electrons or hydrogen atoms to neutralize radicals of DPPH and form neutral DPPH molecules [117]. Hence, it is clear from our results that these fruit crops had a greater potential for radical scavenging compounds with proton-donating abilities.
## 3.2.7. FRAP reducing power
FRAP antioxidants capacity is a simple and inexpensive assay that offers a putative index of the potential antioxidant activity of plant materials. Principally, the FRAP assay treats the antioxidants in the sample as reductants in a redox-linked colourimetric reaction. The reducing power assay, i.e., the transformation of Fe3+ to Fe2+ in the presence of either the extract or the standard (ascorbic acid), is a measure of reducing capability [79]. A significant variation ($p \leq 0.05$) of FRAP reducing power was observed among the underutilized fruits studied, and it ranged from 3.63 ± 0.05 mg AAE/g in E. pyriformis to 13.82 ± 0.04 mg AAE/g in M. nagi (Figure 3H). These results indicated higher FRAP values in these fruits than in the other wild fruits reported (0.0518 ± 0.49 to 0.111 ± 0.00 mg AAE/g) by Mahadkar et al. [ 118] from central India. The differences in antioxidant content between species may be due to genetics and environmental factors, as well as their interactions. Our result showed a strong positive correlation of FRAP value with total sugar (0.563**), ascorbic acid (0.405*), total monomeric anthocyanin (0.635**), total carotenoids (0.851**), total phenolics (0.732**), total flavonoids (0.447**), total flavonol (0.569**), and inversely related to DPPH (IC50, −0.932**) (Table 4). The finding that the DPPH and FRAP assays of fruit extracts were highly correlated agrees with the work of Szydłowska-Czerniak et al. [ 119] and is consistent with the view that the two assays share a similar mechanistic basis, viz., transfer of electrons from the antioxidant to reduce an oxidant, as proposed by Huang et al. [ 120]. The total carotenoids and total phenolic content were the major compounds contributing to the antioxidant activity in our study. Previous report in amla fruits (*Emblica officinalis* Gaertn) showed that carotenoids had reduction potential lower than 0.44 V, allowing them to reduce Fe (III) to Fe (II) while also being oxidized and acting as antioxidants [84]. Similarly, the phenolic compounds largely contribute to the antioxidant activities of these species and therefore could play an important role in the beneficial effects of these fruits. Several studies have found that phenolic compounds are major antioxidant constituents in selected plants and that there are direct relationships between their antioxidant activity and total phenolic content [103]. The antioxidant properties of phenolic compounds are directly linked to their unique structure, which allows them to act as reducing agents, hydrogen donors, and singlet oxygen quenchers [121]. This demonstrated that most of these underutilized fruits have strong reducing capabilities as compared to other fruit crops, which might be due to the presence of high total carotenoids, total phenolic content, and other functional compounds that are responsible for their antioxidant activity [122]. *In* general, among the fruits, M. nagi showed relatively stronger FRAP activity than other fruits.
## 3.3. Color, season of availability and market price of fruits
These fruits have appealing pigments, both in the peel and juice, as evidenced by a significant variation in value of L*, a* and b* in both peel and juice color ($p \leq 0.05$, Table 5). B. sapida had the highest peel L* value (71.80 ± 10.42a) and b* value (30.90 ± 4.78a), while E. latifolia had the highest peel a* value (31.50 ± 3.90a). Similarly, the yellowness and redness of these fruits were found to be higher than many of the Indian commercial mango varieties [123]. The color variation (L*, a, b) among the wild edible fruit trees might be due to a genetic effect. It is well established that the L* value is a suitable indicator of darkening that arises either from increasing pigment concentrations or from oxidative browning reactions [124]. Furthermore, the higher a* and b* values added a decorative effect toward the consumer’s preference. These fruits can be a good potential source for the extraction of natural edible color that is required in the food industry. As per Deka et al. [ 125], the products prepared from P. nepalensis, such as squash and jam, develop an attractive color and remain stable for 1 year (Figure 4). They have also prepared ready-to-serve (RTS) products and cherry wine from P. nepalensis fruits, which impart a unique natural purple color [13]. The suitability of products for processing and extracting natural color helps stabilize the market price. The results also showed that the season of availability of fruits varies from plant to plant. The fruits of these wild edible fruit plants were found to be available throughout the year, with the exception of January and February. The different harvesting periods of these wild edible fruits ensure the year-round availability of these fruits, and particularly during the lean season when other fruits are not available, they provide supplementary food and nutritional security in the region. The yield of wild edible fruit trees varies between 7–26 kg per tree in E. pyriformis and 45–138 kg per tree in B. sapida. The variation in season of fruit availability and yield among wild edible fruit trees might be due to the contribution of genetic makeup and the growing environment [126]. Similarly, the market price of fruits ranged from $ 0.21 per kg in E. pyriformis to $ 1.7 per kg in P. nepalensis. The variation in market price among wild edible fruits might be due to the contribution of fruit quality factors such as taste, TSS-acidity blend, peel appearance, etc., which determine the appealability and preferences amongst consumers. Tarancon et al. [ 127] also reported that the consumer’s perception of fruit quality is exclusively based on appearance. The market price of the fruits of P. nepalensis was about $34.10–$141.5 per tree, which highlights the high potential for income generation from these wild fruit trees. Therefore, expansion of the commercial area under these crops and their utilization may offer an additional source of income, employment generation, and livelihood improvement.
**FIGURE 4:** *Different products developed by farmers from wild edible fruits grown in the eastern Himalayas, India. (A) Jam of Sohiong (P. nepalensis Ser.); (B) Squash of P. nepalensis; (C) Juice of P. nepalensis; (D) Juice of Sohphie (M. esculenta); (E) Wine of P. nepalensis; (F) pickle of Sohphoh (D. indica); (G) Mixed pickles of P. nepalensis and E. latifolia; (H) pickle of Sohshang (E. latifolia).*
## 4. Conclusion
About $12\%$ of the world’s population lives in mountainous regions. Wild edible fruits have been consumed by the mountainous populace since time immemorial. Many of these genetic resources, however, have become rare and endangered as a result of overexploitation in their natural habitat and a lack of consumer understanding of their antioxidant and biochemical values. Our results would aid in a proper understanding of the potential uses and antioxidant activities of wild edible fruit trees. Therefore, it is concluded that: Wild fruits such as H. validus, P. nepalensis, B. sapida, E. latifolia, M. esculenta, and D. indica are high in total soluble solids, total sugar, and acidity. These fruits have the potential to be used as supplementary bases in the fruit processing industry.
The high antioxidant activities such as ascorbic acid, total phenolic content, total flavonoid, total flavonol, DPPH free scavenging capacity, and FRAP reducing power in H. validus, P. nepalensis, M. esculenta, and M. nagi suggest their potential as sources of bioactive compounds.
These fruits can be used to extract attractive natural colors and to make high-value processed products such as jams, squash, pickles, and wine.
A proper understanding of the biochemical and antioxidant properties of these fruits will help in their sustainable utilization and conservation.
## Data availability statement
The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
HR: laboratory studies, design of the figures, and writing the manuscript. VV: data analysis and interpretation of finding. HT, V, RS, and KB: editing of the manuscript. SA, MD, LC, and BM: correction of manuscript. JM, ARS: data collections, laboratory studies, and design of the figures. SH and VM: discussion of the results, critical feedback, and providing help in shaping the content, and evaluation of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Evaluation of mitochondrial biogenesis and ROS generation in high-grade serous
ovarian cancer
authors:
- Zeynep C. Koc
- Vincent E. Sollars
- Nadim Bou Zgheib
- Gary O. Rankin
- Emine C. Koc
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10014927
doi: 10.3389/fonc.2023.1129352
license: CC BY 4.0
---
# Evaluation of mitochondrial biogenesis and ROS generation in high-grade serous ovarian cancer
## Abstract
### Introduction
Ovarian cancer is one of the leading causes of death for women with cancer worldwide. Energy requirements for tumor growth in epithelial high-grade serous ovarian cancer (HGSOC) are fulfilled by a combination of aerobic glycolysis and oxidative phosphorylation (OXPHOS). Although reduced OXPHOS activity has emerged as one of the significant contributors to tumor aggressiveness and chemoresistance, up-regulation of mitochondrial antioxidant capacity is required for matrix detachment and colonization into the peritoneal cavity to form malignant ascites in HGSOC patients. However, limited information is available about the mitochondrial biogenesis regulating OXPHOS capacity and generation of mitochondrial reactive oxygen species (mtROS) in HGSOC.
### Methods
To evaluate the modulation of OXPHOS in HGSOC tumor samples and ovarian cancer cell lines, we performed proteomic analyses of proteins involved in mitochondrial energy metabolism and biogenesis and formation of mtROS by immunoblotting and flow cytometry, respectively.
### Results and discussion
We determined that the increased steady-state expression levels of mitochondrial- and nuclear-encoded OXPHOS subunits were associated with increased mitochondrial biogenesis in HGSOC tumors and ovarian cancer cell lines. The more prominent increase in MT-COII expression was in agreement with significant increase in mitochondrial translation factors, TUFM and DARS2. On the other hand, the ovarian cancer cell lines with reduced OXPHOS subunit expression and mitochondrial translation generated the highest levels of mtROS and significantly reduced SOD2 expression. Evaluation of mitochondrial biogenesis suggested that therapies directed against mitochondrial targets, such as those involved in transcription and translation machineries, should be considered in addition to the conventional chemotherapies in HGSOC treatment.
## Introduction
Ovarian cancer is one of the deadliest gynecological cancers worldwide and is the fifth leading cause of death for women in the United States [1]. Despite success in attaining remission in many cases, over half of the women with ovarian cancer experience resistance to chemotherapy, metastasis, and recurrence. Changes in energy and antioxidant metabolism have been highlighted as major factors in chemoresistance and peritoneal metastasis in recent epithelial high-grade serous ovarian cancer (HGSOC) studies (2–6). Determining the metabolic remodeling of energy generation for metastatic development and tumor growth has the potential to introduce pathway-specific therapies.
In recent biomarker studies, mitochondrial energy metabolism is emerging as one of the major contributors to aggressiveness and chemoresistance in HGSOC [7, 8]. The mitochondrial mass and oxidative phosphorylation (OXPHOS) capacities are increased 3.3-8.4-fold in epithelial ovarian carcinoma [9]. It is believed that tumors preferentially use aerobic glycolysis rather than the much more efficient OXPHOS to generate ATP, described as the Warburg effect (10–12). However, evidence suggests that tumor cells require a metabolically rich microenvironment allowing a combination of aerobic glycolysis and OXPHOS to promote growth and metastasis [13, 14]. In addition to increased OXPHOS, high levels of reactive oxygen species (ROS) generated in HGSOC cause sensitivity to platinum-based chemotherapy [7, 15]. However, HGSOC tumors have been shown to develop resistance to platinum-based chemotherapy over time, possibly due to remodeling of the energy metabolism and apoptotic pathways (5, 7, 16–18).
The metabolic flexibility of HGSOC tumors requires changes in the expression of both nuclear and mitochondrial genomes to encode subunits of OXPHOS complexes (complex I-V). Mitochondrial transcription supports the synthesis of 13 OXPHOS subunits encoded by the mitochondrial genome, two ribosomal RNAs (rRNAs), and 22 tRNAs [19, 20]. Malignant transformation of mitochondrial function and mtDNA mutations have been observed in age-related cancer development [21, 22]. A comprehensive list of mitochondrial genes and proteins causing mitochondrial dysfunction in ovarian cancer can be found in a recent review published by Shukla and Singh [23].
The significant variation in the expression of mitochondrial-specific transcription factors, such as PGC1α and TFAM, implies a highly modulated expression of mt-transcription in HGSOC (9, 24–26). Activation of PGC1α, promoted by chronic oxidative stress and aggregation of PML-nuclear bodies, results in high OXPHOS capacity and chemosensitivity in HGSOC [7]. On the other hand, the knock-down of PGC1α or TFAM diminishes the generation of mitochondrial reactive oxygen species (mtROS) and cisplatin-induced apoptosis [27].
The role of mitochondrial translation in HGSOC is limited. Nuclear-encoded protein factors and mitochondrial-specific 55S ribosomes support mitochondrial translation. While the mitochondrial ribosomal proteins (MRPs) are all nuclear-encoded genes, 55S ribosomes are composed of the two mitochondrial(mt)-encoded rRNAs and 80 MRPs identified in our previous proteomics studies (28–32). For the mitochondrial translation-related genes, high transcript levels of mitochondrial ribosomal small (MRPS) and large (MRPL) subunit proteins, MRPS12, MRPS14, MRPL15, MRPL34, and MRPL49, are suggested as novel prognostic markers predicting reduced overall survival in ovarian cancer patients (33–35). Additionally, a single nucleotide polymorphism (SNP) of the mitochondrial elongation factor Tu (TUFM) gene is associated with epithelial ovarian cancer risk [36]. Therefore, further evaluation of factors involved in mitochondrial biogenesis, specifically mitochondrial translation, is required to determine the mechanism(s) behind the remodeling of energy metabolism in HGSOC and resistance to chemotherapy.
Here, we provide evidence, for the first time, that changes in mitochondrial biogenesis support the metabolic flexibility in HGSOC tumor biopsies and ovarian cancer cell lines. Specifically, mitochondrial translation and transcription factors played an essential role in the modulation of OXPHOS subunit expression. Datamining analyses of mass spectrometry (MS)-based proteomics studies of HGSOC performed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Institute Curie cohort also supported our findings with concurrent changes in mt-encoded subunit II of the complex IV, MT-COII, and mitochondrial translation factors, TUFM and DARS2. We also observed higher mtROS generation in ovarian cancer cell lines with lower OXPHOS subunit expression and mitochondrial biogenesis. These observations suggest that the steady-state expression of mt-encoded OXPHOS subunits and components of the mitochondrial translation could be used as prognostic biomarkers to determine more targeted chemotherapy options in HGSOC.
## Ovarian tissue biopsies
Fifteen de-identified ovarian tumors and normal tissue biopsies were removed by surgical excision from patients treated at the Marshall University Edwards Comprehensive Cancer Center, Huntington, WV. Ethical review and approval were not required for the human de-identified biopsies used in this study in accordance with the local legislation and institutional requirements. Tumor characteristics of biopsies are given in Table S1. Ovarian cancer subtypes were determined by immunohistochemistry, immunofluorescence, and fluorescence in situ hybridization techniques by the Edwards Comprehensive Cancer Center. Tissue protein lysates were prepared by resuspension and sonication of biopsies in RIPA buffer containing $1\%$ SDS and NP40. Protein concentration was determined by the bicinchoninic acid (BCA) assay (Pierce, Rockford, USA).
## Cell culture and [35S]-Met pulse labeling
The NCI-ovarian cancer cell line panel (OVCAR-4, OVCAR-5, OVCAR-8, SKOV-3, and IGROV-1) was purchased from NCI. *Using* gene expression compositional assignment, the OVCAR-5 cell line is also reported as being gastrointestinal in origin [37]; however, NCI did not confirm this report. The OVCAR-3 cell line was obtained from Dr. Sarah Miles (Marshall University). The NCI-60 panel of ovarian cancer cell lines, OVCAR-4, OVCAR-5, OVCAR-8, SKOV-3, and IGROV-1, was cultured in RPMI media (HyClone, Thermo-Scientific, Waltham, MA) as recommended by NCI. OVCAR3 cells were maintained in RPMI media containing $20\%$ fetal bovine serum (FBS) (Rocky Mountain Biologicals, Missoula, MT), 10 mL/mL human insulin, $0.1\%$ penicillin/streptomycin (Corning Cellgro, Manassas, VA). The cells were grown in a humidified incubator at 37°C with $5\%$ CO2. All experiments with the cell lines were limited to passages 5-15 from frozen stocks and repeated with a minimum of nine biological replicates conducted in three separate experiments for all results.
Expression of the 13 mt-encoded subunits of OXPHOS complexes was determined by [35S]-Met pulse labeling described previously [38, 39]. Pulse labeling experiments were performed with breast cancer cell lines grown to 60-$70\%$ confluency in RPMI media. After arresting cytosolic protein synthesis by emetine, cells were incubated in 0.2 mCi/mL of [35S]-EasyTag™ Protein Labeling Mix (Perkin Elmer Inc., Waltham, MA) containing media for 2 h. Cells were lysed in RIPA buffer supplemented with protease and phosphatase inhibitors (Calbiochem, Darmstadt, Germany). Whole-cell lysates (30 μg) were separated on $13\%$ SDS-PAGE. The gels were dried on 3MM chromatography paper (Whatman) after Coomassie Blue staining, and the signal intensities of the bands were quantified by UN-Scan-It (Silk Scientific Inc, Orem, UT).
## Immunoblotting analyses
Tissue lysates obtained from biopsies and cell lines were either diluted further or lysed in RIPA buffer containing 50 mM Tris-HCl (pH 7.6), 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, $1\%$ NP40, $0.1\%$ SDS, $0.5\%$ deoxycholate, and protease and phosphatase inhibitor cocktails (Calbiochem, Darmstadt, Germany). Protein concentrations were determined using BCA assays (Pierce, Rockford, USA). Approximately 20 µg of the protein lysate was separated on $12\%$ SDS-PAGE, transferred to nitrocellulose membranes (Amersham, GE Healthcare, UK), and stained with Ponceau S to ensure equal protein loading (Figures S1, S2). The Ponceau S staining of nitrocellulose membranes was used to normalize total protein loading to signal intensities detected by immunoblotting analyses. Antibodies were commercially obtained as follows: the human OXPHOS antibody cocktail from Abcam (Eugene, OR); mitochondrial NDUSF2, DARS2, TUFM, and TFAM from Santa Cruz (Dallas, TX); PGC1α and SSBP1 from ProteinTech (Rosemont, IL), SOD2 from Cell Signaling Technologies (Danvers, MA), and GAPDH from Fitzgerald (Acton, MA). The secondary anti-rabbit and mouse HRP-conjugate antibodies were obtained from Pierce (Rockford, USA). The protein immunoreactivity was detected using the ECL Western blotting kit (Amersham, GE Healthcare, UK) as directed by the manufacturer. Immunoblotting signal intensities were quantified by UN-Scan-It (Silk Scientific Inc, Orem, UT) and normalized to total protein loading detected by Ponceau S staining of the membranes.
## Flow cytometry analyses
Mitochondrial mass and ROS generation determinations in ovarian cancer cell lines were performed using MitoTracker-Red CMXRos (Invitrogen) and MitoSOX-Red (ThermoFisher), respectively, by flow cytometry analyses using the Agilent Novocyte 3000. Data analysis was performed using NovoExpress Software vX. Optimal concentrations of Mito-SOX-Red and Mito-Tracker-Red were 5 and 0.5 μM, respectively.
## Statistical analyses
Statistical and graphical analyses were performed using Excel and GraphPad Prism 9.3. Statistical significance was determined using unpaired Welch’s t‐tests. Probability values less than 0.05 were regarded as statistically significant. All the values were in triplicates wherever possible and expressed as the mean ± SD unless otherwise described.
## Heterogeneity of mitochondrial energy metabolism in ovarian cancer
Changes in mitochondrial energy metabolism have recently been suggested as possible causes for chemoresistance and tumor recurrence in HGSOC [2, 7, 23, 40, 41]. To further investigate the changes in mitochondrial function, we obtained nine surgically removed normal ovarian and ovarian tumor tissue biopsies from the Tissue Procurement Center at the Marshall University Edwards Comprehensive Cancer Center, Huntington, WV. The tumor characteristics and stages of HGSOC biopsies are given in the Supplemental Table S1.
In our earlier studies, the steady-state expression of OXPHOS subunits by immunoblotting agrees with OXPHOS complex activities and is sufficient for evaluating energy metabolism in mitochondria (7, 42–44). Therefore, we determined the steady-state expression of OXPHOS subunits in HGSOC biopsies by immunoblotting using an antibody cocktail. The antibody cocktail is a mixture of five antibodies recognizing four nuclear-encoded OXPHOS subunits, including complex V (ATP5A1), III (UQCRC2), II (SDHB), and I (NDUFB8) and a mt-encoded subunit of complex IV (MT-COII). The same OXPHOS membrane was also probed with GAPDH antibody (Figure 1A). The signal intensity detected by NDUFB8 antibody would not be quantified in most of the biopsies. Instead, an additional antibody against NDUFS2 was used to confirm changes in another nuclear-encoded subunit of complex I (Figure 1A). Signal intensities obtained for each subunit were normalized to total protein loading detected by Ponceau S staining and mean signal intensities for normal and tumor biopsies rather than a direct comparison of normal and tumor biopsies from the same patient (Figure 1B). Changes in OXPHOS subunit expressions were at least 2-3-fold higher for some of the subunits in tumor biopsies relative to the normal tissue biopsies obtained from the same patient (Figures 1A, B). Specifically, we observed an overall statistically significant increase in complex II and IV subunits, SDHB and MT-COII ($P \leq 0.01$), as well as GAPDH in tumors relative to the normal tissues (Figures 1A, B). Although GAPDH is usually used as a loading control, here we observed an increase in the glycolytic enzyme, GAPDH ($P \leq 0.05$), confirming the metabolic remodeling in HGSOC [10].
**Figure 1:** *Modulation of OXPHOS subunit expression in ovarian cancer. (A) The expression of OXPHOS subunits, including ATP5A1 (CV; Complex V), UQCRC2 (CIII; Complex III), SDHB (CII, Complex II), MT-COII (CIV; Complex IV), NDUFB8 and NDUFS2 (CI; Complex I), and GAPDH were detected by immunoblotting in normal (N; blue) and ovarian tumor (T; red) biopsies. Only a tumor biopsy was available for patient 359. Approximately 20 μg of protein lysates obtained from normal and tumor tissues were separated by 12% SDS-PAGE, and the immunoblotting analyses were performed using antibodies shown on the left side. For patient 782, the normal tissue protein amount was extremely low, and it was not included in the quantitation. The red arrow shows the mt-encoded complex IV subunit, MT-COII. (B) Relative protein expression was quantified by normalizing the signal intensities of antibodies to protein loading and presented as violin graphs after conversion to log 2 values. (C) Log2 protein expression values of OXPHOS subunit expression determined by MS-based proteomics of 20 normal (N; blue) and 83 HGSOC ovarian biopsies (T) published by McDermott et al. [Log 2 values are taken from
Supplemental Data
in reference (45)] as part of the CPTAC. (D) Log2 OXPHOS subunit expression values for 53 low (L; blue) and 74 high (H; red) HGSOC tumors were reported by Gentric et al. [Log 2 values are taken from
Supplemental Data
in reference (7)] as part of the Curie cohort. For the statistical analysis, unpaired Welch’s t-test was used, and the P values ≤ 0.05 were represented as (*), ≤ 0.01 (**) and ≤ 0.0001 (****).*
Recent proteomics data published by CPTAC has provided a more comprehensive MS-based quantitation of 20 normal and 83 ovarian tumor biopsies obtained from HGSOC patients [45]. The Log2 expression values for mitochondrial proteins are reported in Supplemental Data provided by McDermott et al. [ 45]. The majority of the OXPHOS subunit expression slightly increased in tumor biopsies relative to the normal tissues; however, NDUFS2 expression is reduced in tumor biopsies ($p \leq 0.05$) (Figure 1C). The HGSOC proteome analyses by CPTAC compares 83 HGSOC tumor tissues to the mean values determined from 20 normal tissues (Figure 1B). Here, the slight discrepancy in the magnitude of expression changes in our results compared to those by CPTAC is possibly due to the direct comparison of the normal and tumor tissues obtained from the same patient in our analyses rather than a comparison to a mean as performed by CPTAC. However, when Gentric et al. compared the OXPHOS subunit expression by MS-based proteomics, HGSOC biopsies were classified as low- and high- OXPHOS subunit expressing HGSOC tumors [7]. The expression of nuclear-encoded subunits ATP5A1, UCQRC2, SDHB, and NDUFS2, and the mt-encoded subunits, including MT-COII, were graphed to demonstrate a significant increase in OXHOS subunits in HGSOC tumors with high mitochondrial energy metabolism (Figure 1D). The agreement observed between our immunoblotting analyses and MS-based proteomics data by McDermott et al. and Gentric et al. suggests that the modulation of mitochondrial energy metabolism is required for tumor growth and proliferation in HGSOC [7, 45].
## Mitochondrial biogenesis modulates OXPHOS in HGSOC
The up-regulation of mt-encoded MT-COII protein expression shown above (Figure 1A) indicates a role for mitochondrial biogenesis in HGSOC. Due to the presence of seven mt-encoded subunits in complex I, the nuclear-encoded complex I subunit, NDUFS2, was also concurrently affected by the changes in mitochondrial biogenesis (Figure 1A). However, the overall increase in NDUFS2 expression was not statistically significant in tissue biopsies (Figure 1B). Additionally, the transcription factors involved in nuclear- and mt-encoded OXPHOS transcripts, PGC1α and TFAM, respectively, are suggested as putative markers of chemoresistance in epithelial ovarian carcinoma [7, 25, 46]. Therefore, we postulated that the mitochondrial transcription and translation proteins directly related to the biogenesis of 13 mt-encoded subunits also contribute to the modulation of OXPHOS in HGSOC.
To assess the role of mitochondrial biogenesis in HGSOC biopsies, we determined PGC1α and TFAM protein expression and the single-stranded mitochondrial DNA-binding protein (SSBP1) by immunoblotting using the same HGSOC biopsies described above (Figures 2A, S2). Although the PGC1α levels were slightly decreased and a doublet observed, TFAM and SSBP1 protein expression were elevated in some of the HGSOC tumor biopsies relative to the normal values (Figure 2A). The overall mean modulation of protein expression in normal vs. tumor biopsies was not significant ($P \leq 0.05$) (Figure 2B). On the other hand, TFAM and SSBP1 levels were significantly elevated in the HGSOC tumor biopsies reported by the CPTAC proteome ($P \leq 0.0001$) (Figure 2C) [45]. These findings supported the altered mitochondrial transcription and replication in HGSOC.
**Figure 2:** *Expression of Mitochondrial Transcription- and Replication-Related Proteins in HGSOC. (A) Expression of PGC1α, TFAM, and SSBP1 proteins were detected in normal and HGSOC biopsies by immunoblotting analyses as described in
Figure1A
. (B) Log 2 relative protein expression values of PGC1a, TFAM, and SSBP1 shown in (A) were presented using violin plots. (C) MS-based quantitation of TFAM and SSBP1 published by McDermott et al. (45) as part of the CPTAC data set described in
Figure 1C
. The normal (N; blue) and HGSOC biopsies (T; red) were compared using violin plots as described in
Figure 1B
legend. P values ≤ 0.0001 were represented as (****).*
The modulation of OXPHOS requires cooperation between mitochondrial transcription and translation for synthesizing 13 mt-encoded subunits. We next determined the expression of two mitochondrial translation factors, elongation factor Tu (TUFM) and aspartyl-tRNA synthetase 2 (DARS2), in HGSOC biopsies by immunoblotting. Interestingly, the TUFM and DARS2 protein expressions were much higher and significant, $P \leq 0.05$ and $P \leq 0.005$, respectively, in tumor biopsies (Figures 3A, B). The CPTAC proteomics data mining analyses for TUFM and DARS2 were also in agreement with our observation (Figure 3C). In fact, the majority of mitochondrial translation-related proteins and factors are higher in the HGSOC tumor biopsies reported by the CPTAC (data not shown [45]).
**Figure 3:** *Expression of Mitochondrial Translation-Related Proteins in HGSOC. (A) Relative protein expression of TUFM and DARS2 were detected in normal ovarian (N) and HGSOC biopsies (T) by immunoblotting analyses. Equal protein loading was evaluated by Ponceau S staining and GAPDH probing. (B) Log 2 relative protein expression values of TUFM and DARS2 shown in panel A was presented using violin plots. (C) MS-based quantitation of TUFM and DARS2 protein expression, and (D) expression of mitochondrial ribosomal proteins, MRPS12, MRPS14, MRPL15, and MRPL49 in normal (N; blue) and HGSOC (T; red) in HGSOC published by McDermott et al. as part of the CPTAC data set [Log 2 values are taken from
Supplemental Data
in reference (45)]. P values ≤ 0.05 were represented as (*), ≤ 0.01 (**), and ≤ 0.0001 (****).*
Higher expression of several MRP genes has been associated with reduced overall survival and tumor recurrence using the publicly available ovarian cancer transcriptomics databases [33, 34]. We searched the CPTAC proteome data to determine the expression of these MRPs, including MRPS12, MRPS14, MRPL15, and MRPL49, published by McDermott et al. [ 45]. Log2 protein expression values for MRPS12, MRP L15, and MRPL49 were graphed and shown to be significantly elevated in tumor biopsies (Figure 3D). Here, the synergy between the MRP expression and mitochondrial translation factors along with the MT-COII expression confirmed the remodeling of energy metabolism and mitochondrial biogenesis, particularly the protein synthesis, in HGSOC (Figures 1A, 3).
## mtROS generation is increased in ovarian cancer cell lines with reduced OXPHOS subunit expression
Tumor-initiating cells undergo hypoxic conditions as they form spheroids and malignant ascites in the peritoneal cavity, causing changes in mitochondrial morphology and ROS levels during the progression of ovarian cancer [41, 47]. mtROS produced as byproducts of OXPHOS when electrons leak from complexes I and III play a critical role in regulating a wide variety of cellular signaling pathways, including stabilization of hypoxia-inducible factor 1 alpha (HIF1α) in cancer (11, 27, 48–50). Therefore, it is critical to correlate OXPHOS status, mitochondrial biogenesis, and mtROS generation in ovarian cancer cell lines. For this purpose, we acquired the NCI-60 ovarian cancer cell line panel containing OVCAR-3, OVCAR-4, OVCAR-5, OVCAR-8, SKOV-3, and IGROV-1 cells derived from adenocarcinomas and peritoneal ascites (Table S2). Among these cell lines, OVCAR-3 and OVCAR-4 are the closest cell line models to HGSOC by comparing the genomic profiles [51, 52]. The cells originated from ascites form aggressive peritoneal tumors and malignant ascites in animal models (Table S2) (53–55). In fact, the diversity of these cell lines might provide distinct mitochondrial characteristics and allow us to evaluate mitochondrial biogenesis and mtROS in these cell lines and compare it to HGSOC tumors. We first performed the immunoblotting analyses of cell lysates using the OXPHOS antibody cocktail as described in Figure 1A. The steady-state expression of OXPHOS subunits was relatively higher in OVCAR-3, OVCAR-4, and OVCAR-5 cells than that of the OVCAR-8, SKOV-3, and IGROV-1 cell lines (Figure 4A). These observations are all in agreement with high- and low-OXPHOS ovarian cancer cell line classification determined by Gentric et al. [ 7]. Expressions of both mt-encoded, MT-COII (shown by a red arrow) and nuclear-encoded subunits, UQCRC2, NDUFB8, and COX4, were highly modulated between the two groups, confirming the high- and low-OXPHOS capacities in these cell lines (Figure 4A). Additionally, the increase in Mn-superoxide dismutase, SOD2, expression was more prominent in cells with high-OXPHOS capacity except the SKOV-3 cells (Figure 4A). The cells with higher OXPHOS subunit expression (Figure 4A) are suggested to be more sensitive to chemotherapy relative to the low OXPHOS expressing cells [7]. As summarized in Table S2, some of the cell lines, specifically OVCAR-8 and SKOV-3, with reduced OXPHOS subunit expression and mitochondrial mass cause subcutaneous and intraperitoneal tumor formation in mice xenografts [54, 55].
**Figure 4:** *Altered OXPHOS subunit and SOD2 expressions and generation of reactive oxygen species in ovarian cancer cell lines. (A) The OXPHOS subunit and SOD2 expressions were detected by immunoblotting of lysates obtained from ovarian cancer cell lines, OVCAR-3 (OV3), OVCAR-4 (OV4), OVCAR-5 (OV5), OVCAR-8 (OV8), SKOV-3 (SK3), and IGROV-1 (IGR1) as described in
Figure 1A
. The red arrow shows the mt-encoded complex IV subunit, MT-COII. The relative quantitation of OXPHOS subunit and SOD2 expression represents the mean ± SD of at least three experiments. Signal intensity for each antibody was normalized to the mean of high (OV3, OV4, and OV5) and low (OV8, SK3, and IGR1) OXPHOS expressing cell lines and Ponceau S staining (
Figures S2, S3
). (B) mtROS and mitochondrial mass were determined by MitoSOX-Red (MitoSOX) and MitoTracker-Red (MitoTracker) stains, respectively, using flow cytometry of live ovarian cancer cell lines. MitoSOX/MitoTracker ratio reflects mtROS formation per functional mitochondrion for each cell line.*
Above, we demonstrated that the low-OXPHOS subunit expressing cell lines, OVCAR-8 and SKOV-3, are derived from highly malignant and chemo-resistant peritoneal ascites (Figure 4A) (Table S2). Due to the varying levels of OXPHOS subunit expression, one may speculate that the different mtROS levels adapted these cell lines to hypoxic conditions and survival in the peritoneal cavity. To correlate the OXPHOS subunit expression to mtROS generation, we performed flow cytometry analyses using MitoSOX-Red as well as the MitoTracker-Red staining of live ovarian cancer cell lines. The MitoSOX-Red and MitoTracker-Red ratios allowed us to determine the generation of mtROS per functional mitochondrion in these cell lines. This ratio was higher for OVCAR-8 cells relative to the other cell lines, specifically OVCAR-3, OVCAR-4, and OVCAR-5 cells (Figure 4B). In other words, lower MitoSOX/MitoTracker ratio in these cells with higher OXPHOS subunit expression could be either due to the increased mitochondrial mass or mtROS scavenging capacity. The reduced OXPHOS subunit expression was in agreement with the increased mtROS generation, specifically for OVCAR-8 and SKOV3 cells (Figures 4A, B). Although the reduced expression of SOD2 explains the increased mtROS generation in OVCAR-8 cells, the high SOD2 protein expression was not sufficient to suppress mtROS generation in SKOV-3 cells (Figures 4A, B). These cell lines were highly proliferative and resistant to cisplatin treatments [data not shown and [7]]. Again, the OVCAR-8 and SKOV3 cell lines are known to develop subcutaneous and intraperitoneal tumors in mice [54, 55]; the reduced OXPHOS subunit expression can be associated with increased mtROS generation and resistance to chemotherapy, as also suggested by Gentric et al. [ 6, 23]. Our observations and findings from other laboratories indicate that the remodeling of OXPHOS and mtROS generation could be highly informative in explaining tumor aggressiveness, metastasis to the peritoneal cavity, and recurrence in ovarian cancer [3, 18, 34, 41, 56].
## Mitochondrial biogenesis is altered in ovarian cancer cell lines
Similar to our observations with HGSOC biopsies, the substantial change in the steady-state MT-COII expression implied the modulation of mitochondrial biogenesis in some of the ovarian cancer cell lines (Figure 4A). To further investigate this phenomenon, immunoblotting analyses of ovarian cancer cell lysates were carried out using PGC1α, TFAM, SSBP1, TUFM, and DARS2 antibodies. Expression of the major mitochondrial transcription factors, PGC1α and TFAM, and SSBP1 were relatively similar in these cell lines, with some exceptions (Figure 5A). Reduced SSBP1 protein expression was noteworthy in SKOV3 cells (Figure 5A). Like TFAM expression, variation in translation elongation factor TUFM expressions was negligible in these cell lines (Figure 5B). Another translation-related protein DARS2 expression was higher in cell lines with high OXPHOS capacity (Figure 5B). The lower DARS2 expression in OVCAR-3 cell lines is noteworthy and possibly attributable to the reduced aspartate levels in HGSOC [57]. *In* general, the lack of any significant trend in the data suggests mitochondrial biogenesis could be regulated at different stages to modulate OXPHOS subunit expression in these cell lines.
**Figure 5:** *Evaluation of Mitochondrial Biogenesis in Ovarian Cancer Cell Lines. (A) Mitochondrial transcription and replication-related proteins, PGC1a, TFAM, and SSBP1, and (B) mitochondrial translation-related proteins, TUFM and DARS2, were detected by immunoblotting analyses of ovarian cancer cell lines described in
Figure 4A
. Ponceau S staining ensured equal protein loading Results represent the mean ± SD of at least three experiments. (C) Mitochondrial translation is determined by 35S-Met pulse labeling of 13 mt-encoded OXPHOS subunits in ovarian cancer cell lines. The pulse-labeled protein lysates (30 μg) were separated on 13% SDS-PAGE and 13 mt-encoded subunits, ND1-ND6 (complex I), Cyt b (complex III), COI-COIII (complex IV), and ATP6 and ATP8 (complex V), were labeled on the autoradiography of the gel. Total protein loading was visualized by Coomassie Blue. The relative quantitation of de novo synthesized subunits, ND5, COI, Cyt b/ND2, ND1, COII/COIII, and ATP6, was determined from at least three experiments, with the mean ± SD displayed. The signal intensity of each protein band was normalized to the mean of high (OV3, OV4, and OV5) or low (OV8, SK3, and IGR1) OXPHOS expressing cell lines.*
One of the best methods to explore the functionality of mitochondrial biogenesis in situ is to perform pulse-labeling of de novo synthesized mitochondrial proteins in the presence of [35S]-Met. For this purpose, pulse labeling of ovarian cancer cell lines was carried out using cells grown to $70\%$ confluency in regular media, as described previously [58]. The de novo synthesized thirteen mt-encoded subunits were expressed by autoradiography after adding emetine in the media containing [35S]-Met and normalized to the total protein loading stained with Coomassie Blue. ( Figure 5C). The expression of [35S]-Met-labeled subunits was clearly higher in OVCAR-4, OVCAR-5, and SKOV-3 cells relative to OVCAR-3, OVCAR-8, and IGROV-1 cells. Therefore, the reduced de novo protein synthesis observed in OVCAR-8 and IGROV-1 cell lines could be caused by mitochondrial translation and transcription defects (Figure 5C).
The steady-state expression of nuclear and mt-encoded OXPHOS subunits (Figure 4A) supported the results obtained with the de novo expression of 13 mt-encoded subunits for OVCAR-4, OVCAR-5, OVCAR-8, and IGROV-1 cells, with some exceptions (Figure 5C). The discrepancies observed between the steady-state and de novo expression of subunits in OVCAR-3 and SKOV-3 cell lines was conceivably caused by a difference in their proliferation rates (data not shown). For example, although the OVCAR-3 cells had higher steady-state subunit expression levels, the de novo subunit expression of OXPHOS subunits was lower than that of OVCAR-4 and OVCAR-5 cells. On the contrary, the SKOV-3 cell line had shown relatively high de novo OXPHOS subunit expression, possibly due to the proliferation rates of these cell lines.
Among the cells with low OXPHOS subunit expression, OVCAR-8, and SKOV-3, have higher invasion and metastatic capabilities supported by glycolytic energy metabolism rather than OXPHOS compared to the cell lines with higher OXPHOS capacity and resistance to cisplatin-induced apoptosis (Figure 4A and Table S2) [7, 27]. The changes in de novo protein synthesis and the expression of mitochondrial translation components in these cell lines could also be correlated to alterations observed in HGSOC tumor biopsies.
## Conclusions and future directions
HGSOC is one of the most common ovarian cancer subtypes and remains one of the deadliest cancers due to its high metastatic capacity and development of resistance to chemotherapy. With the metabolic heterogeneity of tumors in mind, one of the controversies that need to be resolved is the contribution of OXPHOS to aggressiveness and the development of chemoresistance and recurrence in ovarian cancer. One proposed mechanism for the high metastatic capacity and chemoresistance in ovarian cancer is the formation of spheroids from primary tumors with metabolic flexibility adapted to hypoxia in an ascitic environment. The increased metabolic flexibility of spheroids caused by modulation of mitochondrial function and morphology allows them to disseminate or reattach in the peritoneal cavity [4, 41, 47, 53, 59].
In this study, we investigated the role of mitochondrial biogenesis in the modulation of mitochondrial energy metabolism in HGSOC biopsies and ovarian cancer cell lines derived from adenocarcinomas and peritoneal ascites. The mitochondrial energy metabolism is evaluated by OXPHOS subunit expression belonging to the electron transport chain complexes (complexes I-IV) and ATP synthase (complex V). The primary and significant changes were observed in complex II and IV subunits, SDHB and MT-COII, respectively, in HGSOC biopsies (Figures 1A, B). In MS-based proteomics studies performed by McDermott et al., the overall change was insignificant except for a reduction in NDUFS2 expression (Figure 1C) [45]. On the other hand, the difference and increase between the low- and high-OXPHOS expressing HGSOC biopsies was significant for all the subunits (Figure 1D) [7]. The heterogeneity and the increased OXPHOS subunit expression in most of the HGSOC biopsies reported by Gentric et al. [ 7] strongly agree with the bimodal distribution observed in our analyses, specifically for MT-COII expression (Figure 1). Interestingly, the high-OXPHOS tumors have shown an increased response to conventional chemotherapy and are associated with better prognosis in HGSOC patients [7].
Expression of the mt-encoded OXPHOS subunit quantified in our analysis, MT-COII, depends on mitochondrial biogenesis; thus, the modulation of transcription and translation machineries in mitochondria is essential. Probing normal and HGSOC biopsies for the expression of PGC1α, TFAM, SSBP1, TUFM, and DARS2 allowed us to demonstrate the correlation between the changes in OXPHOS subunit expression and mitochondrial biogenesis for the first time (Figures 1 – 3). The strong agreement between our findings on mitochondrial biogenesis and the data mining analyses of MS-based proteomics studies allowed us to suggest that the remodeling of energy metabolism or metabolic flexibility in HGSOC is regulated by mitochondrial biogenesis (Figures 1 – 3) [7, 45]. Since metabolic flexibility is one of the determinants of the survival of tumor cells in the peritoneal cavity, deciphering mitochondrial biogenesis and its role in the remodeling of energy metabolism is crucial for better prognosis in HGSOC patients.
The mechanism behind the chemotherapy resistance and recurrence is still an unresolved issue in HGSOC [60, 61]. The role of mitochondrial function and oxidative stress in chemoresistance and metastatic capacity is under investigation by many groups using ovarian cancer cell line models [5, 7, 27, 41, 47, 53]. Here, we aimed to correlate mitochondrial biogenesis and mtROS generation to OXPHOS status in commonly used NCI-60 ovarian cancer cell line panel, including highly metastatic and chemoresistant cell lines listed in Table S2. Two of the cell lines with lower OXPHOS subunit expression, OVCAR-8, and SKOV-3 (Figure 4A), are the more aggressive cell lines forming subcutaneous and intraperitoneal tumors in mice xenografts (Table S2) [54, 55]. Gentric et al. have suggested that ovarian cancer cell lines, including OVCAR-8 and SKOV-3, and HGSOC tumors with low-OXPHOS subunit expression have decreased chemosensitivity to platinum-based treatments due to reduced oxidative stress and ferroptosis [7]. Modulating oxidative stress and ROS generation is proposed as one of the mechanisms behind the cis-platin induced cell death [3, 5, 27, 56, 62]; however, it might not be possible to modulate mtROS generation in cells or tumors with low-OXPHOS. Although the overall cellular ROS generation is lower in low-OXPHOS cells [7], we found that the mtROS per functional mitochondrion was higher in OVCAR-8 cells, which is known for its resistance to cis-platin cell death relative to the high-OXPHOS cell lines (Figure 4B). It is conceivable that the low-OXPHOS cells or HGSOC tumors already have leaky electron transport chain generating high mtROS levels due to reduced mitochondrial biogenesis and SOD2 levels (Figure 4A). The decreased SOD2 levels or antioxidant capacity of ovarian cancer cells with low-OXPHOS, such as OVCAR-8, is not sufficient to scavenge mtROS generated in these cells (Figure 4). One possible mechanism for resistance to platinum-based treatments in OVCAR-8 cells could be due to the low SOD2 levels and increased mtROS generation. Dual role of SOD2 expression has been suggested in ovarian cancer as tumor suppressor and protumoregenic factor (53, 63–65); therefore, antioxidant capacity of HGSOC need to be critically evaluated in future studies.
As discussed above, the association between the OXPHOS subunit expression and mitochondrial biogenesis is clearly shown in HGSOC tumor biopsies (Figures 1 – 3). This correlation was not clear with the ovarian cancer cell lines with low-OXPHOS subunit expression; specifically, the OVCAR-8 cells had normal levels and, in some cases, higher levels of PGC1α, TFAM, TUFM, and DARS2 protein expression (Figures 5A, B). On the other hand, the de novo synthesis of mt-encoded proteins determined by the 35[S]-Met pulse labeling experiments was consistent with the MT-COII expression detected by immunoblotting analyses in some of the cell lines (Figures 4A, 5C). The de novo protein synthesis data obtained with the ovarian cancer cells suggests mitochondrial biogenesis, at both transcription and translation levels, remodels the mitochondrial energy metabolism and possibly mtROS generation in ovarian cancer.
Changes in mitochondrial mRNA and biogenesis related transcript levels are previously utilized in predicting overall survival and tumor progression and suggested as possible prognostic biomarkers in HGSOC (33–35). In this study, we validated these predictions at the protein expression levels and have shown the role of mitochondrial biogenesis in metabolic remodeling of HGSOC tumors and ovarian cancer cell lines derived from ascites. One of the major limitations of our study is the absence of normal ovarian and true HGSOC cell lines for a better comparison of changes in OXPHOS and mitochondrial biogenesis. The role of mitochondria in metabolic remodeling of HGSOC is now better-understood [2, 5, 7, 18, 66, 67]; however, the role of mitochondrial biogenesis in formation of peritoneal spheroids and ascites as well as mtROS generation need to be investigated further to improve efficacy of current chemotherapy options in HGSOC treatment.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary materials, further inquiries can be directed to the corresponding author/s.
## Ethics statement
Ethical review and approval were not required for the human de-identified biopsies used in this study in accordance with the local legislation and institutional requirements.
## Author contributions
ZK and EK designed the study. ZK and EK performed immunoblotting analyses of biopsies and ovarian cancer cell lines. EK performed 35S-Met pulse labeling assays. ZK and EK performed the data mining analysis of the CPTAC proteome and prepared the figures. EK and VS performed flow cytometry analyses of ovarian cancer cell lines. ZK, VS, NB, GR, and EK involved in manuscript writing and revision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1129352/full#supplementary-material
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|
---
title: The association of lipid-lowering therapy and blood pressure control among
outpatients with hypertension at the Felege Hiwot Comprehensive Specialized Hospital,
Northwest Ethiopia
authors:
- Rahel Belete Abebe
- Sewnet Adem Kebede
- Mequanent Kassa Birarra
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10014929
doi: 10.3389/fcvm.2023.1071338
license: CC BY 4.0
---
# The association of lipid-lowering therapy and blood pressure control among outpatients with hypertension at the Felege Hiwot Comprehensive Specialized Hospital, Northwest Ethiopia
## Abstract
### Background
The lipid-lowering medications known as statins have been shown in controlled clinical trials to have pleiotropic properties, such as lowering blood pressure, in addition to lowering cholesterol levels. The purpose of this study was to see if there was a possible link between blood pressure control and statin therapy in outpatients with hypertension in a real clinical setting.
### Patients and methods
A retrospective comparative cohort study of 404 patients with hypertension was carried out. A systematic random sampling technique was used. For data entry, Epi-Data version 4.6 was used, and SPSS version 25 was used for further analysis. For group comparisons, chi-square and independent t-tests were computed. To determine the relationship between statin use and blood pressure control, a binary logistic regression model was employed. To declare statistical significance, a $95\%$ confidence interval and a P-value of <0.05 were used.
### Results
Half of the study participants who were using a prescribed statin were assigned to the statin group, whereas the remaining participants who do not take statins were assigned to the control group. After 3 months of statin treatment, BP control to <$\frac{130}{80}$ mmHg was significantly greater ($$P \leq 0.022$$) in the statin group ($52.5\%$) than in the control group ($41.0\%$). The use of statins raises the likelihood of having blood pressure under control by 1.58 times when compared to statin non-users. After controlling for possible confounders, statin therapy still increased the odds of having controlled BP by a factor of 5.98 [OR = 5.98; $95\%$ CI: 2.77–12.92].
### Conclusion
This study revealed that blood pressure control was higher among statin user hypertensive patients. Favorable effects of statin use were independently observed, even after correction for age, presence of dyslipidemia, and duration of antihypertensive therapy. Therefore, the importance of concomitantly added lipid-lowering drugs such as statins and their role in managing poor blood pressure control should be given due emphasis.
## Introduction
Long-term complications including heart failure, myocardial infarction, kidney disease, and stroke are significantly increased when blood pressure is uncontrolled [1, 2]. Despite the high prevalence of hypertension, only a minority of patients in developing nations receive treatment and achieve blood pressure control [3]. Hypertension is a highly prevalent and poorly controlled chronic condition in Ethiopia [4, 5]. Lowering intensive blood pressure (BP) to achieve a target BP of <$\frac{130}{80}$ mmHg is advantageous in decreasing cardiovascular outcomes [6]. The high prevalence of hypertension, continuing evidence that it is undertreated, and a growing awareness of the adverse consequences of inadequately managed and poorly controlled hypertension lead studies into exploring other management options for poorly controlled blood pressure.
The advantages of BP medications for the prevention of cardiovascular events are widely known. However, the degree to which the effect of BP lowering treatments differs by the presence of different drug classes to manage comorbidities in patients with hypertension is less clear. Previous clinical studies have shown that some supplements and medications, including Silymarin, omega-3 fatty acids, Daflon, and febuxostat, have an effect on hypertensive markers, blood glucose control, blood pressure control, and controlling different biomarkers, all of which are associated with an increased risk of cardiovascular disease (7–10). Statins are one of the commonly prescribed drug classes for patients with hypertension with different comorbidities. They inhibit 3-hydroxy-3-methylglutaryl-coenzyme A (HMGCoA), a rate-limiting enzyme in cholesterol synthesis, to reduce cholesterol production in hepatocytes [11]. Statins are recognized to improve cardiovascular protection on top of their lipid-lowering ability; in addition to this, they also have non-lipid-lowering pleiotropic effects [12]. The potential mechanisms that could be involved in this effect include the increase in the endothelial synthesis of nitric oxide, the downregulation of the angiotensin II-type 1 receptor, and the reduction of the vasoconstrictor endothelin-1 level [12, 13].
Due to reports from different studies suggesting the potential role of statin therapy in BP control and reduction, statin use has sparked attention in the field of hypertension (14–17). Although statins are effective in hypertensive animal models and controlled clinical trials, it is important to see if a similar effect is observed in real clinical practice (18–21). However, so far, no studies have been conducted in Ethiopia to investigate the association of statins with blood pressure control. As a result, the purpose of this study was to see if there was a possible link between statin therapy and blood pressure control among patients with hypertension in a real-world clinical setting.
## Study design and area
A retrospective comparative cohort study was employed at the chronic outpatient clinic of the Felege Hiwot Comprehensive Specialized Hospital from 22 June to 21 August 2021. The hospital is located in Bahir Dar, a city in northwest Ethiopia, 490 km from Addis Ababa, Ethiopia's capital city.
## Source population
All patients with hypertension who were receiving antihypertensive therapy and attending the Felege Hiwot Comprehensive Specialized Hospital's chronic follow-up units.
## Study population
All adult patients with hypertension who met the inclusion criteria and were under follow-up at the Felege Hiwot Comprehensive Specialized Hospital during the time of data collection were included in the study.
## Inclusion criteria
All patients with hypertension who were ≥18 years of age and on standard antihypertensive therapy for at least 6 months were included in the control group or the non-statin user group, whereas all patients with hypertension who were ≥18 years and on standard antihypertensive and concurrent statin treatments for at least 6 months before the start of the study were included in the study user group.
## Exclusion criteria
Patients with hypertension who refused to participate at the time of data collection, who had mental health issues, or who were unable to communicate, and patients with incomplete medical reports were excluded.
## Sample size determination
The sample size was calculated by utilizing Epi info software. Because no similar study had been conducted in the study area, $50\%$ of the prevalence of the outcome among the exposed group was considered. When determining the sample size, the following factors were taken into account: $5\%$ for two-tailed type one error (Zα = 1.96) and $80\%$ for the power of study, a two-sided $95\%$ confidence interval (CI), and a 1:1 comparison group ratio. The highest sample size number was found with the Fleiss with CC method, and the calculated sample size was 366. For possible missed data and lost to follow-up, a $10\%$ contingency was considered, and finally, the study enrolled 404 patients.
## Sampling technique and procedure
The study participants who met the inclusion criteria were chosen using a systematic random sampling technique. The “K” value for the sampling interval was derived as K = N/n, where “N” is the estimated number of average monthly follow-ups of patients with hypertension in the hospital which was 814 and “n” is the final sample size which was 404 [22]. Since the data collection period was for 2 months, our N was 1,628 which gives a sampling interval of four.
## Dependent variable
The dependent variable was blood pressure control.
## Independent variables
Sociodemographic characteristics (sex, age, and place of residence), statin use, presence of comorbidities, alcohol drinking status, smoking status, level of physical activity, level of adherence to medications, duration of hypertensive treatment, presence of co-administered drugs, and type, amount, and frequency of antihypertensive drugs.
## Hypertension
It is defined as having a systolic BP of ≥130 mmHg or a diastolic BP of ≥80 mmHg or self-reported ongoing utilization of antihypertensive therapy as per the American College of Cardiology and the American Heart Association (ACC/AHA) 2017 guideline criteria [23].
## Treatment for hypertension
It is characterized as the current usage of antihypertensive therapy as reported on a chart by individuals who have been told that they had high BP by a doctor or other health professional.
## Blood pressure control
As per the ACC/AHA 2017 guideline, adults with proven hypertension and no additional markers of elevated cardiovascular disease (CVD) risk or with known CVD or 10-year arteriosclerotic cardiovascular disease (ASCVD) event risk of $10\%$ or greater are considered as having both SBP <130 mmHg and DBP <80 mmHg [23].
## Data collection tools and procedures
To collect the data, a pretested, structured, interviewer-administered questionnaire developed by reviewing several works of literature was used (14, 15, 20, 24–29).
Three BSC nurses were involved in the data collection, one as a supervisor and the others as data collectors. The purpose of the questionnaire was to obtain information on the sociodemographic characteristics, lifestyle behavior, and clinical characteristics of respondents and their level of BP control with the different components of BP control evaluations. Information about sociodemography and lifestyle behavior was gathered by face-to-face interviews, while clinical parameters including prescribed medicines, BP measurements, and other objective measurements were obtained from the patient's medical records.
Three consecutive systolic and diastolic BP measurements were taken from the patient's medical charts with 3 months gap between them. The baseline BP value was the one recorded just before starting to take antihypertensive treatment for the control group and before starting to take concurrent statin treatment for the statin user group. Then, after 3 and 6 months of taking only antihypertensive treatment and antihypertensive with statin treatment, two different SBP and DBP values were recorded from the patient's charts for the control group and the statin user group, respectively.
The participants' adherence was assessed using the seven-item Adherence to Refills and Medications Scale (ARMS-7), a self-reported validated measure of medication adherence that is a simplified version of the ARMS [28]. Each item was designed for a response on a 4-point Likert scale with replies ranging from “none,” “some,” “most,” or “all” of the time, which were allocated values from 1 to 4, respectively. The total score of the ARMS seven-item version varies between 7 (best adherence) to 28 (worst adherence), and it can be dichotomized as 7 or >7. Any score >7 shows some degree of non-adherence or poor adherence while a score equal to 7 suggests optimal or good adherence.
A questionnaire based on the Diet History Questionnaire-NIH and customized for use in Ethiopian settings was used to collect self-reported data about food consumption habits, with a four-item Likert scale (every day, frequently, rarely, or never). The primary goal of the questionnaire was to assess the overall eating habits in terms of frequency (daily, weekly, and monthly) and amount of fruits, vegetables, meat, and beverages, such as alcohol consumption.
Physical activity was measured by assessing how many minutes and days a patient spent exercising on a daily and weekly basis, respectively. Participants were considered physically active if they could carry out physical activities for at least 30 min per day for at least 5 days per week (≥150 min per week) and insufficient physical activity was defined as <30 min per day of moderate-intensity exercise for <5 days per week; otherwise, they were categorized as physically inactive if they did not exercise for at least 10 min per day [30].
Alcohol intake was considered excessive in this study if men consumed >10 glasses of wine, >21 Birille of Tej, >21 can/Tassa of Tella, >21 shot of spirit/Areki Melekia, or >14 standard drinks of beer per day, and if women consumed >7 glasses of wine, >10 Birille of Tej, >10 can/Tassa of Tella, >10 shot of spirit/Areki, or >7 standards drink of beer per day. One standard alcoholic drink was one medium size glass of wine (120 mL), one Birille of Tej, one Tassa of Tella, one single measure of spirit/Areki Melekia (30 mL), or one 330 mL bottle of regular beer [29, 31].
Adults were classified as never-smokers (if they had never smoked a cigarette in their lives), previous smokers (if they had smoked previously but not in the previous month), and current smokers (if they had smoked cigarettes in the previous month) [29].
## Data quality assurance
The investigators prepared the questionnaire in English, and then forward and backward translations into Amharic were done by English and Amharic-versed individuals to ensure consistency. The primary investigator provided training to the data collectors and supervisor. Before the actual data collection, the questionnaire was pretested on $5\%$ of the study subjects to assess its clarity and sociocultural compatibility. The results of the pretest were not used in the final study. Every stage of the data collection process was checked for accuracy, completeness, and consistency.
## Data processing and analysis
The data were inspected visually for completeness, and the responses were coded and entered into Epi-data version 4.6, of which $10\%$ of the responses were chosen randomly and examined for data entry consistency. Then, data were exported to SPSS version 25 for further analysis. For continuous variables, summary measures were used, whereas percentages and frequencies were used for categorical variables. The percentage of those with controlled blood pressure in each group was calculated. The most likely demographic-, behavioral-, lifestyle-, clinical-, and medication-related variables that have a known association with blood pressure control were compared at baseline between statin user and non-user hypertensive patients to see the presence of any statistically significant difference. To see whether there was a statistically significant difference between the two groups, an independent sample t-test for normally distributed continuous variables and Pearson's chi-square (χ2-test) for categorical variables were used.
According to the AHA/ACC 2017 recommendations, patients with hypertension were further subdivided into either controlled or uncontrolled BP groups based on the attainment of blood pressure targets. The cut point used to demonstrate BP control was three months following the initial BP value record after starting treatment; because of reported clinical evidence that shows BP decreased substantially after 8–12 weeks of statin therapy [32, 33].
The two groups' mean reductions in DBP and SBP were compared. The baseline DBP and SBP were subtracted from the values of DBP and SBP at 3 and 6 months, and the difference was compared to see if there was an association between blood pressure reduction and statin use using an independent sample t-test. The odds ratio (OR) and $95\%$confidence interval (CI) for the association between blood pressure control and statin use were computed by binary logistic regression analysis. For adjusting any confounding effects, the variables that have significant differences (P-value of < 0.05) at baseline between the two groups were analyzed using a multivariable logistic regression model to see if the association exists after adjusting for those variables. In Model 1: age was included as a possible sociodemographic confounder variable; and in Model 2: the added presence of dyslipidemia and duration of antihypertensive treatment. All tests were two-sided and the odds ratio (OR) with a $95\%$ confidence interval (CI) was calculated with a P-value of <0.05 as the cut point for establishing statistical significance association. To determine the model fit of each variable in a logistic regression model, the Hosmer and Lemeshow goodness-of-fit test was used. Homogeneity of variances was assumed for the variables included in the t-test.
## Ethical considerations
The study was carried out in line with the Helsinki Declaration. All participants were given written informed consent after the study's nature was properly explained verbally, and participation was voluntary. The Institutional Review Board of the University of Gondar College of medicine and health sciences granted ethical clearance with the reference number SOP/$\frac{063}{2020.}$ The Felege Hiwot Comprehensive Specialized Hospital provided a permission letter to conduct the study. Furthermore, the confidentiality and privacy of the information obtained from the patient's records were actively protected.
## Study participants' enrollment and sociodemographic factors
A total of 516 patients with hypertension were approached, all of whom had been on standard antihypertensive treatment for at least 6 months. Of this, 112 were excluded from the study, since they did not fulfill the inclusion criteria; and 404 patients were included in the final analysis after being evaluated for eligibility and consenting to participate. The statin group consisted of 202 patients ($50\%$) who had been prescribed a statin for at least 6 months, while the control group (non-statin user group) consisted of the remaining 202 patients who had not been administered a statin (Figure 1).
**Figure 1:** *Participants' enrolment flowchart of patients with hypertension attending outpatient clinic at the Felege Hiwot Comprehensive and Specialized Hospital, northwest Ethiopia.*
The mean age of respondents was 59.8 ± 10.1 years and around one-fourth of them ($25.5\%$) were above 65 years of age. More than one-third ($37.9\%$) of the patients were women and nearly two-thirds ($63.6\%$) were married; and 66 ($16.3\%$) of the patients were rural residents.
There were no significant differences between patients in the control group and statin group regarding gender ($41.6\%$ male vs. $34.2\%$ female participants; $$P \leq 0.124$$), current body mass index (mean 23.4 ± 3.3 kg/m2 vs. 23.3 ± 3.0 kg/m2, $$P \leq 0.571$$), and place of residence ($18.3\%$ urban area vs. $14.3\%$; $$P \leq 0.282$$), respectively.
Likewise, the proportion of patients regarding education level, marital status, religion, and type of occupation did not significantly differ in the two groups.
However, there was a statistically significant age difference between the groups. Statin users were found to be older than non-users (the mean age for statin users was 60.8 ± 10.4 years vs. 58.7 ± 9.8 years for non-statin users; $$P \leq 0.032$$) (Table 1).
**Table 1**
| Sociodemographic variables | Sociodemographic variables.1 | Total participants (n = 404) | Total participants (n = 404).1 | Statin users (n = 202) | Statin users (n = 202).1 | Statin non-users (n = 202) | Statin non-users (n = 202).1 | X2−Test | P-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Frequency | % | Frequency | % | Frequency | % | | |
| Sex | Male | 251 | 62.1 | 133 | 65.8 | 118 | 58.4 | 2.367 | 0.124 |
| | Female | 153 | 37.9 | 69 | 34.2 | 84 | 41.6 | | |
| Age (mean ± SD)* | Age (mean ± SD)* | 59.8 ± 10.1 | 59.8 ± 10.1 | 60.8 ±10.4 | 60.8 ±10.4 | 58.7 ± 9.8 | 58.7 ± 9.8 | 2.163 | 0.032 |
| BMI (mean ± SD)* | BMI (mean ± SD)* | 23.3 ± 3.1 | 23.3 ± 3.1 | 23.2 ± 3.0 | 23.2 ± 3.0 | 23.4 ± 3.3 | 23.4 ± 3.3 | 0.502 | 0.571 |
| Education | Illiterate | 125 | 30.9 | 57 | 28.2 | 68 | 33.7 | 4.457 | 0.216 |
| | Primary education | 99 | 24.5 | 45 | 22.3 | 54 | 26.7 | | |
| | Secondary education | 103 | 25.5 | 55 | 27.2 | 48 | 23.8 | | |
| | Collage/university and above | 77 | 19.1 | 45 | 22.3 | 32 | 15.8 | | |
| Marriage | Married | 257 | 63.6 | 136 | 67.3 | 121 | 59.9 | 3.371 | 0.338 |
| | Single | 68 | 16.8 | 28 | 13.9 | 40 | 19.8 | | |
| | Divorced | 46 | 11.4 | 21 | 10.4 | 25 | 12.4 | | |
| | Widowed | 33 | 8.2 | 17 | 8.4 | 16 | 7.9 | | |
| Religion | Orthodox | 341 | 84.4 | 168 | 83.1 | 173 | 85.6 | 2.002 | 0.368 |
| | Muslim | 44 | 10.9 | 26 | 12.9 | 18 | 9.0 | | |
| | Protestant | 19 | 4.7 | 8 | 4.0 | 11 | 5.4 | | |
| Residence | Rural | 66 | 16.3 | 29 | 14.3 | 37 | 18.3 | 1.159 | 0.282 |
| | Urban | 338 | 83.7 | 173 | 85.7 | 165 | 81.7 | | |
| Occupation | Farmer | 54 | 13.4 | 21 | 10.4 | 33 | 16.3 | 8.545 | 0.129 |
| | Civil servant | 99 | 24.5 | 46 | 22.8 | 53 | 26.2 | | |
| | Housewife | 66 | 16.3 | 29 | 14.3 | 37 | 18.3 | | |
| | Private worker | 107 | 26.5 | 59 | 29.2 | 48 | 23.8 | | |
| | Retired | 30 | 7.4 | 18 | 9.0 | 12 | 6.0 | | |
| | Other** | 48 | 11.9 | 29 | 14.3 | 19 | 9.4 | | |
## Study participants' behavioral/lifestyle characteristics
Of the 404 subjects, only 11 ($2.7\%$) were current smokers, 85 ($21\%$) were not adherent to their medications, and the majority of the respondents ($75.2\%$) were physically inactive. Physically inactive participants predominated, but there was no difference in their distribution between the two groups. There was no statistically significant variation in patients in the control group vs. statin group regarding physical activity since hypertension diagnosis (physically inactive (73.3 vs. $77.2\%$), insufficiently physically active (22.3 vs. $14.9\%$), and physically active (4.4 vs. $7.9\%$), $$P \leq 0.075$$), medication adherence since diagnosis (nonadherent; 22.8 vs. $19.3\%$; $$P \leq 0.393$$), and adding salt in food (rarely; 43.0 vs. $53.5\%$; $$P \leq 0.126$$).
Similarly, patients' proportions regarding smoking status after diagnosis, alcohol drinking, consumption of fruit, vegetables, and grains, and high saturated fat consumption did not differ significantly between the two groups (Table 2).
**Table 2**
| Base line life style behavior variables | Base line life style behavior variables.1 | Statin users (n = 202) | Statin users (n = 202).1 | Statin non-users (n = 202) | Statin non-users (n = 202).1 | X2 − Test | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Frequency | % | Frequency | % | | |
| Physical activity status after diagnosis | Inactive | 156 | 77.2 | 148 | 73.3 | 5.171 | 0.075 |
| Physical activity status after diagnosis | Insufficient | 30 | 14.9 | 45 | 22.3 | 5.171 | 0.075 |
| Physical activity status after diagnosis | Active | 16 | 7.9 | 9 | 4.4 | 5.171 | 0.075 |
| Alcohol drinking status since diagnosis | | 148 | 73.2 | 159 | 78.7 | 2.737 | 0.255 |
| Alcohol drinking status since diagnosis | Moderate | 50 | 24.8 | 37 | 18.3 | 2.737 | 0.255 |
| Alcohol drinking status since diagnosis | Excessive | 4 | 2.0 | 6 | 3.0 | 2.737 | 0.255 |
| Smoking status after diagnosis | Never smoked | 178 | 88.1 | 189 | 93.6 | 3.609 | 0.165 |
| Smoking status after diagnosis | Former smoker | 17 | 8.4 | 9 | 4.4 | 3.609 | 0.165 |
| Smoking status after diagnosis | Current smoker | 7 | 3.5 | 4 | 2.0 | 3.609 | 0.165 |
| Fruits, vegetables, and grains included in diet after diagnosis | Never | 27 | 13.4 | 16 | 7.9 | 7.423 | 0.06 |
| Fruits, vegetables, and grains included in diet after diagnosis | Rarely | 144 | 71.2 | 154 | 76.2 | 7.423 | 0.06 |
| Fruits, vegetables, and grains included in diet after diagnosis | Usually | 19 | 9.4 | 27 | 13.4 | 7.423 | 0.06 |
| Fruits, vegetables, and grains included in diet after diagnosis | Always | 12 | 6.0 | 5 | 2.5 | 7.423 | 0.06 |
| Consumption of foods with high saturated fat | Never | 181 | 89.6 | 190 | 94.0 | 2.673 | 0.102 |
| Consumption of foods with high saturated fat | Rarely | 21 | 10.4 | 12 | 6.0 | 2.673 | 0.102 |
| Added salt in food | Never | 82 | 40.5 | 95 | 47.0 | 5.716 | 0.126 |
| Added salt in food | Rarely | 108 | 53.5 | 87 | 43.0 | 5.716 | 0.126 |
| Added salt in food | Usually | 5 | 2.5 | 11 | 5.6 | 5.716 | 0.126 |
| Added salt in food | Always | 7 | 3.5 | 9 | 4.4 | 5.716 | 0.126 |
| Medication adherence | Good | 163 | 80.7 | 156 | 77.2 | 0.73 | 0.393 |
| Medication adherence | Poor | 39 | 19.3 | 46 | 22.8 | 0.73 | 0.393 |
## Study participants' clinical characteristics
Out of the total respondents, 67 ($16.6\%$) have reported a family history of high BP, whereas $61.4\%$ had another comorbidity, with 98 ($24.3\%$) having diabetes mellitus and $23.8\%$ having cardiovascular disease. There were no differences which were significant between patients in the control group and statin group regarding family history of high BP (have a family history of high BP; 19.3 vs. $13.8\%$; $$P \leq 0.141$$), frequency of antihypertensive drug use per day (once a day 54.9 vs. $62.9\%$; $$P \leq 0.106$$), and presence of diabetes mellitus (Yes; 42.7 vs$.36.6\%$; $$P \leq 0.327$$), respectively.
Furthermore, the patients' proportion with cardiovascular diseases, chronic kidney disease, asthma, and HIV/AIDS did not differ significantly in the two groups. On the other hand, dyslipidemia as comorbidity increased remarkably in the statin group (64.1 vs. $1.7\%$ $P \leq 0.001$). Moreover, the mean duration of antihypertensive medication treatment was higher significantly in the statin user group (4.2 ± 2.0 vs. 3.7 ± 1.7 years, $$P \leq 0.011$$), which could be a confounding variable (Table 3).
**Table 3**
| Variables | Variables.1 | Statin users (n = 202) | Statin users (n = 202).1 | Statin non-users (n = 202) | Statin non-users (n = 202).1 | X2 − Test | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Frequency | % | Frequency | % | | |
| Family history of high BP | Yes | 28 | 13.8 | 39 | 19.3 | 2.165 | 0.141 |
| | No | 174 | 86.2 | 163 | 80.7 | | |
| Stage of HTN at diagnosis | Stage I | 98 | 48.5 | 82 | 40.6 | 2.565 | 0.109 |
| | Stage II | 104 | 51.5 | 120 | 59.4 | | |
| Duration antihypertensive drug treatment* | Duration antihypertensive drug treatment* | 4.2 ± 1.9 | 4.2 ± 1.9 | 3.7 ± 1.7 | 3.7 ± 1.7 | 2.56 | 0.011 |
| Frequency of Health follow up | Monthly | 34 | 16.8 | 24 | 11.9 | 2.013 | 0.156 |
| | Every 3 months | 168 | 83.2 | 178 | 89.1 | | |
| Presence of comorbidities | Yes | 131 | 64.9 | 117 | 58.0 | 2.047 | 0.153 |
| | No | 71 | 35.1 | 85 | 42.0 | | |
| Diabetes mellitus | Yes | 48 | 36.6 | 50 | 42.7 | 0.96 | 0.327 |
| | No | 83 | 63.4 | 67 | 57.3 | | |
| Chronic kidney disease | Yes | 8 | 6.1 | 4 | 3.4 | 0.97 | 0.325 |
| | No | 123 | 93.9 | 113 | 96.6 | | |
| Cardiovascular diseases | Yes | 52 | 39.7 | 44 | 37.6 | 0.114 | 0.736 |
| | No | 79 | 60.3 | 73 | 52.4 | | |
| Asthma | Yes | 3 | 2.3 | 8 | 6.8 | 3.015 | 0.082 |
| | No | 128 | 97.7 | 109 | 93.2 | | |
| HIV/AIDS | Yes | 7 | 5.3 | 6 | 5.1 | 0.006 | 0.939 |
| | No | 124 | 94.7 | 111 | 94.9 | | |
| Dyslipidemia | Yes | 84 | 64.1 | 2 | 1.7 | 106.27 | 0.0 |
| | No | 47 | 35.9 | 115 | 98.3 | | |
| Other** | Yes | 3 | 2.3 | 7 | 6.0 | 2.178 | 0.14 |
## Medication-related features of the study participants
The groups did not differ significantly in terms of the number and type of antihypertensive drugs used. Enalapril was the most frequently prescribed drug among both groups ($52.2\%$) followed by hydrochlorothiazide ($42.5\%$). There was no significant difference between patients in the control group and statin group regarding the frequency of antihypertensive drug use per day (once a day antihypertensive use; 54.9 vs. $62.9\%$, $$P \leq 0.106$$) and the number of antihypertensive drug/s the patient is using (dual therapy; 33.1 vs. $27.2\%$ $$P \leq 0.388$$).
In terms of specific antihypertensive drug use and the presence of medications other than antihypertensive therapy, there was no significant difference. Among statin users, two types of statins were used, atorvastatin and simvastatin; a quarter of statin users 52 ($25.2\%$) take simvastatin and the rest were using atorvastatin (Table 4).
**Table 4**
| Variables | Variables.1 | Variables.2 | Statin users (n = 202) | Statin users (n = 202).1 | Statin non-users (n = 202) | Statin non-users (n = 202).1 | X2 Test | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | Frequency | % | Frequency | % | | |
| Frequency of anti-HTN drug use per day | Frequency of anti-HTN drug use per day | Once | 127 | 62.9 | 111 | 54.9 | 2.618 | 0.106 |
| | | Twice | 75 | 37.1 | 91 | 45.1 | | |
| Number of anti-HTN drug/s the patient is using | Number of anti-HTN drug/s the patient is using | Mono-therapy | 130 | 64.4 | 117 | 57.9 | 1.893 | 0.388 |
| | | Dual therapy | 55 | 27.2 | 67 | 33.1 | | |
| | | Triple therapy | 17 | 8.4 | 18 | 9.0 | | |
| Name of antihypertensive | Hydrocloroizide | Yes | 80 | 39.6 | 92 | 45.5 | 1.458 | 0.227 |
| Name of antihypertensive | Hydrocloroizide | No | 122 | 60.4 | 110 | 54.5 | | |
| Name of antihypertensive | Enalapril | Yes | 111 | 55.0 | 96 | 47.5 | 2.229 | 0.135 |
| Name of antihypertensive | Enalapril | No | 91 | 45.0 | 106 | 52.5 | | |
| Name of antihypertensive | Nifedepine | Yes | 42 | 20.8 | 54 | 26.7 | 1.968 | 0.161 |
| Name of antihypertensive | Nifedepine | No | 160 | 79.2 | 148 | 73.3 | | |
| Name of antihypertensive | Amlodipine | Yes | 39 | 19.3 | 49 | 24.3 | 1.453 | 0.228 |
| Name of antihypertensive | Amlodipine | No | 163 | 80.7 | 153 | 76.7 | | |
| Name of antihypertensive | Atenolol | Yes | 13 | 6.4 | 7 | 3.5 | 1.894 | 0.169 |
| Name of antihypertensive | Atenolol | No | 189 | 93.6 | 195 | 96.5 | | |
| Name of antihypertensive | Other anti-HTN drug use* | Yes | 2 | 1.0 | 4 | 2.0 | 0.677 | 0.411 |
| Presence of medications other than antihypertensive drugs | Metformin | Yes | 35 | 17.3 | 34 | 16.8 | 2.627 | 0.105 |
| Presence of medications other than antihypertensive drugs | Glibenclamide | Yes | 9 | 4.4 | 13 | 6.4 | 0.41 | 0.839 |
| Presence of medications other than antihypertensive drugs | NSAIDs | Yes | 14 | 6.9 | 20 | 10.0 | 0.051 | 0.821 |
| Presence of medications other than antihypertensive drugs | Insulin | Yes | 13 | 6.4 | 13 | 6.4 | 0.611 | 0.435 |
| Presence of medications other than antihypertensive drugs | Clopidogrel | Yes | 5 | 2.4 | 8 | 3.9 | 0.113 | 0.736 |
| Presence of medications other than antihypertensive drugs | ART | Yes | 7 | 3.4 | 6 | 2.9 | 0.677 | 0.411 |
| Presence of medications other than antihypertensive drugs | Other medications** | Yes | 34 | 16.8 | 35 | 17.3 | 1.741 | 0.187 |
## Blood pressure control status of the study participants
Even though there was no statistically significant difference in baseline mean SBP and DBP between statin users (164.5 ± 14.0 vs. 162.3 ± 12.6, $$P \leq 0.107$$) and statin non-users (100.6 ± 8.7 vs. 99.8 ± 7.7, $$P \leq 0.317$$), BP control based on the ACC/AHA 2017 guideline was higher significantly ($$P \leq 0.022$$) in the statin plus antihypertensive drug group ($52.5\%$) than in antihypertensive drug alone group ($41.0\%$) after 3 months of treatment.
Also, isolated SBP and DBP controls were higher among statin users than in the control group ($52.5\%$ for systolic blood pressure control vs. $41.6\%$, $$P \leq 0.028$$ and $60.9\%$ for isolated diastolic control vs. $47.0\%$, $$P \leq 0.005$$) (Table 5).
**Table 5**
| Variables | Variables.1 | Statin users (n = 202) | Statin users (n = 202).1 | Statin non-users (n = 202) | Statin non-users (n = 202).1 | X2 Test | p-value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Frequency | % | Frequency | % | | |
| Systolic BP controlled | Yes | 106 | 52.5 | 84 | 41.6 | 4.809 | 0.028 |
| | No | 96 | 47.5 | 118 | 58.4 | | |
| Diastolic BP controlled | Yes | 123 | 60.9 | 95 | 47.0 | 7.811 | 0.005 |
| | No | 79 | 39.1 | 107 | 53.0 | | |
| Both the systolic and diastolic BP | Yes | 106 | 52.5 | 83 | 41.0 | 5.259 | 0.022 |
| controlled | No | 96 | 47.5 | 119 | 59.0 | | |
## Mean blood pressure reduction status of the study participants
Accordingly, significantly higher mean systolic BP reduction from baseline (30.6 mmHg ± 18.7 vs. 25.24 mmHg ± 13.9 $$P \leq 0.001$$) and mean DBP reduction (20.4 mmHg ± 11.3 vs. 17.2 ± 9.0 $$P \leq 0.002$$) were observed in the statin user group after 3 months of treatment compared to their counterparts (Table 6).
**Table 6**
| Variables | Statin users (n = 202) | Statin non-users (n = 202) | T-test | P-value |
| --- | --- | --- | --- | --- |
| | Mean ±SD | Mean ±SD | | |
| SBP at initiation | 164.5 ± 14.0 | 162.3 ± 12.6 | 1.61 | 0.107 |
| SBP after 3 months | 133.8 ± 16.4 | 137.1 ± 16.1 | 2.01 | 0.044 |
| SBP after 6 months | 131.5 ± 15.2 | 134.6 ± 14.3 | 2.08 | 0.037 |
| DBP at initiation | 100.6 ± 8.7 | 99.84 ± 7.7 | 1.0 | 0.317 |
| DBP after 3 months | 80.1 ± 8.3 | 82.6 ± 8.7 | 2.83 | 0.005 |
| DBP after 6 months | 79.3 ± 7.9 | 81.6 ± 8.4 | 2.77 | 0.006 |
| Mean d/f b/n SBP at initiation and at 3 monthsa | 30.6 ± 18.7 | 25.2 ± 13.6 | 3.3 | 0.001 |
| Mean d/f b/n SBP at initiation and at 6 monthsb | 32.9 ± 18.2 | 27.7 ± 14.0 | 3.22 | 0.001 |
| Mean d/f b/n DBP at initiation and at 3 monthsc | 20.4 ± 11.3 | 17.2 ± 9.0 | 3.17 | 0.002 |
| Mean d/f b/n DBP at initiation and at 6 monthsd | 21.2 ± 11.0 | 18.1 ± 9.1 | 3.06 | 0.002 |
Similarly, the mean systolic and diastolic BP reduction after 6 months of observation from baseline resulted in a higher and more significant reduction among statin users when compared to the control group (32.9 mmHg ± 18.7 vs. 27.7 ± 14.0 $$P \leq 0.001$$ and 21.2 mmHg ± 11.0 vs. 18.1 ± 9.1 $$P \leq 0.002$$) (Figure 2).
**Figure 2:** *Line graph showing the trend of blood pressure mean differences at various times between statin user and non-user hypertensive outpatients at the Felege Hiwot Comprehensive and Specialized Hospital, northwest Ethiopia. *Mean difference between systolic blood pressures at initiation and at 3 months. **Mean difference between systolic blood pressures at initiation and at 6 months. ***Mean difference between diastolic blood pressures at initiation and at 3 months. ****Mean difference between diastolic blood pressures at initiation and at 6 months.*
The use of statin therapy was associated with a higher mean SBP reduction of 5.4 mmHg ($95\%$ CI, 2.2 to 8.6, $$P \leq 0.001$$) and mean DBP reduction of 3.2 mmHg ($95\%$ CI, 1.2 to 5.2 $$P \leq 0.002$$) after 3 months of treatment from baseline; and mean SBP reduction of 5.2 mmHg ($95\%$ CI, 2.0 to 8.4 $$P \leq 0.001$$) and mean DBP reduction of 3.1 mmHg ($95\%$ CI, 1.1 to 5.0 $$P \leq 0.002$$) after 6 months of treatment from baseline (Table 7).
**Table 7**
| Variables | At three month | At three month.1 | At three month.2 | At 6 month | At 6 month.1 | At 6 month.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | Mean | 95% CI | P -value | Mean | 95% CI | P -value |
| Mean difference of Mean SBP reduction (mmHg) for statin users when compared to non-users | 5.4 | 2.2–8.6 | 0.001 | 5.2 | 2.0–8.4 | 0.001 |
| Mean difference of Mean DBP reduction (mmHg) for statin users when compared to non-users | 3.2 | 1.2–5.2 | 0.002 | 3.1 | 1.1–5.0 | 0.002 |
## Logistic regression for blood pressure control and statin use
Because age, the presence of dyslipidemia as comorbidity, and the mean duration of antihypertensive drug treatment were all significantly higher in the statin user group, these variables were integrated into a logistic regression model to adjust the OR of controlled blood pressure associated with statin therapy.
The crude model in Table 8 reveals that statin therapy enhances the likelihood of having blood pressure controlled 1.58 times [OR 1.58; $95\%$ CI, 1.068–2.346]. After controlling for age in Model 1, the odds of having controlled blood pressure were 1.6 times [$95\%$ CI, 1.106–2.451] higher among statin users as compared to their counterparts. This likelihood remained unchanged when adding the presence of dyslipidemia as comorbidity and the mean duration of antihypertensive treatment in Model 2 which were statistically significantly different at baseline between the two groups. Still, statin therapy increased the odds of having controlled blood pressure by a factor of 5.98 [OR 5.986; $95\%$ CI 2.773–12.922] when compared to statin non-users indicating that the use of statins among patients with hypertension was significantly associated with controlled blood pressure.
**Table 8**
| Model type | Adjusted OR | AOR 95% CI | P-value |
| --- | --- | --- | --- |
| Mode 0: unadjusted | 1.583* | 1.068–2.346 | 0.022 |
| Model 1: controlling for age | 1.646 | 1.106–2.451 | 0.014 |
| Model 2: controlling for age, presence of dyslipidemia, and duration of antihypertensive therapy | 5.986 | 2.773–12.922 | <0.001 |
The bivariable and multivariable binary logistic regression analyses show age, presence of dyslipidemia, and duration of antihypertensive therapy affect the association of BP control with statin therapy.
## Discussion
The study was intended to investigate the association between the use of statin therapy and BP control in patients with hypertension. This study revealed that after 3 months of treatment with statins, BP control based on the ACC/AHA 2017 guideline was significantly higher ($$P \leq 0.022$$) in the statin group ($52.5\%$) in comparison to the control group ($41.0\%$). This finding was similar to previous studies [14, 16, 27]. In this study, the proportion of patients concerning gender, presence of CKD, diabetes, and the class and number of antihypertensive medications used did not significantly differ in both groups; and this is consistent with other studies conducted in the United States of America and Portugal [16, 26].
In this study, statin users were on average 2 years older than non-users; this result is in agreement with previous studies [14, 16, 24]. This may be due to the possibility that aging increases the burden of atherosclerosis and other cardiovascular conditions; as a result, elderly people have a higher risk of cardiovascular mortality and morbidity than younger people, and they require more rigorous treatment of modifiable risk factors including dyslipidemia, which may necessitate statin therapy [34]. Antihypertensive drug therapy mean duration was also longer among statin users which is in agreement with a study by Morgado and associates [16]. This may be due to the high probability of statin user hypertensive patients being aged with different comorbidity that may increase the need to use statins as primary and secondary cardiovascular protection.
In the current study, even though there was no difference which was significant in baseline mean SBP and DBP between the two groups after 3 months of statin treatment, blood pressure control (<$\frac{130}{80}$ mmHg) was higher significantly ($$P \leq 0.022$$) in the statin user group ($52.5\%$) than in the control group ($41.0\%$). This is in line with several studies that show statins' ability in reducing significantly the DBP and SBP, beyond their lipid-lowering properties (14–17, 24–27, 35).
The result in this study of superior BP control in patients with hypertension taking a prescribed statin supports the theory that statins may also have an antihypertensive effect [27, 36, 37]. A study conducted in the United States of America found that when compared to people who did not use statins, more statin users had significantly controlled their blood pressure (52.2 vs. $38.0\%$) [27]. This could be explained by statins' pleiotropic effects such as decreased blood pressure, which go beyond cholesterol reduction which in turn has overwhelming benefits in preventing cardiovascular events [12].
The current study indicates that using a statin improves the likelihood of having blood pressure controlled by 1.58 times [OR 1.58; $95\%$ CI, 1.068–2.346]. After adjusting for age, the presence of dyslipidemia as a comorbidity, and the mean duration of antihypertensive treatment which were statistically significantly different at baseline in the two groups, still using statin increased the odds of controlled blood pressure 5.98 times [OR 5.986; $95\%$ CI, 2.773–12.922]. After controlling for potential confounding factors, the relationship was still maintained. This is in line with a previous study done in the United States of America that showed after controlling for demographic factors, statin users were two times ($95\%$ CI, 1.46–2.72) more likely than non-users to have their blood pressure under control ($\frac{140}{90}$ mmHg). In the study, the likelihood of having controlled blood pressure remained more likely among statin users (OR 1.46, $95\%$ CI, 1.05–2.05) after further adjusting for diabetes, BMI, exercise, smoking, antihypertensive medications, and low-salt diet [27].
Adding to the growing body of evidence that statin medication can help with blood pressure control, a study in Portugal indicated that statin therapy enhances the likelihood of having blood pressure under control [OR 4.46; $95\%$ CI, 1.64–12.15]. The study found the same statistically significant relationship after controlling for the length of antihypertensive treatment [OR 5.23; $95\%$ CI 1.86–14.67] [16].
The physiologic effects of statins on the body, which point to the “pleiotropic” effects of statins, such as anti-inflammatory effects, improved endothelial function, stabilization of atherosclerotic plaques, antioxidant properties, and increased nitric oxide (NO) bioavailability, are possible explanations for this association [38]. Effects on the endothelial vaso-reactivity or renin-angiotensin system could also explain association [36]. Statins, on top of their undeniable potential to lower lipid profile, have many other biological effects, mostly related to improving arterial compliance and endothelial function. As shown in Figure 2, the mean systolic and diastolic BP reduction after 6 months of observation from baseline resulted in a higher and more significant reduction among statin users when compared to the control group (32.9 mmHg ± 18.7 vs. 27.7 ± 14.0, $$P \leq 0.001$$ and 21.2 mmHg ± 11.0 vs. 18.1 ± 9.1, $$P \leq 0.002$$). This blood pressure control gap between statin users and non-users over time can be explained by the possible additive effect with antihypertensive and statin drugs on better BP control when used together, which was supported by former studies that found BP can be controlled much better with a combination of statins and antihypertensive medications than with either treatment alone (39–41).
In this retrospective cohort study, the use of statin therapy was associated with not only enhanced BP control but also a higher mean SBP reduction of 5.4 mmHg ($95\%$ CI, 2.2 to 8.6, $$P \leq 0.001$$) and mean DBP reduction of 3.2 mmHg ($95\%$ CI, 1.2 to 5.2 $$P \leq 0.002$$) after 3 months of treatment from baseline; and mean SBP reduction of 5.2 mmHg ($95\%$ CI, 2.0 to 8.4, $$P \leq 0.001$$) and mean DBP reduction of 3.1 mmHg ($95\%$ CI, 1.1 to 5.0, $$P \leq 0.002$$) after 6 months of treatment from baseline was observed with it.
In line with this result, in a randomized, double-blind study, Ferrier et al. reported a mean reduction of −6 mmHg in SBP after 3 months of atorvastatin treatment in a sample of patients with isolated systolic hypertension [33]. Another study also demonstrated that statins reduced SBP by 3.3 mmHg and DBP by an average of 1.9 mmHg ($P \leq 0.01$) among antihypertensive drug users ($$P \leq 0.02$$).
The mean SBP decrement of 5.4 mmHg and mean DBP decrement of 3.2 mmHg after 3 months of treatment from baseline is in agreement with data obtained from a study by Kuklinska and associates, that used patients with normolipid who were taking standard HTN treatment; although both groups' baseline BP scores were similar, after 3 months of atorvastatin therapy, the mean changes in SBP and DBP were 5.7 mmHg ($95\%$ CI, 4.1 to 7.2 mmHg) and 3.9 mmHg ($95\%$ CI, 2.7 to 5.0 mmHg), respectively. This finding indicates the presence of a significant association with atorvastatin use for blood pressure control [42]. In addition, Morgado and associates report significantly lower SBP and DBP (−6.7 mmHg, $$P \leq 0.020$$ and −6.4 mmHg, $$P \leq 0.002$$) levels, respectively, in the statin user group [16]. Similar to this study, a retrospective study conducted in Italy in 2017 demonstrated that the use of statins was linked with the independent and strongest association with 24 h and night-time BP control, even after controlling for sex, BMI, age, number of antihypertensive drugs, and diabetes (model 1), or the presence/absence of antihypertensive therapy [17]. This favorable effect of statins on BP control could also be explained by the positive behavior of patients who are on lipid-lowering therapy regarding cardiovascular prevention strategies; as indicated by former studies that show patients who use lipid-lowering therapy had good treatment persistence and adherence [43, 44].
Statins are likely to benefit patients with uncontrolled BP whose modulation of vascular resistance and peripheral vascular tone is significantly compromised [40, 45]. In addition to this, because of the physiologic effect they share, lipid-lowering therapies other than statins, such as nicotinic acid (niacin), omega-3 fatty acids, and fatty acid esters and fibric acid derivatives (Fenofibrate), have also a favorable effect on reducing and controlling blood pressure (46–50).
Conversely from the above findings, some studies show contradiction about the association of BP control and reduction with statin use. Some earlier studies found no BP-lowering impact of statins in patients with normotension and well-controlled hypertension [32, 39]. A meta-analysis of 936 patients with hypertension and 4,692 patients with normotension found that statin therapy does not result in a substantial decrease in systolic or diastolic BP in either patients with normotension or hypertension [39]. This may be due to high heterogeneity between studies. Another reason for this disparity could be that the participants were predominantly normotensive, which could reduce the statins benefit because the possible hypotensive effect of statins has been hypothesized to be more evident in patients with higher baseline blood pressure [20, 26, 40, 51]. Furthermore, in the presence of comorbid conditions, there may be drug—drug interactions between the antihypertensive medicine and the other medications being used, which might potentially antagonize the antihypertensive medication's therapeutic effect and result in uncontrolled BP.
This study found that when antihypertensive and statin drugs were used together, they had an additive effect for better BP control, which was supported by former studies that found BP can be controlled much better with a combination of statins and antihypertensive medications than with either treatment alone [39, 41].
This could be because of a possible additive effect of these medications. The conflicting findings of some studies indicate that this topic is not fully resolved and that more research is needed.
## Limitations of the study
Due to the retrospective nature of the study, blood pressure values were taken as documented in the patients' medical charts, which reflected actual clinical practice; nonetheless, these values may be subjected to measurement and recording errors. Furthermore, some issues were not explored in this study and will necessitate additional study designs. These include the impact of certain combinations of antihypertensive drugs and statins and the impact of various statin regimens and dosages. The authors strongly encourage future researchers to use prospective study designs to address the issues that were not covered in this study.
## Conclusion
This study concluded that the use of statins is associated with BP control (<$\frac{130}{80}$ mmHg) among patients with hypertension in a real-world clinical setting. The current study found that, in patients with hypertension who need to take a statin concurrently, the use of a statin can enhance blood pressure control and decrease 5.4 and 3.2 mmHg of SBP and DBP, respectively, after 3 months of treatment. This positive blood pressure control and reduction may decrease the number of antihypertensive drugs and doses needed to achieve satisfactory BP control, which could have some therapeutic implications. The results of this study may have important implications for the safe and effective prevention of cardiovascular diseases, especially in patients with hypertension whose blood pressure is not adequately controlled by antihypertensive treatment alone.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Review Board of the University of Gondar's School of Pharmacy, who approved this study under the reference number SOPS $\frac{063}{2020.}$ The patients/participants provided their written informed consent to participate in this study.
## Author contributions
RA, SK, and MB conceived the study and were involved in its design, coordination, and review of the article, analysis, report writing, and manuscript preparation. The final manuscript was read and approved by all authors.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: A phase I open-label clinical trial to study drug-drug interactions of Dorzagliatin
and Sitagliptin in patients with type 2 diabetes and obesity
authors:
- Li Chen
- Jiayi Zhang
- Yu Sun
- Yu Zhao
- Xiang Liu
- Zhiyin Fang
- Lingge Feng
- Bin He
- Quanfei Zou
- Gregory J. Tracey
journal: Nature Communications
year: 2023
pmcid: PMC10014962
doi: 10.1038/s41467-023-36946-7
license: CC BY 4.0
---
# A phase I open-label clinical trial to study drug-drug interactions of Dorzagliatin and Sitagliptin in patients with type 2 diabetes and obesity
## Abstract
This is a phase 1, open-label, single-sequence, multiple-dose, single-center trial conducted in the US (NCT03790839), to evaluate the clinical pharmacokinetics, safety and pharmacodynamics of dorzagliatin co-administered with sitagliptin in patients with T2D and obesity. The trial has completed. 15 patients with T2D and obesity were recruited and treated with sitagliptin 100 mg QD on Day 1-5, followed by a combination of sitagliptin 100 mg QD with dorzagliatin 75 mg BID at second stage on Day 6-10 and the third stage of dorzagliatin 75 mg BID alone on Day 11-15. Primary outcomes include pharmacokinetic geometric mean ratio (GMR), safety and tolerability. Secondary outcomes include the incremental area under the curve for 4 hours post oral glucose tolerance test (iAUC) of pharmacodynamic biomarkers and glucose sensitivity. GMR for AUC0-24h and Cmax were 92.63 ($90\%$ CI, 85.61, 100.22) and 98.14 ($90\%$ CI, 83.73, 115.03) in combination/sitagliptin, and 100.34 ($90\%$ CI, 96.08, 104.79) and 102.34 ($90\%$ CI, 86.92, 120.50) in combination/dorzagliatin, respectively. Combination treatment did not increase the adverse events and well-tolerated in T2D patients. Lack of clinically meaningful pharmacokinetic interactions between dorzagliatin and sitagliptin, and an improvement of glycemic control under combination potentially support their co-administration for diabetes management.
Dorzagliatin, which acts on the glucose sensor glucokinase, is a new class of anti-diabetic medicine. Here the authors report that in a phase I open-label trial co-administration of dorzagliatin and sitagliptin (a different class of anti-diabetic medicine) does not significantly change the pharmacokinetics of either medicine in patients with type 2 diabetes and obesity.
## Introduction
Glucokinase (GK) is a glucose sensor and plays a central role in glucose homeostasis1. It is expressed in the endocrine organs of the pancreas alpha and beta cells to regulate glucagon and insulin secretion, and in intestinal L cells to regulate glucagon-like peptide-1 (GLP-1) secretion, as a metabolic sensor to convert the signal of glucose metabolism into glucose threshold-controlled hormone release. The majority of GK is expressed in the liver to convert the post-meal glucose into glycogen mediated by insulin and glucagon. GLP-1 and glucagon-like peptide-1 receptor agonist (GLP-1RA) have been studied intensively for the treatment of diabetes and obesity through peripheral and central receptors2–5. Current therapies have been evolved into a dual active peptide of GLP-1 and glucose-dependent insulinotropic peptide (GIP)6,7. GLP-1 originated from intestinal L cell and pancreatic alpha-cell, together with its circulating metabolites generated by dipeptidyl peptidase 4 (DPP-4) enzyme, were considered to play an important role in glucose homeostasis through glucagon-like peptide-1 receptor (GLP-1R), as well as having cardiovascular and neurological protection effects through non-GLP-1R regulated functions8–10. Therefore, the strategy of developing GLP-1R-based therapy may have limited the physiological function of incretins and its metabolites beyond their role in glucose homeostasis. Defects of GLP-1 secretion in response to glucose challenge have been reported in western and eastern patients with impaired glucose tolerance (IGT) and type 2 diabetes (T2D)11,12, which can be rescued by either GLP-1RA, or ideally a glucose-dependent regulator on GLP-1 secretion. Dorzagliatin is an orally active allosteric glucokinase activator (GKA) which acts on GK in the pancreas and liver for the treatment of T2D13–16. As a new class of diabetes medicine, dorzagliatin was considered safe and effective in T2D patients for glycemic control and may have a unique advantage in the treatment of diabetic kidney disease patients, given its minimal excretion from the kidney17,18. It has shown that dorzagliatin improves glycemic control and increases early-phase insulin secretion in T2D patients in Chinese T2D patients after 1 month of treatment13. We have also observed a sustained effect in the improvement of disposition index and HOMA-IR one week after drug withdrawal in a 3-month monotherapy study in which dorzagliatin showed a dose-dependent glycated hemoglobin (HbA1c) reduction of $1.2\%$ in a 75 mg BID group14. In the two phase 3 studies, T2D subjects received 75 mg dorzagliatin twice a day either alone in the drug naïve patients (SEED Study) or combined with metformin in the metformin-tolerated patients (DAWN Study) for 6 months in the double-blinded, placebo-controlled and randomized study followed by an open-label 28-week extension with dorzagliatin for its safety evaluation. Dorzagliatin is safe and well tolerated with an average HbA1c reduction of $1\%$ from baseline with a 42–$44\%$ glycemic control rate in both trials with minimum hypoglycemia15,16. A logistic regression study showed that the improvement of early-phase insulin secretion measured by insulinogenic index and disposition index is the major factor for achieving glycemic control19. Additional clinical studies to investigate its mechanism of action include a double-blinded crossover trial in glucokinase–maturity-onset diabetes of the young (GCK-MODY) patients who suffered from a heterozygous GK gene inactive mutation and defect in second phase insulin secretion in response to a glucose challenge. Dorzagliatin improves the second phase insulin secretion and glucose sensitivity in this study20.
DPP-4 is an enzyme that converts the active GLP-1 and GIP peptide hormone into its inactive metabolite GLP-1 [9-36] amide (GLP-1m), and thus a therapeutic target for diabetes. Sitagliptin is a first-in-class DPP-4 inhibitor launched in 2006 for T2D through its effect to increase incretin levels of GLP-1 and GIP, which increase insulin secretion and decrease gastric emptying. The combination of sitagliptin with other oral antidiabetic drugs (OADs) in glycemic control has become a common clinical practice.
Here, we show there is a lack of clinically meaningful pharmacokinetic interactions between dorzagliatin and sitagliptin. Dorzagliatin regulates glucose-stimulated GLP-1 release and improves glycemic control with good tolerance when combined with sitagliptin in patients with T2D and obesity, suggesting the role of dorzagliatin in the regulation of GLP-1 release in response to oral glucose challenge in a triple acting role of regulation of glucose homeostasis.
## Study population
The subject demographics and baseline characteristics are summarized in Table 1.Table 1Demographic characteristics of study subjectsSubjectsN = 15Age (yrs)56.70 (5.39)Gender, n (%)Male4 (26.67) Age 40–64Female11 (73.33) Age 54–62BMI (kg/m2)32.06 (3.57)RaceBlack/African American3 (20.00)White12 (80.00)Ethnicity, n (%)Hispanic/Latino14 (93.33)Not Hispanic/Latino1 (6.67)HbA1c (%)8.24 (0.99)FBG (mg/dL)178.90 (44.79)Values are presented as the mean (SD) or n (%).BMI body mass index, FBG fasting blood glucose, HbA1c glycated hemoglobin.
A total of 15 subjects were enrolled and included in pharmacodynamics (PD) and safety analysis. Fourteen subjects were included in the pharmacokinetics (PK) analysis, as one subject discontinued due to an adverse event (AE) (an erythematous rash) on Day 11. The cohort had an average age (mean ± SD) of 56.70 ± 5.39 years, body mass index (BMI) of 32.06 ± 3.57 kg/m2, HbA1c 8.24 ± $0.99\%$, and fasting blood glucose 178.90 ± 44.79 mg/dL.
## PK
PK parameters maximum plasma concentration (Cmax) and area under the concentration-time curve from 0 to 24 h (AUC0–24h) for sitagliptin were similar when sitagliptin was administrated alone or co-administrated with dorzagliatin, which are illustrated in Table 2. The corresponding adjusted geometric mean ratio (GMR) (combination/monotherapy ratio) of sitagliptin were 92.63 ($90\%$ confidence interval (CI), 85.61, and 100.22) for AUC0–24h and 98.14 ($90\%$ CI, 83.73, 115.03) for Cmax, respectively. Table 2Pharmacokinetic parameters for sitagliptin and dorzagliatin, measured at steady state, after the last dose in a 5-day interval of sitagliptin only, dorzagliatin only, or sitagliptin + dorzagliatin treatmentGeometric meanGMR ($90\%$ Cl)Sitagliptin aloneSitagliptin + DorzagliatinDorzagliatin aloneSitagliptinCmax (ng/mL)$\frac{410403}{98.14}$ (83.73–115.03)AUC0–24h (ng*h/mL)$\frac{29382722}{92.63}$ (85.61–100.22)DorzagliatinCmax (ng/mL)/833814102.34 (86.92–120.50)AUC0-24h (ng*h/mL)/65936571100.34 (96.08–104.79)*Cmax maximum* plasma concentration, AUC0–24 area under the concentration-time curve from 0 to 24 h, GMR adjusted geometric mean ratios (combination/monotherapy ratio), CI confidence interval.
PK parameters Cmax and AUC0–24h for dorzagliatin were similar when dorzagliatin was administrated alone and co-administrated with sitagliptin, which are illustrated in Table 2. The GMR of dorzagliatin were 100.34 ($90\%$ CI, 96.08, 104.79) for AUC0–24h and 102.34 ($90\%$ CI, 86.92, 120.50) for Cmax, respectively.
The GMR and $90\%$ CIs for the AUC0–24h and Cmax for sitagliptin or dorzagliatin fell within the standard bioequivalence boundaries of 80–$125\%$21 in Supplementary Fig. S2, indicating that co-administration of sitagliptin and dorzagliatin did not significantly affect the PK of either of them.
## Safety and tolerability
Safety and tolerability were assessed by reviewing individual data from all the enrolled subjects in the study. No clinically significant findings or overall changes was seen in clinical laboratory tests, vital signs, physical examination, or standard 12-lead electrocardiograms (ECGs) examinations, and no deaths or drug-related serious treatment-emergent adverse events (TEAEs) occurred in the study (Supplementary Table S1). All TEAEs were in mild or moderate severity and mostly unrelated to study drugs.
There is no increased frequency or severity of any AE during the combination treatment of sitagliptin and dorzagliatin observed, compared with either monotherapy of sitagliptin or dorzagliatin. One case of hypoglycemia was reported during the combination treatment (sitagliptin + dorzagliatin) in mild severity and relieved quickly without intervention, which was assessed possibly due to an inadequate/untimely dietary intake as it occurred very close to lunch time on the testing day when a glucose solution was given in lieu of a regular breakfast. Thus, the combination of dorzagliatin with sitagliptin did not increase the risk of severe hypoglycemia in the safety profiles. Multiple doses of dorzagliatin alone (75 mg BID), or in combination with sitagliptin (100 mg QD) were safe and well-tolerated.
## PD
A clear result was observed on the glucose-lowering effect with combination treatment compared with sitagliptin or dorzagliatin monotherapy. The incremental area under the curve for 4 h (iAUC0–4h) of glucose in oral glucose tolerance test (OGTT) under sitagliptin alone, sitagliptin+dorzagliatin and dorzagliatin alone were 378.00 ± 87.80, 253.00 ± 116.00, and 339.00 ± 124.00 mg × h/dL, respectively (Fig. 1). Similar trends were observed in the incremental maximum concentration from fasting state (at time of 0) before OGTT (iCmax) of glucose with a reduction to 142 mg/dL in combination from 165 mg/dL in sitagliptin alone ($p \leq 0.05$). The effect of improved glycemic control was associated with a significant increase of C-peptide iCmax from 5.10 ng/mL in sitagliptin to 6.33 ng/mL in combination ($p \leq 0.05$), as shown in Table 3. The mean serum glucose concentration-time curve is illustrated in Supplementary Fig. S3a. Fig. 1iAUC and plasma concentration of glucose, C-peptide, and GLP-1.iAUC0-4h of a glucose (sitagliptin, $$n = 9$$; sitagliptin + dorzagliatin, $$n = 15$$; dorzagliatin, $$n = 14$$), b C-peptide (sitagliptin, $$n = 13$$; sitagliptin + dorzagliatin, $$n = 15$$; dorzagliatin, $$n = 14$$), c GLP-1total (sitagliptin, $$n = 12$$; sitagliptin + dorzagliatin, $$n = 12$$; dorzagliatin, $$n = 14$$), and d GLP-1active (sitagliptin, $$n = 10$$; sitagliptin + dorzagliatin, $$n = 10$$; dorzagliatin, $$n = 10$$) in OGTT studies. Diagonal bar and empty circles represent mean and individual iAUC under sitagliptin monotherapy, gray bar and empty squares represent mean and individual iAUC under sitagliptin+dorzagliatin therapy, white bar and empty triangles represent mean and individual iAUC under dorzagliatin monotherapy. Error bars represent standard deviation; P values were calculated for the comparisons between the PD parameters (log-difference) using the mixed models; Statistical tests were two-sided at a significance level of 0.05, and no adjustments were made for multiplicity. The plasma concentration-time curve of GLP-1 and C-peptide e under sitagliptin monotherapy, f under sitagliptin+dorzagliatin therapy, and g under dorzagliatin monotherapy. An empty square line represents C-peptide concentration, a filled triangle line represents GLP-1total concentration, and empty triangle line represents GLP-1active concentration. Data were presented as mean values ± SD. * $p \leq 0.05$, **$p \leq 0.01$ compared with combination treatment. iAUC0–4h incremental area under the curve for 4 h, OGTT oral glucose tolerance test. Table 3Pharmacodynamic parameters and surrogate estimates of glucose sensitivity during OGTT after sitagliptin, dorzagliatin, or sitagliptin + dorzagliatin treatmentSitagliptinSitagliptinDorzagliatinalone+DorzagliatinalonePD parametersGlucoseiCmax (mg/dL)165.00 (34.70)*142.00 (44.90)163.00 (39.00)iCav (mg/dL)94.50 (22.00)**63.20 (29.00)84.80 (31.00)*iAUC0-4h (mg*h/dL)378.00 (87.80)**253.00 (116.00)339.00 (124.00)*C-peptideiCmax (ng/mL)5.10 (2.40)*6.33 (3.77)4.15 (1.86)**iCav (ng/mL)3.04 (1.34)3.61 (1.80)2.46 (0.97)**iAUC0-4h (ng*h/mL)12.10 (5.35)14.40 (7.18)9.82 (3.86)**GLP-1totaliCmax (pmol/L)13.30 (18.50)9.17 (7.96)22.10 (13.20)**iCav (pmol/L)3.67 (3.89)2.81 (2.46)5.70 (2.94)*iAUC0-4h (pmol*h/L)14.70 (15.50)11.20 (9.85)22.80 (11.70)*GLP-1activeiCmax (pmol/L)8.24 (3.76)9.81 (6.17)6.64 (5.29)iCav (pmol/L)2.70 (1.48)3.06 (2.05)1.57 (0.93)iAUC0-4h (pmol*h/L)10.80 (5.93)12.20 (8.21)6.26 (3.73)Glucose sensitivityΔC30/ΔG30 (ng/mL per mg/dL)0.02*(0.01)0.04 (0.04)0.02*(0.01)ΔGLP-1total30/ΔG30 (pmol/L per mg/dL)0.12 (0.18)0.11 (0.10)0.23*(0.14)ΔGLP-1active30/ΔG30 (pmol/L per mg/dL)0.04 (0.008)0.09 (0.05)0.07 (0.05)Values are presented as the mean (SD).iCmax incremental maximum concentration from fasting state (at time of 0) before OGTT, iCav average concentration (calculated as iAUC0–4h/4) from fasting state (at time of 0) before OGTT, iAUC0–4h incremental area under curve for 4 h, OGTT oral glucose tolerance test, ΔC30/ΔG30 the level at 30 min subtract 0 min of C-peptide divided by 30 min subtract 0 min glucose level, ΔGLP-1total30/ΔG30 the level at 30 min subtract 0 min of GLP-1total divided by 30 min subtract 0 min glucose level, ΔGLP-1active30/ΔG30, the level at 30 min subtract 0 min of GLP-1active divided by 30 min subtract 0 min glucose level.*$p \leq 0.05$, **$p \leq 0.01$ compared with the combination treatment.
These results manifest the combination treatment of dorzagliatin with sitagliptin achieved a greater glucose-lowering effect than sitagliptin or dorzagliatin monotherapy.
Consistent with glucose outcomes, combination treatment showed higher C-peptide secretion compared with sitagliptin or dorzagliatin monotherapy under glucose stimulation, as shown in Fig. 1.
The iAUC0-4h of C-peptide in OGTT under sitagliptin alone, sitagliptin+dorzagliatin, and dorzagliatin alone were 12.10 ± 5.35, 14.40 ± 7.18, and 9.82 ± 3.86 ng × h/mL, respectively. Similar trends were also observed in iCmax, which are summarized in Table 3 that the incremental maximum C-peptide level was observed in the combination treatment over the monotherapy significantly ($p \leq 0.05$ with sitagliptin and $P \leq 0.01$ with dorzagliatin). The serum C-peptide concentration-time curve is illustrated in Supplementary Fig. S3b.
These results manifest the combination treatment of dorzagliatin with sitagliptin improved glucose-stimulated insulin secretion (GSIS) to achieve a greater glucose-lowering effect than either monotherapy, thus indicating the synergic potential for glycemic control in T2D patients.
GLP-1 was measured in total and active forms. Interestingly, for GLP-1 secretion in the OGTT, dorzagliatin monotherapy resulted in a significantly higher GLP-1total level compared with combination treatment. The iAUC0-4h of GLP-1total in OGTT under sitagliptin alone, sitagliptin + dorzagliatin, and dorzagliatin alone were 14.70 ± 15.50, 11.20 ± 9.85, and 22.80 ± 11.70 pmol × h/L, respectively (Fig. 1), with similar trends in iCmax as shown in Table 3, in which dorzagliatin resulted in the highest level of glucose-stimulated GLP-1 release of 22.10 pmol/L compared with the combination therapy of 9.17 pmol/L in iCmax ($p \leq 0.05$). The plasma GLP-1total concentration-time curve is illustrated in Supplementary Fig. S3c, showing the time to reach maximum plasma concentration (Tmax) is 30 min after glucose challenge or 1 h after drug administration. Correlation of PK-PD analysis showed that dorzagliatin drug concentration maintained above 500 ng/mL from 30 min post-drug dosing to 4 h, where total GLP-1 secretion mainly occurred from 30 min to 2 h with Tmax at 1 h (Supplementary Fig. S4), suggesting a dorzagliatin regulated glucose-stimulated GLP-1 secretion.
Combination treatment obtained numerically increased GLP-1active compared with sitagliptin or dorzagliatin monotherapy (Fig. 1 and Supplementary Fig. S3d). The iAUC0-4h of GLP-1active in OGTT under sitagliptin alone, sitagliptin + dorzagliatin, and dorzagliatin alone were 10.80 ± 5.93, 12.20 ± 8.21, and 6.26 ± 3.73 pmol × h/L, respectively (Fig. 1), with similar trends in iCmax (Table 3), in which sitagliptin combination with dorzagliatin resulted in the highest level of circulating GLP-1active. The plasma GLP-1active concentration-time curve is illustrated in Supplementary Fig. S3d.
The serum C-peptide and plasma GLP-1 response to glucose under the three treatment regimens post-oral glucose challenge are illustrated in Fig. 2, and glucose sensitivity comparisons are detailed in Table 3.Fig. 2Glucose sensitivity.a The insulinogenic index (ΔC30/ΔG30) (sitagliptin, $$n = 11$$; sitagliptin + dorzagliatin, $$n = 15$$; dorzagliatin, $$n = 14$$), b total GLP-1 secretion index (ΔGLP-1total.30/ΔG30) (sitagliptin, $$n = 11$$; sitagliptin + dorzagliatin, $$n = 15$$; dorzagliatin, $$n = 14$$), and c active GLP-1 secretion index (ΔGLP-1active.30/ΔG30) (sitagliptin, $$n = 8$$; sitagliptin + dorzagliatin, $$n = 9$$; dorzagliatin, $$n = 10$$) are calculated based on the C-peptide and GLP-1 levels in the OGTT study. A diagonal bar and empty circles represent the mean and individual index under sitagliptin monotherapy, gray bar, and empty squares represent the mean and individual index under sitagliptin + dorzagliatin therapy, white bar and empty triangles represent the mean and individual index under dorzagliatin monotherapy. Error bars represent standard deviation. The p value was calculated based on the paired Wilcoxon test between combination therapy and each monotherapy. * $p \leq 0.05$, **$p \leq 0.01$ compared with combination treatment. OGTT oral glucose tolerance test.
Combination treatment obtained significantly increased early insulinogenic index ΔC30/ΔG30 compared with sitagliptin or dorzagliatin monotherapy, with 0.04 ± 0.04 ng/mL per mg/dL, 0.02 ± 0.01 ng/mL per mg/dL, and 0.02 ± 0.01 ng/mL per mg/dL, respectively ($p \leq 0.05$). Similar trends were observed in GLP-1 secretion index ΔGLP-1active.30/ΔG30, with numerically higher GLP-1active response to glucose in combination treatment compared with either monotherapy, whereas the total GLP-1 secretion index is significantly higher in dorzagliatin monotherapy with 0.23 ± 0.14 pmol/L per mg/dL, and 0.12 ± 0.18 pmol/L per mg/dL in sitagliptin monotherapy and 0.11 ± 0.10 pmol/L per mg/dL in combination therapy ($p \leq 0.05$), respectively.
## Discussion
Dorzagliatin demonstrated additional benefits in blood glucose reduction when it is combined with sitagliptin. Although both drugs act on the decrease of postprandial glucose, an additional $30\%$ reduction of glucose is observed in the OGTT when dorzagliatin was added to sitagliptin over either monotherapy ($p \leq 0.05$). The benefit in glycemic control is correlated with an increased GSIS in iAUC0–4h and improvement of early-phase C-peptide secretion index (ΔC30/ΔG30) in the combination over each monotherapy with statistical significance. Based on the data from the current study, the plasma concentration of GLP-1active is increased almost onefold for the combination of dorzagliatin and sitagliptin over dorzagliatin alone, which is correlated with an increased glucose-stimulated C-peptide secretion by nearly $50\%$. Accordingly, the enhanced beta-cell secretion function was associated with an improved GLP-1 secretion index in combination treatment (Fig. 2 and Table 3).
The statistically significant improvement of early-phase C-peptide secretion index and the increased GLP-1active secretion should have positively contributed to the glucose sensitivity and glycemic control when dorzagliatin is combined with a DPP-4 inhibitor sitagliptin. It is conceivable that the combination worked together to restoration of GK function in the pancreas and intestine simultaneously, which triggered the GK-mediated GSIS in beta cells and engaged the GLP-1 secretion to further enhance the GSIS function22. Improvement of hepatic glucose metabolism by dorzagliatin is found in a relationship with effective reduction of fasting plasma glucose in a healthy subject without change of insulin secretion, and reduction of postprandial glucose in T2D patients13,23, and will be further studied in subsequent trials (R Basu, ClinicalTrials.gov identifier: NCT05098470). It has been reported that a substantial reduction of hepatic GK expression in T2D patients correlated with their hyperglycemia and hepatic insulin resistance24,25. Epigenetic modification of hepatic GK promoter caused a reduction of hepatic GK expressions and a reduced glycogen content in the liver26. Dorzagliatin restored the hepatic GK expression in diabetes rats, increased the numbers of insulin-secreting cells in the pancreas, and improved glycemic control after 28-day treatment27. In the same animal model, dorzagliatin increased fasting GLP-1 level in the small intestine and pancreas, either alone or in combination with sitagliptin28. The effect on glucose-stimulated GLP-1 secretion was not reported in other GKAs and was considered a unique feature of dorzagliatin. In the preclinical studies, we discovered its effect on glucose-stimulated GLP-1 secretion in the C5757BL/6 J mice under OGTT conditions29 and its signal on glucose-stimulated GLP-1 secretion in healthy Chinese subjects (ClinicalTrials.gov identifier: NCT01952535)30. Data from the whole-body radiography (WBR) in rats showed a high organ distribution of dorzagliatin in the pancreas, small intestine, and liver after an oral dose in the first 4 h, and the drug were then quickly cleared with a t$\frac{1}{2}$ of 4.4 h in plasma. The mass balance study (ClinicalTrials.gov identifier: NCT03158506) was conducted in the US and results are consistent with fast clearance of dorzagliatin in humans and minimum renal clearance (less than $10\%$). We, therefore, suggest that the effect of dorzagliatin on glucose-stimulated GLP-1 secretion in patients with T2D and obesity is a combination of the role of GK in GLP-1 regulation and the pharmacokinetic property of dorzagliatin with intrinsic high organ distribution in the small intestine.
Unlike most of the clinical trials of dorzagliatin conducted in the population with T2D and non-obesity with BMI around 25 kg/m214–16, this study was conducted in patients with T2D and obesity in which dorzagliatin also showed its effectiveness in the glycemic control and improved insulinogenic effect and glucose sensitivity when combined with sitagliptin. There is no PK interaction between these two oral drugs and no increases in AEs. This offers an opportunity to treat patients with T2D and obesity through a combination of dorzagliatin and sitagliptin.
GK plays a central role in glucose homeostasis in pancreatic beta-cell, intestinal L cells, as well as hepatocytes1. The role of GK in the regulation of GLP-1 secretion has not been fully understood and the results from different studies are not consistent31. The most commonly accepted mechanism for GLP-1 secretion in L cells is supported by a sodium-glucose cotransporter-1 (SGLT-1) glucose transporter-regulated coupling of glucose-sodium flood into entero-L cells with the membrane depolarization through a KATP channel blockage and voltage-gated calcium channel opening, which lead to GLP-1 release32. Theodorakis and colleagues reported that GK is expressed in human L and K cells, and may play an important role in incretin secretion in the entero-endocrine cells33. A significant increase of glucose-stimulated GLP-1 secretion by dorzagliatin alone suggested its effect on GK in entero-endocrine L cells. It is observed that the time for GLP-1 peak value of 30 min upon glucose load is correlated with the Tmax of dorzagliatin exposure (Supplementary Fig. S4).
Ferrannini and colleagues reported that GLP-1 secretion quickly reached the maximum level of GLP-1total in healthy Caucasians in a range of 25–30 pmol/L within 30 min after 75-g glucose oral mixed meal, compared with a Cmax of 10–12 pmol/L for T2D subjects with obesity11, although it was reported that the GLP-1 and GIP secretion in the GCK-MODY patients was not impaired in these subjects with non-obesity31,34. The inconsistency in the GLP-1 secretion in GCK-MODY and T2D could arise from the expression state of GK in T2D vs GCK-MODY subjects. A substantial reduction of GK expression in the pancreas and liver in T2D patients has been reported, which leads to the loss of glucose sensitivity in the pancreas and reduced hepatic glycogen production24,25,35. In the current study, dorzagliatin alone improved the impaired glucose-stimulated GLP-1 secretion in patients with T2D and obesity with an iCmax of GLP-1total reached 22.10 pmol/L (~27 pmmol/L in Cmax, Supplementary Fig. S4), approaching to the healthy range of maximum GLP-1 level described above.
Recently, GLP-1 and its most abundant inactive GLP-1 metabolite GLP-1 [9-36]NH2 has been reported to have biological activities in protecting human aortic endothelial cells and cardiomyocytes in vivo and ex vivo studies36–38. The cleaved peptide is found in almost twofold magnitude higher concentrations than active GLP-1 in peripheral blood and shows cardioprotection, and antioxidant properties31 as well as demonstrates the capability to inhibit hepatic neoglucogenesis39. The benefits of GLP-1 and metabolites regulated by dorzagliatin shall be further evaluated when it is used for the treatment of T2D as a monotherapy.
The fast clearance of GLP-1 and relatively low concentration of GLP-1active in the circulation can be explained by the effect of DPP-4 activity. However, the pharmacological effect of dorzagliatin on GLP-1 secretion measured by GLP-1total was substantially suppressed by the addition of sitagliptin. The increase of GLP-1active by the combination of dorzagliatin and sitagliptin compared with either monotherapy should result from the DPP-4 inhibition effect of sitagliptin to slow down the GLP-1 degradation under an increased GLP-1 production modulated by dorzagliatin. It has been reported that elevated GLP-1active concentrations restrict the GLP-1 secretion to some degree40–43. This feedback effect has also been observed in other studies in humans, where an exogenous infusion of active GLP-1 (7–37) led to a reduction in levels of endogenous GLP-144, and in the clinical trial studying sitagliptin45. Brubaker and Hansen have reported that GLP-1 can stimulate somatostatin release from isolated rat intestinal cultures46 and somatostatin inhibits GLP-1 secretion, indicating that GLP-1 limits its own secretion through a somatostatin-mediated paracrine-inhibitory pathway47. Although the GLP-1total level is reduced in the combination treatment with dorzagliatin and sitagliptin, the overall secretion of C-peptide in response to glucose challenge has been significantly improved over monotherapies in this study. This effect may result from the GLP-1 secretion from pancreatic alpha-cell through a paracrine mode of action on beta-cell.
In conclusion, dorzagliatin regulates glucose homeostasis not only via its dual-activating GK activities in the pancreas and liver but also through the improvement of glucose-stimulated GLP-1 release in T2D patients. It increases the effectiveness of glycemic control and glucose sensitivity when combined with a DPP-4 inhibitor sitagliptin. The lack of PK interaction between dorzagliatin and sitagliptin further supports the combination of dorzagliatin with sitagliptin for the treatment of patients with T2D and obesity.
## Methods
This study was designed to evaluate the PK and PD effects of dorzagliatin and sitagliptin either alone or in combination in patients with T2D and obesity who were on standard anti-diabetes drug (clinical trials identifier: NCT03790839). The protocol was approved by an Institutional Review Board (IntegReview Ethics Review Board, Austin, USA) at the study site, and conducted in accordance with the Declaration of Helsinki and International Conference on Harmonization Good Clinical Practice (ICH-GCP) guidelines, as well as US Food and Drug Administration regulations. All participants provided written informed consent prior to participating in the study and were compensated for completed study procedures. The patient enrollment was from 21 December 2018 to 30 August 2019.
## Key eligibility criteria/study population
Male and female adults eligible for inclusion had to meet the following criteria: patients aged between 30 and 65 years, in general, good health who had been diagnosed as T2D for at least 3 months, with HbA1c between 7.0 and $10.5\%$, body mass index (BMI) between 19.0 and 38.0 kg/m2, taking a stable dose of metformin ≥1000 mg per day, or a DPP-4 inhibitor, or a sodium-glucose cotransporter-2 (SGLT-2) inhibitor, or metformin plus a DPP-4 inhibitor with no change in the dose for at least 4 weeks prior to screening, and accepting to change their current therapy to 100 mg sitagliptin QD for at least 14 days prior to dosing on Day 1.
The key exclusion criteria included: fasting blood glucose (FBG) ≤110 or ≥270 mg/dL, the reported incidence of severe or serious hypoglycemia within 3 months prior to screening, type 1 diabetes or latent autoimmune diabetes, known hypersensitivity/contraindication to study drugs, evidence of any clinically significant medical illness or functional disorders, and pregnant or breast-feeding women.
## Study design
This is a phase 1, open-label, single-sequence, multiple-dose study conducted at a single clinical center in the US (Frontage Clinical Services, Inc., Secaucus, NJ).
Eligible subjects had a minimum 12-day sitagliptin run-in period (sitagliptin 100 mg QD) prior to admission to the clinical research center, and each subject completed the medical diary to record study drug (sitagliptin) doses taken every day and the results of the blood glucose monitoring. Following completion of the run-in period, eligible subjects were admitted to the clinical research center on Day −2 for a total of 18 overnight stays, and discharged after completion of end-of-study (EOS) procedures on Day 17. All eligible subjects received 3 treatment regimens sequentially as shown in Supplementary Fig. S1: sitagliptin 100 mg QD for 5 days (Day 1–5), then sitagliptin 100 mg QD + dorzagliatin 75 mg BID for 5 days (Day 6–10), followed by dorzagliatin 75 mg BID for 5 days (Day 11–15). Only the morning dose was administered, and sampling was performed on Days 5, 10, and 15 for up to 24 h post-dose. All treatments were administered 60 min prior to meals except on Days 5, 10, and 15; when OGTT was conducted, subjects rapidly (within 5 min) drank a solution containing 75-g glucose 30 min after study drug oral administration instead of a breakfast.
The primary outcomes of this study include potential PK drug–durg interaction between dorzagliatin and sitagliptin by GMR, and the assessment of safety and tolerability of dorzagliatin with simultaneous administration of sitagliptin in subjects with T2D. The secondary outcomes include the PD responses of glucose, GLP-1, and C-peptide by iAUC0-4h and Cmax, and glucose sensitivity following dorzagliatin, sitagliptin, or simultaneous administration of dorzagliatin and sitagliptin in subjects with T2D.
Sample size calculations were based on study design and intra-subject variability. At least ten evaluable subjects in the sequence would be required to achieve a power of at least 0.8 for GMR between two treatments (sitagliptin + dorzagliatin vs. dorzagliatin alone or sitagliptin + dorzagliatin vs. sitagliptin alone) for Cmax or AUC0-24h, with the equivalence bounds of 0.8 to 1.25. Assuming a drop-out rate of $20\%$. 15 eligible subjects were planned to enroll by aiming to obtain 12 evaluable subjects for pharmacokinetic drug–drug interaction assessment.
## PK and PD sample collection
Blood samples for PK analysis were collected in dipotassium ethylenediaminetetra acetic acid (K2EDTA) tubes on Days 5, 10, and 15 at pre-dose, 0.25, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 10, 12, 18, and 24 h post-dose.
In the OGTT studies on Days 5, 10, and 15, 75 g of glucose was administered orally 30 min after experimental drug administration, and the blood samples were collected at pre-dose, 0.5, 1, 1.5, 2, 2.5, 3, and 4 h post-oral glucose intake. The samples were used to measure serum glucose, C-peptide, and plasma GLP-1. The preparation of the sample is detailed in the supplementary material.
## Analytical methods
The concentrations of dorzagliatin and sitagliptin in plasma were determined by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods (Frontage Laboratories, Inc., Pennsylvania, USA) (refer to supplementary material for details).
Serum glucose was analyzed using a hexokinase enzymatic method, serum C-peptide was assessed using a chemiluminescence assay (BioReference Laboratories, Inc., New Jersey, USA). The determinations of plasma GLP-1 concentrations were performed by using validated enzyme-linked immunosorbent assays (ELISA) (Mercodia AB, Uppsala, Sweden) (refer to supplementary material for details).
## PK and PD assessment
The non-compartmental analysis was applied to determine the PK using WinNonlin software (Certara, Princeton, NJ, USA). PK parameters were derived from the plasma concentration-time curve, Cmax and Tmax were directly determined from the plasma concentration-time profile of each subject. The AUC0–24h was calculated using the linear trapezoidal method.
PD parameters were evaluated by measurement of serum glucose, C-peptide, and plasma GLP-1 concentrations, using the incremental area under curve for 4 h, iAUC0–4h, incremental maximum concentration, iCmax, and average concentration (iCav, calculated as iAUC0–4h/4) from fasting state (at time of 0) before OGTT.
## Safety assessment
Safety evaluations were conducted throughout each study based on clinical laboratory tests, vital signs, physical examinations, 12-lead ECG, and AE. Any AE reported, especially TEAE, was recorded and coded using the Medical Dictionary for Drug Regulatory Activities (MedDRA), and its relationship to the drug treatment was determined by the investigator.
All AEs were monitored after the administration of the study drugs.
## Statistical analysis
For statistical analysis, subjects who received at least one dose of the study drug and had at least one post-enrollment safety assessment were included in the safety analysis set. The PK analysis set included the subjects who had no major protocol deviations and had sufficient PK data to obtain estimates of key PK parameters. The PD analysis set included those subjects who had sufficient glucose, C-peptide, and GLP-1 concentrations to obtain estimates of PD parameters.
Mixed models under the sequential design were used to analyze Cmax and AUC0–24h with treatments as fixed effects and subjects as random effects. The GMR (combination/monotherapy ratio) and the corresponding $90\%$ CI for Cmax and AUC0–24h were obtained by exponentiating the adjusted mean difference in logarithms. The incremental PD parameters were calculated by subtracting the fasting state (time of 0 before OGTT) value from the values at each sampling time-point. A comparison was performed using two mixed models corresponding to sitagliptin + dorzagliatin versus sitagliptin, or sitagliptin + dorzagliatin versus dorzagliatin. P values were calculated based on the test of the ratio (log-difference) from the models.
Parameters for glucose sensitivity are evaluated through the C-peptide index (ΔC30/ΔG30) and GLP-1 index (ΔGLP-1total.30/ΔG30, ΔGLP-1active.30/ΔG30), which were accessed through the level at 30 min subtract 0 min of C-peptide, GLP-1total, and GLP-1active respectively and then divide by 30 min subtract 0 min glucose level, which data for calculating were all collected in OGTT. The p value was calculated based on the paired Wilcoxon test between combination therapy and each monotherapy.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-36946-7.
## Source data
Source Data
## Peer review information
Nature Communications thanks Lærke Smidt Gasbjerg, Yasuo Terauchi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: 1,2,4,5-Tetrazine-tethered probes for fluorogenically imaging superoxide in
live cells with ultrahigh specificity
authors:
- Xuefeng Jiang
- Min Li
- Yule Wang
- Chao Wang
- Yingchao Wang
- Tianruo Shen
- Lili Shen
- Xiaogang Liu
- Yi Wang
- Xin Li
journal: Nature Communications
year: 2023
pmcid: PMC10014963
doi: 10.1038/s41467-023-37121-8
license: CC BY 4.0
---
# 1,2,4,5-Tetrazine-tethered probes for fluorogenically imaging superoxide in live cells with ultrahigh specificity
## Abstract
Superoxide (O2·−) is the primary reactive oxygen species in mammal cells. Detecting superoxide is crucial for understanding redox signaling but remains challenging. Herein, we introduce a class of activity-based sensing probes. The probes utilize 1,2,4,5-tetrazine as a superoxide-responsive trigger, which can be modularly tethered to various fluorophores to tune probe sensitivity and emission color. These probes afford ultra-specific and ultra-fluorogenic responses towards superoxide, and enable multiplexed imaging of various cellular superoxide levels in an organelle-resolved way. Notably, the probes reveal the aberrant superoxide generation in the pathology of myocardial ischemia/reperfusion injury, and facilitate the establishment of a high-content screening pipeline for mediators of superoxide homeostasis. One such identified mediator, coprostanone, is shown to effectively ameliorating oxidative stress-induced injury in mice with myocardial ischemia/reperfusion injury. Collectively, these results showcase the potential of 1,2,4,5-tetrazine-tethered probes as versatile tools to monitor superoxide in a range of pathophysiological settings.
Specific detection of cellular superoxide is challenging. Here, the authors designed 1,2,4,5-tetrazine based fluorogenic probes for specific and sensitive imaging of superoxide, and applied them in high throughput screening of modulators of oxidative stress.
## Introduction
Reactive oxygen species (ROS) include a broad variety of oxygen derivatives that are labile, oxidative, and short-lived1. Their similar structures and reactivity make the specific detection of a single ROS highly challenging. Yet, to accurately elucidate the molecular mechanism underlying the pleiotropic roles of various ROS in physiology and pathology2, and to translate redox signaling into effective therapies, it is essential to develop robust tools that specifically detect a ROS of interest while being silent towards others3–6, ideally in live cells with desirable spatiotemporal resolution.
Probably the most important ROS is superoxide (O2·−). Superoxide is generated by a variety of enzymes, such as the mitochondrial electron transport chain (ETC) and NADPH oxidases (NOX)7,8. Its dismutation by superoxide dismutase (SOD) yields H2O2 and fuels other ROS for redox signaling (Fig. 1A)9–12. Imbalanced superoxide homeostasis initiates oxidative stress which is implicated in the etiology of aging13, cancer14, neurodegenerative diseases15, diabetes16, cardiovascular diseases17, etc. Given the pivotal roles of superoxide in redox signaling and redox homeostasis, the specific detection of superoxide has long been a hot topic attracting continuous research efforts18.Fig. 1Design of tetrazine-based probes for sensing superoxide. A Superoxide as a primary ROS in cells. This figure was created with BioRender.com. B Reactivity of various ROS. C Nitrogen substitution of benzene ring up-shifts its electron affinity (EA); the inset shows the ionization potential (IP) of the superoxide radical anion. D HPLC traces of Tz1 before and after the treatment of superoxide. E Proposed reaction mechanism between 1,2,4,5-tetrazine and superoxide to yield 2,5-diphenyl-l,3,4-oxadiazole (O1). F Reactivity of Tz1 when treated with various analytes. Data shown were the normalized yields of O1. G Superoxide dose-dependent conversion of Tz1 to O1. H Superoxide transformed Tz1-Tz4 to their oxadiazole counterparts, and the stereoelectronic effects on their reactivity towards superoxide. Data are presented as mean value ± SD, $$n = 3$$ independent experiments.
Various methods have been developed to detect superoxide, including electron paramagnetic resonance (EPR) spin-trapping technique19, electrochemical sensors20, spectrophotometric assays21, chemiluminescent assays22, and fluorescent imaging23. Among these diverse methods, fluorescent imaging is the most desirable, because it is compatible with live cells and permits the direct in situ tracing of superoxide dynamics with unprecedented spatiotemporal resolution. This advantage is critical, given the labile nature and the compartmentalized presence of superoxide in cells. To this end, fluorescent probes dihydroethidium (DHE) and its mitochondria-anchored analog MitoSOX have become the most popular tools for detecting superoxide in biology24. Blue-emissive DHE is readily oxidized by superoxide to form red emissive product(s). Recording the total intensities of red fluorescence in cells has therefore been routinely performed for assessing cellular superoxide production. Although this method has been used for about two decades, studies show that DHE can also be oxidized by other ROS to form red emissive product(s), interfering with the imaging of superoxide25. Further studies identified 2-hydroxyethidium as a specific product from the oxidation of DHE via superoxide, and thereafter the high-performance liquid chromatography (HPLC) analysis of 2-hydroxyethidium was proposed to detect superoxide26. This assay achieved the desired specificity, but unfortunately, at the expense of losing spatiotemporal resolution. Indeed, since oxidation is a common feature of the ROS family, oxidation-based probes for superoxide generally show suboptimal specificity. To address this issue, several probes were developed based on detecting nucleophilic ROS by using sulfonylated and phosphinated probes27–29. Though the development of these probes improved selectivity towards superoxide over other ROS; the tendency of hydrolysis and the abundant presence of biological nucleophiles (such as glutathione) still compromise their specificity. The development of fluorescent probes with desirable specificity for the spatiotemporal imaging of intracellular superoxide remains a daunting challenge.
Herein, we report the design and development of a family of activity-based sensing (ABS) probes for imaging superoxide in live cells with unprecedented specificity. The probes are developed based on the reductive nature of superoxide30, and utilize the single electron transfer from superoxide to 1,2,4,5-tetrazine (Tz) as a responsive mechanism. Due to the inherent fluorescence-quenching ability of Tz31,32, these probes show ultra-fluorogenic responses towards superoxide. By tuning probe reactivity and emission color, multiplexed imaging of cellular superoxide levels is realized with unprecedented spatial resolution. Given the robustness of the probes, we build a high-content drug screening model and identify a natural product to alleviate oxidative stress-induced injury in the pathology of ischemic heart disease. We envision that the specificity and ultra-fluorogenic response of these Tz-based probes will make them highly useful tools for tracking superoxide in a range of pathophysiological settings.
## Design of tetrazine-based probes for sensing superoxide
Activity-based sensing (ABS) conceptualized by Chang and co-workers33,34, has proven powerful for the selective imaging of bio-analytes in live cells. ABS utilizes chemical reactivity to develop probes for the selective and sensitive detection of analytes and is especially applicable for labile species as demonstrated by the success of boronate-based probes for H2O235,36.
Inspired by the ABS strategy, we outlined four key requirements for developing superoxide probes. First, these probes should show desirable biocompatibility, including low cytotoxicity and high self-stability. Second, they should be highly specific to superoxide in a complex biological system, with no or minimal interference from other ROS or reactive biomolecules. Third, given the extremely transient and low concentrations of superoxide in cells, these probes should react with superoxide with ultrafast kinetics in the biological environment. Fourth, the probes should emit distinct signals, either fluorogenic or ratiometric, upon the detection of superoxide.
To design probes fulfilling these criteria, we turned our attention to the reductive property of superoxide. Superoxide may act as a moderate one-electron reducing agent30. It has been used to reduce benzothiazolium salts via one-electron transfer37. This reductive property is unique for superoxide among the ROS family and could be utilized to construct dedicated superoxide probes (Fig. 1B). In addition, 1,2,4,5-tetrazine (Tz) possesses a significant reduction potential (Fig. 1C). Studies suggest that the nitrogen substitution of the benzene ring up-shifts its reduction potential, with more nitrogen substitutions (especially the N=N bond) leading to higher reduction potentials38. We calculated the electron affinity (EA) of Tz and its analogs. Our results show that Tz exhibits the largest EA (3.32 eV), suggesting its strong oxidative properties (Fig. 1C). In addition, we also calculated the ionization potential (IP) of the superoxide radical anion (3.15 eV). This IP is smaller than the EA of Tz. Based on these reducing/oxidizing properties, we hypothesized that Tz may enable selective detection of superoxide by a single electron transfer (SET) mechanism. It is worth mentioning that *Tz is* well known for its bioorthogonal reaction with trans-cyclooctene for labeling proteins, and has demonstrated robust biocompatibility39,40. Moreover, *Tz is* a good fluorescence dark quencher for constructing fluorogenic probes31. We could thus develop a platter of colorful fluorogenic probes to monitor superoxide activities in live cells.
To test this hypothesis, we prepared 3,6-diphenyl-1,2,4,5-tetrazine (Tz1) and utilized it as a model compound to interrogate its reactivity with superoxide. Tz1 was treated with an excess of superoxide (administrated as KO2) and the reaction was analyzed via both HPLC and liquid-chromatography-mass spectrometry (LC-MS). Our results showed the disappearance of Tz1 and the emergence of a new peak; mass analysis showed that this peak was attributed to 2,5-diphenyl-l,3,4-oxadiazole (Fig. 1D, Supplementary Fig. 1). Three additional experiments further confirmed the molecular structure of 2,5-diphenyl-l,3,4-oxadiazole. First, the product was analyzed by high-resolution mass spectrometry (HRMS), from which we obtained consistent results (Supplementary Fig. 2). Next, 2,5-diphenyl-l,3,4-oxadiazole was prepared by a literature procedure and was used as a standard. A comparison of the standard and the product between Tz1 and superoxide gave the same retention time during HPLC analysis (Supplementary Fig. 3). Finally, after the evaporation of the reaction solvent between Tz1 and superoxide, the product residue was directly analyzed by proton nuclear magnetic resonance (NMR) spectroscopy, and the signals overlapped with those of the standard (Supplementary Fig. 4). These results collectively demonstrated that superoxide reacted with Tz1 with high efficiency to yield oxadiazole as the dominant product. We have proposed a tandem pathway initiated by one electron transfer from superoxide to Tz to explain the reaction mechanism (Fig. 1E).
Given the labile nature of superoxide, we also performed experiments to confirm that the transformation from Tz1 to the oxadiazole was indeed induced by superoxide but not its descents. To this end, the reaction between Tz1 and superoxide was conducted in the presence of TEMPO or Tiron, with the former being a superoxide dismutase mimic and the latter as an electron trap41,42. The presence of excess TEMPO or Tiron completely blocked this transformation (Supplementary Fig. 5), leaving Tz1 intact. These results suggested that the reactions were indeed executed by superoxide. Inspired by these results, we further investigated the specificity of Tz1 towards superoxide among the ROS family and the reductive glutathione (GSH). Aliquots of Tz1 were treated with an excess of various ROS or GSH and the reactions were analyzed by HPLC. Among all tested species, only superoxide transformed Tz1 into oxadiazole, and Tz1 remained almost inert to other species (Fig. 1F, Supplementary Figs. 6, 7, Supplementary Table 1). These results demonstrated the desirable selectivity of tetrazine-based probes towards superoxide. We also investigated the dependence of the conversion rate on the dose of superoxide, and a positive correlation was observed (Fig. 1G, Supplementary Fig. 8). This relationship suggested the potency of the probe to quantify superoxide concentrations.
To explore how the electronic effects of the substituents on Tz affected its reactivity with superoxide, Tz2 and Tz3 bearing either an electron-donating or an electron-withdrawing group on the phenyl ring were prepared. Tz1-Tz3 reacted with an insufficient amount of superoxide (0.5 eq), respectively, and these reactions were monitored both by HPLC and LCMS. As expected, all compounds reacted with superoxide to yield the oxadiazole derivatives (Supplementary Figs. 9–11). However, their conversion rates varied considerably. Tz3 bearing an electron-withdrawing group showed the highest reduction potential and the best conversion rate (Supplementary Table 2, Fig. 1H, Supplementary Fig. 12).
To test how the steric effects affected this reactivity, one phenyl group in Tz1 was changed into a methyl group to reduce the steric hindrance, affording Tz4. Tz4 reacted with superoxide with a much-improved conversion rate in comparison to Tz1 (Supplementary Fig. 13, Supplementary Table 2). It should be noted that in addition to the less steric effect, the methyl group is also less electron-donating than the phenyl group in Tz1. These observations agreed with our proposed reaction mechanism (Fig. 1E) and shed light on the molecular design of the superoxide probes with tunable sensitivity.
## Development of Tz-based fluorogenic probes for imaging superoxide in live cells
After identifying Tz as an ultra-specific responsive trigger for superoxide, we set out to design fluorogenic probes for imaging superoxide in live cells by tethering Tz to various fluorophores. We first chose naphthalene and quinoline (Fig. 2A). These two fluorophores are structurally similar but emit in different spectra ranges. The electron-withdrawing N atom at the quinolone fluorophore could render the quinoline-tetrazine probes more sensitive toward superoxide than the naphthalene-tetrazine analogs, according to our initial results (Fig. 1H). These varied sensitivities as well as their distinct emission colors could enable the multiplexed imaging of superoxide levels in live cells. With these considerations, Tz was tethered at the C-2 position of either the naphthalene (F-Tz1, F-Tz2) or the quinoline fluorophore (F-Tz3, F-Tz4) (Fig. 2A). To tune the electron push-pull effect, amines of different electron-donating abilities were introduced at their C-6 positions. These amino groups are essential for the final naphthyl or quinolyl oxadiazoles to emit bright fluorescence, by stabilizing π–π* transitions. We facilely synthesized these probes via the procedures shown in Supplementary Information. Fig. 2Design of Tz-based fluorogenic probes for imaging superoxide in live cells. A Structures of the probes and their sensing mechanism. B Photophysical properties of the probes before and after sensing superoxide. [ a]Ф: Quantum yields were determined with quinine sulfate (Фstandard = 0.577 in 0.1 M H2SO4) as a standard (λex 365 nm). C Normalized emission spectra of the probes (5 μM) before and after the treatment of superoxide (20 eq). The data were normalized to the maximum emission after the treatment of superoxide. D Plot of probe (5 μM) fluorescence intensity at their peak emission wavelengths as a function of superoxide doses. Data are presented as mean value ± SD, $$n = 3$$ independent experiments. E Fluorescence response of the probes toward various reactive analytes in comparison to DCFHDA and DHE. All probes were used at 5 μM and the reactive analytes were used at 100 μM except ONOO− (10 μM) and NO (20 μM). The reactions were carried out in PBS (pH 7.4, 10 mM) at ambient temperature for 30 min before measurement. Data were the normalized emission intensities at their peak emission wavelengths. Data are presented as mean value ± SD, $$n = 3$$ independent experiments. F Confocal microscopy images of HepG2 cells pretreated with H2O2 (2 mM) for various durations and then stained with either the blue emissive F-Tz1 or F-Tz2, or green emissive F-Tz3 or F-Tz4. Probes were used at a final concentration of 5 μM and were incubated with the cells for 30 min before imaging. Representative images are shown from $$n = 3$$ independent experiments. G Fluorescence images of HepG2 cells stained with F-Tz1, F-Tz4, DCFHDA, or DHE (each 5 μM, 30 min). Before being stained with the probes, cells were intact (control), or pretreated with H2O2 (2 mM) for 2 h, or first pretreated with tiron (100 μM) or TEMPO (300 μM) for 1 h, and then the co-treatment of tiron (100 μM) or TEMPO (300 μM) together with H2O2 (2 mM) for 2 h. Scale bar: 25 μm. Representative images are shown from $$n = 3$$ independent experiments.
After obtaining these probes, we measured their optical response toward superoxide in phosphate-buffered saline (PBS) (Fig. 2B). Intact probes generally exhibited their UV-vis absorption peaks in the range of 350–405 nm; whereas superoxide treatment caused blueshifts of ~20–50 nm, due to the modification of π-conjugations (Supplementary Figs. 14–17). Since the oxadiazole scaffold generally features a shorter absorption band than the Tz moiety, these spectral changes suggested the superoxide-induced transformation of the Tz trigger into the oxadiazole moiety. These structural changes were experimentally confirmed by LCMS (Supplementary Figs. 18–21). Next, fluorescence measurements show that unreacted probes were barely emissive. However, after the treatment of superoxide, dramatic fluorescence switch-on response was observed. While F-Tz3 and F-Tz4 emit green fluorescence, the fluorescence of F-Tz1 and F-Tz2 is in the blue range (Fig. 2C, Supplementary Fig. 22). The fluorogenicity of these probes was further rationalized via quantum chemical calculations: before the reactions with superoxide, the Tz moiety generates a low-lying dark state, quenching the fluorescence; after the reactions, the Tz moiety is destroyed and the associated dark states are thus removed, activating bright fluorescence from the resulting fluorophores (Supplementary Figs. 23–26)43.
We noted that the fluorescence intensities of all these four probes depend on the dose of superoxide (Fig. 2D, Supplementary Fig. 27). Moreover, their fluorogenic responses are highly specific towards superoxide, as other biologically relevant species posed almost no interference (Fig. 2E, Supplementary Fig. 28). This specificity is superior to the commercial probes DHE and DCFHDA. DHE is regarded as a superoxide-specific probe and DCFHDA is a nonselective ROS probe. However, parallel experiments showed that both DHE and DCFHDA can be turned on by various ROS species, exhibiting poor selectivity (Fig. 2E, Supplementary Figs. 29, 30). We noted that the tetrazine-based probes demonstrated much improved storage stability than DHE and DCFHDA (Supplementary Figs. 31–34), in addition to their desirable selectivity.
Before cell imaging, cell viability assay was performed to confirm the safety of these probes (Supplementary Fig. 35). Moreover, their oxadiazole derivatives which were prepared by treating the probes with superoxide in flasks, were also confirmed to show negligible cytotoxicity (Supplementary Fig. 35). We then evaluated their performance in detecting superoxide in live cells. To induce robust upregulation of cellular superoxide, HepG2 cells were stimulated with H2O2 (2 mM) for various durations. We first confirmed that H2O2 even at this high concentration couldn’t react with the Tz-based probes by both fluorescence spectra (Supplementary Fig. 36) and LC-MS analysis (Supplementary Fig. 37). Then the cells were stained with the probes for 30 min and imaged under confocal microscopy. All probes except F-Tz3 were able to image superoxide in HepG2 cells (Fig. 2F), and their fluorescence intensity positively correlated with the H2O2-incubation time (Supplementary Fig. 38). The failure of F-Tz3 in the cell imaging assay was probably due to the low brightness of the reaction product or the low retainability in cells. Based on the brightness data, the highly emissive blue probe F-Tz1 and the green probe F-Tz4 were used in the subsequent studies.
Live cells undergoing oxidative stress inevitably express a variety of ROS. Specific probes for a single ROS are thus highly sought-after. To confirm the specificity of F-Tz1 and F-Tz4 towards superoxide in live cells, we used the method of superoxide-specific scavenging. Tiron and TEMPO are two reagents that efficiently scavenge superoxide41,42. These two reagents were then used in the H2O2-treated cells to decrease cellular superoxide levels. Accordingly, both Tiron and TEMPO worked efficiently to inhibit the fluorogenic response of probes F-Tz1 and F-Tz4 in such live HepG2 cells. In contrast, they only partially inhibited the fluorescent response of DHE and DCFHDA (Fig. 2G and Supplementary Fig. 39). These results are in accordance with the better selectivity of the tetrazine-based probes towards superoxide than DCFHDA and DHE (Fig. 2E) and highlight the unprecedented specificity of the Tz-based probes F-Tz1 and F-Tz4 for imaging cellular superoxide.
In addition to monitoring the elevated superoxide levels in live cells with exogenous oxidant treatment, probe F-Tz4 was also confirmed to be capable of imaging endogenous superoxide during toxin-induced oxidative stress or physiological redox signaling. Paraquat is known to upregulate cellular superoxide44. HepG2 cells pretreated with paraquat (0.5, 1, or 3 mM) for 24 h were then stained with F-Tz4. Confocal imaging results showed that the cellular probe fluorescence intensity was positively correlated to paraquat doses that the cells were exposed (Supplementary Fig. 40). Next, a moderate upregulation of cellular superoxide levels was induced by treating A549 cells expressing the epidermal growth factor receptor (EGFR) with epidermal growth factor (EGF), a process known to activate NOX activity. A549 cells were first stimulated with EGF (0.5 μg/mL) and then stained with F-Tz4. Compared with the vehicle control, the EGF group showed significantly increased cellular probe fluorescence, and this increase could be compromised by the pretreatment of cells either with DPI (5 μM) which is an unspecific NOX inhibitor, or by VAS2870 which inhibits NOX with fair specificity (Supplementary Fig. 41)45. These results suggest that F-Tz4 is sensitive enough to image endogenous superoxide.
## Multiplexed imaging of cellular superoxide with high spatial resolution
Inspired by the different emission colors of F-Tz1 and F-Tz4 and their considerably different limit of detection (LOD) towards superoxide (Fig. 2B, Supplementary Figs. 42, 43), we evaluated the possibility to quantify cellular superoxide levels. For this purpose, HepG2 cells were pretreated with various concentrations of H2O2 for 2 h to induce varying levels of cellular superoxide. The feasibility of this model was first confirmed by feeding the cells with DHE and then quantifying cellular 2-hydroxyethidium levels with LCMS analysis which is a known superoxide-specific product. While 2-hydroxyethidium was almost absent in the starting reagent, its levels increased as the cells were stimulated with increasing doses of H2O2 (Supplementary Fig. 44). This result implied that adjusting H2O2 doses or duration time should be feasible to tune cellular superoxide levels. After establishing this model, the cells pretreated with various doses of H2O2 were then stained with either F-Tz1 or F-Tz4. As shown by the confocal imaging results (Fig. 3A, C, and Supplementary Figs. 45, 46 for brightfield images), a significant fluorogenic response from F-Tz1 was observed only in the high H2O2-dosage group, while F-Tz4 turned on bright fluorescence even in the low H2O2-dosage group. Fig. 3Multiplexed imaging of cellular superoxide levels with probes F-Tz1 and F-Tz4.A, B Fluorescence images of HepG2 cells stained with either F-Tz1 or F-Tz4 (each 5 μM, 30 min). Cells were pretreated with various doses of H2O2 for 2 h (A) or 2 mM H2O2 for various time (B). Scale bar: 25 μm. C, D The statistically quantified data on the cellular fluorescence intensity in (A) and (B). The data were normalized to the control group, and P values were analyzed by two-tailed unpaired t-test, $95\%$ Confidence interval. F-Tz1: $$n = 97$$ cells for 0 mM H2O2 0 h, $$n = 45$$ cells for 0.5 mM H2O2 2 h, $$n = 66$$ cells for 1 mM H2O2 2 h, $$n = 49$$ cells for 2 mM H2O2 2 h, $$n = 56$$ cells for 2 mM H2O2 1 h, $$n = 41$$ cells for 2 mM H2O2 4 h; F-Tz4: $$n = 116$$ cells for 0 mM H2O2 0 h, $$n = 78$$ cells for 0.5 mM H2O2 2 h, $$n = 68$$ cells for 1 mM H2O2 2 h, $$n = 77$$ cells for 2 mM H2O2 2 h, $$n = 68$$ cells for 2 mM H2O2 1 h, $$n = 74$$ cells for 2 mM H2O2 4 h. All cell numbers are over three biologically independent experiments. E, F Fluorescence images of HepG2 cells co-stained with F-Tz4 (5 μM, 30 min) and various organelle markers (50 nM Mito Tracker Red CMXRos, 50 nM Lyso Tracker Red, or 5 μM DHE for nuclei, 15 min). Cells were pretreated either with a low (0.5 mM) or high (2 mM) dose of H2O2 for 2 h before being stained with the probes. The right panel showed the intensity profile and PCC (Pearson’s correlation coefficient) along the white rectangle highlighted in the left panel. Scale bars, 10 μm.
Similar results were observed when cells were treated with the same dose of H2O2 (2 mM) for different durations (Fig. 3B, D, and Supplementary Figs. 45, 46 for brightfield images). These results were in good agreement with the different sensitivity of the two probes towards superoxide (Fig. 2B) and confirmed the possibility of detecting cellular superoxide levels with the combinational applications of various Tz-based probes.
Interestingly, the subcellular distribution of probe F-Tz4 alone could also be used to indicate oxidative stress levels. When HepG2 cells were treated with H2O2 of low dose (0.5 mM) for 2 h and then stained with F-Tz4, fluorescence was observed only in the cytoplasm compartment and colocalized well with the signal from Mito Tracker (Fig. 3E). When HepG2 cells were stimulated with H2O2 of high dose (2 mM) for 2 h followed by similar staining procedures, fluorescence could also be observed in the nucleus, as shown by the co-localized signals from DHE. Herein DHE was used as a nuclear stain because oxidized DHE tends to accumulate in the nucleus via intercalating into DNA46. Meanwhile, the colocalization of F-Tz4 with Mito Tracker greatly decreased (Fig. 3F). We assumed that under low levels of oxidative stress, mitochondria remained the dominant place for superoxide production. Therefore, the fluorescence from F-Tz4 colocalized well with that of Mito Tracker. However, when cells were challenged with a high degree of oxidative stress, a variety of oxidases were activated including those localized in the nucleus. The burst of superoxide production by these oxidases then changed the distribution of fluorescence signals from F-Tz4. The emergence of fluorescence signals in the nucleus is thus associated with a high degree of cellular oxidative stress.
## Extension of the strategy to other fluorophores
Encouraged by the desirable specificity of Tz towards superoxide detection, its excellent fluorescence-quenching ability, and module design with various fluorophores, we then extended this probe strategy to other fluorophores with emission wavelengths ranging from bluish violet to red (Fig. 4A). Coumarine, naphthalimides, rhodamine, and acridine orange have been frequently used in cell imaging experiments, owing to their good photophysical properties. After being tethered with Tz (Fig. 4A), these fluorophores all demonstrated negligible fluorescence. After the treatment of superoxide, an ultra-fluorogenic response was observed (Fig. 4B, C), with fluorescence turn-on ratios up to ~1400 times. Quantum chemical calculations show that this ultrafluorogenicity is related to the reaction of the Tz moiety (Supplementary Figs. 47–51)43,47.Fig. 4Extension of Tz to other fluorophores to tune probe emission spectra. A Structures of the Tz-tethered probes. B *Fluorescence spectra* of the probes before or after the treatment of various analytes. C Photophysical properties of the probes before or after the treatment of superoxide. [ a]Ф: Quantum yields of F-Tz$\frac{5}{6}$ were determined at an excitation wavelength of 365 nm and using quinine sulfate (Фstandard = 0.577 in 0.1 M H2SO4) as a standard. Quantum yields of F-Tz7 were determined at an excitation wavelength of 460 nm and using fluorescein (Фstandard = 0.95 in 0.1 M NaOH) as a standard. Quantum yields of F-Tz$\frac{8}{9}$ were determined at an excitation wavelength of 470 nm and using fluorescein (Фstandard = 0.95 in 0.1 M NaOH) as a standard. D Imaging superoxide in live cells with these probes. Scale bar: 25 μm. Representative images are shown from $$n = 3$$ independent experiments.
Our subsequent experiments confirmed that this fluorescence turn-on response is highly specific towards superoxide (Fig. 4B), resulting in the conversion from Tz to oxadiazole (Supplementary Fig. 52). The ability of these probes to image superoxide in live cells was also demonstrated (Fig. 4D, Supplementary Fig. 53).
## Tz-based probes facilitated the high-content screening for superoxide modulators to suppress myocardial infarction-induced injury
After verifying the desirable specificity of Tz-based probes for imaging superoxide in live cells, we then tested if these probes could be utilized to construct a high-content screening model for superoxide modulators, by monitoring the fluorescence intensity changes. It should be noted that with the clinical failure of most low-molecule weight antioxidants that stoichiometrically scavenge ROS un-selectively, and with the more recognized notion that ROS are also important messengers in redox signaling, it emerges as a more plausible way to search for redox modulators to conteract oxidative stress4. We therefore ascertain that a high-content screening model should be highly relevant for such a purpose.
Myocardial infarction is a deadly medical condition with high incidence. A major cause of myocardial infarction is oxidative stress48. To confirm the etiological role of superoxide in myocardial infarction, the accumulation of superoxide during myocardial ischemia/reperfusion (I/R) injury was ex vivo imaged with our probes. F-Tz4 (2 mg/kg) was intra-cardic injected into the left ventricle of mice suffering from myocardial I/R injury, and cardiac sections were harvested and observed via a spinning disk confocal microscope. An approximately 2.81-fold increase of fluorescent intensity was found in mice with myocardial I/R injury compared with that of a sham-operated group (Fig. 5A). These results suggest the participation of superoxide in myocardial infarction. Fig. 5High-content screening for superoxide modulators from natural products by F-Tz4 staining. A Ex vivo F-Tz4 imaging of superoxide production during myocardial I/R injury. $$n = 4$$ mice. B The illustration of cell imaging schematic and high content screening workflow (created with BioRender.com). For the screening results, the inhibition rate greater than $50\%$ was set as the screening standard. Each compound was repeated three times.
After confirming the implication of superoxide in the etiology of myocardial infarction, we then set out to formulate a high-content screening model for superoxide modulators. It is important to note that many antioxidants exhibited therapeutic potential in preclinical studies but hardly achieved success in clinical trials. This discrepancy is presumably due to the ineffective scavenging of ROS, or their delayed administration at late reperfusion4,48. However, this shouldn’t diminish the potential of antioxidants as cardioprotective agents. In this context, the search for effective superoxide modulators remains crucial for the development of cardioprotective agents. We chose to use H9C2 mice cardiomyocytes for the high-content screen. Although H9C2 cells are non-contractile and lack the ROS-generating property of normal cardiac contractility49–51, however, considering that the elevated ROS level during oxidative stress injury was much higher than that in physiological cardiac contractility, and the availability of cultured myocardial cells, we think a high-content screening model employing H9C2 cells should be acceptable; and further validation experiments can be performed in primary neonatal rat cardiomyocytes.
First, cells were subjected to tert-butyl hydroperoxide (tBHP) treatment to trigger superoxide accumulation. The magnitude of this accumulation was monitored by either the classical dye MitoSox or our probe F-Tz4. tBHP-induced superoxide generation can be represented by the elevated fluorogenic response of both probes in a dose-dependent manner (Supplementary Fig. 54). Noteworthy, F-Tz4 was more sensitive than MitoSox by exhibiting higher variations in fluorogenic responses, suggesting the super sensitivity of F-Tz4. Through an orthogonal experiment (Supplementary Fig. 55), we confirmed the optimized concentrations of tBHP and F-Tz4 were 150 μM and 1 μM, respectively, for this assay.
The high-content screening was then carried out over a natural product library containing 223 compounds using the ImageXpress Micro Confocal system (Molecular Devices). After incubated with each of these compounds (25 μM) for 24 h, H9C2 cells were treated with 150 μM tBHP for 2 h to trigger superoxide over-generation, and subsequently stained with F-Tz4 (Fig. 5B). The inhibitory rate of each candidate was calculated as [(IM − IA)/IM] × $100\%$, where IM was the signal of cells with only tBHP treatment, and IA was the signal of cells with pre-incubation of various compounds. The majority of compounds exhibited few or no effects on superoxide accumulation (Supplementary Data 1). However, four compounds showed over $50\%$ inhibitory activity at 25 μM (Supplementary Fig. 56). Noteworthy, three of the four hits, i.e., methylcobalamin, 7,8-dihydroxyflavone, and sinomenine have been previously reported to inhibit oxidative stress52–54, suggesting the reliability of this model. The fourth hit, coprostanone (5α-Cholestan-3-one, 5αCh3), is a microbial metabolite of cholesterol (Fig. 6A). Coprostanone has not been reported for its redox modulating activity55.Fig. 6Verifying the effect of coprostanone to inhibit superoxide overproduction and to ameliorate myocardial I/R injury in mice. A The molecular structure of coprostanone. B Representative images of H9C2 cells stimulated with tBHP and stained with F-Tz4 (1 μM). Cells were intact (control), treated with tBHP (150 μM), or pretreated with coprostanone (10, 25, 50 µM) for 24 h and then treated with tBHP (150 μM). All cells were co-treated with probe F-Tz4 at the time tBHP was adminstrated. Scale bar: 50 μm. C The statistically quantified data on the cellular fluorescence intensity of cells pretreated with coprostanone (24 h) of different doses, and then treated with tBHP and F-Tz4. The data were the mean ± SD and were normalized to the control group. D Representative photographs of TTC staining of myocardial infarction in mice subjected to myocardial I/R injury with coprostanone pretreatment for 3 days before ischemia. All the mice were anesthetized with tribromoethanol at a dose of 150 mg/kg before surgery. Mice myocardial ischemia and reperfusion injury were induced by the occlusion of the left anterior descending coronary artery for 45 min followed by 24 h reperfusion. The sham-operated group underwent the same procedure without ligation of the left anterior descending coronary artery. E TTC staining ratios of myocardial infarction in mice subjected to myocardial I/R injury ($$n = 16$$ mice for I/R group, 15 for 50 mg/kg and 100 mg/kg 5αCh3 group). Data were expressed as mean ± SD and analyzed with one-way ANOVA follwed by Dunnett’s multiple comparisons test. ** $P \leq 0.01$ vs. I/R group, ***$P \leq 0.001$ vs. I/R group, ****$P \leq 0.0001$ vs. I/R group; TTC, 2,3,5-triphenyltetrazolium chloride. F Representative Western Blot of NRF2, HO-1, and SOD2 in cardiac tissue lysates, $$n = 4$$ mice per group. All data were expressed as means ± SD and were analyzed using unpaired two-tailed t-test, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ versus I/R group.
To validate this activity of coprostanone, we tracked time-dependent overproduction of superoxide in H9C2 cells induced by 150 µM tBHP, with the cells pretreated (24 h) with coprostanone from 10 to 50 µM. Fluorescence images were recorded every 15 min by the high-content screening system. As shown in Fig. 6B, C and Supplementary Fig. 57, coprostanone inhibited the increase of probe fluorescence intensity in a dose-dependent manner, demonstrating its potency in preventing superoxide overproduction. Furthermore, we confirmed the effect of coprostanone to inhibit tBHP-induced superoxide overproduction in primary mice cardiomyocytes. While 30 µM tBHP treatment significantly upregulated cellular probe fluorescence, pretreating the primary mice cardiomyocytes with coprostanone dose-dependently compromised this effect (Supplementary Fig. 58). This result suggested that the screening results obtained in the H9C2 cell line should be translated to primary mice cardiomyocytes.
Inspired by this result, we moved on to test the cardioprotective effects of coprostanone in a mice model with myocardial I/R injury. Mice were gavage administrated with coprostanone (50 mg/kg/d or 100 mg/kg/d) for 3 days, and then were subjected to surgical I/R injury. Compared with I/R mice, coprostanone pretreatment significantly reduced TTC-stained infarct size (Fig. 6D, E). To test the effects of coprostanone on hemodynamics in mice of sham operation or I/R injury, mice were randomly divided into four groups, including sham, sham + 5αCh3 (100 mg/kg), I/R, and I/R + 5αCh3 (100 mg/kg) groups. After 3 days of pretreatment with 5αCh3 or vehicle, I/R and sham operations were performed. After 45 min ischemia followed by 24 h reperfusion, each anesthetized mouse was micro-cannulated with a 1.4 F microcatheter. Parameters, including left ventricular systolic pressure (LVSP), left ventricular end-diastolic pressure (LVEDP) and the maximum and minimum rates of left ventricular pressure changes (dp/dt max and dp/dt min), were automatically recorded. Compared with the sham-operated group, the abnormal values of LVSP and dp/dt max were observed in mice subjected to I/R injury. The pretreatment of 5αCh3 effectively improved these parameters in mice subjected to I/R injury; while it caused no significant effects on these parameters in the sham group (Supplementary Fig. 59). Apart from eliminating the side effects of 5αCh3 on hemodynamics of sham-operated mice, these data highlighted the beneficial effects of 5αCh3 against the impaired hemodynamic parameters of I/R mice.
To further investigate the potential pharmacological mechanism of coprostanone, the expression of oxidative stress-related proteins in myocardial tissues was analyzed by Western blotting. We found that the NRF2-mediated transcriptional antioxidant program, including its downstream factors HO-1 and SOD2, was significantly regulated by coprostanone treatment (Fig. 6F). Moreover, an immunofluorescence assay was carried out, which showed that the heart tissues from mice pretreated with coprostanone and then subjected to I/R injury expressed HO-1 and SOD2 at higher levels than the vehicle group (Supplementary Fig. 60). While the exact mechanism by which coprostanone induces cardioprotection remains to be explored, its effect to inhibit superoxide overload has been established, and the enhancement of the NRF2-HO-1/SOD2 signaling pathway may play a role in this effect.
## Discussion
Redox regulation is key to maintain cell homeostasis. Imbalanced redox contributes to the occurrence and development of various aging-related diseases. Although antioxidants are reported to reduce oxidative stress and increase healthy longevity56, several antioxidants showed detrimental effects in clinical trials57. These controversial results suggest the complexity of redox signaling and advocate the necessity of in-depth studies of various redox species with precision58. It is therefore urgent to develop reliable assays to monitor each of these redox species with high selectivity.
Superoxide is the major initial form of ROS. It is readily converted to H2O2 via superoxide dismutase enzyme9, which is further transformed into •OH through Fenton reaction or into HClO by myeloperoxidases10,11. Superoxide can also interact with nitric oxide to form peroxynitrite12. Evidence suggests that approximately 0.2–$2\%$ O2 consumed by mitochondria is converted into superoxide under normal physiological conditions, fueling the generation of H2O2 and other ROS for redox signaling59. Its excessive generation or inefficient dismutation ignites oxidative stress, causing the pathogenesis of many diseases13–17. Therefore, the spatiotemporal-resolved detection of superoxide is of primary importance.
There has been continuous efforts to develop assays for detecting superoxide18, as summarized in Supplementary Data 2. However, no current method is without limitation. Assays such as the DHE-HPLC assay26, or the EPR assay19, fulfill the selectivity requirement but lack spatiotemporal resolution. Assays such as DHE/MitoSox imaging can monitor superoxide in live cells, but the selectivity is compromised due to their cross-reactivity towards various ROS24.
Fluorescent imaging is a desirable modality to realize both the selective and spatiotemporal-resolved detection of superoxide in live cells. We thus set out to develop selective superoxide-responsive fluorescent probes. Judiciously comparing the chemical reactivity of various ROS revealed that superoxide possesses unique reducibility1. It is capable of transferring one electron to other compounds with suitable reduction potentials. This reactivity is opposite to other ROS which tends to extract one electron from other compounds. Based on this distinct reactivity and with the knowledge that multiple nitrogen-substituted benzenes have a strong tendency to be reduced, we hypothesized that 1,2,4,5-tetrazine could act as a selective superoxide-responsive trigger. We calculated the electron affinity of 1,2,4,5-tetrazine to be 3.326 eV, which is considerably greater than the ionization potential of the superoxide radical anion (3.156 eV), suggesting the potential of 1,2,4,5-tetrazine to accept one electron transferred from superoxide radical anion for its detection.
By first preparing a model compound Tz1 and studying its reactivity towards superoxide, we excitedly confirmed the selective reaction between 1,2,4,5-tetrazine and superoxide to produce l,3,4-oxadiazole, and confirmed that this tetrazine was basically inert towards other ROS species (Fig. 1F). Importantly, the conversion rate was observed positively correlating to superoxide doses (Fig. 1G), suggesting the potiential of this reaction to make a quantitative detection. Furthermore, we revealed that tetrazines with larger reduction potentials and less steric hindrance should react with superoxide more efficiently (Fig. 1H). This simple structure-sensitivity relationship should inspire the design of probes with different sensitivity towards superoxide.
1,2,4,5-Tetrazines usually have sufficient biocompatibility and stability, as evidenced by their wide application as bioorthogonal compounds for labeling proteins40. Due to their inherent and effective fluorescence quenching ability31, their translation into superoxide-responsive fluorogenic probes is straightforward. We have confirmed that the tetrazine moiety can be simply tethered to a platter of fluorophores from bluish violet to red (Fig. 4A), converting them into fluorogenic superoxide probes, which demonstrates the generalizability and modularity of our design strategy. 1,2,4,5-Tetrazine-based probes fluorogenically responded to superoxide in a dose-dependent way, and this response could only be triggered on by superoxide but not other ROS species. Specifically, we have confirmed the superior selectivity of these probes to commercial probes DHE or DCFHDA by both solution-based and cell-imaging based assays (Fig. 2E, G). In addition, the probes also favor a high degree of sensitivity towards endogenous superoxide. When cells were first stimulated with paraquat to induce the upregulation of superoxide44, and then stained with F-Tz4, paraquat-dose dependent increase of cellular probe fluorescence was observed (Supplementary Fig. 40). Moreover, when cells highly expressing EGFR were stimulated with EGF to induce a fast redox signaling, the burst of cellular superoxide could also be monitored by F-Tz4 (Supplementary Fig. 41). These results suggest the high sensitivity of the probe. Moreover, we observed that NOX inhibitor DPI and VAS2870 could compromise cellular F-Tz4 fluorescence induced by EGF, further suggesting the high selectivity of this probe towards superoxide.
Interestingly, Tz-based fluorogenic probes with different sensitivity could be used in combination to discriminate cellular oxidative states, as shown by probe F-Tz4 and F-Tz1, with the former being green-emissive and highly sensitive (LOD 10 nM), while the latter blue-emissive and less sensitive (LOD 800 nM). Low levels of cellular oxidative stress resulted in green fluorescence of F-Tz4; while high levels of oxidative stress were visible by both channels (Fig. 3A, B). Further, F-Tz4 itself was observed to be able to detect cellular oxidative states by the organelle distribution of its fluorescence, with the emergence of nuclear fluorescence suggesting the worsening of oxidative stress (Fig. 3F).
Given the high selectivity and sensitivity of F-Tz4 in cell imaging experiments, it was further used to image the aberrant superoxide generation in the pathology of mice myocardial I/R injury, and thereafter to construct a high-content screening model for superoxide modulators. By employing the fluorescence output to monitor superoxide concentrations and infer the effect of candidate compounds in inhibiting oxidative stress, this screening model enabled the identification of coprostanone as a promising compound for preventing superoxide overload and ameliorating myocardial I/R injury at least in part by inducing native antioxidant enzymes. Specifically, with the failure of many ROS scavengers in clinical trials, the search for inducers of endogenous antioxidant enzymes is emerging as a more promising strategy for antioxidant development. The results herein suggests that this superoxide-specific-probe-facilitated high-content screening model should be especially appealing for finding such inducers from either natural product libraries or traditional Chinese medicines.
These results underscore the versatility and utilities of Tz-based superoxide probes. We envision that the unprecedented specificity and spatial/temporal resolution of these Tz-based probes would warrant numerous applications to a wide range of pathological conditions. Meanwhile, it should also be noted that although we carried out a simple probe structure-activity relationship study in this work, this is far from being sufficient to obtain the most sensitive probes. This is especially true for those probes emitting in the blue-violet or red region. As shown by the data in Fig. 4C, the quantum yields of most probes after reacting with superoxide remained low, and further work to improve their sensitivity is needed.
## Ethical statement
All animal studies were approved by the Ethics Committee for Animal Experiments of Zhejiang University in China, and performed in accordance with the Guidelines for the Care and Use of Laboratory Animals of Zhejiang University. The approved protocal number is ZJU20220096. All mice used in this study were male adult C57BL/6 mice. Male mice were used because the basic studies demonstrated estrogen treatment prevents apotosis and necrosis of cardiac and endothelial cells causing unexplained impact on drug efficacy studies.
## Cell culture
A549, H9C2, and HepG2 cells were kindly provided by Stem cell bank, Chinese Academy of Sciences. All of them were STR-proved by stem Cell Bank, Chinese Academy of Sciences. HepG2 and A549 cells were cultured in high glucose Dulbecco’s Modified Eagle Medium (DMEM, Gibico) supplemented with $10\%$ fetal bovine serum (FBS, PAN) with $1\%$ antibiotics (100 U/mL penicillin and 100 µg/mL streptomycin) at 37 °C and $5\%$ CO2. Cells were carefully harvested and split when they reached $80\%$ confluence to maintain exponential growth. H9C2 cells were cultured in high glucose Dulbecco’s modified *Eagle medium* (DMEM, Corning) supplemented with $10\%$ fetal bovine serum (FBS, Corning) with $1\%$ antibiotics (100 U/mL penicillin and 100 µg/mL streptomycin, Giboco) at 37 °C and $5\%$ CO2. Cells were carefully harvested and split when they reached $80\%$ confluence to maintain exponential growth.
The cardiac myocytes were prepared from SD rats born at 0-3 d (P0-P3) (provided by Zhejiang Academy of Medical Sciences) using the Neonatal Heart Dissociation Kit (MACS, Germany) according to the manufacturer’s protocol. Cardiomyocytes were plated in 96-well plates in a plating medium containing $10\%$ serum. After 24 h of plating, the medium was then replaced with a serum‐free maintenance medium and incubated for another 24 h before being used for further study.
## MTT assay
Cells of the logarithmic growth phase were taken and inoculated in 96-well culture plates with edge-well PBS replenishment and incubated in a 37 °C incubator with $5\%$ CO2. After cell apposition, the cells were administrated with complete medium containing the tested compounds and incubated for 24 h. Then replace the liquid in all wells with DMEM medium containing 0.5 mg/mL MTT. Put back into the incubator at 37 °C, and then replace the liquid in the wells with 100 μL DMSO after 4 h. After shaking for 10 min at 37 °C, the absorbance of 580 nm was measured by an microplate reader TECAN infinite M1000 Multi-function microplate reader. The absorbance of each well was compared with the absorbance of normal wells, and the ratio obtained was calculated as the cell survival rate. Cell survival (%) = (Absorbance intensity of test sample/Absorbance intensity of control) × $100\%$.
## Fluorescence confocal imaging
HepG2 cells were seeded in 15 mm glass-bottomed dishes and cultured for 24 h. The medium was then changed into a serum-free medium containing various concentrations of H2O2 (0, 0.5, 1, or 2 mM). After a further incubation time of 1, 2, or 4 h, cells were washed with PBS three times. Then, these cells were incubated with the probe (5 μM) in serum-free medium for 30 min. After three times of washing with PBS, fresh medium without serum was added into the wells, and fluorescence images were recorded on a Leica TCS SP8 confocal micropscope using Leica Application Suite X software. The ∞ /0.17/OFN25/E, HC PL APO, 63×/1.40 OIL CS2 objective len was used. F-Tz1 and F-Tz2 channel: λex = 405 nm, λem = 410–550 nm, F-Tz3 and F-Tz4 channel: λex = 405 nm, λem = 450–600 nm, F-Tz5 and F-Tz6 channel: λex = 405 nm, λem = 410–550 nm, F-Tz7 channel: λex = 488 nm, λem = 495–600 nm, F-Tz8 channel: λex = 552 nm, λem = 560–650 nm, F-Tz9 channel: λex = 488 nm, λem = 500–600 nm. Each experiment was performed three times. Three frames were taken each time for a single focal plane without Z-stack by random selection. For various conditions in one experiment, the same microscope parameters were used to keep the background signal constant. Images were analyzed via the software “-ImageJ” to quantify the fluorescence intensity by densitometry. Briefly, the cellular cytoplastmic regions were outlined freehand and each cell fluorescence measured manually. Mean gray value was used for measuring the fluorescence intensity. No background subtraction was used. No gaussian blur filter was applied. The measurements of the raw data were pooled across various conditions and across the three parallels in one experiment.
For the pretreatment of Tiron or TEMPO, HepG2 cells were seeded in 15 mm glass-bottomed dishes and cultured for 24 h. Then cells were first treated with Tiron (100 μM) or TEMPO (300 μM) in serum-free DMEM for 1 h. The medium was then changed into fresh medium without serum containing Tiron (100 μM) or TEMPO (300 μM) together with H2O2 (2 mM). After a further incubation time of 2 h, cells were washed with PBS three times and stained with the probe as above described. ( F-Tz1 channel: λex = 405 nm, λem = 410–550 nm, F-Tz4 channel: λex = 405 nm, λem = 450–600 nm, DCFHDA channel: λex = 488 nm, λem = 500–550 nm, DHE channel: λex = 552 nm, λem = 560–630 nm).
For the cell experiment stimulated with paraquat, HepG2 cells were seeded in 15 mm glass-bottomed dishes and cultured for 24 h. The medium was then changed into a serum-free medium containing various concentrations of paraquat (0, 0.5, 1, or 3 mM). After 24 h, cells were washed with PBS three times. Then, these cells were incubated with the probe F-Tz4 (5 μM) in serum-free medium for 30 min. After three times of washing with PBS, fresh medium without serum was added into the wells, and fluorescence images were recorded on a Leica TCS SP8 confocal micropscope. F-Tz4 channel: λex = 405 nm, λem = 450–600 nm.
For the cell experiment stimulated with EGF, A549 cells were seeded in 96-well culture plates with edge-well PBS replenishment and incubated in a 37 °C incubator with $5\%$ CO2 for 12 h. For DPI inhibition experiment, the cells were divided into three groups. The control group and EGF group were treated with 0 or 0.5 μg/mL of EGF in serum-free DMEM for 30 min. The DPI group was pretreated with NOX inhibitor DPI (5 μM) in serum-free DMEM for 30 min, and then the medium was changed into fresh medium without serum but containing DPI (5 μM) together with 0.5 μg/mL EGF for 30 min. For the VAS2870 inhibition experiment, cells were divided into four groups. The control group and EGF group were treated with 0 or 0.5 μg/mL of EGF in serum-free DMEM for 30 min. The VAS2870 group was pretreated with NOX inhibitor VAS2870 (10 or 20 μM) in serum-free DMEM for 60 min, and then the medium was changed into fresh medium without serum but containing VAS2870 (10 or 20 μM) together with 0.5 μg/mL EGF for 30 min. The cells were washed with PBS three times and incubated with probe F-Tz4 (5 μM) in serum-free medium for 30 min. After three times of washing with PBS, fresh medium without serum was added into the wells, and fluorescence images were recorded on a Leica TCS SP8 confocal micropscope. F-Tz4 channel: λex = 405 nm, λem = 450–600 nm.
For the organelle tracker co-localization imaging experiment, HepG2 cells were seeded in 15 mm glass-bottomed dishes and cultured for 24 h. Then cells were treated with H2O2 (0.5 or 2 mM) in a serum-free medium for 2 h. After that, cells were washed with PBS three times, Then, the cells were stained with F-Tz4 (5 μM) in serum-free medium for 30 min. After washing with PBS three times, the cells were further stained with the commercial organelle tracker (50 nM Mito-Tracker Red CMXRos, 50 nM Lyso-Tracker Red, or 5 μM DHE for nucleus) in serum-free medium for 15 min. Fluorescence images were recorded on a Leica TCS SP8 microscopy (F-Tz4 channel: λex = 405 nm, λem = 450–580 nm, The Mito-Tracker Red CMXRos channel: λex = 552 nm, λem = 580–700 nm, The Lyso-Tracker Red channel: λex = 552 nm, λem = 580–700 nm, The DHE channel: λex = 552 nm, λem = 560–630 nm).
## Detection of 2-hydroxyethidium in HepG2 cells by LC-MS
HepG2 cells (6 × 105) were seeded in 60 mm diameter dishes and cultured for 24 h. The medium was then changed into a serum-free medium containing various concentrations of H2O2 (0, 0.5, 1, or 2 mM). For the TEMPO group, cells were first treated with TEMPO (300 μM) in serum-free DMEM for 1 h. The medium was then changed into fresh medium without serum containing TEMPO (300 μM) together with H2O2 (2 mM). After a further incubation time of 2 h, cells were washed with PBS three times. Then, these cells were incubated with DHE (10 μM) in serum-free medium for 30 min. After 30 min, remove the medium and wash the cells with ice-cold DPBS. Then scrape the cells in 1 ml of ice-cold DPBS. Transfer the cell suspension into 1.5 mL Eppendorf tube and centrifuge (5 min × 94 g, 4 °C). Remove the supernatant and then add 150 μL of ice-cold DPBS containing $0.1\%$ (v/v) Triton X-100. Draw the mixture in and out of the insulin syringe (ten times) to lyse the cells and centrifuge (5 min × 94 g, 4 °C). Transfer 100 μL of the lysate supernatant into the tube containing 100 μL of 0.2 M HClO4 in ice-cold MeOH. Votex 10 s and place in ice for 2 h to allow protein precipitation. During this time, transfer 2 μL of the same lysate supernatant to quantify protein used BCA Kit. After 2 h, pellete the protein precipitate by centrifugation (30 min × 376 g, 4 °C). Transfer 100 μL of resulting supernatant to the tube containing 100 μL of 1 M PBS (pH 2.6). Votex 5 s and remove the KClO4 precipitate by centrifugation (15 min × 94 g, 4 °C). Transfer 150 μL of supernatant into LCMS vial equipped with 200-μL conical glass insert and analyze by LCMS (Acquisition Mode: SIM). The calculated and detected m/z values are as follows: DHE: [M + H]+: 316; [2-OH-E]+: 330; [E]+: 314.The specific product of the reaction between DHE and superoxide is 2-hydroxyethidiun (2-OH-E+), and ethidium E+ is produced as a nonspecific product. For the 2-OH-E+ quantification, the peak area detected at m/$z = 330$ was used. The calculation of the amount of 2-OH-E+ = peak area/protein concentration (mg/mL). We also used cell-free samples as controls to guarantee that no 2-OH-E+ was produced during the procedures.
## In vivo imaging with F-Tz4
Male adult C57B6/L mice (8 weeks old) were obtained from SLAC ANIMAL Company, Shanghai. The mice were first acclimatized and fed for one week. The mice were randomly divided into two groups, one for the sham-operated group and the other for the I/R injury group. Before surgery, the mice were opened and intracardially injected with F-Tz4 probe. To assess the myocardial injury, mice were subjected to 45 min of myocardial ischemia followed by 15 min of reperfusion. The sham-operated group underwent the same procedure. Afterward, the heart was removed, frozen, and placed under a slide, and images were acquired using fluorescence confocal microscopy. Fluorescence intensity was quantified by ImageJ software.
## High-content screening methods
H9C2 cells were cultured in high glucose Dulbecco’s modified *Eagle medium* (DMEM, Gibico) supplemented with $10\%$ fetal bovine serum (FBS, PAN), and $1\%$ antibiotics (100 U/ml penicillin and 100 µg/ml streptomycin) at 37 °C and $5\%$ CO2. Cells were carefully harvested and split when they reached $80\%$ confluence to maintain exponential growth.
For drug screening, H9C2 cells were first seeded into a 96-well black plate with a clear bottom at a density of 5000/well. After culturing the cells for 24 h, the compounds were administrated at 25 µM, and incubated with the cells for 24 h in DMEM. Then the medium was removed, and cells were treated with 150 µM tBHP in DMEM for 2 h. After removing the medium, cells were washed with DMEM three times, then stained with 1 µM Nuclear Red in DMEM for 10 min. The medium was removed and cells were washed with DMEM three times. Then the cells were stained with F-Tz4 (1 µM) in DMEM for 30 min. All Fluorescent images of cell were captured with an ImageXpress Micro Confocal High-Content Imaging System (F-Tz4 channel: λex = 405 nm, λem = 450–580 nm, the nuclear red channel: λex = 552 nm, λem = 580–700 nm) with the 40× air objective len (S PLAN FLUOR ELWD, 0.60NA). Each condition was run in three replicates. The image analysis software of the high-content imaging system (MetaXpress PowerCore) was used to calculate the green fluorescence intensity in the cell fluorescence images and count the number of cells according to the number of fine nuclei. The inhibition rate was calculated according to the following formula: inhibition rate = [(IM − Icompound)/IM] × $100\%$. Icompound represents the average fluorescence intensity of green fluorescence per cell in the administered group, IM represents the average fluorescence intensity of green fluorescence per cell in the administered group.
## Protective effect of coprostanone against mice myocardial I/R injury
Male adult C57BL/6 mice (8 weeks old) were obtained from SHANGHAI SLAC ANIMAL CO. LTD (Certificate No.: SCXK [Hu] 2017-0005). The mice were first adaptive feeding for one week. According to the random number table method, 132 mice were randomly divided into sham operation group, I/R group, 5αCh3 low dose group (50 mg/kg) and 5αCh3 high dose group (100 mg/kg). 5αCh3 was dissolved with $5\%$ Macrogol [15]-Hydroxystearate (AA Blocks)-Saline. The drug was administered by gavage once a day for 3 days. The sham-operated and I/R groups were given equal amounts of the blank solvent.
After 3 days pretreatment with or without 5αCh3, mice were subjected to myocardial ischemia and reperfusion injury by occlusion of the left anterior descending coronary artery for 45 min followed by 24 h reperfusion. The sham-operated group underwent the same procedure without ligation of the left anterior descending coronary artery. All the mice were anesthetized with tribromoethanol (Sigma-Aldrich) at a dose of 150 mg/kg before surgery.
After 24 h, the survived mice for TTC staining were injected with Evans blue dye (Sigma-Aldrich) through the aortic arch retrogradely into the heart to depict the area at risk. Hearts were rapidly excised and transferred to −80 °C. Then the hearts were cut into 5 pieces and incubated in $1.0\%$ 2,3,5-triphenyltetrazolium chloride (Sigma-Aldrich) solution at 37 °C for 10 min. The TTC staining solution was aspirated dry, and the sections were fixed in $4\%$ Paraformaldehyde Fix Solution (Sangon Biotech) and photographed 2 h later. The area of Infarct size (INF), left ventricular(LV), and area at risk (AAR) were measured using ImageJ. The ratio of INF/LV (%), AAR/LV(%), and INF/AAR(%) were calculated.
The remaining saline-washed hearts of mice were divided into two parts, the upper part of the heart was reserved for immunofluorescence staining and the lower part was reserved for western blot analysis.
## Testing the effects of coprostanone on hemodynamics in mice
41 Mice were randomly divided into four groups, including sham, sham + 5αCh3 (100 mg/kg), I/R, and I/R + 5αCh3 (100 mg/kg) groups. After 3 days of pretreatment with 5αCh3 or vehicle, I/R and sham operations were performed. After 45 min ischemia followed by 24 h reperfusion, all the survived mouse were anesthetized and then micro-cannulated with a 1.4F microcatheter (Millar Instrument Inc, USA) and measured using a Powerlab multichannel physiological recorder. Briefly, the right common carotid artery (CCA) was carefully isolated and adequately exposed, followed by transiently blocking the proximal end of CCA using a microvascular clip. After ligation and traction of the distal end of CCA, a small hole was cut in the right CCA to allow the microcatheter to insert. Loosening the clip and gently advancing the microcatheter to reach the left ventricle. The key parameters, including left ventricular systolic pressure (LVSP), left ventricular end-diastolic pressure (LVEDP) and the maximum and minimum rates of left ventricular pressure changes (dp/dt max and dp/dt min), were automatically recorded. 1-3 mice in each group failed to record the hemodynamic parameters because of the failure of micro-cannulation.
## Western blot
The heart tissues of mice collected from sham-operated group, I/R group, 5αCh3 low dose group (50 mg/kg), 5αCh3 high dose group (100 mg/kg) with four mice in each group were removed from −80 °C and thawed on ice. Weigh and cut the tissues into homogenization tubes, add a small amount of small magnetic beads, and use a homogenizer to obtain a tissue homogenate. After centrifugation, the supernatant was taken and BCA assay was used to quantify the protein. Protein samples were mixed with loading buffer and reducing agent, then separated on $10\%$ Bris-tris gels and blotted on PVDF membranes. Subsequently, membranes were closed in closure buffer ($5\%$ Non-Fat milk) and incubated overnight with primary antibodies to NRF2 (Cat. no. AF7623, Beyotime), SOD2 (Cat. no. 24127-1-AP, Proteintech), HO-1(Cat. no. 10701-1-AP, Proteintech), and β-tubulin (Cat. no. 10094-1-AP, Proteintech) (1:1000 in primary antibody diluent), washed 3 times, and then incubated with HRP-conjugated anti-Rabbit secondary antibody (Cat. no. A0208, Beyotime) (1:2000 in $5\%$BSA-TBST) at room temperature for 1 h. ECL chemiluminescent reagents were used to show the blots. These bands were exposed by Bio-Rad ChemiDoc XRS.
## Immunofluorescence staining
After 24 h reperfusion, the mice were anesthetized and the hearts ($$n = 4$$ for each group) were extracted and fixed in $4\%$ paraformaldehyde at 4 °C for at least 48 h. Briefly, the cardiac tissues were dehydrated, embedded, and cut into 4 μm-thick slices. Then, the paraffin sections were dewaxed and rehydrated followed by treated with EDTA Antigen Repair Solution (Servicebio, China). After blocking with bovine serum albumin solution (BSA, Sangon Biotech, China) for 30 min at room temperature, the slices were incubated overnight at 4 °C with primary antibodies against NRF2 (1:5000, Cat. no. AF7623, Beyotime)/SOD2(1:5000, Cat. no. 24127-1-AP, Proteintech)/HO-1 (1:6000, Cat. no. 10701-1-AP, Proteintech). Subsequently, the corresponding HRP conjugated Goat Anti-Rabbit IgG (H + L) (1:500, Cat. no. GB23303, Servicebio) was applied to treat the slices at room temperature for 50 min. After incubating with FITC reagent at room temperature in dark for 10 min, the sections were treated with Tris-EDTA followed by blocking. Next step, the slices continued to be incubated overnight at 4 °C with anti-cTnT antibody (1:300, Cat. no. GB11364, Servicebio) followed by treated with Cy3 conjugated Goat Anti-Rabbit IgG (H + L) (1:300, Cat. no. GB21303, Servicebio) for 50 min at room temperature. Cell nuclei was stained with 4′,6-diamidino-2-phenylindole (DAPI) for 10 min at room temperature in dark. The images were obtained and observed using a Orthofluorescence microscope (NIKON ECLIPSE C1, Japan). For the cells with expression of NRF2, HO-1 and SOD2, the cells were presented with green color in cytoplasm, blue color in nucleus and the red color represented cTnT.
## HPLC analysis procedures
To record the reactivity of Tz1-Tz4 towards O2•−, each compound stock solution (50 mM, in DMSO) was diluted in MeCN to make a 100 μM solution. Then O2•− (0–20 eq) was added. The mixture was stored at ambient temperature for 30 min, followed by HPLC analysis. The peak area of the remaining residue was used to calculate the conversion rate. The detailed liquid chromatography methods were reported in the Supplementary Information. To record the reactivity of Tz1 towards various analytes, each analyte was added to an aliquot of Tz1 in DMSO. After 30 min, the mixture was diluted with a mixed solution of MeCN and PBS (1:1, v/v, PBS of pH 7.4, 10 mM). Then the mixture was analyzed by HPLC. The peak area corresponding to 2,5-diphenyl-1,3,4-oxadiazole was recorded, which was normalized to the KO2 group to calculate the normalized yield of the oxadiazole product. The peak area corresponding to Tz1 residue was also recorded, which was normalized to the blank group to calculate the residue of Tz1.
## Optical response analysis
To measure the absorption spectra, the stock solution of F-Tz1-F-Tz9 was diluted with MeCN containing $1\%$ 18-crown-6. O2•− was added to this solution. After 30 min of incubation, the mixture was diluted with PBS buffer (10 mM, pH 7.4) and then measured. The final concentration of the probe was 20 μM while that of O2•− was 400 μM. To record the fluorescence spectra, various doses of O2•− (0–20 eq) were added to probe F-Tz1-F-Tz9 solutions in MeCN containing $1\%$ 18-crown-6. After 30 min of incubation, the mixture was diluted with PBS buffer (10 mM, pH 7.4) to make sure that the final probe concentration was 5 μM. The mixture was then measured for fluorescence (F-Tz1: λex/λem = $\frac{323}{470}$ nm; F-Tz2: λex/λem = $\frac{384}{460}$ nm; F-Tz3: λex/λem = $\frac{350}{530}$ nm; F-Tz4: λex/λem = $\frac{385}{510}$ nm; F-Tz5: λex/λem = $\frac{405}{445}$ nm; F-Tz6: λex/λem = $\frac{380}{505}$ nm; F-Tz7: λex/λem = $\frac{445}{540}$ nm; F-Tz8: λex/λem = $\frac{565}{585}$ nm; F-Tz9: λex/λem = $\frac{488}{600}$ nm). To determine the quantum yields, quinine sulfate (Фstandard = 0.577 in 0.1 M H2SO4) and fluorescein (Фstandard = 0.95 in 0.1 M NaOH) were used as standards according to a published method. For the probe, fluorescein, and quinine sulfate, the absorbance spectra were measured within an absorbance range of 0.01 to 0.05. The quantum yield was calculated according to the equation:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\Phi }_{{{{{{\rm{sample}}}}}}}={\Phi }_{{{{{{\rm{standard}}}}}}}\frac{\sum {{{{{{\rm{F}}}}}}}_{{{{{{\rm{sample}}}}}}}}{\sum {{{{{{\rm{F}}}}}}}_{{{{{{\rm{standard}}}}}}}}\frac{{{{{{{\rm{Abs}}}}}}}_{{{{{{\rm{standard}}}}}}}}{{{{{{{\rm{Abs}}}}}}}_{{{{{{\rm{sample}}}}}}}}{\left(\frac{{{{{{{\rm{n}}}}}}}_{{{{{{\rm{sample}}}}}}}}{{{{{{{\rm{n}}}}}}}_{{{{{{\rm{standard}}}}}}}}\right)}^{2}$$\end{document}Φsample=Φstandard ∑Fsample ∑FstandardAbsstandardAbssamplensamplenstandard2where Ф is the quantum yield, ΣF is the integrated fluorescence intensity, *Abs is* the absorbance at the excitation wavelength, and n represents the refractive index of the solvent.
To test the selectivity of the probes, F-Tz1-F-Tz9 was diluted with PBS buffer (10 mM, pH 7.4) to make a solution of 5 μM. Aliquots of this solution were then treated with various analytes whose stock solutions was prepared according to the methods described in the Supplementary Information. The volume change (ca. 1‰) caused by the addition of analytes could be negligible. After incubating at room temperature for 30 min, the fluorescence spectrum was collected. All the fluorescence and absorption spectra data were processed via the software Origin 2021.
## Reduction potentials and cyclic voltammograms
Tz1-Tz3 (1 mmol) was dissolved in 10 ml of 0.1 M Et4NClO4 solution of CH3CN. Reduction potentials and cyclic voltammograms were recorded on an Electrochemical workstation (CHI660E, produced by Shanghai YueCi Electronic Technology Company) using glassy carbon as working, platinum as counter, and Ag/AgCl as reference electrode, respectively.
## Computational methods
Quantum chemical calculations based on density functional theory (DFT) and time-dependent density functional theory (TD-DFT) were employed to rationalize the fluorogenicity of tetrazine-tethered probes. All structure optimizations were performed without constraints using the ωB97XD functionals and def2SVP basis set in the ground states. Solvation effects (in water) were taken into account using the SMD model. The vertical excitation energies of all molecules were calculated using linear solvation formalism at TDDFT-ωB97XD/def2SVP. All DFT and TD-DFT calculations were carried out using Gaussian 16A.
## Statistics and reproducibility
All the statistical analysis was perfomed with Graphpad Prism 8.0 software. The two-tailed unpaired Student’s t-test and one-way Anova followed by Dunnett’s multiple comparisons test or uncorrected Fisher’s LSD multiple comparisons test were used for data statistical handled. P value <0.05 was considered as statistically significant. All the cell and animal experiments was performed at least three biologically independent times.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary information Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37121-8.
## Source data
Source Data
## Peer review information
Nature Communications thanks Maan Hayyan and the other anonymous reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.
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